diff --git a/.devcontainer/Dockerfile b/.devcontainer/Dockerfile
index 61c8d14039db..03849995735d 100644
--- a/.devcontainer/Dockerfile
+++ b/.devcontainer/Dockerfile
@@ -31,7 +31,7 @@ RUN apt-get install -y curl wget gnupg python3 python-is-python3 python3-pip git
 RUN python -m pip install \
     pip==23.3.1 \
     setuptools==68.2.2 \
-    poetry==1.5.1
+    poetry==1.7.1
 
 USER $USERNAME
 ENV PATH="/home/$USERNAME/.local/bin:${PATH}"
diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS
index a5eadadf8604..34af632814a3 100644
--- a/.github/CODEOWNERS
+++ b/.github/CODEOWNERS
@@ -3,5 +3,17 @@
 # Default code owners
 * @danieljanes @tanertopal
 
+# README.md
+README.md @jafermarq @tanertopal @danieljanes
+
 # Flower Baselines
 /baselines @jafermarq @tanertopal @danieljanes
+
+# Flower Datasets
+/datasets @jafermarq @tanertopal @danieljanes
+
+# Flower Examples
+/examples @jafermarq @tanertopal @danieljanes
+
+# Changelog
+/doc/source/ref-changelog.md @jafermarq @tanertopal @danieljanes
diff --git a/.github/ISSUE_TEMPLATE/baseline_request.yml b/.github/ISSUE_TEMPLATE/baseline_request.yml
index 49ae922a94ad..8df8eec22a21 100644
--- a/.github/ISSUE_TEMPLATE/baseline_request.yml
+++ b/.github/ISSUE_TEMPLATE/baseline_request.yml
@@ -38,18 +38,18 @@ body:
     attributes:
       label: For first time contributors
       value: |
-        - [ ]  Read the [`first contribution` doc](https://flower.dev/docs/first-time-contributors.html)
+        - [ ]  Read the [`first contribution` doc](https://flower.ai/docs/first-time-contributors.html)
         - [ ]  Complete the Flower tutorial
         - [ ]  Read the Flower Baselines docs to get an overview:
-            - [ ]  [How to use Flower Baselines](https://flower.dev/docs/baselines/how-to-use-baselines.html)
-            - [ ]  [How to contribute a Flower Baseline](https://flower.dev/docs/baselines/how-to-contribute-baselines.html)
+            - [ ]  [How to use Flower Baselines](https://flower.ai/docs/baselines/how-to-use-baselines.html)
+            - [ ]  [How to contribute a Flower Baseline](https://flower.ai/docs/baselines/how-to-contribute-baselines.html)
   - type: checkboxes
     attributes:
       label: Prepare - understand the scope
       options:
         - label: Read the paper linked above
         - label: Decide which experiments you'd like to reproduce. The more the better!
-        - label: Follow the steps outlined in [Add a new Flower Baseline](https://flower.dev/docs/baselines/how-to-contribute-baselines.html#add-a-new-flower-baseline).
+        - label: Follow the steps outlined in [Add a new Flower Baseline](https://flower.ai/docs/baselines/how-to-contribute-baselines.html#add-a-new-flower-baseline).
         - label: You can use as reference [other baselines](https://github.com/adap/flower/tree/main/baselines) that the community merged following those steps.
   - type: checkboxes
     attributes:
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
index d0ab46d60b57..9e0ed23d4dbb 100644
--- a/.github/ISSUE_TEMPLATE/config.yml
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -1,14 +1,14 @@
 blank_issues_enabled: false
 contact_links:
   - name: Slack Channel
-    url: https://flower.dev/join-slack
+    url: https://flower.ai/join-slack
     about: Connect with other Flower users and contributors and discuss with them or ask them questions.
   - name: Discussion
     url: https://github.com/adap/flower/discussions
-    about: Ask about new features or general questions. Please use the discussion area in most of the cases instead of the issues. 
+    about: Ask about new features or general questions. Please use the discussion area in most of the cases instead of the issues.
   - name: Flower Issues
     url: https://github.com/adap/flower/issues
     about: Contribute new features/enhancements, report bugs, or improve the documentation.
   - name: Flower Mail
-    url: https://flower.dev/
-    about: If your project needs professional support please contact the Flower team (hello@flower.dev).
+    url: https://flower.ai/
+    about: If your project needs professional support please contact the Flower team (hello@flower.ai).
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index 8d73ed618919..0077bbab0909 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -36,10 +36,29 @@ Example: The variable `rnd` was renamed to `server_round` to improve readability
 
 - [ ] Implement proposed change
 - [ ] Write tests
-- [ ] Update [documentation](https://flower.dev/docs/writing-documentation.html)
-- [ ] Update [changelog](https://github.com/adap/flower/blob/main/doc/source/changelog.rst)
+- [ ] Update [documentation](https://flower.ai/docs/writing-documentation.html)
+- [ ] Update the changelog entry below
 - [ ] Make CI checks pass
-- [ ] Ping maintainers on [Slack](https://flower.dev/join-slack/) (channel `#contributions`)
+- [ ] Ping maintainers on [Slack](https://flower.ai/join-slack/) (channel `#contributions`)
+
+<!--
+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.
+
+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. If the section contains some text other than tokens, it will use it to add a description to the change. 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:
+
+<general> is for classifying a PR as a general improvement.
+<skip> is to not add the PR to the changelog
+<baselines> is to add a general baselines change to the PR
+<examples> is to add a general examples change to the PR
+<sdk> is to add a general sdk change to the PR
+<simulations> is to add a general simulations change to the PR
+
+Note that only one token should be used.
+-->
+
+### Changelog entry
+
+
 
 ### Any other comments?
 
@@ -49,7 +68,7 @@ Smaller PRs with good descriptions can be considered much more easily.
 
 If you have an urgent request or question, please use the Flower Slack:
 
-    https://flower.dev/join-slack/ (channel: #contributions)
+    https://flower.ai/join-slack/ (channel: #contributions)
 
 Thank you for contributing to Flower!
 -->
diff --git a/.github/actions/bootstrap/action.yml b/.github/actions/bootstrap/action.yml
index bab5113bc567..7b1716bbc954 100644
--- a/.github/actions/bootstrap/action.yml
+++ b/.github/actions/bootstrap/action.yml
@@ -12,7 +12,7 @@ inputs:
     default: 68.2.2
   poetry-version:
     description: "Version of poetry to be installed using pip"
-    default: 1.5.1
+    default: 1.7.1
 outputs:
   python-version:
     description: "Version range or exact version of Python or PyPy"
@@ -30,7 +30,7 @@ runs:
   using: "composite"
   steps:
     - name: Set up Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ inputs.python-version }}
     - name: Install build tools
diff --git a/.github/workflows/_docker-build.yml b/.github/workflows/_docker-build.yml
index d36076444746..99ec0671db66 100644
--- a/.github/workflows/_docker-build.yml
+++ b/.github/workflows/_docker-build.yml
@@ -1,4 +1,4 @@
-name: Reusable docker server image build workflow
+name: Reusable docker image build workflow
 
 on:
   workflow_call:
@@ -35,19 +35,32 @@ permissions:
 # based on https://docs.docker.com/build/ci/github-actions/multi-platform/#distribute-build-across-multiple-runners
 jobs:
   build:
-    name: Build server image
+    name: Build image
     runs-on: ubuntu-22.04
     timeout-minutes: 60
+    outputs:
+      build-id: ${{ steps.build-id.outputs.id }}
     strategy:
       fail-fast: true
       matrix:
         platform: [
             # build-push action and qemu use different platform names
             # therefore we create a map
-            { qemu: "", docker: "linux/amd64" },
-            { qemu: "arm64", docker: "linux/arm64" },
+            { name: "amd64", qemu: "", docker: "linux/amd64" },
+            { name: "arm64", qemu: "arm64", docker: "linux/arm64" },
           ]
     steps:
+      - name: Create build id
+        id: build-id
+        shell: python
+        run: |
+          import hashlib
+          import os
+
+          hash = hashlib.sha256('''${{ inputs.build-args }}'''.encode())
+          with open(os.environ['GITHUB_OUTPUT'], 'a') as fh:
+              print(f"id={hash.hexdigest()}", file=fh)
+
       - name: Set up QEMU
         if: matrix.platform.qemu != ''
         uses: docker/setup-qemu-action@68827325e0b33c7199eb31dd4e31fbe9023e06e3 # v3.0.0
@@ -85,9 +98,9 @@ jobs:
           touch "/tmp/digests/${digest#sha256:}"
 
       - name: Upload digest
-        uses: actions/upload-artifact@a8a3f3ad30e3422c9c7b888a15615d19a852ae32 # v3.1.3
+        uses: actions/upload-artifact@1eb3cb2b3e0f29609092a73eb033bb759a334595 # v4.1.0
         with:
-          name: digests
+          name: digests-${{ steps.build-id.outputs.id }}-${{ matrix.platform.name }}
           path: /tmp/digests/*
           if-no-files-found: error
           retention-days: 1
@@ -101,10 +114,11 @@ jobs:
       metadata: ${{ steps.meta.outputs.json }}
     steps:
       - name: Download digests
-        uses: actions/download-artifact@9bc31d5ccc31df68ecc42ccf4149144866c47d8a # v3.0.2
+        uses: actions/download-artifact@eaceaf801fd36c7dee90939fad912460b18a1ffe # v4.1.2
         with:
-          name: digests
+          pattern: digests-${{ needs.build.outputs.build-id }}-*
           path: /tmp/digests
+          merge-multiple: true
 
       - name: Docker meta
         id: meta
diff --git a/.github/workflows/baselines.yml b/.github/workflows/baselines.yml
index bfb26053836d..c4485fe72d10 100644
--- a/.github/workflows/baselines.yml
+++ b/.github/workflows/baselines.yml
@@ -1,14 +1,5 @@
 name: Baselines
-# The aim of this workflow is to test only the changed (or added) baseline.
-# Here is the rough idea of how it works (more details are presented later in the comments):
-# 1. Checks for the changes between the current branch and the main - in case of PR -
-#  or between the HEAD and HEAD~1 (main last commit and the previous one) - in case of
-#  a push to main.
-# 2. Fails the test if there are changes to more than one baseline. Passes the test
-#  (skips the rests) if there are no changes to any baselines. Follows the test if only
-#  one baseline is added or modified.
-# 3. Sets up the env specified for the baseline.
-# 4. Runs the tests.
+
 on:
   push:
     branches:
@@ -24,112 +15,75 @@ concurrency:
 env:
   FLWR_TELEMETRY_ENABLED: 0
 
-defaults:
-  run:
-    working-directory: baselines
-
 jobs:
-  test_baselines:
-    name: Test
+  changes:
     runs-on: ubuntu-22.04
+    permissions:
+      pull-requests: read
+    outputs:
+      baselines: ${{ steps.filter.outputs.changes }}
     steps:
       - uses: actions/checkout@v4
-        # The depth two of the checkout is needed in case of merging to the main
-        # because we compare the HEAD (current version) with HEAD~1 (version before
-        # the PR was merged)
-        with:
-          fetch-depth: 2
-      - name: Fetch main branch
-        run: |
-          # The main branch is needed in case of the PR to make a comparison (by 
-          # default the workflow takes as little information as possible - it does not
-          # have the history
-          if [ ${{ github.event_name }} == "pull_request" ]
-          then
-            git fetch origin main:main
-          fi
-      - name: Find changed/new baselines
-        id: find_changed_baselines_dirs
+
+      - shell: bash
         run: |
-          if [ ${{ github.event_name }} == "push" ]
-          then
-            # Push event triggered when merging to main
-            change_references="HEAD..HEAD~1"
-          else
-            # Pull request event triggered for any commit to a pull request
-            change_references="main..HEAD"
-          fi
-          dirs=$(git diff --dirstat=files,0 ${change_references} . | awk '{print $2}' | grep -E '^baselines/[^/]*/$' | \
-          grep -v \
-            -e '^baselines/dev' \
-            -e '^baselines/baseline_template' \
-            -e '^baselines/flwr_baselines' \
-            -e '^baselines/doc' \
-          | sed 's/^baselines\///')
-          # git diff --dirstat=files,0 ${change_references} . - checks the differences
-          #   and a file is counted as changed if more than 0 lines were changed
-          #   it returns the results in the format x.y% path/to/dir/
-          # awk '{print $2}' - takes only the directories (skips the percentages)
-          # grep -E '^baselines/[^/]*/$' - takes only the paths that start with 
-          #   baseline (and have at least one subdirectory)
-          # grep -v -e ... - excludes the `baseline_template`, `dev`, `flwr_baselines`
-          # sed 's/^baselines\///' - narrows down the path to baseline/<subdirectory>
-          echo "Detected changed directories: ${dirs}"
-          # Save changed dirs to output of this step
-          EOF=$(dd if=/dev/urandom bs=15 count=1 status=none | base64)
-          echo "dirs<<EOF" >> "$GITHUB_OUTPUT"
-          for dir in $dirs
-          do
-            echo "$dir" >> "$GITHUB_OUTPUT"
-          done
-          echo "EOF" >> "$GITHUB_OUTPUT"
-      - name: Validate changed/new baselines
-        id: validate_changed_baselines_dirs
+          # create a list of all directories in baselines
+          {
+            echo 'FILTER_PATHS<<EOF'
+            FILTER=""
+            while read -d $'\0' BASELINES_PATH; do
+                DIR=$(basename $BASELINES_PATH)
+                FILTER+=$(echo "$DIR: ${BASELINES_PATH}/**\n")
+            done < <(find baselines -maxdepth 1 \
+                -name ".*" -prune -o \
+                -path "baselines/doc" -prune -o \
+                -path "baselines/dev" -prune -o \
+                -path "baselines/baseline_template" -prune -o \
+                -path "baselines/flwr_baselines" -prune -o \
+                -type d -print0)
+            FILTER=$(echo -e "$FILTER")
+            # remove first line
+            FILTER=${FILTER#*$'\n'}
+            echo "$FILTER"
+            echo EOF
+          } >> "$GITHUB_ENV"
+
+      - uses: dorny/paths-filter@v3
+        id: filter
+        with:
+          filters: ${{ env.FILTER_PATHS }}
+
+      - if: ${{ github.event.pull_request.head.repo.fork }}
         run: |
-          dirs="${{ steps.find_changed_baselines_dirs.outputs.dirs }}"
-          dirs_array=()
-          if [[ -n $dirs ]]; then
-            while IFS= read -r line; do
-              dirs_array+=("$line")
-            done <<< "$dirs"
-          fi
-          length=${#dirs_array[@]}
-          echo "The number of changed baselines is $length"
-          
-          if [ $length -gt 1 ]; then
-          echo "The changes should only apply to a single baseline"
-          exit 1
+          CHANGES=$(echo "${{ toJson(steps.filter.outputs.changes) }}" | jq '. | length')
+          if [ "$CHANGES" -gt 1 ]; then
+            echo "::error ::The changes should only apply to a single baseline."
+            exit 1
           fi
-          
-          if [ $length -eq 0 ]; then
-          echo "The baselines were not changed - skipping the remaining steps."
-          echo "baseline_changed=false" >> "$GITHUB_OUTPUT"
-          exit 0
-          fi
-          
-          echo "changed_dir=${dirs[0]}" >> "$GITHUB_OUTPUT"
-          echo "baseline_changed=true" >> "$GITHUB_OUTPUT"
+
+  test:
+    runs-on: ubuntu-22.04
+    needs: changes
+    if: ${{ needs.changes.outputs.baselines != '' && toJson(fromJson(needs.changes.outputs.baselines)) != '[]' }}
+    strategy:
+      matrix:
+        baseline: ${{ fromJSON(needs.changes.outputs.baselines) }}
+    steps:
+      - uses: actions/checkout@v4
+
       - name: Bootstrap
-        if: steps.validate_changed_baselines_dirs.outputs.baseline_changed == 'true'
         uses: ./.github/actions/bootstrap
         with:
           python-version: '3.10'
+
       - name: Install dependencies
-        if: steps.validate_changed_baselines_dirs.outputs.baseline_changed == 'true'
-        run: |
-          changed_dir="${{ steps.validate_changed_baselines_dirs.outputs.changed_dir }}"
-          cd "${changed_dir}"
-          python -m poetry install
-      - name: Test
-        if: steps.validate_changed_baselines_dirs.outputs.baseline_changed == 'true'
-        run: |
-          dir="${{ steps.validate_changed_baselines_dirs.outputs.changed_dir }}"
-          echo "Testing ${dir}"
-          ./dev/test-baseline.sh $dir
-      - name: Test Structure
-        if: steps.validate_changed_baselines_dirs.outputs.baseline_changed == 'true'
-        run: |
-          dir="${{ steps.validate_changed_baselines_dirs.outputs.changed_dir }}"
-          echo "Testing ${dir}"
-          ./dev/test-baseline-structure.sh $dir
+        working-directory: baselines/${{ matrix.baseline }}
+        run: python -m poetry install
+
+      - name: Testing ${{ matrix.baseline }}
+        working-directory: baselines
+        run: ./dev/test-baseline.sh ${{ matrix.baseline }}
 
+      - name: Test Structure of ${{ matrix.baseline }}
+        working-directory: baselines
+        run: ./dev/test-baseline-structure.sh ${{ matrix.baseline }}
diff --git a/.github/workflows/cpp.yml b/.github/workflows/cpp.yml
index a1d918aa5b29..7efc879bcee2 100644
--- a/.github/workflows/cpp.yml
+++ b/.github/workflows/cpp.yml
@@ -19,13 +19,13 @@ jobs:
       uses: ./.github/actions/bootstrap
 
     - name: Cache restore SDK build
-      uses: actions/cache/restore@v3
+      uses: actions/cache/restore@v4
       with:
         path: build/
         key: ${{ runner.os }}-sdk-build
 
     - name: Cache restore example build
-      uses: actions/cache/restore@v3
+      uses: actions/cache/restore@v4
       with:
         path: examples/quickstart-cpp/build/
         key: ${{ runner.os }}-example-build
@@ -82,14 +82,14 @@ jobs:
         fi
 
     - name: Cache save SDK build
-      uses: actions/cache/save@v3
+      uses: actions/cache/save@v4
       if: github.ref_name == 'main'
       with:
         path: build/
         key: ${{ runner.os }}-sdk-build
 
     - name: Cache save example build
-      uses: actions/cache/save@v3
+      uses: actions/cache/save@v4
       if: github.ref_name == 'main'
       with:
         path: examples/quickstart-cpp/build/
diff --git a/.github/workflows/datasets-e2e.yml b/.github/workflows/datasets-e2e.yml
new file mode 100644
index 000000000000..2a73a8538b14
--- /dev/null
+++ b/.github/workflows/datasets-e2e.yml
@@ -0,0 +1,52 @@
+name: Datasets-E2E
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - "datasets/flwr_datasets/**"
+  pull_request:
+    branches:
+      - main
+    paths:
+      - "datasets/flwr_datasets/**"
+
+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
+
+jobs:
+  frameworks:
+    runs-on: ubuntu-22.04
+    timeout-minutes: 10
+    # Using approach described here:
+    # https://docs.github.com/en/actions/using-jobs/using-a-matrix-for-your-jobs
+    strategy:
+      matrix:
+        include:
+          - directory: pytorch
+
+          - directory: tensorflow
+
+          - directory: scikit-learn
+
+    name: Framework / ${{matrix.directory}}
+
+    defaults:
+      run:
+        working-directory: datasets/e2e/${{ matrix.directory }}
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Bootstrap
+        uses: ./.github/actions/bootstrap
+        with:
+          python-version: 3.8
+      - name: Install dependencies
+        run: python -m poetry install
+      - name: Run tests
+        run: python -m unittest discover -p '*_test.py'
diff --git a/.github/workflows/docker-base.yml b/.github/workflows/docker-base.yml
index 7b23340ec7d2..f2cd2ef99d08 100644
--- a/.github/workflows/docker-base.yml
+++ b/.github/workflows/docker-base.yml
@@ -39,13 +39,13 @@ jobs:
           echo "ubuntu-version=${{ env.DEFAULT_UBUNTU }}" >> "$GITHUB_OUTPUT"
 
   build-base-images:
-    name: Build images
+    name: Build base images
     uses: ./.github/workflows/_docker-build.yml
     needs: parameters
     strategy:
-      fail-fast: true
+      fail-fast: false
       matrix:
-        python-version: [3.8, 3.9, 3.10, 3.11]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
     with:
       namespace-repository: flwr/base
       file-dir: src/docker/base
diff --git a/.github/workflows/docker-client.yml b/.github/workflows/docker-client.yml
new file mode 100644
index 000000000000..3c2d83596733
--- /dev/null
+++ b/.github/workflows/docker-client.yml
@@ -0,0 +1,36 @@
+name: Build docker client image
+
+on:
+  workflow_dispatch:
+    inputs:
+      flwr-version:
+        description: "Version of Flower e.g. (1.7.0)."
+        required: true
+        type: string
+
+permissions:
+  contents: read
+
+jobs:
+  build-client-images:
+    name: Build client 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:
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
+    with:
+      namespace-repository: flwr/client
+      file-dir: src/docker/client
+      build-args: |
+        FLWR_VERSION=${{ github.event.inputs.flwr-version }}
+        BASE_IMAGE_TAG=py${{ matrix.python-version }}-ubuntu22.04
+      tags: |
+        ${{ github.event.inputs.flwr-version }}-py${{ matrix.python-version }}-ubuntu22.04
+        ${{ github.event.inputs.flwr-version }}
+        latest
+    secrets:
+      dockerhub-user: ${{ secrets.DOCKERHUB_USERNAME }}
+      dockerhub-token: ${{ secrets.DOCKERHUB_TOKEN }}
diff --git a/.github/workflows/docker-server.yml b/.github/workflows/docker-server.yml
index 093f148c4c34..1e43715207d4 100644
--- a/.github/workflows/docker-server.yml
+++ b/.github/workflows/docker-server.yml
@@ -4,11 +4,11 @@ on:
   workflow_dispatch:
     inputs:
       flwr-version:
-        description: "Version of Flower e.g. (1.6.0)."
+        description: "Version of Flower e.g. (1.7.0)."
         required: true
         type: string
-      base-image-version:
-        description: "Version of the Flower base image."
+      base-image-tag:
+        description: "The tag of the Flower base image."
         required: false
         type: string
         default: "py3.11-ubuntu22.04"
@@ -20,14 +20,16 @@ jobs:
   build-server-images:
     name: Build images
     uses: ./.github/workflows/_docker-build.yml
+    # run only on default branch when using it with workflow_dispatch
+    if: github.ref_name == github.event.repository.default_branch
     with:
       namespace-repository: flwr/server
       file-dir: src/docker/server
       build-args: |
         FLWR_VERSION=${{ github.event.inputs.flwr-version }}
-        BASE_IMAGE_VERSION=${{ github.event.inputs.base-image-version }}
+        BASE_IMAGE_TAG=${{ github.event.inputs.base-image-tag }}
       tags: |
-        ${{ github.event.inputs.flwr-version }}-${{ github.event.inputs.base-image-version }}
+        ${{ github.event.inputs.flwr-version }}-${{ github.event.inputs.base-image-tag }}
         ${{ github.event.inputs.flwr-version }}
         latest
     secrets:
diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 52944ffecf70..a4c769fdf850 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -23,6 +23,8 @@ jobs:
       - uses: actions/checkout@v4
         with:
           fetch-depth: 0
+      - name: Check copyright line
+        run: ./dev/test-copyright.sh
       - name: Bootstrap
         uses: ./.github/actions/bootstrap
       - name: Install pandoc
@@ -41,8 +43,9 @@ jobs:
           AWS_DEFAULT_REGION: ${{ secrets. AWS_DEFAULT_REGION }}
           AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
           AWS_SECRET_ACCESS_KEY: ${{ secrets. AWS_SECRET_ACCESS_KEY }}
+          DOCS_BUCKET: flower.ai
         run: |
-          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./doc/build/html/ s3://flower.dev/docs/framework
-          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./baselines/doc/build/html/ s3://flower.dev/docs/baselines
-          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./examples/doc/build/html/ s3://flower.dev/docs/examples
-          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./datasets/doc/build/html/ s3://flower.dev/docs/datasets
+          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./doc/build/html/ s3://${{ env.DOCS_BUCKET }}/docs/framework
+          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./baselines/doc/build/html/ s3://${{ env.DOCS_BUCKET }}/docs/baselines
+          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./examples/doc/build/html/ s3://${{ env.DOCS_BUCKET }}/docs/examples
+          aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./datasets/doc/build/html/ s3://${{ env.DOCS_BUCKET }}/docs/datasets
diff --git a/.github/workflows/e2e.yml b/.github/workflows/e2e.yml
index a5121ad71b38..62f3c0a78ce4 100644
--- a/.github/workflows/e2e.yml
+++ b/.github/workflows/e2e.yml
@@ -14,6 +14,7 @@ concurrency:
 
 env:
   FLWR_TELEMETRY_ENABLED: 0
+  ARTIFACT_BUCKET: artifact.flower.ai
 
 jobs:
   wheel:
@@ -30,12 +31,12 @@ jobs:
       - name: Test wheel
         run: ./dev/test-wheel.sh
       - name: Upload wheel
-        if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork }}
+        if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork && github.actor != 'dependabot[bot]' }}
         id: upload
         env:
-          AWS_DEFAULT_REGION: ${{ secrets. AWS_DEFAULT_REGION }}
+          AWS_DEFAULT_REGION: ${{ secrets.AWS_DEFAULT_REGION }}
           AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
-          AWS_SECRET_ACCESS_KEY: ${{ secrets. AWS_SECRET_ACCESS_KEY }}
+          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
         run: |
           cd ./dist
           echo "WHL_PATH=$(ls *.whl)" >> "$GITHUB_OUTPUT"
@@ -43,7 +44,8 @@ jobs:
           echo "SHORT_SHA=$sha_short" >> "$GITHUB_OUTPUT"
           [ -z "${{ github.head_ref }}" ] && dir="${{ github.ref_name }}" || dir="pr/${{ github.head_ref }}"
           echo "DIR=$dir" >> "$GITHUB_OUTPUT"
-          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./ s3://artifact.flower.dev/py/$dir/$sha_short --recursive
+          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./ s3://${{ env.ARTIFACT_BUCKET }}/py/$dir/$sha_short --recursive
+          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./ s3://${{ env.ARTIFACT_BUCKET }}/py/$dir/latest --recursive
     outputs:
       whl_path: ${{ steps.upload.outputs.WHL_PATH }}
       short_sha: ${{ steps.upload.outputs.SHORT_SHA }}
@@ -73,7 +75,7 @@ jobs:
             dataset: |
               import tensorflow as tf
               tf.keras.datasets.cifar10.load_data()
-              
+
           - directory: tabnet
             dataset: |
               import tensorflow_datasets as tfds
@@ -83,17 +85,12 @@ jobs:
             dataset: |
               from torchvision.datasets import CIFAR10
               CIFAR10('./data', download=True)
-              
+
           - directory: pytorch-lightning
             dataset: |
               from torchvision.datasets import MNIST
               MNIST('./data', download=True)
 
-          - directory: mxnet
-            dataset: |
-              import mxnet as mx
-              mx.test_utils.get_mnist()
-
           - directory: scikit-learn
             dataset: |
               import openml
@@ -102,7 +99,7 @@ jobs:
           - directory: fastai
             dataset: |
               from fastai.vision.all import untar_data, URLs
-              untar_data(URLs.MNIST) 
+              untar_data(URLs.MNIST)
 
           - directory: pandas
             dataset: |
@@ -126,9 +123,9 @@ jobs:
       - name: Install dependencies
         run: python -m poetry install
       - name: Install Flower wheel from artifact store
-        if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork }}
+        if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork && github.actor != 'dependabot[bot]' }}
         run: |
-          python -m pip install https://artifact.flower.dev/py/${{ needs.wheel.outputs.dir }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
+          python -m pip install https://${{ env.ARTIFACT_BUCKET }}/py/${{ needs.wheel.outputs.dir }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Download dataset
         if: ${{ matrix.dataset }}
         run: python -c "${{ matrix.dataset }}"
@@ -138,6 +135,12 @@ jobs:
         run: python simulation.py
       - name: Run driver test
         run: ./../test_driver.sh "${{ matrix.directory }}"
+      - name: Run driver test with REST
+        if: ${{ matrix.directory == 'bare' }}
+        run: ./../test_driver.sh bare rest
+      - name: Run driver test with SQLite database
+        if: ${{ matrix.directory == 'bare' }}
+        run: ./../test_driver.sh bare sqlite
 
   strategies:
     runs-on: ubuntu-22.04
@@ -161,11 +164,11 @@ jobs:
         run: |
           python -m poetry install
       - name: Install Flower wheel from artifact store
-        if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork }}
+        if: ${{ github.repository == 'adap/flower' && !github.event.pull_request.head.repo.fork && github.actor != 'dependabot[bot]' }}
         run: |
-          python -m pip install https://artifact.flower.dev/py/${{ needs.wheel.outputs.dir }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
+          python -m pip install https://${{ env.ARTIFACT_BUCKET }}/py/${{ needs.wheel.outputs.dir }}/${{ needs.wheel.outputs.short_sha }}/${{ needs.wheel.outputs.whl_path }}
       - name: Cache Datasets
-        uses: actions/cache@v3
+        uses: actions/cache@v4
         with:
           path: "~/.keras"
           key: keras-datasets
diff --git a/.github/workflows/framework-draft-release.yml b/.github/workflows/framework-draft-release.yml
new file mode 100644
index 000000000000..91a89953cf96
--- /dev/null
+++ b/.github/workflows/framework-draft-release.yml
@@ -0,0 +1,66 @@
+name: Draft release
+
+on:
+  push:
+    tags:
+      - "v*.*.*"
+
+env:
+  ARTIFACT_BUCKET: artifact.flower.ai
+
+jobs:
+  publish:
+    if: ${{ github.repository == 'adap/flower' }}
+    name: Publish draft
+    runs-on: ubuntu-22.04
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - name: Wait for wheel to be built
+        uses: lewagon/wait-on-check-action@v1.3.3
+        with:
+          ref: ${{ github.ref }}
+          check-name: 'Build, test and upload wheel'
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          wait-interval: 10
+      - name: Download wheel
+        run: |
+          tag_name=$(echo "${GITHUB_REF_NAME}" | cut -c2-)
+          echo "TAG_NAME=$tag_name" >> "$GITHUB_ENV"
+
+          wheel_name="flwr-${tag_name}-py3-none-any.whl"
+          echo "WHEEL_NAME=$wheel_name" >> "$GITHUB_ENV"
+
+          tar_name="flwr-${tag_name}.tar.gz"
+          echo "TAR_NAME=$tar_name" >> "$GITHUB_ENV"
+
+          wheel_url="https://${{ env.ARTIFACT_BUCKET }}/py/main/${GITHUB_SHA::7}/${wheel_name}"
+          tar_url="https://${{ env.ARTIFACT_BUCKET }}/py/main/${GITHUB_SHA::7}/${tar_name}"
+
+          curl $wheel_url --output $wheel_name
+          curl $tar_url --output $tar_name
+      - name: Upload wheel
+        env:
+          AWS_DEFAULT_REGION: ${{ secrets. AWS_DEFAULT_REGION }}
+          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          AWS_SECRET_ACCESS_KEY: ${{ secrets. AWS_SECRET_ACCESS_KEY }}
+        run: |
+          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./${{ env.WHEEL_NAME }} s3://${{ env.ARTIFACT_BUCKET }}/py/release/v${{ env.TAG_NAME }}/${{ env.WHEEL_NAME }}
+          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./${{ env.TAR_NAME }} s3://${{ env.ARTIFACT_BUCKET }}/py/release/v${{ env.TAG_NAME }}/${{ env.TAR_NAME }}
+
+      - name: Generate body
+        run: |
+          ./dev/get-latest-changelog.sh > body.md
+          cat body.md
+
+      - name: Release
+        uses: softprops/action-gh-release@v1
+        with:
+          body_path: ./body.md
+          draft: true
+          name: Flower ${{ env.TAG_NAME }}
+          files: |
+            ${{ env.WHEEL_NAME }}
+            ${{ env.TAR_NAME }}
diff --git a/.github/workflows/framework-release.yml b/.github/workflows/framework-release.yml
index eab15a51d217..04b68fd38af9 100644
--- a/.github/workflows/framework-release.yml
+++ b/.github/workflows/framework-release.yml
@@ -1,63 +1,45 @@
-name: Release Framework
+name: Publish `flwr` release on PyPI
 
 on:
-  push:
-    tags:
-      - "v*.*.*"
+  release:
+    types: [released]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref == 'refs/heads/main' && github.run_id || github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+
+env:
+  ARTIFACT_BUCKET: artifact.flower.ai
 
 jobs:
   publish:
     if: ${{ github.repository == 'adap/flower' }}
-    name: Publish draft
+    name: Publish release
     runs-on: ubuntu-22.04
     steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-      - name: Wait for wheel to be built
-        uses: lewagon/wait-on-check-action@v1.3.1
-        with:
-          ref: ${{ github.ref }}
-          check-name: 'Build, test and upload wheel'
-          repo-token: ${{ secrets.GITHUB_TOKEN }}
-          wait-interval: 10
-      - name: Download wheel
-        run: |
-          tag_name=$(echo "${GITHUB_REF_NAME}" | cut -c2-)
-          echo "TAG_NAME=$tag_name" >> "$GITHUB_ENV"
-          
-          wheel_name="flwr-${tag_name}-py3-none-any.whl"
-          echo "WHEEL_NAME=$wheel_name" >> "$GITHUB_ENV"
-          
-          tar_name="flwr-${tag_name}.tar.gz"
-          echo "TAR_NAME=$tar_name" >> "$GITHUB_ENV"
-
-          wheel_url="https://artifact.flower.dev/py/main/${GITHUB_SHA::7}/${wheel_name}"
-          tar_url="https://artifact.flower.dev/py/main/${GITHUB_SHA::7}/${tar_name}"
-          
-          curl $wheel_url --output $wheel_name
-          curl $tar_url --output $tar_name
-      - name: Upload wheel
-        env:
-          AWS_DEFAULT_REGION: ${{ secrets. AWS_DEFAULT_REGION }}
-          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
-          AWS_SECRET_ACCESS_KEY: ${{ secrets. AWS_SECRET_ACCESS_KEY }}
-        run: |
-          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./${{ env.WHEEL_NAME }} s3://artifact.flower.dev/py/release/v${{ env.TAG_NAME }}/${{ env.WHEEL_NAME }}
-          aws s3 cp --content-disposition "attachment" --cache-control "no-cache" ./${{ env.TAR_NAME }} s3://artifact.flower.dev/py/release/v${{ env.TAG_NAME }}/${{ env.TAR_NAME }}
-          
-      - name: Generate body
-        run: |
-          ./dev/get-latest-changelog.sh > body.md
-          cat body.md
-          
-      - name: Release
-        uses: softprops/action-gh-release@v1
-        with:
-          body_path: ./body.md
-          draft: true
-          name: Flower ${{ env.TAG_NAME }}
-          files: |
-            ${{ env.WHEEL_NAME }}
-            ${{ env.TAR_NAME }}
+    - name: Checkout code
+      uses: actions/checkout@v4
+      with:
+        fetch-depth: 0
+
+    - name: Bootstrap
+      uses: ./.github/actions/bootstrap
+
+    - name: Get artifacts and publish
+      env:
+        GITHUB_REF: ${{ github.ref }}
+      run: |
+        TAG_NAME=$(echo "${GITHUB_REF_NAME}" | cut -c2-)
+
+        wheel_name="flwr-${TAG_NAME}-py3-none-any.whl"
+        tar_name="flwr-${TAG_NAME}.tar.gz"
+
+        wheel_url="https://${{ env.ARTIFACT_BUCKET }}/py/release/v${TAG_NAME}/${wheel_name}"
+        tar_url="https://${{ env.ARTIFACT_BUCKET }}/py/release/v${TAG_NAME}/${tar_name}"
+
+        mkdir -p dist
+
+        curl $wheel_url --output dist/$wheel_name
+        curl $tar_url --output dist/$tar_name
+
+        python -m poetry publish -u __token__ -p ${{ secrets.PYPI_TOKEN }}
diff --git a/.github/workflows/framework.yml b/.github/workflows/framework.yml
index 0899940636d2..784f04750c5e 100644
--- a/.github/workflows/framework.yml
+++ b/.github/workflows/framework.yml
@@ -25,7 +25,7 @@ jobs:
         # In case of a mismatch, the job has to download Python to install it.
         # Note: Due to a bug in actions/setup-python we have to put 3.10 in
         # qoutes as it will otherwise will assume 3.1
-        python: [3.8, 3.9, '3.10']
+        python: [3.8, 3.9, '3.10', '3.11']
 
     name: Python ${{ matrix.python }}
 
diff --git a/.github/workflows/swift.yml b/.github/workflows/swift.yml
index 8758d0e1c5c7..2ca596a59361 100644
--- a/.github/workflows/swift.yml
+++ b/.github/workflows/swift.yml
@@ -20,7 +20,7 @@ jobs:
     name: Test
     runs-on: macos-latest
     steps:
-      - uses: fwal/setup-swift@f51889efb55dccf13be0ee727e3d6c89a096fb4c
+      - uses: fwal/setup-swift@cdbe0f7f4c77929b6580e71983e8606e55ffe7e4
         with:
           swift-version: 5
       - uses: actions/checkout@v4
@@ -31,7 +31,7 @@ jobs:
     runs-on: macos-latest
     name: Build docs
     steps:
-      - uses: fwal/setup-swift@f51889efb55dccf13be0ee727e3d6c89a096fb4c
+      - uses: fwal/setup-swift@cdbe0f7f4c77929b6580e71983e8606e55ffe7e4
         with:
           swift-version: 5
       - uses: actions/checkout@v4
@@ -44,7 +44,7 @@ jobs:
     runs-on: macos-latest
     name: Deploy docs 
     steps:
-      - uses: fwal/setup-swift@f51889efb55dccf13be0ee727e3d6c89a096fb4c
+      - uses: fwal/setup-swift@cdbe0f7f4c77929b6580e71983e8606e55ffe7e4
         with:
           swift-version: 5
       - uses: actions/checkout@v4
diff --git a/.github/workflows/update-pr.yml b/.github/workflows/update-pr.yml
index 78ef5bc86772..64b16aeabebf 100644
--- a/.github/workflows/update-pr.yml
+++ b/.github/workflows/update-pr.yml
@@ -9,7 +9,7 @@ jobs:
     runs-on: ubuntu-22.04
     steps:
       - name: Automatically update mergeable PRs
-        uses: adRise/update-pr-branch@v0.7.0
+        uses: adRise/update-pr-branch@cd305ecbd76bf63056c9400ce2c725293fc3e0c0 # v0.7.0
         with:
           token: ${{ secrets.FLWRMACHINE_TOKEN }}
           base: 'main'
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 264548c87669..1f01c0e9717d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,5 @@
 # Changelog
 
-Flower changes are tracked as part of the documentation: [Flower Changelog](https://flower.dev/docs/changelog.html).
+Flower changes are tracked as part of the documentation: [Flower Changelog](https://flower.ai/docs/changelog.html).
 
 The changelog source can be edited here: `doc/source/changelog.rst`
diff --git a/README.md b/README.md
index b8b62e8c0c43..90faa2358fa6 100644
--- a/README.md
+++ b/README.md
@@ -1,16 +1,16 @@
 # Flower: A Friendly Federated Learning Framework
 
 <p align="center">
-  <a href="https://flower.dev/">
-    <img src="https://flower.dev/_next/image/?url=%2F_next%2Fstatic%2Fmedia%2Fflower_white_border.c2012e70.png&w=640&q=75" width="140px" alt="Flower Website" />
+  <a href="https://flower.ai/">
+    <img src="https://flower.ai/_next/image/?url=%2F_next%2Fstatic%2Fmedia%2Fflower_white_border.c2012e70.png&w=640&q=75" width="140px" alt="Flower Website" />
   </a>
 </p>
 <p align="center">
-    <a href="https://flower.dev/">Website</a> |
-    <a href="https://flower.dev/blog">Blog</a> |
-    <a href="https://flower.dev/docs/">Docs</a> |
-    <a href="https://flower.dev/conf/flower-summit-2022">Conference</a> |
-    <a href="https://flower.dev/join-slack">Slack</a>
+    <a href="https://flower.ai/">Website</a> |
+    <a href="https://flower.ai/blog">Blog</a> |
+    <a href="https://flower.ai/docs/">Docs</a> |
+    <a href="https://flower.ai/conf/flower-summit-2022">Conference</a> |
+    <a href="https://flower.ai/join-slack">Slack</a>
     <br /><br />
 </p>
 
@@ -18,7 +18,7 @@
 [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/adap/flower/blob/main/CONTRIBUTING.md)
 ![Build](https://github.com/adap/flower/actions/workflows/framework.yml/badge.svg)
 [![Downloads](https://static.pepy.tech/badge/flwr)](https://pepy.tech/project/flwr)
-[![Slack](https://img.shields.io/badge/Chat-Slack-red)](https://flower.dev/join-slack)
+[![Slack](https://img.shields.io/badge/Chat-Slack-red)](https://flower.ai/join-slack)
 
 Flower (`flwr`) is a framework for building federated learning systems. The
 design of Flower is based on a few guiding principles:
@@ -33,14 +33,13 @@ design of Flower is based on a few guiding principles:
 
 - **Framework-agnostic**: Different machine learning frameworks have different
   strengths. Flower can be used with any machine learning framework, for
-  example, [PyTorch](https://pytorch.org),
-  [TensorFlow](https://tensorflow.org), [Hugging Face Transformers](https://huggingface.co/), [PyTorch Lightning](https://pytorchlightning.ai/), [MXNet](https://mxnet.apache.org/), [scikit-learn](https://scikit-learn.org/), [JAX](https://jax.readthedocs.io/), [TFLite](https://tensorflow.org/lite/), [fastai](https://www.fast.ai/), [Pandas](https://pandas.pydata.org/) for federated analytics, or even raw [NumPy](https://numpy.org/)
+  example, [PyTorch](https://pytorch.org), [TensorFlow](https://tensorflow.org), [Hugging Face Transformers](https://huggingface.co/), [PyTorch Lightning](https://pytorchlightning.ai/), [scikit-learn](https://scikit-learn.org/), [JAX](https://jax.readthedocs.io/), [TFLite](https://tensorflow.org/lite/), [MONAI](https://docs.monai.io/en/latest/index.html), [fastai](https://www.fast.ai/), [MLX](https://ml-explore.github.io/mlx/build/html/index.html), [XGBoost](https://xgboost.readthedocs.io/en/stable/), [Pandas](https://pandas.pydata.org/) for federated analytics, or even raw [NumPy](https://numpy.org/)
   for users who enjoy computing gradients by hand.
 
 - **Understandable**: Flower is written with maintainability in mind. The
   community is encouraged to both read and contribute to the codebase.
 
-Meet the Flower community on [flower.dev](https://flower.dev)!
+Meet the Flower community on [flower.ai](https://flower.ai)!
 
 ## Federated Learning Tutorial
 
@@ -56,11 +55,11 @@ Flower's goal is to make federated learning accessible to everyone. This series
 
 2. **Using Strategies in Federated Learning**
 
-   [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb))
+   [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb))
 
 3. **Building Strategies for Federated Learning**
 
-   [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb))
+   [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adap/flower/blob/main/doc/source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/doc/source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb))
 
 4. **Custom Clients for Federated Learning**
 
@@ -74,20 +73,19 @@ Stay tuned, more tutorials are coming soon. Topics include **Privacy and Securit
 
 ## Documentation
 
-[Flower Docs](https://flower.dev/docs):
-
-- [Installation](https://flower.dev/docs/framework/how-to-install-flower.html)
-- [Quickstart (TensorFlow)](https://flower.dev/docs/framework/tutorial-quickstart-tensorflow.html)
-- [Quickstart (PyTorch)](https://flower.dev/docs/framework/tutorial-quickstart-pytorch.html)
-- [Quickstart (Hugging Face)](https://flower.dev/docs/framework/tutorial-quickstart-huggingface.html)
-- [Quickstart (PyTorch Lightning [code example])](https://flower.dev/docs/framework/tutorial-quickstart-pytorch-lightning.html)
-- [Quickstart (MXNet)](https://flower.dev/docs/framework/example-mxnet-walk-through.html)
-- [Quickstart (Pandas)](https://flower.dev/docs/framework/tutorial-quickstart-pandas.html)
-- [Quickstart (fastai)](https://flower.dev/docs/framework/tutorial-quickstart-fastai.html)
-- [Quickstart (JAX)](https://flower.dev/docs/framework/tutorial-quickstart-jax.html)
-- [Quickstart (scikit-learn)](https://flower.dev/docs/framework/tutorial-quickstart-scikitlearn.html)
-- [Quickstart (Android [TFLite])](https://flower.dev/docs/framework/tutorial-quickstart-android.html)
-- [Quickstart (iOS [CoreML])](https://flower.dev/docs/framework/tutorial-quickstart-ios.html)
+[Flower Docs](https://flower.ai/docs):
+
+- [Installation](https://flower.ai/docs/framework/how-to-install-flower.html)
+- [Quickstart (TensorFlow)](https://flower.ai/docs/framework/tutorial-quickstart-tensorflow.html)
+- [Quickstart (PyTorch)](https://flower.ai/docs/framework/tutorial-quickstart-pytorch.html)
+- [Quickstart (Hugging Face)](https://flower.ai/docs/framework/tutorial-quickstart-huggingface.html)
+- [Quickstart (PyTorch Lightning)](https://flower.ai/docs/framework/tutorial-quickstart-pytorch-lightning.html)
+- [Quickstart (Pandas)](https://flower.ai/docs/framework/tutorial-quickstart-pandas.html)
+- [Quickstart (fastai)](https://flower.ai/docs/framework/tutorial-quickstart-fastai.html)
+- [Quickstart (JAX)](https://flower.ai/docs/framework/tutorial-quickstart-jax.html)
+- [Quickstart (scikit-learn)](https://flower.ai/docs/framework/tutorial-quickstart-scikitlearn.html)
+- [Quickstart (Android [TFLite])](https://flower.ai/docs/framework/tutorial-quickstart-android.html)
+- [Quickstart (iOS [CoreML])](https://flower.ai/docs/framework/tutorial-quickstart-ios.html)
 
 ## Flower Baselines
 
@@ -100,17 +98,24 @@ Flower Baselines is a collection of community-contributed projects that reproduc
 - [FedMLB](https://github.com/adap/flower/tree/main/baselines/fedmlb)
 - [FedPer](https://github.com/adap/flower/tree/main/baselines/fedper)
 - [FedProx](https://github.com/adap/flower/tree/main/baselines/fedprox)
+- [FedNova](https://github.com/adap/flower/tree/main/baselines/fednova)
+- [HeteroFL](https://github.com/adap/flower/tree/main/baselines/heterofl)
+- [FedAvgM](https://github.com/adap/flower/tree/main/baselines/fedavgm)
+- [FedStar](https://github.com/adap/flower/tree/main/baselines/fedstar)
 - [FedWav2vec2](https://github.com/adap/flower/tree/main/baselines/fedwav2vec2)
 - [FjORD](https://github.com/adap/flower/tree/main/baselines/fjord)
 - [MOON](https://github.com/adap/flower/tree/main/baselines/moon)
 - [niid-Bench](https://github.com/adap/flower/tree/main/baselines/niid_bench)
 - [TAMUNA](https://github.com/adap/flower/tree/main/baselines/tamuna)
+- [FedVSSL](https://github.com/adap/flower/tree/main/baselines/fedvssl)
+- [FedXGBoost](https://github.com/adap/flower/tree/main/baselines/hfedxgboost)
+- [FedPara](https://github.com/adap/flower/tree/main/baselines/fedpara)
 - [FedAvg](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist)
 - [FedOpt](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization)
 
-Please refer to the [Flower Baselines Documentation](https://flower.dev/docs/baselines/) for a detailed categorization of baselines and for additional info including:
-* [How to use Flower Baselines](https://flower.dev/docs/baselines/how-to-use-baselines.html)
-* [How to contribute a new Flower Baseline](https://flower.dev/docs/baselines/how-to-contribute-baselines.html)
+Please refer to the [Flower Baselines Documentation](https://flower.ai/docs/baselines/) for a detailed categorization of baselines and for additional info including:
+* [How to use Flower Baselines](https://flower.ai/docs/baselines/how-to-use-baselines.html)
+* [How to contribute a new Flower Baseline](https://flower.ai/docs/baselines/how-to-contribute-baselines.html)
 
 ## Flower Usage Examples
 
@@ -124,24 +129,32 @@ Quickstart examples:
 - [Quickstart (PyTorch Lightning)](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch-lightning)
 - [Quickstart (fastai)](https://github.com/adap/flower/tree/main/examples/quickstart-fastai)
 - [Quickstart (Pandas)](https://github.com/adap/flower/tree/main/examples/quickstart-pandas)
-- [Quickstart (MXNet)](https://github.com/adap/flower/tree/main/examples/quickstart-mxnet)
 - [Quickstart (JAX)](https://github.com/adap/flower/tree/main/examples/quickstart-jax)
+- [Quickstart (MONAI)](https://github.com/adap/flower/tree/main/examples/quickstart-monai)
 - [Quickstart (scikit-learn)](https://github.com/adap/flower/tree/main/examples/sklearn-logreg-mnist)
 - [Quickstart (Android [TFLite])](https://github.com/adap/flower/tree/main/examples/android)
 - [Quickstart (iOS [CoreML])](https://github.com/adap/flower/tree/main/examples/ios)
+- [Quickstart (MLX)](https://github.com/adap/flower/tree/main/examples/quickstart-mlx)
+- [Quickstart (XGBoost)](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart)
 
 Other [examples](https://github.com/adap/flower/tree/main/examples):
 
 - [Raspberry Pi & Nvidia Jetson Tutorial](https://github.com/adap/flower/tree/main/examples/embedded-devices)
 - [PyTorch: From Centralized to Federated](https://github.com/adap/flower/tree/main/examples/pytorch-from-centralized-to-federated)
-- [MXNet: From Centralized to Federated](https://github.com/adap/flower/tree/main/examples/mxnet-from-centralized-to-federated)
+- [Vertical FL](https://github.com/adap/flower/tree/main/examples/vertical-fl)
+- [Federated Finetuning of OpenAI's Whisper](https://github.com/adap/flower/tree/main/examples/whisper-federated-finetuning)
+- [Federated Finetuning of Large Language Model](https://github.com/adap/flower/tree/main/examples/fedllm-finetune)
+- [Federated Finetuning of a Vision Transformer](https://github.com/adap/flower/tree/main/examples/vit-finetune)
 - [Advanced Flower with TensorFlow/Keras](https://github.com/adap/flower/tree/main/examples/advanced-tensorflow)
 - [Advanced Flower with PyTorch](https://github.com/adap/flower/tree/main/examples/advanced-pytorch)
-- 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))
+- 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)
 
 ## Community
 
-Flower is built by a wonderful community of researchers and engineers. [Join Slack](https://flower.dev/join-slack) to meet them, [contributions](#contributing-to-flower) are welcome.
+Flower is built by a wonderful community of researchers and engineers. [Join Slack](https://flower.ai/join-slack) to meet them, [contributions](#contributing-to-flower) are welcome.
 
 <a href="https://github.com/adap/flower/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=adap/flower" />
diff --git a/baselines/README.md b/baselines/README.md
index a18c0553b2b4..3a84df02d8de 100644
--- a/baselines/README.md
+++ b/baselines/README.md
@@ -1,7 +1,7 @@
 # Flower Baselines
 
 
-> We are changing the way we structure the Flower baselines. While we complete the transition to the new format, you can still find the existing baselines in the `flwr_baselines` directory. Currently, you can make use of baselines for [FedAvg](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist), [FedOpt](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization), and [LEAF-FEMNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist). 
+> We are changing the way we structure the Flower baselines. While we complete the transition to the new format, you can still find the existing baselines in the `flwr_baselines` directory. Currently, you can make use of baselines for [FedAvg](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist), [FedOpt](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization), and [LEAF-FEMNIST](https://github.com/adap/flower/tree/main/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist).
 
 > The documentation below has been updated to reflect the new way of using Flower baselines.
 
@@ -23,7 +23,7 @@ Please note that some baselines might include additional files (e.g. a `requirem
 
 ## Running the baselines
 
-Each baseline is self-contained in its own directory. Furthermore, each baseline defines its own Python environment using [Poetry](https://python-poetry.org/docs/) via a `pyproject.toml` file and  [`pyenv`](https://github.com/pyenv/pyenv). If you haven't setup `Poetry` and `pyenv` already on your machine, please take a look at the [Documentation](https://flower.dev/docs/baselines/how-to-use-baselines.html#setting-up-your-machine) for a guide on how to do so. 
+Each baseline is self-contained in its own directory. Furthermore, each baseline defines its own Python environment using [Poetry](https://python-poetry.org/docs/) via a `pyproject.toml` file and  [`pyenv`](https://github.com/pyenv/pyenv). If you haven't setup `Poetry` and `pyenv` already on your machine, please take a look at the [Documentation](https://flower.ai/docs/baselines/how-to-use-baselines.html#setting-up-your-machine) for a guide on how to do so.
 
 Assuming `pyenv` and `Poetry` are already installed on your system. Running a baseline can be done by:
 
@@ -54,7 +54,7 @@ The steps to follow are:
     ```bash
     # This will create a new directory with the same structure as `baseline_template`.
     ./dev/create-baseline.sh <baseline-name>
-    ``` 
+    ```
 3. Then, go inside your baseline directory and continue with the steps detailed in `EXTENDED_README.md` and `README.md`.
 4. Once your code is ready and you have checked that following the instructions in your `README.md` the Python environment can be created correctly and that running the code following your instructions can reproduce the experiments in the paper, you just need to create a Pull Request (PR). Then, the process to merge your baseline into the Flower repo will begin!
 
diff --git a/baselines/baseline_template/pyproject.toml b/baselines/baseline_template/pyproject.toml
index da7516437f09..31f1ee7bfe6d 100644
--- a/baselines/baseline_template/pyproject.toml
+++ b/baselines/baseline_template/pyproject.toml
@@ -7,11 +7,11 @@ name = "<BASELINE_NAME>" # <----- Ensure it matches the name of your baseline di
 version = "1.0.0"
 description = "Flower Baselines"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -42,9 +42,9 @@ flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
-docformatter = "==1.5.1"
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
@@ -80,10 +80,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/dasha/pyproject.toml b/baselines/dasha/pyproject.toml
index 3ef24e4b985a..f03ad06e26e4 100644
--- a/baselines/dasha/pyproject.toml
+++ b/baselines/dasha/pyproject.toml
@@ -9,9 +9,9 @@ description = "DASHA: Distributed nonconvex optimization with communication comp
 license = "Apache-2.0"
 authors = ["Alexander Tyurin <alexandertiurin@gmail.com>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -49,9 +49,9 @@ torchvision = [{ url = "https://download.pytorch.org/whl/cu118/torchvision-0.15.
                { url = "https://download.pytorch.org/whl/cpu/torchvision-0.15.0-cp39-cp39-macosx_11_0_arm64.whl", markers="sys_platform == 'darwin'"}]
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
-docformatter = "==1.5.1"
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
@@ -60,6 +60,7 @@ pytest-watch = "==4.2.0"
 types-requests = "==2.27.7"
 py-spy = "==0.3.14"
 ruff = "==0.0.272"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
@@ -88,8 +89,8 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias,no-self-use,too-many-locals,too-many-instance-attributes"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
-generated-members="numpy.*, torch.*"
+signature-mutators = "hydra.main.main"
+generated-members = "numpy.*, torch.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/depthfl/README.md b/baselines/depthfl/README.md
index b8ab7ed18571..ab26f9c8f8c9 100644
--- a/baselines/depthfl/README.md
+++ b/baselines/depthfl/README.md
@@ -124,7 +124,7 @@ python -m depthfl.main --multirun exclusive_learning=true model_size=1,2,3,4
 
 **Table 2** 
 
-100% (a), 75%(b), 50%(c), 25% (d) cases are exclusive learning scenario. 100% (a) exclusive learning means, the global model and every local model are equal to the smallest local model, and 100% clients participate in learning. Likewise, 25% (d) exclusive learning means, the global model and every local model are equal to the larget local model, and only 25% clients participate in learning.
+100% (a), 75%(b), 50%(c), 25% (d) cases are exclusive learning scenario. 100% (a) exclusive learning means, the global model and every local model are equal to the smallest local model, and 100% clients participate in learning. Likewise, 25% (d) exclusive learning means, the global model and every local model are equal to the largest local model, and only 25% clients participate in learning.
 
 | Scaling Method | Dataset | Global Model | 100% (a) | 75% (b) | 50% (c) | 25% (d) | 
 | :---: | :---: | :---: | :---: | :---: | :---: | :---: | 
diff --git a/baselines/depthfl/depthfl/models.py b/baselines/depthfl/depthfl/models.py
index df3eebf9f9ce..5ce5eedb360e 100644
--- a/baselines/depthfl/depthfl/models.py
+++ b/baselines/depthfl/depthfl/models.py
@@ -1,6 +1,5 @@
 """ResNet18 model architecutre, training, and testing functions for CIFAR100."""
 
-
 from typing import List, Tuple
 
 import torch
diff --git a/baselines/depthfl/pyproject.toml b/baselines/depthfl/pyproject.toml
index 2f928c2d3553..e59d16fef88e 100644
--- a/baselines/depthfl/pyproject.toml
+++ b/baselines/depthfl/pyproject.toml
@@ -9,9 +9,9 @@ description = "DepthFL: Depthwise Federated Learning for Heterogeneous Clients"
 license = "Apache-2.0"
 authors = ["Minjae Kim <mjkim@snu.ac.kr>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -37,18 +37,17 @@ classifiers = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.10.0, <3.11.0" 
+python = ">=3.10.0, <3.11.0"
 flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 matplotlib = "3.7.1"
-torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.13.1%2Bcu116-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.14.1%2Bcu116-cp310-cp310-linux_x86_64.whl"}
-
+torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.13.1%2Bcu116-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.14.1%2Bcu116-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
-docformatter = "==1.5.1"
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
@@ -56,6 +55,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
@@ -84,10 +84,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/doc/source/_templates/base.html b/baselines/doc/source/_templates/base.html
index 1cee99053fbe..cc171be9e6b0 100644
--- a/baselines/doc/source/_templates/base.html
+++ b/baselines/doc/source/_templates/base.html
@@ -5,7 +5,7 @@
     <meta charset="utf-8"/>
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark">
-    <link rel="canonical" href="https://flower.dev/docs/baselines/{{ pagename }}.html">
+    <link rel="canonical" href="https://flower.ai/docs/baselines/{{ pagename }}.html">
 
     {%- if metatags %}{{ metatags }}{% endif -%}
 
@@ -99,6 +99,6 @@
     {%- endblock -%}
 
     {%- endblock scripts -%}
-    <script defer data-domain="flower.dev" data-api="/static/pla/api/event" src="/static/pla/js/script.js"></script>
+    <script defer data-domain="flower.ai" data-api="/static/pla/api/event" src="/static/pla/js/script.js"></script>
   </body>
 </html>
diff --git a/baselines/doc/source/conf.py b/baselines/doc/source/conf.py
index e1055aaed216..dad8650cddaa 100644
--- a/baselines/doc/source/conf.py
+++ b/baselines/doc/source/conf.py
@@ -14,6 +14,7 @@
 # ==============================================================================
 
 
+import datetime
 import os
 import sys
 from sphinx.application import ConfigError
@@ -32,7 +33,7 @@
 # -- Project information -----------------------------------------------------
 
 project = "Flower"
-copyright = "2022 Flower Labs GmbH"
+copyright = f"{datetime.date.today().year} Flower Labs GmbH"
 author = "The Flower Authors"
 
 # The full version, including alpha/beta/rc tags
@@ -84,7 +85,7 @@
 html_title = f"Flower Baselines {release}"
 html_logo = "_static/flower-logo.png"
 html_favicon = "_static/favicon.ico"
-html_baseurl = "https://flower.dev/docs/baselines/"
+html_baseurl = "https://flower.ai/docs/baselines/"
 
 html_theme_options = {
     #
diff --git a/baselines/doc/source/how-to-use-baselines.rst b/baselines/doc/source/how-to-use-baselines.rst
index ed47438ad5a9..4704a9b6074e 100644
--- a/baselines/doc/source/how-to-use-baselines.rst
+++ b/baselines/doc/source/how-to-use-baselines.rst
@@ -33,7 +33,7 @@ Setting up your machine
 
 Common to all baselines is `Poetry <https://python-poetry.org/docs/>`_, a tool to manage Python dependencies. Baselines also make use of `Pyenv <https://github.com/pyenv/pyenv>`_. You'll need to install both on your system before running a baseline. What follows is a step-by-step guide on getting :code:`pyenv` and :code:`Poetry` installed on your system.
 
-Let's begin by installing :code:`pyenv`. We'll be following the standard procedure. Please refere to the `pyenv docs <https://github.com/pyenv/pyenv#installation>`_ for alternative ways of installing it.
+Let's begin by installing :code:`pyenv`. We'll be following the standard procedure. Please refer to the `pyenv docs <https://github.com/pyenv/pyenv#installation>`_ for alternative ways of installing it.
 
 .. code-block:: bash
 
@@ -49,7 +49,7 @@ Let's begin by installing :code:`pyenv`. We'll be following the standard procedu
   command -v pyenv >/dev/null || export PATH="$PYENV_ROOT/bin:$PATH"
   eval "$(pyenv init -)"
 
-Verify your installtion by opening a new terminal and
+Verify your installation by opening a new terminal and
 
 .. code-block:: bash
 
@@ -63,7 +63,7 @@ Then you can proceed and install any version of Python. Most baselines currently
 
   pyenv install 3.10.6
   # this will take a little while
-  # once done, you should see that that version is avaialble
+  # once done, you should see that that version is available
   pyenv versions
   # system
   # * 3.10.6  # <-- you just installed this
diff --git a/baselines/doc/source/index.rst b/baselines/doc/source/index.rst
index 335cfacef1ab..3a19e74b891e 100644
--- a/baselines/doc/source/index.rst
+++ b/baselines/doc/source/index.rst
@@ -1,7 +1,7 @@
 Flower Baselines Documentation
 ==============================
 
-Welcome to Flower Baselines' documentation. `Flower <https://flower.dev>`_ is a friendly federated learning framework.
+Welcome to Flower Baselines' documentation. `Flower <https://flower.ai>`_ is a friendly federated learning framework.
 
 
 Join the Flower Community
@@ -9,7 +9,7 @@ Join the Flower Community
 
 The Flower Community is growing quickly - we're a friendly group of researchers, engineers, students, professionals, academics, and other enthusiasts.
 
-.. button-link:: https://flower.dev/join-slack
+.. button-link:: https://flower.ai/join-slack
     :color: primary
     :shadow:
 
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new file mode 100644
index 000000000000..d64569567334
--- /dev/null
+++ b/baselines/fedavgm/LICENSE
@@ -0,0 +1,202 @@
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diff --git a/baselines/fedavgm/README.md b/baselines/fedavgm/README.md
new file mode 100644
index 000000000000..5b8ddfcad6a8
--- /dev/null
+++ b/baselines/fedavgm/README.md
@@ -0,0 +1,220 @@
+---
+title: Measuring the effects of non-identical data distribution for federated visual classification
+url: https://arxiv.org/abs/1909.06335
+labels: [non-iid, image classification]
+dataset: [CIFAR-10, Fashion-MNIST]
+---
+
+# Measuring the effects of non-identical data distribution for federated visual classification
+
+> Note: If you use this baseline in your work, please remember to cite the original authors of the paper as well as the Flower paper.
+
+**Paper:** [arxiv.org/abs/1909.06335](https://arxiv.org/abs/1909.06335)
+
+**Authors:** Tzu-Ming Harry Hsu, Hang Qi, Matthew Brown
+
+**Abstract:** Federated Learning enables visual models to be trained in a privacy-preserving way using real-world data from mobile devices. Given their distributed nature, the statistics of the data across these devices is likely to differ significantly. In this work, we look at the effect such non-identical data distributions has on visual classification via Federated Learning. We propose a way to synthesize datasets with a continuous range of identicalness and provide performance measures for the Federated Averaging algorithm. We show that performance degrades as distributions differ more, and propose a mitigation strategy via server momentum. Experiments on CIFAR-10 demonstrate improved classification performance over a range of non-identicalness, with classification accuracy improved from 30.1% to 76.9% in the most skewed settings.
+
+
+## About this baseline
+
+**What’s implemented:** The code in this directory evaluates the effects of non-identical data distribution for visual classification task based on paper _Measuring the effects of non-identical data distribution for federated visual classification_ (Hsu et al., 2019). It reproduces the FedAvgM and FedAvg performance curves for different non-identical-ness of the dataset (CIFAR-10 and Fashion-MNIST). _Figure 5 in the paper, section 4.2._
+
+**Datasets:** CIFAR-10, and Fashion-MNIST
+
+**Hardware Setup:** This baseline was evaluated in a regular PC without GPU (Intel i7-10710U CPU, and 32 Gb RAM). The major constraint is to run a huge number of rounds such as the reference paper that reports 10.000 round for each case evaluated. 
+
+**Contributors:** Gustavo Bertoli [(@gubertoli)](https://github.com/gubertoli)
+
+## Experimental Setup
+
+**Task:** Image Classification
+
+**Model:** This directory implements a CNN model similar to the one used on the seminal FedAvg paper (`models.py`):
+
+- McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017, April). Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics (pp. 1273-1282). PMLR. ([Link](http://proceedings.mlr.press/v54/mcmahan17a/mcmahan17a.pdf)):
+
+As the following excerpt:
+
+> "*We also ran experiments on the CIFAR-10 dataset... The model architecture was taken from the TensorFlow tutorial [38], which consists of two convolutional layers followed by two fully connected layers and then a linear transformation layer to produce logits, for a total of about 10 parameters."*
+
+Regarding this architecture, the historical references mentioned on the FedAvg and FedAvgM papers are [this](https://web.archive.org/web/20190415103404/https://www.tensorflow.org/tutorials/images/deep_cnn) and [this](https://web.archive.org/web/20170807002954/https://github.com/tensorflow/models/blob/master/tutorials/image/cifar10/cifar10.py).
+
+Important to highlight the rationale with this CNN model stated on FedAvgM paper:
+
+> "*This model is not the state-of-the-art on the CIFAR-10 dataset, but is sufficient to show relative performance for the purposes of our investigation."*
+
+**The default CNN model in use on this baseline have a centralized accuracy of ~0.74. That is different from the reported 0.86 accuracy from the original FedAvg paper. But it is still sufficient to show the relative performance for the purposes of FedAvgM investigation.**
+
+**Dataset:** This baseline includes the CIFAR-10 and Fashion-MNIST datasets. By default it will run with the CIFAR-10. The data partition uses a configurable Latent Dirichlet Allocation (LDA) distribution (`concentration` parameter equals 0.1 as default) to create **non-iid distributions** between the clients. The understanding for this `concentration` (α) is that α→∞ all clients have identical distribution, and α→𝟢 each client hold samples from only one class.
+
+| Dataset | # classes | # partitions | partition method | partition settings|
+| :------ | :---: | :---: | :---: | :---: |
+| CIFAR-10 | 10 | `num_clients` | Latent Dirichlet Allocation (LDA) | `concentration` |
+| Fashion-MNIST | 10 | `num_clients` | Latent Dirichlet Allocation (LDA) | `concentration` |
+
+**Data distribution:** The following figure illustrates the use of multiple `concentration` values to generate the data distribution over 30 clients for CIFAR-10 (10 classes) - [source code](fedavgm/utils.py):
+
+![](_static/concentration_cifar10_v2.png)
+
+**Training Hyperparameters:**
+The following table shows the main hyperparameters for this baseline with their default value (i.e. the value used if you run `python main.py` directly)
+
+| Description | Default Value |
+| ----------- | ----- |
+| total clients | 10 |
+| number of rounds | 5 |
+| model | CNN |
+| strategy | Custom FedAvgM |
+| dataset | CIFAR-10 |
+| concentration | 0.1 |
+| fraction evaluate | 0 |
+| num cpus | 1 |
+| num gpus | 0 |
+| server momentum | 0.9 |
+| server learning rate | 1.0 |
+| server reporting fraction | 0.05 |
+| client local epochs | 1 |
+| client batch size | 64 |
+| client learning rate | 0.01 |
+
+### Custom FedAvgM
+In contrast to the initial implementation found in Flower v1.5.0, our baseline incorporates the Nesterov accelerated gradient as a pivotal component of the momentum applied to the server model. It is worth emphasizing that the inclusion of Nesterov momentum aligns with the original definition of FedAvgM in the research paper.
+
+To use the original Flower implementation, use the argument `strategy=fedavgm`. By default, the custom implementation is used. But, you can also refer to it on the command line as `strategy=custom-fedavgm`.
+
+## Environment Setup
+
+### Specifying the Python Version
+
+This baseline was tested with Python 3.10.6 and following the steps below to construct the Python environment and install all dependencies. Both [`pyenv`](https://github.com/pyenv/pyenv) and [`poetry`](https://python-poetry.org/docs/) are assumed to be already present in your system.
+
+```bash
+# Cd to your baseline directory (i.e. where the `pyproject.toml` is), then
+pyenv local 3.10.6
+
+# Set that version for poetry
+poetry env use 3.10.6
+
+# Install the base Poetry environment
+poetry install
+
+# Activate the environment
+poetry shell
+```
+
+### Google Colab
+If you want to setup the environment on Google Colab, please executed the script `conf-colab.sh`, just use the Colab terminal and the following:
+
+```bash
+chmod +x conf-colab.sh
+./conf-colab.sh
+```
+
+## Running the Experiments
+
+To run this FedAvgM with CIFAR-10 baseline, first ensure you have activated your Poetry environment (execute `poetry shell` from this directory), then:
+
+```bash  
+python -m fedavgm.main # this will run using the default setting in the `conf/base.yaml`
+
+# you can override settings directly from the command line
+
+python -m fedavgm.main strategy=fedavg num_clients=1000 num_rounds=50 # will set the FedAvg with 1000 clients and 50 rounds
+
+python -m fedavgm.main dataset=fmnist noniid.concentration=10 # use the Fashion-MNIST dataset and a different concentration for the LDA-based partition
+
+python -m fedavgm.main server.reporting_fraction=0.2 client.local_epochs=5 # will set the reporting fraction to 20% and the local epochs in the clients to 5
+```
+
+## Expected Results
+
+### CIFAR-10
+Similar to FedAvgM paper as reference, the CIFAR-10 evaluation runs 10,000 rounds.
+
+> In order to speedup the execution of these experiments, the evaluation of the _global model_ on the test set only takes place after the last round. The highest accuracy is achieved towards the last rounds, not necessarily in the last. If you wish to evaluate the _global model_ on the test set (or a validation set) more frequently, edit `get_evaluate_fn` in `server.py`. Overal, running the experiments as shown below demonstrate that `FedAvgM` is consistently superior to `FedAvg`.
+
+For FedAvgM evaluation, it was performed a hyperparameter search of server momentum and client learning rate (similar to Figure 6 reported below) for each of the concentrations under analysis, using the following commands:
+
+- Concentration = 1e-5 and 1e-9 (extreme non-iid)
+```bash
+python -m fedavgm.main --multirun client.local_epochs=1 noniid.concentration=1e-5,1e-9 strategy=custom-fedavgm,fedavg \
+server.reporting_fraction=0.05 num_rounds=10000 num_clients=100 \
+dataset=cifar10 client.lr=0.0003 server.momentum=0.99
+```
+
+- Concentration = 0.01
+```bash
+python -m fedavgm.main --multirun client.local_epochs=1 noniid.concentration=0.01 strategy=custom-fedavgm,fedavg \
+server.reporting_fraction=0.05 num_rounds=10000 num_clients=100 \
+dataset=cifar10 client.lr=0.003 server.momentum=0.97
+```
+
+- Concentration = 0.1
+```bash
+python -m fedavgm.main --multirun client.local_epochs=1 noniid.concentration=0.1 strategy=custom-fedavgm,fedavg \
+server.reporting_fraction=0.05 num_rounds=10000 num_clients=100 \
+dataset=cifar10 client.lr=0.0003 server.momentum=0.99
+```
+
+- Concentration = 1
+```bash
+python -m fedavgm.main --multirun client.local_epochs=1 noniid.concentration=1 strategy=custom-fedavgm,fedavg \
+server.reporting_fraction=0.05 num_rounds=10000 num_clients=100 \
+dataset=cifar10 client.lr=0.0003 server.momentum=0.997
+```
+
+- Concentration = 10
+```bash
+python -m fedavgm.main --multirun client.local_epochs=1 noniid.concentration=10 strategy=custom-fedavgm,fedavg \
+server.reporting_fraction=0.05 num_rounds=10000 num_clients=100 \
+dataset=cifar10 client.lr=0.003 server.momentum=0.9
+```
+
+Summarizing all the results:
+
+![](_static/fedavgm_vs_fedavg_rounds=10000_cifar10_w_1e-9.png)
+
+The findings aligns with the report on the original FedAvgM paper that *"To prevent client updates from diverging, we additionally have to use a combination of low absolute learning rate and high momentum"*.
+
+The following command reproduces the same behavior of Figure 6 from FedAvgM paper for the case of Local Epoch E=1, Reporting Fraction C=0.05, and concentration (α) = 1. In this example, it runs just 1,000 rounds:
+
+```bash
+python -m fedavgm.main --multirun client.local_epochs=1 noniid.concentration=1 \
+strategy=custom-fedavgm server.reporting_fraction=0.05 num_rounds=100 num_clients=100 \
+dataset=cifar10 client.lr=0.0001,0.0003,0.001,0.003,0.01,0.03,0.1,0.3 \
+server.momentum=0.7,0.9,0.97,0.99,0.997
+```
+
+![](_static/Figure6_cifar10_num-rounds=1000_concentration=1.png)
+
+
+---
+### Fashion-MNIST
+
+```bash
+python -m fedavgm.main --multirun client.local_epochs=1 \
+noniid.concentration=0.001,0.01,0.1,1,10,100 strategy=custom-fedavgm,fedavg \
+server.reporting_fraction=0.05 num_rounds=1000 \
+num_clients=100 dataset=fmnist server.momentum=0.97 client.lr=0.003
+```
+The above command will evaluate the custom FedAvgM versus FedAvg on Fashion-MNIST datasets. It uses 100 clients with a reporting fraction of 5% during 1000 rounds. To evaluate the non-iid aspects, this exececution exercises concentration of [100, 10, 1, 0.1, 0.01, 0.001]:
+
+![](_static/fedavgm_vs_fedavg_rounds=1000_fmnist.png)
+
+#### Comparison between the Custom-FedAvgM and FedAvgM
+
+To compare the improvement of the FedAvgM with Nesterov momentum (`strategy=custom-fedavgm`) and the FedAvgM without the Nesterov momentum (`strategy=fedavgm`), here we use the results of previous running with addition of the same conditions for the `fedavgm` strategy as follows:
+
+```bash
+python -m fedavgm.main --multirun client.local_epochs=1 \
+noniid.concentration=0.001,0.01,0.1,1,10,100 strategy=fedavgm \
+server.reporting_fraction=0.05 num_rounds=1000 \
+num_clients=100 dataset=fmnist server.momentum=0.97 client.lr=0.003
+```
+
+![](_static/custom-fedavgm_vs_fedavgm_rounds=1000_fmnist.png)
+
+Overall, FedAvgM with Nesterov momentum outperforms the FedAvgM without Nesterov momentum, being clear this behavior for higher non-iidness (0.01 and 0.001). In these higher non-iidness, the test accuracy for FedAvg without Nesterov momentum are worse than the FedAvg.
+For larger concentrations (1, 10, 100), it was observed some runs that the centralized evaluation resulted in a loss equal NaN or Inf, thus it was required multiple runs to guarantee the accuracies reported.
+
diff --git a/baselines/fedavgm/_static/Comparison_CNN_vs_TF_v1_x_Example_for_CIFAR_10.ipynb b/baselines/fedavgm/_static/Comparison_CNN_vs_TF_v1_x_Example_for_CIFAR_10.ipynb
new file mode 100644
index 000000000000..fac837d145e3
--- /dev/null
+++ b/baselines/fedavgm/_static/Comparison_CNN_vs_TF_v1_x_Example_for_CIFAR_10.ipynb
@@ -0,0 +1,1851 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "import numpy as np\n",
+        "from keras.optimizers import SGD\n",
+        "from keras.regularizers import l2\n",
+        "from tensorflow import keras\n",
+        "from tensorflow.nn import local_response_normalization\n",
+        "from keras.utils import to_categorical\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "Rp9LUn54SUTu"
+      },
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "7tTxE8D6bD6g"
+      },
+      "outputs": [],
+      "source": [
+        "def tf_example(input_shape, num_classes):\n",
+        "    \"\"\"CNN Model from TensorFlow v1.x example.\n",
+        "\n",
+        "    This is the model referenced on the FedAvg paper.\n",
+        "\n",
+        "    Reference:\n",
+        "    https://web.archive.org/web/20170807002954/https://github.com/tensorflow/models/blob/master/tutorials/image/cifar10/cifar10.py\n",
+        "    \"\"\"\n",
+        "    input_shape = tuple(input_shape)\n",
+        "\n",
+        "    weight_decay = 0.004\n",
+        "    model = keras.Sequential(\n",
+        "        [\n",
+        "            keras.layers.Conv2D(\n",
+        "                64,\n",
+        "                (5, 5),\n",
+        "                padding=\"same\",\n",
+        "                activation=\"relu\",\n",
+        "                input_shape=input_shape,\n",
+        "            ),\n",
+        "            keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding=\"same\"),\n",
+        "            keras.layers.Lambda(\n",
+        "                local_response_normalization,\n",
+        "                arguments={\n",
+        "                    \"depth_radius\": 4,\n",
+        "                    \"bias\": 1.0,\n",
+        "                    \"alpha\": 0.001 / 9.0,\n",
+        "                    \"beta\": 0.75,\n",
+        "                },\n",
+        "            ),\n",
+        "            keras.layers.Conv2D(\n",
+        "                64,\n",
+        "                (5, 5),\n",
+        "                padding=\"same\",\n",
+        "                activation=\"relu\",\n",
+        "            ),\n",
+        "            keras.layers.Lambda(\n",
+        "                local_response_normalization,\n",
+        "                arguments={\n",
+        "                    \"depth_radius\": 4,\n",
+        "                    \"bias\": 1.0,\n",
+        "                    \"alpha\": 0.001 / 9.0,\n",
+        "                    \"beta\": 0.75,\n",
+        "                },\n",
+        "            ),\n",
+        "            keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding=\"same\"),\n",
+        "            keras.layers.Flatten(),\n",
+        "            keras.layers.Dense(\n",
+        "                384, activation=\"relu\", kernel_regularizer=l2(weight_decay)\n",
+        "            ),\n",
+        "            keras.layers.Dense(\n",
+        "                192, activation=\"relu\", kernel_regularizer=l2(weight_decay)\n",
+        "            ),\n",
+        "            keras.layers.Dense(num_classes, activation=\"softmax\"),\n",
+        "        ]\n",
+        "    )\n",
+        "    optimizer = SGD(learning_rate=0.1)\n",
+        "    model.compile(\n",
+        "        loss=\"categorical_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"]\n",
+        "    )\n",
+        "\n",
+        "    return model\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def cifar10(num_classes, input_shape):\n",
+        "    \"\"\"Prepare the CIFAR-10.\n",
+        "\n",
+        "    This method considers CIFAR-10 for creating both train and test sets. The sets are\n",
+        "    already normalized.\n",
+        "    \"\"\"\n",
+        "    print(f\">>> [Dataset] Loading CIFAR-10. {num_classes} | {input_shape}.\")\n",
+        "    (x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()\n",
+        "    x_train = x_train.astype(\"float32\") / 255\n",
+        "    x_test = x_test.astype(\"float32\") / 255\n",
+        "    input_shape = x_train.shape[1:]\n",
+        "    num_classes = len(np.unique(y_train))\n",
+        "\n",
+        "    return x_train, y_train, x_test, y_test, input_shape, num_classes"
+      ],
+      "metadata": {
+        "id": "vuQykx1uSXHk"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "FMph7H-qbHHR",
+        "outputId": "45cf4a68-7054-460e-bcd7-c353338dc387"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            ">>> [Dataset] Loading CIFAR-10. 10 | (32, 32, 3).\n"
+          ]
+        }
+      ],
+      "source": [
+        "x_train, y_train, x_test, y_test, input_shape,num_classes = cifar10(10, (32,32,3))\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "EPOCHS=350\n",
+        "BATCH_SIZE=128"
+      ],
+      "metadata": {
+        "id": "AD2qsybwX6uR"
+      },
+      "execution_count": 19,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "---"
+      ],
+      "metadata": {
+        "id": "531ZRrY2SY85"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "1BO5D4ZBbJJo"
+      },
+      "outputs": [],
+      "source": [
+        "model = tf_example(input_shape, num_classes)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8DMMAgw6bK2C",
+        "outputId": "9c1203a2-7152-4c25-dc1c-1c58b1cf8b8b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/350\n",
+            "391/391 [==============================] - 8s 18ms/step - loss: 4.8242 - accuracy: 0.2914\n",
+            "Epoch 2/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 3.0276 - accuracy: 0.4814\n",
+            "Epoch 3/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 2.1395 - accuracy: 0.5609\n",
+            "Epoch 4/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 1.6463 - accuracy: 0.6129\n",
+            "Epoch 5/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 1.3656 - accuracy: 0.6504\n",
+            "Epoch 6/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 1.1851 - accuracy: 0.6868\n",
+            "Epoch 7/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 1.0698 - accuracy: 0.7147\n",
+            "Epoch 8/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.9918 - accuracy: 0.7350\n",
+            "Epoch 9/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.9465 - accuracy: 0.7551\n",
+            "Epoch 10/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.8991 - accuracy: 0.7747\n",
+            "Epoch 11/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.8534 - accuracy: 0.7971\n",
+            "Epoch 12/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.8305 - accuracy: 0.8111\n",
+            "Epoch 13/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.8070 - accuracy: 0.8265\n",
+            "Epoch 14/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.7805 - accuracy: 0.8434\n",
+            "Epoch 15/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.7672 - accuracy: 0.8527\n",
+            "Epoch 16/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.7504 - accuracy: 0.8647\n",
+            "Epoch 17/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.7418 - accuracy: 0.8715\n",
+            "Epoch 18/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.7244 - accuracy: 0.8819\n",
+            "Epoch 19/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.7205 - accuracy: 0.8871\n",
+            "Epoch 20/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.7032 - accuracy: 0.8966\n",
+            "Epoch 21/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6965 - accuracy: 0.8999\n",
+            "Epoch 22/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6998 - accuracy: 0.9026\n",
+            "Epoch 23/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6952 - accuracy: 0.9065\n",
+            "Epoch 24/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6795 - accuracy: 0.9120\n",
+            "Epoch 25/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6913 - accuracy: 0.9100\n",
+            "Epoch 26/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6822 - accuracy: 0.9144\n",
+            "Epoch 27/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6773 - accuracy: 0.9174\n",
+            "Epoch 28/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6885 - accuracy: 0.9155\n",
+            "Epoch 29/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6588 - accuracy: 0.9239\n",
+            "Epoch 30/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6631 - accuracy: 0.9230\n",
+            "Epoch 31/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6819 - accuracy: 0.9193\n",
+            "Epoch 32/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6557 - accuracy: 0.9271\n",
+            "Epoch 33/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6806 - accuracy: 0.9224\n",
+            "Epoch 34/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6525 - accuracy: 0.9299\n",
+            "Epoch 35/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6500 - accuracy: 0.9303\n",
+            "Epoch 36/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6701 - accuracy: 0.9234\n",
+            "Epoch 37/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6627 - accuracy: 0.9297\n",
+            "Epoch 38/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6507 - accuracy: 0.9321\n",
+            "Epoch 39/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6497 - accuracy: 0.9323\n",
+            "Epoch 40/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6593 - accuracy: 0.9304\n",
+            "Epoch 41/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6528 - accuracy: 0.9325\n",
+            "Epoch 42/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6294 - accuracy: 0.9365\n",
+            "Epoch 43/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6596 - accuracy: 0.9304\n",
+            "Epoch 44/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6493 - accuracy: 0.9343\n",
+            "Epoch 45/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6440 - accuracy: 0.9351\n",
+            "Epoch 46/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6233 - accuracy: 0.9392\n",
+            "Epoch 47/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6631 - accuracy: 0.9301\n",
+            "Epoch 48/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6341 - accuracy: 0.9397\n",
+            "Epoch 49/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6440 - accuracy: 0.9351\n",
+            "Epoch 50/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6540 - accuracy: 0.9354\n",
+            "Epoch 51/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6371 - accuracy: 0.9407\n",
+            "Epoch 52/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6464 - accuracy: 0.9373\n",
+            "Epoch 53/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6489 - accuracy: 0.9371\n",
+            "Epoch 54/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6471 - accuracy: 0.9386\n",
+            "Epoch 55/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6342 - accuracy: 0.9414\n",
+            "Epoch 56/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6447 - accuracy: 0.9379\n",
+            "Epoch 57/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6291 - accuracy: 0.9431\n",
+            "Epoch 58/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6447 - accuracy: 0.9376\n",
+            "Epoch 59/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6493 - accuracy: 0.9401\n",
+            "Epoch 60/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6317 - accuracy: 0.9425\n",
+            "Epoch 61/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6179 - accuracy: 0.9450\n",
+            "Epoch 62/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6549 - accuracy: 0.9370\n",
+            "Epoch 63/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6333 - accuracy: 0.9449\n",
+            "Epoch 64/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6345 - accuracy: 0.9409\n",
+            "Epoch 65/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6320 - accuracy: 0.9440\n",
+            "Epoch 66/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6361 - accuracy: 0.9423\n",
+            "Epoch 67/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6285 - accuracy: 0.9444\n",
+            "Epoch 68/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6324 - accuracy: 0.9427\n",
+            "Epoch 69/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6427 - accuracy: 0.9397\n",
+            "Epoch 70/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6429 - accuracy: 0.9436\n",
+            "Epoch 71/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6226 - accuracy: 0.9465\n",
+            "Epoch 72/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6406 - accuracy: 0.9411\n",
+            "Epoch 73/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6197 - accuracy: 0.9470\n",
+            "Epoch 74/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6285 - accuracy: 0.9434\n",
+            "Epoch 75/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6307 - accuracy: 0.9447\n",
+            "Epoch 76/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6243 - accuracy: 0.9465\n",
+            "Epoch 77/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6274 - accuracy: 0.9468\n",
+            "Epoch 78/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6397 - accuracy: 0.9432\n",
+            "Epoch 79/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6282 - accuracy: 0.9468\n",
+            "Epoch 80/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6408 - accuracy: 0.9434\n",
+            "Epoch 81/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6368 - accuracy: 0.9468\n",
+            "Epoch 82/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6158 - accuracy: 0.9499\n",
+            "Epoch 83/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6100 - accuracy: 0.9478\n",
+            "Epoch 84/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6321 - accuracy: 0.9429\n",
+            "Epoch 85/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6260 - accuracy: 0.9477\n",
+            "Epoch 86/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6235 - accuracy: 0.9463\n",
+            "Epoch 87/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6186 - accuracy: 0.9493\n",
+            "Epoch 88/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6155 - accuracy: 0.9481\n",
+            "Epoch 89/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6702 - accuracy: 0.9374\n",
+            "Epoch 90/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6188 - accuracy: 0.9502\n",
+            "Epoch 91/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6410 - accuracy: 0.9439\n",
+            "Epoch 92/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6052 - accuracy: 0.9528\n",
+            "Epoch 93/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6349 - accuracy: 0.9431\n",
+            "Epoch 94/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6216 - accuracy: 0.9486\n",
+            "Epoch 95/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6128 - accuracy: 0.9497\n",
+            "Epoch 96/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6286 - accuracy: 0.9469\n",
+            "Epoch 97/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6095 - accuracy: 0.9515\n",
+            "Epoch 98/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6124 - accuracy: 0.9487\n",
+            "Epoch 99/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6267 - accuracy: 0.9482\n",
+            "Epoch 100/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6323 - accuracy: 0.9459\n",
+            "Epoch 101/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6116 - accuracy: 0.9507\n",
+            "Epoch 102/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6192 - accuracy: 0.9478\n",
+            "Epoch 103/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6229 - accuracy: 0.9482\n",
+            "Epoch 104/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6261 - accuracy: 0.9486\n",
+            "Epoch 105/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6140 - accuracy: 0.9521\n",
+            "Epoch 106/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6256 - accuracy: 0.9476\n",
+            "Epoch 107/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6118 - accuracy: 0.9525\n",
+            "Epoch 108/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6064 - accuracy: 0.9502\n",
+            "Epoch 109/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6161 - accuracy: 0.9487\n",
+            "Epoch 110/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6105 - accuracy: 0.9513\n",
+            "Epoch 111/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6302 - accuracy: 0.9468\n",
+            "Epoch 112/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6022 - accuracy: 0.9534\n",
+            "Epoch 113/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5993 - accuracy: 0.9518\n",
+            "Epoch 114/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6260 - accuracy: 0.9462\n",
+            "Epoch 115/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6026 - accuracy: 0.9538\n",
+            "Epoch 116/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6144 - accuracy: 0.9499\n",
+            "Epoch 117/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6054 - accuracy: 0.9516\n",
+            "Epoch 118/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6122 - accuracy: 0.9504\n",
+            "Epoch 119/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6187 - accuracy: 0.9506\n",
+            "Epoch 120/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6030 - accuracy: 0.9524\n",
+            "Epoch 121/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6078 - accuracy: 0.9513\n",
+            "Epoch 122/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6113 - accuracy: 0.9503\n",
+            "Epoch 123/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6080 - accuracy: 0.9525\n",
+            "Epoch 124/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5991 - accuracy: 0.9539\n",
+            "Epoch 125/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5985 - accuracy: 0.9529\n",
+            "Epoch 126/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6103 - accuracy: 0.9509\n",
+            "Epoch 127/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5947 - accuracy: 0.9557\n",
+            "Epoch 128/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5945 - accuracy: 0.9532\n",
+            "Epoch 129/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6059 - accuracy: 0.9520\n",
+            "Epoch 130/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6076 - accuracy: 0.9517\n",
+            "Epoch 131/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6134 - accuracy: 0.9520\n",
+            "Epoch 132/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5950 - accuracy: 0.9546\n",
+            "Epoch 133/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5881 - accuracy: 0.9557\n",
+            "Epoch 134/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6095 - accuracy: 0.9494\n",
+            "Epoch 135/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6116 - accuracy: 0.9537\n",
+            "Epoch 136/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5860 - accuracy: 0.9554\n",
+            "Epoch 137/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6058 - accuracy: 0.9519\n",
+            "Epoch 138/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6043 - accuracy: 0.9542\n",
+            "Epoch 139/350\n",
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+            "Epoch 140/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5983 - accuracy: 0.9530\n",
+            "Epoch 141/350\n",
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+            "Epoch 142/350\n",
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+            "Epoch 143/350\n",
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+            "Epoch 144/350\n",
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+            "Epoch 145/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6068 - accuracy: 0.9519\n",
+            "Epoch 146/350\n",
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+            "Epoch 147/350\n",
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+            "Epoch 148/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5833 - accuracy: 0.9547\n",
+            "Epoch 149/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6056 - accuracy: 0.9511\n",
+            "Epoch 150/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6060 - accuracy: 0.9517\n",
+            "Epoch 151/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5907 - accuracy: 0.9567\n",
+            "Epoch 152/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5922 - accuracy: 0.9541\n",
+            "Epoch 153/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5961 - accuracy: 0.9527\n",
+            "Epoch 154/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5878 - accuracy: 0.9580\n",
+            "Epoch 155/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5790 - accuracy: 0.9580\n",
+            "Epoch 156/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5977 - accuracy: 0.9523\n",
+            "Epoch 157/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5968 - accuracy: 0.9540\n",
+            "Epoch 158/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5950 - accuracy: 0.9547\n",
+            "Epoch 159/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5907 - accuracy: 0.9554\n",
+            "Epoch 160/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5817 - accuracy: 0.9560\n",
+            "Epoch 161/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5962 - accuracy: 0.9536\n",
+            "Epoch 162/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5876 - accuracy: 0.9572\n",
+            "Epoch 163/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5818 - accuracy: 0.9558\n",
+            "Epoch 164/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5896 - accuracy: 0.9541\n",
+            "Epoch 165/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5915 - accuracy: 0.9552\n",
+            "Epoch 166/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5928 - accuracy: 0.9555\n",
+            "Epoch 167/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5773 - accuracy: 0.9576\n",
+            "Epoch 168/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5817 - accuracy: 0.9560\n",
+            "Epoch 169/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5817 - accuracy: 0.9563\n",
+            "Epoch 170/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5877 - accuracy: 0.9565\n",
+            "Epoch 171/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5893 - accuracy: 0.9554\n",
+            "Epoch 172/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5946 - accuracy: 0.9543\n",
+            "Epoch 173/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5841 - accuracy: 0.9571\n",
+            "Epoch 174/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5745 - accuracy: 0.9598\n",
+            "Epoch 175/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5715 - accuracy: 0.9580\n",
+            "Epoch 176/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5809 - accuracy: 0.9552\n",
+            "Epoch 177/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5845 - accuracy: 0.9557\n",
+            "Epoch 178/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5720 - accuracy: 0.9591\n",
+            "Epoch 179/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5901 - accuracy: 0.9541\n",
+            "Epoch 180/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5667 - accuracy: 0.9608\n",
+            "Epoch 181/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5857 - accuracy: 0.9552\n",
+            "Epoch 182/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5694 - accuracy: 0.9613\n",
+            "Epoch 183/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5732 - accuracy: 0.9574\n",
+            "Epoch 184/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5861 - accuracy: 0.9562\n",
+            "Epoch 185/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5737 - accuracy: 0.9580\n",
+            "Epoch 186/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5816 - accuracy: 0.9584\n",
+            "Epoch 187/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5566 - accuracy: 0.9602\n",
+            "Epoch 188/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5664 - accuracy: 0.9576\n",
+            "Epoch 189/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5911 - accuracy: 0.9535\n",
+            "Epoch 190/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5742 - accuracy: 0.9595\n",
+            "Epoch 191/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5748 - accuracy: 0.9559\n",
+            "Epoch 192/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5606 - accuracy: 0.9604\n",
+            "Epoch 193/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.6116 - accuracy: 0.9508\n",
+            "Epoch 194/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5868 - accuracy: 0.9591\n",
+            "Epoch 195/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5400 - accuracy: 0.9650\n",
+            "Epoch 196/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5624 - accuracy: 0.9574\n",
+            "Epoch 197/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5864 - accuracy: 0.9554\n",
+            "Epoch 198/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5773 - accuracy: 0.9585\n",
+            "Epoch 199/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5699 - accuracy: 0.9580\n",
+            "Epoch 200/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5703 - accuracy: 0.9595\n",
+            "Epoch 201/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5723 - accuracy: 0.9601\n",
+            "Epoch 202/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5641 - accuracy: 0.9591\n",
+            "Epoch 203/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5812 - accuracy: 0.9565\n",
+            "Epoch 204/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5653 - accuracy: 0.9612\n",
+            "Epoch 205/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5697 - accuracy: 0.9592\n",
+            "Epoch 206/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5726 - accuracy: 0.9591\n",
+            "Epoch 207/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5570 - accuracy: 0.9612\n",
+            "Epoch 208/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5598 - accuracy: 0.9599\n",
+            "Epoch 209/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5709 - accuracy: 0.9578\n",
+            "Epoch 210/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5836 - accuracy: 0.9563\n",
+            "Epoch 211/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5621 - accuracy: 0.9613\n",
+            "Epoch 212/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5722 - accuracy: 0.9582\n",
+            "Epoch 213/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5483 - accuracy: 0.9624\n",
+            "Epoch 214/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5708 - accuracy: 0.9563\n",
+            "Epoch 215/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5746 - accuracy: 0.9572\n",
+            "Epoch 216/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5840 - accuracy: 0.9584\n",
+            "Epoch 217/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5545 - accuracy: 0.9623\n",
+            "Epoch 218/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5402 - accuracy: 0.9628\n",
+            "Epoch 219/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5546 - accuracy: 0.9591\n",
+            "Epoch 220/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5762 - accuracy: 0.9552\n",
+            "Epoch 221/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5596 - accuracy: 0.9604\n",
+            "Epoch 222/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5548 - accuracy: 0.9610\n",
+            "Epoch 223/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5586 - accuracy: 0.9608\n",
+            "Epoch 224/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5504 - accuracy: 0.9612\n",
+            "Epoch 225/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5496 - accuracy: 0.9607\n",
+            "Epoch 226/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5763 - accuracy: 0.9562\n",
+            "Epoch 227/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5664 - accuracy: 0.9602\n",
+            "Epoch 228/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5404 - accuracy: 0.9648\n",
+            "Epoch 229/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5603 - accuracy: 0.9580\n",
+            "Epoch 230/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5574 - accuracy: 0.9610\n",
+            "Epoch 231/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5575 - accuracy: 0.9586\n",
+            "Epoch 232/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5660 - accuracy: 0.9585\n",
+            "Epoch 233/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5427 - accuracy: 0.9640\n",
+            "Epoch 234/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5468 - accuracy: 0.9611\n",
+            "Epoch 235/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5678 - accuracy: 0.9581\n",
+            "Epoch 236/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5472 - accuracy: 0.9622\n",
+            "Epoch 237/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5561 - accuracy: 0.9601\n",
+            "Epoch 238/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5471 - accuracy: 0.9621\n",
+            "Epoch 239/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5539 - accuracy: 0.9601\n",
+            "Epoch 240/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5492 - accuracy: 0.9619\n",
+            "Epoch 241/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5674 - accuracy: 0.9581\n",
+            "Epoch 242/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5451 - accuracy: 0.9618\n",
+            "Epoch 243/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5280 - accuracy: 0.9646\n",
+            "Epoch 244/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5628 - accuracy: 0.9579\n",
+            "Epoch 245/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5504 - accuracy: 0.9625\n",
+            "Epoch 246/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5284 - accuracy: 0.9647\n",
+            "Epoch 247/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5277 - accuracy: 0.9629\n",
+            "Epoch 248/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5490 - accuracy: 0.9599\n",
+            "Epoch 249/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5582 - accuracy: 0.9601\n",
+            "Epoch 250/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5701 - accuracy: 0.9587\n",
+            "Epoch 251/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5274 - accuracy: 0.9664\n",
+            "Epoch 252/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5343 - accuracy: 0.9618\n",
+            "Epoch 253/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5601 - accuracy: 0.9586\n",
+            "Epoch 254/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5544 - accuracy: 0.9608\n",
+            "Epoch 255/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5447 - accuracy: 0.9631\n",
+            "Epoch 256/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5355 - accuracy: 0.9634\n",
+            "Epoch 257/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5321 - accuracy: 0.9625\n",
+            "Epoch 258/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5554 - accuracy: 0.9593\n",
+            "Epoch 259/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5533 - accuracy: 0.9608\n",
+            "Epoch 260/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5511 - accuracy: 0.9618\n",
+            "Epoch 261/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5180 - accuracy: 0.9667\n",
+            "Epoch 262/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5495 - accuracy: 0.9582\n",
+            "Epoch 263/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5347 - accuracy: 0.9640\n",
+            "Epoch 264/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5289 - accuracy: 0.9639\n",
+            "Epoch 265/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5340 - accuracy: 0.9623\n",
+            "Epoch 266/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5463 - accuracy: 0.9604\n",
+            "Epoch 267/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5383 - accuracy: 0.9639\n",
+            "Epoch 268/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5421 - accuracy: 0.9614\n",
+            "Epoch 269/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5213 - accuracy: 0.9651\n",
+            "Epoch 270/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5470 - accuracy: 0.9599\n",
+            "Epoch 271/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5388 - accuracy: 0.9634\n",
+            "Epoch 272/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5384 - accuracy: 0.9630\n",
+            "Epoch 273/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5326 - accuracy: 0.9638\n",
+            "Epoch 274/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5442 - accuracy: 0.9609\n",
+            "Epoch 275/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5384 - accuracy: 0.9634\n",
+            "Epoch 276/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5302 - accuracy: 0.9627\n",
+            "Epoch 277/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5403 - accuracy: 0.9617\n",
+            "Epoch 278/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5325 - accuracy: 0.9647\n",
+            "Epoch 279/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5370 - accuracy: 0.9619\n",
+            "Epoch 280/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5357 - accuracy: 0.9640\n",
+            "Epoch 281/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5287 - accuracy: 0.9640\n",
+            "Epoch 282/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5315 - accuracy: 0.9613\n",
+            "Epoch 283/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5361 - accuracy: 0.9649\n",
+            "Epoch 284/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5382 - accuracy: 0.9614\n",
+            "Epoch 285/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5313 - accuracy: 0.9637\n",
+            "Epoch 286/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5414 - accuracy: 0.9618\n",
+            "Epoch 287/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5197 - accuracy: 0.9667\n",
+            "Epoch 288/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5287 - accuracy: 0.9613\n",
+            "Epoch 289/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5433 - accuracy: 0.9610\n",
+            "Epoch 290/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5371 - accuracy: 0.9637\n",
+            "Epoch 291/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5274 - accuracy: 0.9636\n",
+            "Epoch 292/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5302 - accuracy: 0.9638\n",
+            "Epoch 293/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5418 - accuracy: 0.9611\n",
+            "Epoch 294/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5264 - accuracy: 0.9648\n",
+            "Epoch 295/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5397 - accuracy: 0.9614\n",
+            "Epoch 296/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5217 - accuracy: 0.9652\n",
+            "Epoch 297/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5193 - accuracy: 0.9648\n",
+            "Epoch 298/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5296 - accuracy: 0.9643\n",
+            "Epoch 299/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5312 - accuracy: 0.9621\n",
+            "Epoch 300/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5321 - accuracy: 0.9632\n",
+            "Epoch 301/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5151 - accuracy: 0.9664\n",
+            "Epoch 302/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5239 - accuracy: 0.9634\n",
+            "Epoch 303/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5264 - accuracy: 0.9640\n",
+            "Epoch 304/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5168 - accuracy: 0.9652\n",
+            "Epoch 305/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5238 - accuracy: 0.9649\n",
+            "Epoch 306/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5178 - accuracy: 0.9635\n",
+            "Epoch 307/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5119 - accuracy: 0.9650\n",
+            "Epoch 308/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5237 - accuracy: 0.9634\n",
+            "Epoch 309/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5284 - accuracy: 0.9635\n",
+            "Epoch 310/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5121 - accuracy: 0.9660\n",
+            "Epoch 311/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5394 - accuracy: 0.9599\n",
+            "Epoch 312/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5048 - accuracy: 0.9697\n",
+            "Epoch 313/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5069 - accuracy: 0.9650\n",
+            "Epoch 314/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5089 - accuracy: 0.9657\n",
+            "Epoch 315/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5242 - accuracy: 0.9627\n",
+            "Epoch 316/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5258 - accuracy: 0.9638\n",
+            "Epoch 317/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5221 - accuracy: 0.9643\n",
+            "Epoch 318/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5045 - accuracy: 0.9666\n",
+            "Epoch 319/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5195 - accuracy: 0.9652\n",
+            "Epoch 320/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5005 - accuracy: 0.9680\n",
+            "Epoch 321/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5238 - accuracy: 0.9615\n",
+            "Epoch 322/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5321 - accuracy: 0.9618\n",
+            "Epoch 323/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5160 - accuracy: 0.9674\n",
+            "Epoch 324/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5245 - accuracy: 0.9628\n",
+            "Epoch 325/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5109 - accuracy: 0.9669\n",
+            "Epoch 326/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5138 - accuracy: 0.9656\n",
+            "Epoch 327/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.4976 - accuracy: 0.9667\n",
+            "Epoch 328/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5200 - accuracy: 0.9624\n",
+            "Epoch 329/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.4939 - accuracy: 0.9700\n",
+            "Epoch 330/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.4973 - accuracy: 0.9646\n",
+            "Epoch 331/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5258 - accuracy: 0.9619\n",
+            "Epoch 332/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5384 - accuracy: 0.9623\n",
+            "Epoch 333/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5265 - accuracy: 0.9655\n",
+            "Epoch 334/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5038 - accuracy: 0.9678\n",
+            "Epoch 335/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5162 - accuracy: 0.9643\n",
+            "Epoch 336/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5066 - accuracy: 0.9665\n",
+            "Epoch 337/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5063 - accuracy: 0.9660\n",
+            "Epoch 338/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5078 - accuracy: 0.9658\n",
+            "Epoch 339/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5310 - accuracy: 0.9632\n",
+            "Epoch 340/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.4861 - accuracy: 0.9703\n",
+            "Epoch 341/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5143 - accuracy: 0.9631\n",
+            "Epoch 342/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5199 - accuracy: 0.9637\n",
+            "Epoch 343/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.4992 - accuracy: 0.9685\n",
+            "Epoch 344/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5109 - accuracy: 0.9644\n",
+            "Epoch 345/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5066 - accuracy: 0.9657\n",
+            "Epoch 346/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5142 - accuracy: 0.9651\n",
+            "Epoch 347/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5092 - accuracy: 0.9649\n",
+            "Epoch 348/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5188 - accuracy: 0.9636\n",
+            "Epoch 349/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.5069 - accuracy: 0.9677\n",
+            "Epoch 350/350\n",
+            "391/391 [==============================] - 7s 18ms/step - loss: 0.4872 - accuracy: 0.9686\n"
+          ]
+        }
+      ],
+      "source": [
+        "history = model.fit(x_train, to_categorical(y_train, num_classes), epochs=EPOCHS, batch_size=BATCH_SIZE)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "loss = history.history['loss']\n",
+        "epochs = range(1, len(loss) + 1)\n",
+        "\n",
+        "plt.plot(epochs, loss, 'b', label='Training Loss')\n",
+        "plt.title('Training Loss')\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "6lrFuQrNRCyv",
+        "outputId": "3bc66200-18f3-483e-8c8c-3b44072fe7bb"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3SRXs6V6bNEX",
+        "outputId": "4ed1f452-e232-41a3-caa9-ea01e89369d0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "313/313 [==============================] - 1s 3ms/step - loss: 1.5913 - accuracy: 0.7413\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[1.5913232564926147, 0.7412999868392944]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ],
+      "source": [
+        "model.evaluate(x_test, to_categorical(y_test, num_classes))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "---"
+      ],
+      "metadata": {
+        "id": "XyPoVUwrRRj5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "vXA26MZUbOXO"
+      },
+      "outputs": [],
+      "source": [
+        "def cnn(input_shape, num_classes):\n",
+        "    \"\"\"CNN Model from (McMahan et. al., 2017).\n",
+        "\n",
+        "    Communication-efficient learning of deep networks from decentralized data\n",
+        "    \"\"\"\n",
+        "    input_shape = tuple(input_shape)\n",
+        "\n",
+        "    weight_decay = 0.004\n",
+        "    model = keras.Sequential(\n",
+        "        [\n",
+        "            keras.layers.Conv2D(\n",
+        "                64,\n",
+        "                (5, 5),\n",
+        "                padding=\"same\",\n",
+        "                activation=\"relu\",\n",
+        "                input_shape=input_shape,\n",
+        "            ),\n",
+        "            keras.layers.MaxPooling2D((3, 3), strides=(2, 2)),\n",
+        "            keras.layers.BatchNormalization(),\n",
+        "            keras.layers.Conv2D(\n",
+        "                64,\n",
+        "                (5, 5),\n",
+        "                padding=\"same\",\n",
+        "                activation=\"relu\",\n",
+        "            ),\n",
+        "            keras.layers.BatchNormalization(),\n",
+        "            keras.layers.MaxPooling2D((3, 3), strides=(2, 2)),\n",
+        "            keras.layers.Flatten(),\n",
+        "            keras.layers.Dense(\n",
+        "                384, activation=\"relu\", kernel_regularizer=l2(weight_decay)\n",
+        "            ),\n",
+        "            keras.layers.Dense(\n",
+        "                192, activation=\"relu\", kernel_regularizer=l2(weight_decay)\n",
+        "            ),\n",
+        "            keras.layers.Dense(num_classes, activation=\"softmax\"),\n",
+        "        ]\n",
+        "    )\n",
+        "    optimizer = SGD(learning_rate=0.1)\n",
+        "    model.compile(\n",
+        "        loss=\"categorical_crossentropy\", optimizer=optimizer, metrics=[\"accuracy\"]\n",
+        "    )\n",
+        "\n",
+        "    return model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_cnn = cnn(input_shape, num_classes)"
+      ],
+      "metadata": {
+        "id": "t098yVNYRxPu"
+      },
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "history_cnn = model_cnn.fit(x_train, to_categorical(y_train, num_classes), epochs=350, batch_size=100)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JinRA8quR2mr",
+        "outputId": "edc6a49c-3fa4-498d-fbb4-21cb439c9b38"
+      },
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/350\n",
+            "500/500 [==============================] - 4s 7ms/step - loss: 4.1634 - accuracy: 0.4622\n",
+            "Epoch 2/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 2.3282 - accuracy: 0.6234\n",
+            "Epoch 3/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 1.5241 - accuracy: 0.6978\n",
+            "Epoch 4/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 1.1409 - accuracy: 0.7442\n",
+            "Epoch 5/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.9513 - accuracy: 0.7783\n",
+            "Epoch 6/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.8526 - accuracy: 0.8004\n",
+            "Epoch 7/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7955 - accuracy: 0.8228\n",
+            "Epoch 8/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7653 - accuracy: 0.8402\n",
+            "Epoch 9/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7479 - accuracy: 0.8540\n",
+            "Epoch 10/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7359 - accuracy: 0.8678\n",
+            "Epoch 11/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7267 - accuracy: 0.8774\n",
+            "Epoch 12/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7274 - accuracy: 0.8839\n",
+            "Epoch 13/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7191 - accuracy: 0.8918\n",
+            "Epoch 14/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7182 - accuracy: 0.8971\n",
+            "Epoch 15/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7166 - accuracy: 0.9014\n",
+            "Epoch 16/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7239 - accuracy: 0.9033\n",
+            "Epoch 17/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7214 - accuracy: 0.9069\n",
+            "Epoch 18/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7103 - accuracy: 0.9122\n",
+            "Epoch 19/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7023 - accuracy: 0.9168\n",
+            "Epoch 20/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7128 - accuracy: 0.9147\n",
+            "Epoch 21/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7064 - accuracy: 0.9197\n",
+            "Epoch 22/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7090 - accuracy: 0.9177\n",
+            "Epoch 23/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7103 - accuracy: 0.9190\n",
+            "Epoch 24/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6981 - accuracy: 0.9232\n",
+            "Epoch 25/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7015 - accuracy: 0.9234\n",
+            "Epoch 26/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.7026 - accuracy: 0.9253\n",
+            "Epoch 27/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6889 - accuracy: 0.9264\n",
+            "Epoch 28/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6924 - accuracy: 0.9275\n",
+            "Epoch 29/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6818 - accuracy: 0.9303\n",
+            "Epoch 30/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6961 - accuracy: 0.9273\n",
+            "Epoch 31/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6967 - accuracy: 0.9277\n",
+            "Epoch 32/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6932 - accuracy: 0.9318\n",
+            "Epoch 33/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6812 - accuracy: 0.9331\n",
+            "Epoch 34/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6779 - accuracy: 0.9321\n",
+            "Epoch 35/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6898 - accuracy: 0.9312\n",
+            "Epoch 36/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6800 - accuracy: 0.9328\n",
+            "Epoch 37/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6785 - accuracy: 0.9340\n",
+            "Epoch 38/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6713 - accuracy: 0.9370\n",
+            "Epoch 39/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6832 - accuracy: 0.9345\n",
+            "Epoch 40/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6778 - accuracy: 0.9349\n",
+            "Epoch 41/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6610 - accuracy: 0.9378\n",
+            "Epoch 42/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6612 - accuracy: 0.9385\n",
+            "Epoch 43/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6545 - accuracy: 0.9393\n",
+            "Epoch 44/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6609 - accuracy: 0.9369\n",
+            "Epoch 45/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6648 - accuracy: 0.9382\n",
+            "Epoch 46/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6587 - accuracy: 0.9385\n",
+            "Epoch 47/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6492 - accuracy: 0.9420\n",
+            "Epoch 48/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6523 - accuracy: 0.9404\n",
+            "Epoch 49/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6648 - accuracy: 0.9378\n",
+            "Epoch 50/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6571 - accuracy: 0.9397\n",
+            "Epoch 51/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6493 - accuracy: 0.9413\n",
+            "Epoch 52/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6590 - accuracy: 0.9388\n",
+            "Epoch 53/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6542 - accuracy: 0.9412\n",
+            "Epoch 54/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6526 - accuracy: 0.9427\n",
+            "Epoch 55/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6311 - accuracy: 0.9462\n",
+            "Epoch 56/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6459 - accuracy: 0.9412\n",
+            "Epoch 57/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6436 - accuracy: 0.9438\n",
+            "Epoch 58/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6429 - accuracy: 0.9440\n",
+            "Epoch 59/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6459 - accuracy: 0.9421\n",
+            "Epoch 60/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6418 - accuracy: 0.9432\n",
+            "Epoch 61/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6357 - accuracy: 0.9444\n",
+            "Epoch 62/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6316 - accuracy: 0.9452\n",
+            "Epoch 63/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6348 - accuracy: 0.9451\n",
+            "Epoch 64/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6293 - accuracy: 0.9447\n",
+            "Epoch 65/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6339 - accuracy: 0.9453\n",
+            "Epoch 66/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6223 - accuracy: 0.9482\n",
+            "Epoch 67/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6169 - accuracy: 0.9483\n",
+            "Epoch 68/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6217 - accuracy: 0.9456\n",
+            "Epoch 69/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6262 - accuracy: 0.9456\n",
+            "Epoch 70/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6168 - accuracy: 0.9488\n",
+            "Epoch 71/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6166 - accuracy: 0.9465\n",
+            "Epoch 72/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6248 - accuracy: 0.9458\n",
+            "Epoch 73/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6089 - accuracy: 0.9510\n",
+            "Epoch 74/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6155 - accuracy: 0.9472\n",
+            "Epoch 75/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6207 - accuracy: 0.9480\n",
+            "Epoch 76/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6123 - accuracy: 0.9502\n",
+            "Epoch 77/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6173 - accuracy: 0.9474\n",
+            "Epoch 78/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6020 - accuracy: 0.9510\n",
+            "Epoch 79/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5970 - accuracy: 0.9512\n",
+            "Epoch 80/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6211 - accuracy: 0.9454\n",
+            "Epoch 81/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5945 - accuracy: 0.9522\n",
+            "Epoch 82/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6178 - accuracy: 0.9460\n",
+            "Epoch 83/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6082 - accuracy: 0.9504\n",
+            "Epoch 84/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5934 - accuracy: 0.9522\n",
+            "Epoch 85/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5979 - accuracy: 0.9512\n",
+            "Epoch 86/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5985 - accuracy: 0.9506\n",
+            "Epoch 87/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5924 - accuracy: 0.9520\n",
+            "Epoch 88/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5885 - accuracy: 0.9514\n",
+            "Epoch 89/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5934 - accuracy: 0.9515\n",
+            "Epoch 90/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.6033 - accuracy: 0.9507\n",
+            "Epoch 91/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5958 - accuracy: 0.9523\n",
+            "Epoch 92/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5970 - accuracy: 0.9505\n",
+            "Epoch 93/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5900 - accuracy: 0.9536\n",
+            "Epoch 94/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5916 - accuracy: 0.9512\n",
+            "Epoch 95/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5955 - accuracy: 0.9519\n",
+            "Epoch 96/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5943 - accuracy: 0.9520\n",
+            "Epoch 97/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5852 - accuracy: 0.9523\n",
+            "Epoch 98/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5784 - accuracy: 0.9533\n",
+            "Epoch 99/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5800 - accuracy: 0.9535\n",
+            "Epoch 100/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5691 - accuracy: 0.9552\n",
+            "Epoch 101/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5720 - accuracy: 0.9531\n",
+            "Epoch 102/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5766 - accuracy: 0.9541\n",
+            "Epoch 103/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5696 - accuracy: 0.9543\n",
+            "Epoch 104/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5753 - accuracy: 0.9538\n",
+            "Epoch 105/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5765 - accuracy: 0.9540\n",
+            "Epoch 106/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5590 - accuracy: 0.9576\n",
+            "Epoch 107/350\n",
+            "500/500 [==============================] - 4s 7ms/step - loss: 0.5675 - accuracy: 0.9537\n",
+            "Epoch 108/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5797 - accuracy: 0.9523\n",
+            "Epoch 109/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5745 - accuracy: 0.9549\n",
+            "Epoch 110/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5634 - accuracy: 0.9565\n",
+            "Epoch 111/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5626 - accuracy: 0.9556\n",
+            "Epoch 112/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5731 - accuracy: 0.9542\n",
+            "Epoch 113/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5737 - accuracy: 0.9539\n",
+            "Epoch 114/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5690 - accuracy: 0.9557\n",
+            "Epoch 115/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5670 - accuracy: 0.9558\n",
+            "Epoch 116/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5583 - accuracy: 0.9550\n",
+            "Epoch 117/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5479 - accuracy: 0.9570\n",
+            "Epoch 118/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5639 - accuracy: 0.9541\n",
+            "Epoch 119/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5530 - accuracy: 0.9580\n",
+            "Epoch 120/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5579 - accuracy: 0.9562\n",
+            "Epoch 121/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5522 - accuracy: 0.9573\n",
+            "Epoch 122/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5641 - accuracy: 0.9542\n",
+            "Epoch 123/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5519 - accuracy: 0.9582\n",
+            "Epoch 124/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5387 - accuracy: 0.9588\n",
+            "Epoch 125/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5470 - accuracy: 0.9570\n",
+            "Epoch 126/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5583 - accuracy: 0.9545\n",
+            "Epoch 127/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5439 - accuracy: 0.9590\n",
+            "Epoch 128/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5541 - accuracy: 0.9557\n",
+            "Epoch 129/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5357 - accuracy: 0.9598\n",
+            "Epoch 130/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5512 - accuracy: 0.9564\n",
+            "Epoch 131/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5377 - accuracy: 0.9593\n",
+            "Epoch 132/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5414 - accuracy: 0.9568\n",
+            "Epoch 133/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5476 - accuracy: 0.9556\n",
+            "Epoch 134/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5348 - accuracy: 0.9583\n",
+            "Epoch 135/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5451 - accuracy: 0.9572\n",
+            "Epoch 136/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5413 - accuracy: 0.9579\n",
+            "Epoch 137/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5257 - accuracy: 0.9604\n",
+            "Epoch 138/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5314 - accuracy: 0.9585\n",
+            "Epoch 139/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5326 - accuracy: 0.9591\n",
+            "Epoch 140/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5399 - accuracy: 0.9575\n",
+            "Epoch 141/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5402 - accuracy: 0.9588\n",
+            "Epoch 142/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5342 - accuracy: 0.9576\n",
+            "Epoch 143/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5380 - accuracy: 0.9577\n",
+            "Epoch 144/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5313 - accuracy: 0.9587\n",
+            "Epoch 145/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5303 - accuracy: 0.9589\n",
+            "Epoch 146/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5219 - accuracy: 0.9595\n",
+            "Epoch 147/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5418 - accuracy: 0.9567\n",
+            "Epoch 148/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5300 - accuracy: 0.9600\n",
+            "Epoch 149/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5088 - accuracy: 0.9607\n",
+            "Epoch 150/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5319 - accuracy: 0.9577\n",
+            "Epoch 151/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5168 - accuracy: 0.9621\n",
+            "Epoch 152/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5161 - accuracy: 0.9606\n",
+            "Epoch 153/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5109 - accuracy: 0.9618\n",
+            "Epoch 154/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5190 - accuracy: 0.9593\n",
+            "Epoch 155/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5273 - accuracy: 0.9586\n",
+            "Epoch 156/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5093 - accuracy: 0.9630\n",
+            "Epoch 157/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5210 - accuracy: 0.9589\n",
+            "Epoch 158/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5046 - accuracy: 0.9636\n",
+            "Epoch 159/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5142 - accuracy: 0.9598\n",
+            "Epoch 160/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5294 - accuracy: 0.9588\n",
+            "Epoch 161/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5079 - accuracy: 0.9622\n",
+            "Epoch 162/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4972 - accuracy: 0.9635\n",
+            "Epoch 163/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5120 - accuracy: 0.9601\n",
+            "Epoch 164/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5091 - accuracy: 0.9624\n",
+            "Epoch 165/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5115 - accuracy: 0.9612\n",
+            "Epoch 166/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5071 - accuracy: 0.9614\n",
+            "Epoch 167/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5067 - accuracy: 0.9628\n",
+            "Epoch 168/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5002 - accuracy: 0.9623\n",
+            "Epoch 169/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5094 - accuracy: 0.9602\n",
+            "Epoch 170/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5032 - accuracy: 0.9618\n",
+            "Epoch 171/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5024 - accuracy: 0.9618\n",
+            "Epoch 172/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4839 - accuracy: 0.9649\n",
+            "Epoch 173/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4931 - accuracy: 0.9620\n",
+            "Epoch 174/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5061 - accuracy: 0.9614\n",
+            "Epoch 175/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5023 - accuracy: 0.9620\n",
+            "Epoch 176/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5021 - accuracy: 0.9625\n",
+            "Epoch 177/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4821 - accuracy: 0.9651\n",
+            "Epoch 178/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4813 - accuracy: 0.9626\n",
+            "Epoch 179/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4906 - accuracy: 0.9630\n",
+            "Epoch 180/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4973 - accuracy: 0.9611\n",
+            "Epoch 181/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4970 - accuracy: 0.9629\n",
+            "Epoch 182/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4841 - accuracy: 0.9644\n",
+            "Epoch 183/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4872 - accuracy: 0.9631\n",
+            "Epoch 184/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4845 - accuracy: 0.9647\n",
+            "Epoch 185/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4756 - accuracy: 0.9648\n",
+            "Epoch 186/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4821 - accuracy: 0.9626\n",
+            "Epoch 187/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4819 - accuracy: 0.9633\n",
+            "Epoch 188/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.5000 - accuracy: 0.9617\n",
+            "Epoch 189/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4783 - accuracy: 0.9652\n",
+            "Epoch 190/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4778 - accuracy: 0.9641\n",
+            "Epoch 191/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4815 - accuracy: 0.9623\n",
+            "Epoch 192/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4892 - accuracy: 0.9640\n",
+            "Epoch 193/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4850 - accuracy: 0.9637\n",
+            "Epoch 194/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4871 - accuracy: 0.9641\n",
+            "Epoch 195/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4739 - accuracy: 0.9651\n",
+            "Epoch 196/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4779 - accuracy: 0.9636\n",
+            "Epoch 197/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4658 - accuracy: 0.9663\n",
+            "Epoch 198/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4821 - accuracy: 0.9623\n",
+            "Epoch 199/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4826 - accuracy: 0.9635\n",
+            "Epoch 200/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4732 - accuracy: 0.9656\n",
+            "Epoch 201/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4790 - accuracy: 0.9648\n",
+            "Epoch 202/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4675 - accuracy: 0.9658\n",
+            "Epoch 203/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4743 - accuracy: 0.9633\n",
+            "Epoch 204/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4667 - accuracy: 0.9653\n",
+            "Epoch 205/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4760 - accuracy: 0.9624\n",
+            "Epoch 206/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4736 - accuracy: 0.9651\n",
+            "Epoch 207/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4744 - accuracy: 0.9636\n",
+            "Epoch 208/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4623 - accuracy: 0.9664\n",
+            "Epoch 209/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4591 - accuracy: 0.9670\n",
+            "Epoch 210/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4700 - accuracy: 0.9645\n",
+            "Epoch 211/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4690 - accuracy: 0.9653\n",
+            "Epoch 212/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4686 - accuracy: 0.9649\n",
+            "Epoch 213/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4546 - accuracy: 0.9667\n",
+            "Epoch 214/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4710 - accuracy: 0.9645\n",
+            "Epoch 215/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4677 - accuracy: 0.9653\n",
+            "Epoch 216/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4796 - accuracy: 0.9629\n",
+            "Epoch 217/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4601 - accuracy: 0.9673\n",
+            "Epoch 218/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4571 - accuracy: 0.9667\n",
+            "Epoch 219/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4652 - accuracy: 0.9648\n",
+            "Epoch 220/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4613 - accuracy: 0.9658\n",
+            "Epoch 221/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4510 - accuracy: 0.9679\n",
+            "Epoch 222/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4689 - accuracy: 0.9653\n",
+            "Epoch 223/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4490 - accuracy: 0.9677\n",
+            "Epoch 224/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4579 - accuracy: 0.9645\n",
+            "Epoch 225/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4465 - accuracy: 0.9682\n",
+            "Epoch 226/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4486 - accuracy: 0.9673\n",
+            "Epoch 227/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4628 - accuracy: 0.9638\n",
+            "Epoch 228/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4438 - accuracy: 0.9689\n",
+            "Epoch 229/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4528 - accuracy: 0.9650\n",
+            "Epoch 230/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4560 - accuracy: 0.9656\n",
+            "Epoch 231/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4532 - accuracy: 0.9670\n",
+            "Epoch 232/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4497 - accuracy: 0.9671\n",
+            "Epoch 233/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4474 - accuracy: 0.9675\n",
+            "Epoch 234/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4517 - accuracy: 0.9672\n",
+            "Epoch 235/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4531 - accuracy: 0.9660\n",
+            "Epoch 236/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4524 - accuracy: 0.9662\n",
+            "Epoch 237/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4423 - accuracy: 0.9669\n",
+            "Epoch 238/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4500 - accuracy: 0.9658\n",
+            "Epoch 239/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4564 - accuracy: 0.9653\n",
+            "Epoch 240/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4247 - accuracy: 0.9709\n",
+            "Epoch 241/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4395 - accuracy: 0.9670\n",
+            "Epoch 242/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4506 - accuracy: 0.9656\n",
+            "Epoch 243/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4332 - accuracy: 0.9697\n",
+            "Epoch 244/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4385 - accuracy: 0.9674\n",
+            "Epoch 245/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4414 - accuracy: 0.9672\n",
+            "Epoch 246/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4494 - accuracy: 0.9664\n",
+            "Epoch 247/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4429 - accuracy: 0.9677\n",
+            "Epoch 248/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4385 - accuracy: 0.9683\n",
+            "Epoch 249/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4283 - accuracy: 0.9697\n",
+            "Epoch 250/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4362 - accuracy: 0.9677\n",
+            "Epoch 251/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4360 - accuracy: 0.9678\n",
+            "Epoch 252/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4337 - accuracy: 0.9684\n",
+            "Epoch 253/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4485 - accuracy: 0.9664\n",
+            "Epoch 254/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4364 - accuracy: 0.9686\n",
+            "Epoch 255/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4394 - accuracy: 0.9681\n",
+            "Epoch 256/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4211 - accuracy: 0.9692\n",
+            "Epoch 257/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4226 - accuracy: 0.9694\n",
+            "Epoch 258/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4358 - accuracy: 0.9669\n",
+            "Epoch 259/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4260 - accuracy: 0.9696\n",
+            "Epoch 260/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4276 - accuracy: 0.9690\n",
+            "Epoch 261/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4286 - accuracy: 0.9683\n",
+            "Epoch 262/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4297 - accuracy: 0.9690\n",
+            "Epoch 263/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4210 - accuracy: 0.9696\n",
+            "Epoch 264/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4301 - accuracy: 0.9681\n",
+            "Epoch 265/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4283 - accuracy: 0.9687\n",
+            "Epoch 266/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4182 - accuracy: 0.9713\n",
+            "Epoch 267/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4202 - accuracy: 0.9681\n",
+            "Epoch 268/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4292 - accuracy: 0.9686\n",
+            "Epoch 269/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4213 - accuracy: 0.9699\n",
+            "Epoch 270/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4239 - accuracy: 0.9688\n",
+            "Epoch 271/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4213 - accuracy: 0.9686\n",
+            "Epoch 272/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4120 - accuracy: 0.9706\n",
+            "Epoch 273/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4196 - accuracy: 0.9697\n",
+            "Epoch 274/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4181 - accuracy: 0.9694\n",
+            "Epoch 275/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4176 - accuracy: 0.9692\n",
+            "Epoch 276/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4199 - accuracy: 0.9693\n",
+            "Epoch 277/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4298 - accuracy: 0.9686\n",
+            "Epoch 278/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4201 - accuracy: 0.9696\n",
+            "Epoch 279/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4046 - accuracy: 0.9715\n",
+            "Epoch 280/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4024 - accuracy: 0.9707\n",
+            "Epoch 281/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4200 - accuracy: 0.9685\n",
+            "Epoch 282/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4064 - accuracy: 0.9710\n",
+            "Epoch 283/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3937 - accuracy: 0.9725\n",
+            "Epoch 284/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4107 - accuracy: 0.9690\n",
+            "Epoch 285/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4096 - accuracy: 0.9709\n",
+            "Epoch 286/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4103 - accuracy: 0.9696\n",
+            "Epoch 287/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4256 - accuracy: 0.9673\n",
+            "Epoch 288/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4131 - accuracy: 0.9715\n",
+            "Epoch 289/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3974 - accuracy: 0.9726\n",
+            "Epoch 290/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4112 - accuracy: 0.9703\n",
+            "Epoch 291/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3966 - accuracy: 0.9720\n",
+            "Epoch 292/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4136 - accuracy: 0.9687\n",
+            "Epoch 293/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4141 - accuracy: 0.9709\n",
+            "Epoch 294/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4057 - accuracy: 0.9714\n",
+            "Epoch 295/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3946 - accuracy: 0.9728\n",
+            "Epoch 296/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4176 - accuracy: 0.9682\n",
+            "Epoch 297/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4128 - accuracy: 0.9701\n",
+            "Epoch 298/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4058 - accuracy: 0.9712\n",
+            "Epoch 299/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3820 - accuracy: 0.9740\n",
+            "Epoch 300/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4019 - accuracy: 0.9694\n",
+            "Epoch 301/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4020 - accuracy: 0.9713\n",
+            "Epoch 302/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4151 - accuracy: 0.9681\n",
+            "Epoch 303/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3961 - accuracy: 0.9724\n",
+            "Epoch 304/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3941 - accuracy: 0.9709\n",
+            "Epoch 305/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3992 - accuracy: 0.9710\n",
+            "Epoch 306/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3982 - accuracy: 0.9722\n",
+            "Epoch 307/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3842 - accuracy: 0.9727\n",
+            "Epoch 308/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4010 - accuracy: 0.9696\n",
+            "Epoch 309/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4085 - accuracy: 0.9690\n",
+            "Epoch 310/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3894 - accuracy: 0.9731\n",
+            "Epoch 311/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4014 - accuracy: 0.9696\n",
+            "Epoch 312/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3918 - accuracy: 0.9729\n",
+            "Epoch 313/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3906 - accuracy: 0.9708\n",
+            "Epoch 314/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3815 - accuracy: 0.9746\n",
+            "Epoch 315/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3944 - accuracy: 0.9701\n",
+            "Epoch 316/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.4006 - accuracy: 0.9704\n",
+            "Epoch 317/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3836 - accuracy: 0.9748\n",
+            "Epoch 318/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3836 - accuracy: 0.9722\n",
+            "Epoch 319/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3873 - accuracy: 0.9715\n",
+            "Epoch 320/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3858 - accuracy: 0.9728\n",
+            "Epoch 321/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3900 - accuracy: 0.9710\n",
+            "Epoch 322/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3927 - accuracy: 0.9719\n",
+            "Epoch 323/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3863 - accuracy: 0.9711\n",
+            "Epoch 324/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3857 - accuracy: 0.9726\n",
+            "Epoch 325/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3778 - accuracy: 0.9728\n",
+            "Epoch 326/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3951 - accuracy: 0.9698\n",
+            "Epoch 327/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3871 - accuracy: 0.9726\n",
+            "Epoch 328/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3910 - accuracy: 0.9707\n",
+            "Epoch 329/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3787 - accuracy: 0.9735\n",
+            "Epoch 330/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3874 - accuracy: 0.9707\n",
+            "Epoch 331/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3901 - accuracy: 0.9715\n",
+            "Epoch 332/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3710 - accuracy: 0.9741\n",
+            "Epoch 333/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3874 - accuracy: 0.9715\n",
+            "Epoch 334/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3874 - accuracy: 0.9722\n",
+            "Epoch 335/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3768 - accuracy: 0.9730\n",
+            "Epoch 336/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3739 - accuracy: 0.9738\n",
+            "Epoch 337/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3883 - accuracy: 0.9711\n",
+            "Epoch 338/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3715 - accuracy: 0.9732\n",
+            "Epoch 339/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3740 - accuracy: 0.9730\n",
+            "Epoch 340/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3902 - accuracy: 0.9715\n",
+            "Epoch 341/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3779 - accuracy: 0.9727\n",
+            "Epoch 342/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3883 - accuracy: 0.9708\n",
+            "Epoch 343/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3739 - accuracy: 0.9741\n",
+            "Epoch 344/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3823 - accuracy: 0.9714\n",
+            "Epoch 345/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3729 - accuracy: 0.9736\n",
+            "Epoch 346/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3730 - accuracy: 0.9731\n",
+            "Epoch 347/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3716 - accuracy: 0.9722\n",
+            "Epoch 348/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3841 - accuracy: 0.9722\n",
+            "Epoch 349/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3659 - accuracy: 0.9750\n",
+            "Epoch 350/350\n",
+            "500/500 [==============================] - 3s 7ms/step - loss: 0.3740 - accuracy: 0.9721\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model_cnn.evaluate(x_test, to_categorical(y_test, num_classes))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eIzsv0QLR_tt",
+        "outputId": "0a7eb8e7-4d7f-40ef-e1b0-b3979cc65854"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "313/313 [==============================] - 1s 2ms/step - loss: 1.4919 - accuracy: 0.7581\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[1.491928219795227, 0.7580999732017517]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "loss = history_cnn.history['loss']\n",
+        "epochs = range(1, len(loss) + 1)\n",
+        "\n",
+        "plt.plot(epochs, loss, 'b', label='Training Loss')\n",
+        "plt.title('Training Loss')\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('Loss')\n",
+        "plt.legend()\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "rOXE49XhSGBy",
+        "outputId": "7bf4879e-632d-4762-977f-522402eee644"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "dXPARCwlSpqV"
+      },
+      "execution_count": 28,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "machine_shape": "hm",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/baselines/fedavgm/_static/Figure6_cifar10_num-rounds=1000_concentration=1.png b/baselines/fedavgm/_static/Figure6_cifar10_num-rounds=1000_concentration=1.png
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diff --git a/baselines/fedavgm/conf-colab.sh b/baselines/fedavgm/conf-colab.sh
new file mode 100644
index 000000000000..822fe2f273e1
--- /dev/null
+++ b/baselines/fedavgm/conf-colab.sh
@@ -0,0 +1,24 @@
+#!/bin/bash
+# Shellscript to configure the environment on the Google Colab terminal
+
+# fix issue with ctypes on Colab instance
+apt-get update
+apt-get install -y libffi-dev
+
+# Install pyenv
+curl https://pyenv.run | bash
+export PYENV_ROOT="$HOME/.pyenv"
+command -v pyenv >/dev/null || export PATH="$PYENV_ROOT/bin:$PATH"
+eval "$(pyenv init -)"
+
+# this version is specific to the FedAvgM baseline
+pyenv install 3.10.6
+pyenv global 3.10.6
+
+# install Poetry
+curl -sSL https://install.python-poetry.org | python3 -
+export PATH="/root/.local/bin:$PATH"
+
+# install and set environment with Poetry
+poetry install
+poetry shell
diff --git a/baselines/fedavgm/fedavgm/__init__.py b/baselines/fedavgm/fedavgm/__init__.py
new file mode 100644
index 000000000000..a5e567b59135
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/__init__.py
@@ -0,0 +1 @@
+"""Template baseline package."""
diff --git a/baselines/fedavgm/fedavgm/client.py b/baselines/fedavgm/fedavgm/client.py
new file mode 100644
index 000000000000..6500bdc9c737
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/client.py
@@ -0,0 +1,70 @@
+"""Define the Flower Client and function to instantiate it."""
+
+import math
+
+import flwr as fl
+from hydra.utils import instantiate
+from keras.utils import to_categorical
+
+
+class FlowerClient(fl.client.NumPyClient):
+    """Standard Flower client."""
+
+    # pylint: disable=too-many-arguments
+    def __init__(self, x_train, y_train, x_val, y_val, model, num_classes) -> None:
+        # local model
+        self.model = instantiate(model)
+
+        # local dataset
+        self.x_train, self.y_train = x_train, to_categorical(
+            y_train, num_classes=num_classes
+        )
+        self.x_val, self.y_val = x_val, to_categorical(y_val, num_classes=num_classes)
+
+    def get_parameters(self, config):
+        """Return the parameters of the current local model."""
+        return self.model.get_weights()
+
+    def fit(self, parameters, config):
+        """Implement distributed fit function for a given client."""
+        self.model.set_weights(parameters)
+
+        self.model.fit(
+            self.x_train,
+            self.y_train,
+            epochs=config["local_epochs"],
+            batch_size=config["batch_size"],
+            verbose=False,
+        )
+        return self.model.get_weights(), len(self.x_train), {}
+
+    def evaluate(self, parameters, config):
+        """Implement distributed evaluation for a given client."""
+        self.model.set_weights(parameters)
+        loss, acc = self.model.evaluate(self.x_val, self.y_val, verbose=False)
+        return loss, len(self.x_val), {"accuracy": acc}
+
+
+def generate_client_fn(partitions, model, num_classes):
+    """Generate the client function that creates the Flower Clients."""
+
+    def client_fn(cid: str) -> FlowerClient:
+        """Create a Flower client representing a single organization."""
+        full_x_train_cid, full_y_train_cid = partitions[int(cid)]
+
+        # Use 10% of the client's training data for validation
+        split_idx = math.floor(len(full_x_train_cid) * 0.9)
+        x_train_cid, y_train_cid = (
+            full_x_train_cid[:split_idx],
+            full_y_train_cid[:split_idx],
+        )
+        x_val_cid, y_val_cid = (
+            full_x_train_cid[split_idx:],
+            full_y_train_cid[split_idx:],
+        )
+
+        return FlowerClient(
+            x_train_cid, y_train_cid, x_val_cid, y_val_cid, model, num_classes
+        )
+
+    return client_fn
diff --git a/baselines/fedavgm/fedavgm/common.py b/baselines/fedavgm/fedavgm/common.py
new file mode 100644
index 000000000000..0ce9d04dc544
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/common.py
@@ -0,0 +1,494 @@
+# Copyright 2020 Adap 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.
+# ==============================================================================
+"""Commonly used functions for generating partitioned datasets."""
+
+# pylint: disable=invalid-name
+
+
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+from numpy.random import BitGenerator, Generator, SeedSequence
+
+XY = Tuple[np.ndarray, np.ndarray]
+XYList = List[XY]
+PartitionedDataset = Tuple[XYList, XYList]
+
+
+def float_to_int(i: float) -> int:
+    """Return float as int but raise if decimal is dropped."""
+    if not i.is_integer():
+        raise Exception("Cast would drop decimals")
+
+    return int(i)
+
+
+def sort_by_label(x: np.ndarray, y: np.ndarray) -> XY:
+    """Sort by label.
+
+    Assuming two labels and four examples the resulting label order would be 1,1,2,2
+    """
+    idx = np.argsort(y, axis=0).reshape((y.shape[0]))
+    return (x[idx], y[idx])
+
+
+def sort_by_label_repeating(x: np.ndarray, y: np.ndarray) -> XY:
+    """Sort by label in repeating groups.
+
+    Assuming two labels and four examples the resulting label order would be 1,2,1,2.
+
+    Create sorting index which is applied to by label sorted x, y
+
+    .. code-block:: python
+
+        # given:
+        y = [
+            0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9
+        ]
+
+        # use:
+        idx = [
+            0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 1, 3, 5, 7, 9, 11, 13, 15, 17, 19
+        ]
+
+        # so that y[idx] becomes:
+        y = [
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9
+        ]
+    """
+    x, y = sort_by_label(x, y)
+
+    num_example = x.shape[0]
+    num_class = np.unique(y).shape[0]
+    idx = (
+        np.array(range(num_example), np.int64)
+        .reshape((num_class, num_example // num_class))
+        .transpose()
+        .reshape(num_example)
+    )
+
+    return (x[idx], y[idx])
+
+
+def split_at_fraction(x: np.ndarray, y: np.ndarray, fraction: float) -> Tuple[XY, XY]:
+    """Split x, y at a certain fraction."""
+    splitting_index = float_to_int(x.shape[0] * fraction)
+    # Take everything BEFORE splitting_index
+    x_0, y_0 = x[:splitting_index], y[:splitting_index]
+    # Take everything AFTER splitting_index
+    x_1, y_1 = x[splitting_index:], y[splitting_index:]
+    return (x_0, y_0), (x_1, y_1)
+
+
+def shuffle(x: np.ndarray, y: np.ndarray) -> XY:
+    """Shuffle x and y."""
+    idx = np.random.permutation(len(x))
+    return x[idx], y[idx]
+
+
+def partition(x: np.ndarray, y: np.ndarray, num_partitions: int) -> List[XY]:
+    """Return x, y as list of partitions."""
+    return list(zip(np.split(x, num_partitions), np.split(y, num_partitions)))
+
+
+def combine_partitions(xy_list_0: XYList, xy_list_1: XYList) -> XYList:
+    """Combine two lists of ndarray Tuples into one list."""
+    return [
+        (np.concatenate([x_0, x_1], axis=0), np.concatenate([y_0, y_1], axis=0))
+        for (x_0, y_0), (x_1, y_1) in zip(xy_list_0, xy_list_1)
+    ]
+
+
+def shift(x: np.ndarray, y: np.ndarray) -> XY:
+    """Shift x_1, y_1.
+
+    so that the first half contains only labels 0 to 4 and the second half 5 to 9.
+    """
+    x, y = sort_by_label(x, y)
+
+    (x_0, y_0), (x_1, y_1) = split_at_fraction(x, y, fraction=0.5)
+    (x_0, y_0), (x_1, y_1) = shuffle(x_0, y_0), shuffle(x_1, y_1)
+    x, y = np.concatenate([x_0, x_1], axis=0), np.concatenate([y_0, y_1], axis=0)
+    return x, y
+
+
+def create_partitions(
+    unpartitioned_dataset: XY,
+    iid_fraction: float,
+    num_partitions: int,
+) -> XYList:
+    """Create partitioned version of a training or test set.
+
+    Currently tested and supported are MNIST, FashionMNIST and CIFAR-10/100
+    """
+    x, y = unpartitioned_dataset
+
+    x, y = shuffle(x, y)
+    x, y = sort_by_label_repeating(x, y)
+
+    (x_0, y_0), (x_1, y_1) = split_at_fraction(x, y, fraction=iid_fraction)
+
+    # Shift in second split of dataset the classes into two groups
+    x_1, y_1 = shift(x_1, y_1)
+
+    xy_0_partitions = partition(x_0, y_0, num_partitions)
+    xy_1_partitions = partition(x_1, y_1, num_partitions)
+
+    xy_partitions = combine_partitions(xy_0_partitions, xy_1_partitions)
+
+    # Adjust x and y shape
+    return [adjust_xy_shape(xy) for xy in xy_partitions]
+
+
+def create_partitioned_dataset(
+    keras_dataset: Tuple[XY, XY],
+    iid_fraction: float,
+    num_partitions: int,
+) -> Tuple[PartitionedDataset, XY]:
+    """Create partitioned version of keras dataset.
+
+    Currently tested and supported are MNIST, FashionMNIST and CIFAR-10/100
+    """
+    xy_train, xy_test = keras_dataset
+
+    xy_train_partitions = create_partitions(
+        unpartitioned_dataset=xy_train,
+        iid_fraction=iid_fraction,
+        num_partitions=num_partitions,
+    )
+
+    xy_test_partitions = create_partitions(
+        unpartitioned_dataset=xy_test,
+        iid_fraction=iid_fraction,
+        num_partitions=num_partitions,
+    )
+
+    return (xy_train_partitions, xy_test_partitions), adjust_xy_shape(xy_test)
+
+
+def log_distribution(xy_partitions: XYList) -> None:
+    """Print label distribution for list of paritions."""
+    distro = [np.unique(y, return_counts=True) for _, y in xy_partitions]
+    for d in distro:
+        print(d)
+
+
+def adjust_xy_shape(xy: XY) -> XY:
+    """Adjust shape of both x and y."""
+    x, y = xy
+    if x.ndim == 3:
+        x = adjust_x_shape(x)
+    if y.ndim == 2:
+        y = adjust_y_shape(y)
+    return (x, y)
+
+
+def adjust_x_shape(nda: np.ndarray) -> np.ndarray:
+    """Turn shape (x, y, z) into (x, y, z, 1)."""
+    nda_adjusted = np.reshape(nda, (nda.shape[0], nda.shape[1], nda.shape[2], 1))
+    return nda_adjusted
+
+
+def adjust_y_shape(nda: np.ndarray) -> np.ndarray:
+    """Turn shape (x, 1) into (x)."""
+    nda_adjusted = np.reshape(nda, (nda.shape[0]))
+    return nda_adjusted
+
+
+def split_array_at_indices(
+    x: np.ndarray, split_idx: np.ndarray
+) -> List[List[np.ndarray]]:
+    """Split the array `x`.
+
+    into list of elements using starting indices from
+    `split_idx`.
+
+        This function should be used with `unique_indices` from `np.unique()` after
+        sorting by label.
+
+    Args:
+        x (np.ndarray): Original array of dimension (N,a,b,c,...)
+        split_idx (np.ndarray): 1-D array contaning increasing number of
+            indices to be used as partitions. Initial value must be zero. Last value
+            must be less than N.
+
+    Returns
+    -------
+        List[List[np.ndarray]]: List of list of samples.
+    """
+    if split_idx.ndim != 1:
+        raise ValueError("Variable `split_idx` must be a 1-D numpy array.")
+    if split_idx.dtype != np.int64:
+        raise ValueError("Variable `split_idx` must be of type np.int64.")
+    if split_idx[0] != 0:
+        raise ValueError("First value of `split_idx` must be 0.")
+    if split_idx[-1] >= x.shape[0]:
+        raise ValueError(
+            """Last value in `split_idx` must be less than
+            the number of samples in `x`."""
+        )
+    if not np.all(split_idx[:-1] <= split_idx[1:]):
+        raise ValueError("Items in `split_idx` must be in increasing order.")
+
+    num_splits: int = len(split_idx)
+    split_idx = np.append(split_idx, x.shape[0])
+
+    list_samples_split: List[List[np.ndarray]] = [[] for _ in range(num_splits)]
+    for j in range(num_splits):
+        tmp_x = x[split_idx[j] : split_idx[j + 1]]  # noqa: E203
+        for sample in tmp_x:
+            list_samples_split[j].append(sample)
+
+    return list_samples_split
+
+
+def exclude_classes_and_normalize(
+    distribution: np.ndarray, exclude_dims: List[bool], eps: float = 1e-5
+) -> np.ndarray:
+    """Excludes classes from a distribution.
+
+    This function is particularly useful when sampling without replacement.
+    Classes for which no sample is available have their probabilities are set to 0.
+    Classes that had probabilities originally set to 0 are incremented with
+     `eps` to allow sampling from remaining items.
+
+    Args:
+        distribution (np.array): Distribution being used.
+        exclude_dims (List[bool]): Dimensions to be excluded.
+        eps (float, optional): Small value to be addad to non-excluded dimensions.
+            Defaults to 1e-5.
+
+    Returns
+    -------
+        np.ndarray: Normalized distributions.
+    """
+    if np.any(distribution < 0) or (not np.isclose(np.sum(distribution), 1.0)):
+        raise ValueError("distribution must sum to 1 and have only positive values.")
+
+    if distribution.size != len(exclude_dims):
+        raise ValueError(
+            """Length of distribution must be equal
+            to the length `exclude_dims`."""
+        )
+    if eps < 0:
+        raise ValueError("""The value of `eps` must be positive and small.""")
+
+    distribution[[not x for x in exclude_dims]] += eps
+    distribution[exclude_dims] = 0.0
+    sum_rows = np.sum(distribution) + np.finfo(float).eps
+    distribution = distribution / sum_rows
+
+    return distribution
+
+
+def sample_without_replacement(
+    distribution: np.ndarray,
+    list_samples: List[List[np.ndarray]],
+    num_samples: int,
+    empty_classes: List[bool],
+) -> Tuple[XY, List[bool]]:
+    """Sample from a list without replacement.
+
+    using a given distribution.
+
+    Args:
+        distribution (np.ndarray): Distribution used for sampling.
+        list_samples(List[List[np.ndarray]]): List of samples.
+        num_samples (int): Total number of items to be sampled.
+        empty_classes (List[bool]): List of booleans indicating which classes are empty.
+            This is useful to differentiate which classes should still be sampled.
+
+    Returns
+    -------
+        XY: Dataset contaning samples
+        List[bool]: empty_classes.
+    """
+    if np.sum([len(x) for x in list_samples]) < num_samples:
+        raise ValueError(
+            """Number of samples in `list_samples` is less than `num_samples`"""
+        )
+
+    # Make sure empty classes are not sampled
+    # and solves for rare cases where
+    if not empty_classes:
+        empty_classes = len(distribution) * [False]
+
+    distribution = exclude_classes_and_normalize(
+        distribution=distribution, exclude_dims=empty_classes
+    )
+
+    data: List[np.ndarray] = []
+    target: List[np.ndarray] = []
+
+    for _ in range(num_samples):
+        sample_class = np.where(np.random.multinomial(1, distribution) == 1)[0][0]
+        sample: np.ndarray = list_samples[sample_class].pop()
+
+        data.append(sample)
+        target.append(sample_class)
+
+        # If last sample of the class was drawn, then set the
+        #  probability density function (PDF) to zero for that class.
+        if len(list_samples[sample_class]) == 0:
+            empty_classes[sample_class] = True
+            # Be careful to distinguish between classes that had zero probability
+            # and classes that are now empty
+            distribution = exclude_classes_and_normalize(
+                distribution=distribution, exclude_dims=empty_classes
+            )
+    data_array: np.ndarray = np.concatenate([data], axis=0)
+    target_array: np.ndarray = np.array(target, dtype=np.int64)
+
+    return (data_array, target_array), empty_classes
+
+
+def get_partitions_distributions(partitions: XYList) -> Tuple[np.ndarray, List[int]]:
+    """Evaluate the distribution over classes for a set of partitions.
+
+    Args:
+        partitions (XYList): Input partitions
+
+    Returns
+    -------
+        np.ndarray: Distributions of size (num_partitions, num_classes)
+    """
+    # Get largest available label
+    labels = set()
+    for _, y in partitions:
+        labels.update(set(y))
+    list_labels = sorted(labels)
+    bin_edges = np.arange(len(list_labels) + 1)
+
+    # Pre-allocate distributions
+    distributions = np.zeros((len(partitions), len(list_labels)), dtype=np.float32)
+    for idx, (_, _y) in enumerate(partitions):
+        hist, _ = np.histogram(_y, bin_edges)
+        distributions[idx] = hist / hist.sum()
+
+    return distributions, list_labels
+
+
+def create_lda_partitions(
+    dataset: XY,
+    dirichlet_dist: Optional[np.ndarray] = None,
+    num_partitions: int = 100,
+    concentration: Union[float, np.ndarray, List[float]] = 0.5,
+    accept_imbalanced: bool = False,
+    seed: Optional[Union[int, SeedSequence, BitGenerator, Generator]] = None,
+) -> Tuple[XYList, np.ndarray]:
+    r"""Create imbalanced non-iid partitions using Latent Dirichlet Allocation (LDA).
+
+    without resampling.
+
+    Args:
+        dataset (XY): Dataset containing samples X and labels Y.
+        dirichlet_dist (numpy.ndarray, optional): previously generated distribution to
+            be used. This is useful when applying the same distribution for train and
+            validation sets.
+        num_partitions (int, optional): Number of partitions to be created.
+            Defaults to 100.
+        concentration (float, np.ndarray, List[float]): Dirichlet Concentration
+            (:math:`\\alpha`) parameter. Set to float('inf') to get uniform partitions.
+            An :math:`\\alpha \\to \\Inf` generates uniform distributions over classes.
+            An :math:`\\alpha \\to 0.0` generates one class per client. Defaults to 0.5.
+        accept_imbalanced (bool): Whether or not to accept imbalanced output classes.
+            Default False.
+        seed (None, int, SeedSequence, BitGenerator, Generator):
+            A seed to initialize the BitGenerator for generating the Dirichlet
+            distribution. This is defined in Numpy's official documentation as follows:
+            If None, then fresh, unpredictable entropy will be pulled from the OS.
+            One may also pass in a SeedSequence instance.
+            Additionally, when passed a BitGenerator, it will be wrapped by Generator.
+            If passed a Generator, it will be returned unaltered.
+            See official Numpy Documentation for further details.
+
+    Returns
+    -------
+        Tuple[XYList, numpy.ndarray]: List of XYList containing partitions
+            for each dataset and the dirichlet probability density functions.
+    """
+    # pylint: disable=too-many-arguments,too-many-locals
+
+    x, y = dataset
+    x, y = shuffle(x, y)
+    x, y = sort_by_label(x, y)
+
+    if (x.shape[0] % num_partitions) and (not accept_imbalanced):
+        raise ValueError(
+            """Total number of samples must be a multiple of `num_partitions`.
+               If imbalanced classes are allowed, set
+               `accept_imbalanced=True`."""
+        )
+
+    num_samples = num_partitions * [0]
+    for j in range(x.shape[0]):
+        num_samples[j % num_partitions] += 1
+
+    # Get number of classes and verify if they matching with
+    classes, start_indices = np.unique(y, return_index=True)
+
+    # Make sure that concentration is np.array and
+    # check if concentration is appropriate
+    concentration = np.asarray(concentration)
+
+    # Check if concentration is Inf, if so create uniform partitions
+    partitions: List[XY] = [(_, _) for _ in range(num_partitions)]
+    if float("inf") in concentration:
+        partitions = create_partitions(
+            unpartitioned_dataset=(x, y),
+            iid_fraction=1.0,
+            num_partitions=num_partitions,
+        )
+        dirichlet_dist = get_partitions_distributions(partitions)[0]
+
+        return partitions, dirichlet_dist
+
+    if concentration.size == 1:
+        concentration = np.repeat(concentration, classes.size)
+    elif concentration.size != classes.size:  # Sequence
+        raise ValueError(
+            f"The size of the provided concentration ({concentration.size}) ",
+            f"must be either 1 or equal number of classes {classes.size})",
+        )
+
+    # Split into list of list of samples per class
+    list_samples_per_class: List[List[np.ndarray]] = split_array_at_indices(
+        x, start_indices
+    )
+
+    if dirichlet_dist is None:
+        dirichlet_dist = np.random.default_rng(seed).dirichlet(
+            alpha=concentration, size=num_partitions
+        )
+
+    if dirichlet_dist.size != 0:
+        if dirichlet_dist.shape != (num_partitions, classes.size):
+            raise ValueError(
+                f"""The shape of the provided dirichlet distribution
+                 ({dirichlet_dist.shape}) must match the provided number
+                  of partitions and classes ({num_partitions},{classes.size})"""
+            )
+
+    # Assuming balanced distribution
+    empty_classes = classes.size * [False]
+    for partition_id in range(num_partitions):
+        partitions[partition_id], empty_classes = sample_without_replacement(
+            distribution=dirichlet_dist[partition_id].copy(),
+            list_samples=list_samples_per_class,
+            num_samples=num_samples[partition_id],
+            empty_classes=empty_classes,
+        )
+
+    return partitions, dirichlet_dist
diff --git a/baselines/fedavgm/fedavgm/conf/base.yaml b/baselines/fedavgm/fedavgm/conf/base.yaml
new file mode 100644
index 000000000000..3c2c281911a3
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/conf/base.yaml
@@ -0,0 +1,24 @@
+---
+num_clients: 10
+num_rounds: 5  # original experiments (paper) uses 10000
+fraction_evaluate: 0 # fraction of clients usied during validation
+num_cpus: 1
+num_gpus: 0
+
+noniid:
+  concentration: 0.1 # concentrations used in the paper [100., 10., 1., 0.5, 0.2, 0.1, 0.05, 0.0]
+  
+server:
+  momentum: 0.9
+  learning_rate: 1.0
+  reporting_fraction: 0.05  # values used in the paper 0.05, 0.1, 0.2 (not used for Figure 5), 0.4
+
+client:
+  local_epochs: 1   # in the paper it is used 1 or 5
+  batch_size: 64    # in the paper fixed at 64
+  lr: 0.01          # client learning rate
+
+defaults:
+  - strategy: custom-fedavgm
+  - model: cnn
+  - dataset: cifar10
diff --git a/baselines/fedavgm/fedavgm/conf/dataset/cifar10.yaml b/baselines/fedavgm/fedavgm/conf/dataset/cifar10.yaml
new file mode 100644
index 000000000000..4894ba5d675f
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/conf/dataset/cifar10.yaml
@@ -0,0 +1,4 @@
+---
+_target_: fedavgm.dataset.cifar10
+num_classes: 10
+input_shape: [32, 32, 3]
\ No newline at end of file
diff --git a/baselines/fedavgm/fedavgm/conf/dataset/fmnist.yaml b/baselines/fedavgm/fedavgm/conf/dataset/fmnist.yaml
new file mode 100644
index 000000000000..2dfa07f1c60a
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/conf/dataset/fmnist.yaml
@@ -0,0 +1,4 @@
+---
+_target_: fedavgm.dataset.fmnist
+num_classes: 10
+input_shape: [28, 28, 1]
\ No newline at end of file
diff --git a/baselines/fedavgm/fedavgm/conf/model/cnn.yaml b/baselines/fedavgm/fedavgm/conf/model/cnn.yaml
new file mode 100644
index 000000000000..c25463693c7f
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/conf/model/cnn.yaml
@@ -0,0 +1,5 @@
+---
+_target_: fedavgm.models.cnn
+input_shape: ${dataset.input_shape}
+num_classes: ${dataset.num_classes}
+learning_rate: ${client.lr}
\ No newline at end of file
diff --git a/baselines/fedavgm/fedavgm/conf/model/tf_example.yaml b/baselines/fedavgm/fedavgm/conf/model/tf_example.yaml
new file mode 100644
index 000000000000..8c2a670ee978
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/conf/model/tf_example.yaml
@@ -0,0 +1,5 @@
+---
+_target_: fedavgm.models.tf_example
+input_shape: ${dataset.input_shape}
+num_classes: ${dataset.num_classes}
+learning_rate: ${client.lr}
\ No newline at end of file
diff --git a/baselines/fedavgm/fedavgm/conf/strategy/custom-fedavgm.yaml b/baselines/fedavgm/fedavgm/conf/strategy/custom-fedavgm.yaml
new file mode 100644
index 000000000000..526c9714ed73
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/conf/strategy/custom-fedavgm.yaml
@@ -0,0 +1,13 @@
+---
+_target_: fedavgm.strategy.CustomFedAvgM
+min_available_clients: ${num_clients}
+fraction_fit: ${server.reporting_fraction}
+fraction_evaluate: ${fraction_evaluate}
+server_learning_rate: ${server.learning_rate}
+server_momentum: ${server.momentum}
+on_fit_config_fn:
+  _target_: fedavgm.server.get_on_fit_config
+  config: ${client}
+initial_parameters:
+  _target_: fedavgm.models.model_to_parameters
+  model: ${model}
\ No newline at end of file
diff --git a/baselines/fedavgm/fedavgm/conf/strategy/fedavg.yaml b/baselines/fedavgm/fedavgm/conf/strategy/fedavg.yaml
new file mode 100644
index 000000000000..1b2cde85fe6c
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/conf/strategy/fedavg.yaml
@@ -0,0 +1,8 @@
+---
+_target_: flwr.server.strategy.FedAvg
+min_available_clients: ${num_clients}
+fraction_fit: ${server.reporting_fraction}
+fraction_evaluate: ${fraction_evaluate}
+on_fit_config_fn:
+  _target_: fedavgm.server.get_on_fit_config
+  config: ${client}
\ No newline at end of file
diff --git a/baselines/fedavgm/fedavgm/conf/strategy/fedavgm.yaml b/baselines/fedavgm/fedavgm/conf/strategy/fedavgm.yaml
new file mode 100644
index 000000000000..ce88887c02ab
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/conf/strategy/fedavgm.yaml
@@ -0,0 +1,13 @@
+---
+_target_: flwr.server.strategy.FedAvgM
+min_available_clients: ${num_clients}
+fraction_fit: ${server.reporting_fraction}
+fraction_evaluate: ${fraction_evaluate}
+server_learning_rate: ${server.learning_rate}
+server_momentum: ${server.momentum}
+on_fit_config_fn:
+  _target_: fedavgm.server.get_on_fit_config
+  config: ${client}
+initial_parameters:
+  _target_: fedavgm.models.model_to_parameters
+  model: ${model}
\ No newline at end of file
diff --git a/baselines/fedavgm/fedavgm/dataset.py b/baselines/fedavgm/fedavgm/dataset.py
new file mode 100644
index 000000000000..939a42fda5ae
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/dataset.py
@@ -0,0 +1,57 @@
+"""Dataset utilities for federated learning."""
+
+import numpy as np
+from tensorflow import keras
+
+from fedavgm.common import create_lda_partitions
+
+
+def cifar10(num_classes, input_shape):
+    """Prepare the CIFAR-10.
+
+    This method considers CIFAR-10 for creating both train and test sets. The sets are
+    already normalized.
+    """
+    print(f">>> [Dataset] Loading CIFAR-10. {num_classes} | {input_shape}.")
+    (x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
+    x_train = x_train.astype("float32") / 255
+    x_test = x_test.astype("float32") / 255
+    input_shape = x_train.shape[1:]
+    num_classes = len(np.unique(y_train))
+
+    return x_train, y_train, x_test, y_test, input_shape, num_classes
+
+
+def fmnist(num_classes, input_shape):
+    """Prepare the FMNIST.
+
+    This method considers FMNIST for creating both train and test sets. The sets are
+    already normalized.
+    """
+    print(f">>> [Dataset] Loading FMNIST. {num_classes} | {input_shape}.")
+    (x_train, y_train), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()
+    x_train = x_train.astype("float32") / 255
+    x_test = x_test.astype("float32") / 255
+    input_shape = x_train.shape[1:]
+    num_classes = len(np.unique(y_train))
+
+    return x_train, y_train, x_test, y_test, input_shape, num_classes
+
+
+def partition(x_train, y_train, num_clients, concentration):
+    """Create non-iid partitions.
+
+    The partitions uses a LDA distribution based on concentration.
+    """
+    print(
+        f">>> [Dataset] {num_clients} clients, non-iid concentration {concentration}..."
+    )
+    dataset = [x_train, y_train]
+    partitions, _ = create_lda_partitions(
+        dataset,
+        num_partitions=num_clients,
+        # concentration=concentration * num_classes,
+        concentration=concentration,
+        seed=1234,
+    )
+    return partitions
diff --git a/baselines/fedavgm/fedavgm/dataset_preparation.py b/baselines/fedavgm/fedavgm/dataset_preparation.py
new file mode 100644
index 000000000000..dab1967d8399
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/dataset_preparation.py
@@ -0,0 +1 @@
+"""Require to download dataset or additional preparation."""
diff --git a/baselines/fedavgm/fedavgm/main.py b/baselines/fedavgm/fedavgm/main.py
new file mode 100644
index 000000000000..915cad28f212
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/main.py
@@ -0,0 +1,100 @@
+"""Create and connect the building blocks for your experiments; start the simulation.
+
+It includes processioning the dataset, instantiate strategy, specify how the global
+model is going to be evaluated, etc. At the end, this script saves the results.
+"""
+
+import pickle
+from pathlib import Path
+
+import flwr as fl
+import hydra
+import numpy as np
+from hydra.core.hydra_config import HydraConfig
+from hydra.utils import instantiate
+from omegaconf import DictConfig, OmegaConf
+
+from fedavgm.client import generate_client_fn
+from fedavgm.dataset import partition
+from fedavgm.server import get_evaluate_fn
+
+
+# pylint: disable=too-many-locals
+@hydra.main(config_path="conf", config_name="base", version_base=None)
+def main(cfg: DictConfig) -> None:
+    """Run the baseline.
+
+    Parameters
+    ----------
+    cfg : DictConfig
+        An omegaconf object that stores the hydra config.
+    """
+    np.random.seed(2020)
+
+    # 1. Print parsed config
+    print(OmegaConf.to_yaml(cfg))
+
+    # 2. Prepare your dataset
+    x_train, y_train, x_test, y_test, input_shape, num_classes = instantiate(
+        cfg.dataset
+    )
+
+    partitions = partition(x_train, y_train, cfg.num_clients, cfg.noniid.concentration)
+
+    print(f">>> [Model]: Num. Classes {num_classes} | Input shape: {input_shape}")
+
+    # 3. Define your clients
+    client_fn = generate_client_fn(partitions, cfg.model, num_classes)
+
+    # 4. Define your strategy
+    evaluate_fn = get_evaluate_fn(
+        instantiate(cfg.model), x_test, y_test, cfg.num_rounds, num_classes
+    )
+
+    strategy = instantiate(cfg.strategy, evaluate_fn=evaluate_fn)
+
+    # 5. Start Simulation
+    history = fl.simulation.start_simulation(
+        client_fn=client_fn,
+        num_clients=cfg.num_clients,
+        config=fl.server.ServerConfig(num_rounds=cfg.num_rounds),
+        strategy=strategy,
+        client_resources={"num_cpus": cfg.num_cpus, "num_gpus": cfg.num_gpus},
+    )
+
+    _, final_acc = history.metrics_centralized["accuracy"][-1]
+
+    # 6. Save your results
+    save_path = HydraConfig.get().runtime.output_dir
+
+    strategy_name = strategy.__class__.__name__
+    dataset_type = "cifar10" if cfg.dataset.input_shape == [32, 32, 3] else "fmnist"
+
+    def format_variable(x):
+        return f"{x!r}" if isinstance(x, bytes) else x
+
+    file_suffix: str = (
+        f"_{format_variable(strategy_name)}"
+        f"_{format_variable(dataset_type)}"
+        f"_clients={format_variable(cfg.num_clients)}"
+        f"_rounds={format_variable(cfg.num_rounds)}"
+        f"_C={format_variable(cfg.server.reporting_fraction)}"
+        f"_E={format_variable(cfg.client.local_epochs)}"
+        f"_alpha={format_variable(cfg.noniid.concentration)}"
+        f"_server-momentum={format_variable(cfg.server.momentum)}"
+        f"_client-lr={format_variable(cfg.client.lr)}"
+        f"_acc={format_variable(final_acc):.4f}"
+    )
+
+    filename = "results" + file_suffix + ".pkl"
+
+    print(f">>> Saving {filename}...")
+    results_path = Path(save_path) / filename
+    results = {"history": history}
+
+    with open(str(results_path), "wb") as hist_file:
+        pickle.dump(results, hist_file, protocol=pickle.HIGHEST_PROTOCOL)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/baselines/fedavgm/fedavgm/models.py b/baselines/fedavgm/fedavgm/models.py
new file mode 100644
index 000000000000..a151c4d9db76
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/models.py
@@ -0,0 +1,121 @@
+"""CNN model architecture."""
+
+from flwr.common import ndarrays_to_parameters
+from keras.optimizers import SGD
+from keras.regularizers import l2
+from tensorflow import keras
+from tensorflow.nn import local_response_normalization  # pylint: disable=import-error
+
+
+def cnn(input_shape, num_classes, learning_rate):
+    """CNN Model from (McMahan et. al., 2017).
+
+    Communication-efficient learning of deep networks from decentralized data
+    """
+    input_shape = tuple(input_shape)
+
+    weight_decay = 0.004
+    model = keras.Sequential(
+        [
+            keras.layers.Conv2D(
+                64,
+                (5, 5),
+                padding="same",
+                activation="relu",
+                input_shape=input_shape,
+            ),
+            keras.layers.MaxPooling2D((3, 3), strides=(2, 2)),
+            keras.layers.BatchNormalization(),
+            keras.layers.Conv2D(
+                64,
+                (5, 5),
+                padding="same",
+                activation="relu",
+            ),
+            keras.layers.BatchNormalization(),
+            keras.layers.MaxPooling2D((3, 3), strides=(2, 2)),
+            keras.layers.Flatten(),
+            keras.layers.Dense(
+                384, activation="relu", kernel_regularizer=l2(weight_decay)
+            ),
+            keras.layers.Dense(
+                192, activation="relu", kernel_regularizer=l2(weight_decay)
+            ),
+            keras.layers.Dense(num_classes, activation="softmax"),
+        ]
+    )
+    optimizer = SGD(learning_rate=learning_rate)
+    model.compile(
+        loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"]
+    )
+
+    return model
+
+
+def tf_example(input_shape, num_classes, learning_rate):
+    """CNN Model from TensorFlow v1.x example.
+
+    This is the model referenced on the FedAvg paper.
+
+    Reference:
+    https://web.archive.org/web/20170807002954/https://github.com/tensorflow/models/blob/master/tutorials/image/cifar10/cifar10.py
+    """
+    input_shape = tuple(input_shape)
+
+    weight_decay = 0.004
+    model = keras.Sequential(
+        [
+            keras.layers.Conv2D(
+                64,
+                (5, 5),
+                padding="same",
+                activation="relu",
+                input_shape=input_shape,
+            ),
+            keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding="same"),
+            keras.layers.Lambda(
+                local_response_normalization,
+                arguments={
+                    "depth_radius": 4,
+                    "bias": 1.0,
+                    "alpha": 0.001 / 9.0,
+                    "beta": 0.75,
+                },
+            ),
+            keras.layers.Conv2D(
+                64,
+                (5, 5),
+                padding="same",
+                activation="relu",
+            ),
+            keras.layers.Lambda(
+                local_response_normalization,
+                arguments={
+                    "depth_radius": 4,
+                    "bias": 1.0,
+                    "alpha": 0.001 / 9.0,
+                    "beta": 0.75,
+                },
+            ),
+            keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding="same"),
+            keras.layers.Flatten(),
+            keras.layers.Dense(
+                384, activation="relu", kernel_regularizer=l2(weight_decay)
+            ),
+            keras.layers.Dense(
+                192, activation="relu", kernel_regularizer=l2(weight_decay)
+            ),
+            keras.layers.Dense(num_classes, activation="softmax"),
+        ]
+    )
+    optimizer = SGD(learning_rate=learning_rate)
+    model.compile(
+        loss="categorical_crossentropy", optimizer=optimizer, metrics=["accuracy"]
+    )
+
+    return model
+
+
+def model_to_parameters(model):
+    """Retrieve model weigths and convert to ndarrays."""
+    return ndarrays_to_parameters(model.get_weights())
diff --git a/baselines/fedavgm/fedavgm/server.py b/baselines/fedavgm/fedavgm/server.py
new file mode 100644
index 000000000000..c997c035f638
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/server.py
@@ -0,0 +1,45 @@
+"""Define the Flower Server and function to instantiate it."""
+
+from keras.utils import to_categorical
+from omegaconf import DictConfig
+
+
+def get_on_fit_config(config: DictConfig):
+    """Generate the function for config.
+
+    The config dict is sent to the client fit() method.
+    """
+
+    def fit_config_fn(server_round: int):  # pylint: disable=unused-argument
+        # option to use scheduling of learning rate based on round
+        # if server_round > 50:
+        #     lr = config.lr / 10
+        return {
+            "local_epochs": config.local_epochs,
+            "batch_size": config.batch_size,
+        }
+
+    return fit_config_fn
+
+
+def get_evaluate_fn(model, x_test, y_test, num_rounds, num_classes):
+    """Generate the function for server global model evaluation.
+
+    The method evaluate_fn runs after global model aggregation.
+    """
+
+    def evaluate_fn(
+        server_round: int, parameters, config
+    ):  # pylint: disable=unused-argument
+        if server_round == num_rounds:  # evaluates global model just on the last round
+            # instantiate the model
+            model.set_weights(parameters)
+
+            y_test_cat = to_categorical(y_test, num_classes=num_classes)
+            loss, accuracy = model.evaluate(x_test, y_test_cat, verbose=False)
+
+            return loss, {"accuracy": accuracy}
+
+        return None
+
+    return evaluate_fn
diff --git a/baselines/fedavgm/fedavgm/strategy.py b/baselines/fedavgm/fedavgm/strategy.py
new file mode 100644
index 000000000000..cd0a27254fce
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/strategy.py
@@ -0,0 +1,201 @@
+"""Optionally define a custom strategy.
+
+Needed only when the strategy is not yet implemented in Flower or because you want to
+extend or modify the functionality of an existing strategy.
+"""
+
+from logging import WARNING
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+from flwr.common import (
+    FitRes,
+    MetricsAggregationFn,
+    NDArrays,
+    Parameters,
+    Scalar,
+    ndarrays_to_parameters,
+    parameters_to_ndarrays,
+)
+from flwr.common.logger import log
+from flwr.server.client_manager import ClientManager
+from flwr.server.client_proxy import ClientProxy
+from flwr.server.strategy import FedAvg
+from flwr.server.strategy.aggregate import aggregate
+
+
+class CustomFedAvgM(FedAvg):
+    """Re-implmentation of FedAvgM.
+
+    This implementation of FedAvgM diverges from original (Flwr v1.5.0) implementation.
+    Here, the re-implementation introduces the Nesterov Accelerated Gradient (NAG),
+    same as reported in the original FedAvgM paper:
+
+    https://arxiv.org/pdf/1909.06335.pdf
+    """
+
+    def __init__(
+        self,
+        *,
+        fraction_fit: float = 1.0,
+        fraction_evaluate: float = 1.0,
+        min_fit_clients: int = 2,
+        min_evaluate_clients: int = 2,
+        min_available_clients: int = 2,
+        evaluate_fn: Optional[
+            Callable[
+                [int, NDArrays, Dict[str, Scalar]],
+                Optional[Tuple[float, Dict[str, Scalar]]],
+            ]
+        ] = None,
+        on_fit_config_fn: Optional[Callable[[int], Dict[str, Scalar]]] = None,
+        on_evaluate_config_fn: Optional[Callable[[int], Dict[str, Scalar]]] = None,
+        accept_failures: bool = True,
+        initial_parameters: Parameters,
+        fit_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
+        evaluate_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
+        server_learning_rate: float = 1.0,
+        server_momentum: float = 0.9,
+    ) -> None:
+        """Federated Averaging with Momentum strategy.
+
+        Implementation based on https://arxiv.org/pdf/1909.06335.pdf
+
+        Parameters
+        ----------
+        fraction_fit : float, optional
+            Fraction of clients used during training. Defaults to 0.1.
+        fraction_evaluate : float, optional
+            Fraction of clients used during validation. Defaults to 0.1.
+        min_fit_clients : int, optional
+            Minimum number of clients used during training. Defaults to 2.
+        min_evaluate_clients : int, optional
+            Minimum number of clients used during validation. Defaults to 2.
+        min_available_clients : int, optional
+            Minimum number of total clients in the system. Defaults to 2.
+        evaluate_fn : Optional[Callable[[int, NDArrays, Dict[str, Scalar]],
+        Optional[Tuple[float, Dict[str, Scalar]]]]]
+            Optional function used for validation. Defaults to None.
+        on_fit_config_fn : Callable[[int], Dict[str, Scalar]], optional
+            Function used to configure training. Defaults to None.
+        on_evaluate_config_fn : Callable[[int], Dict[str, Scalar]], optional
+            Function used to configure validation. Defaults to None.
+        accept_failures : bool, optional
+            Whether or not accept rounds containing failures. Defaults to True.
+        initial_parameters : Parameters
+            Initial global model parameters.
+        server_learning_rate: float
+            Server-side learning rate used in server-side optimization.
+            Defaults to 1.0.
+        server_momentum: float
+            Server-side momentum factor used for FedAvgM. Defaults to 0.9.
+        """
+        super().__init__(
+            fraction_fit=fraction_fit,
+            fraction_evaluate=fraction_evaluate,
+            min_fit_clients=min_fit_clients,
+            min_evaluate_clients=min_evaluate_clients,
+            min_available_clients=min_available_clients,
+            evaluate_fn=evaluate_fn,
+            on_fit_config_fn=on_fit_config_fn,
+            on_evaluate_config_fn=on_evaluate_config_fn,
+            accept_failures=accept_failures,
+            initial_parameters=initial_parameters,
+            fit_metrics_aggregation_fn=fit_metrics_aggregation_fn,
+            evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation_fn,
+        )
+        self.server_learning_rate = server_learning_rate
+        self.server_momentum = server_momentum
+        self.momentum_vector: Optional[NDArrays] = None
+
+    def __repr__(self) -> str:
+        """Compute a string representation of the strategy."""
+        rep = f"FedAvgM(accept_failures={self.accept_failures})"
+        return rep
+
+    def initialize_parameters(
+        self, client_manager: ClientManager
+    ) -> Optional[Parameters]:
+        """Initialize global model parameters."""
+        return self.initial_parameters
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        """Aggregate fit results using weighted average."""
+        if not results:
+            return None, {}
+
+        # Do not aggregate if there are failures and failures are not accepted
+        if not self.accept_failures and failures:
+            return None, {}
+
+        # Convert results
+        weights_results = [
+            (parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples)
+            for _, fit_res in results
+        ]
+
+        fedavg_result = aggregate(weights_results)  # parameters_aggregated from FedAvg
+
+        # original implementation follows convention described in
+        # https://pytorch.org/docs/stable/generated/torch.optim.SGD.html
+
+        # do the check for self.initial_parameters being set
+        assert (
+            self.initial_parameters is not None
+        ), "Initial parameters must be set for CustomFedAvgM strategy"
+
+        # remember that updates are the opposite of gradients
+        pseudo_gradient: NDArrays = [
+            x - y
+            for x, y in zip(
+                parameters_to_ndarrays(self.initial_parameters), fedavg_result
+            )
+        ]
+
+        if server_round > 1:
+            assert self.momentum_vector, "Momentum should have been created on round 1."
+
+            self.momentum_vector = [
+                self.server_momentum * v + w
+                for w, v in zip(pseudo_gradient, self.momentum_vector)
+            ]
+        else:  # Round 1
+            # Initialize server-side model
+            assert (
+                self.initial_parameters is not None
+            ), "When using server-side optimization, model needs to be initialized."
+            # Initialize momentum vector
+            self.momentum_vector = pseudo_gradient
+
+        # Applying Nesterov
+        pseudo_gradient = [
+            g + self.server_momentum * v
+            for g, v in zip(pseudo_gradient, self.momentum_vector)
+        ]
+
+        # Federated Averaging with Server Momentum
+        fedavgm_result = [
+            w - self.server_learning_rate * v
+            for w, v in zip(
+                parameters_to_ndarrays(self.initial_parameters), pseudo_gradient
+            )
+        ]
+
+        # Update current weights
+        self.initial_parameters = ndarrays_to_parameters(fedavgm_result)
+
+        parameters_aggregated = ndarrays_to_parameters(fedavgm_result)
+
+        # Aggregate custom metrics if aggregation fn was provided
+        metrics_aggregated = {}
+        if self.fit_metrics_aggregation_fn:
+            fit_metrics = [(res.num_examples, res.metrics) for _, res in results]
+            metrics_aggregated = self.fit_metrics_aggregation_fn(fit_metrics)
+        elif server_round == 1:  # Only log this warning once
+            log(WARNING, "No fit_metrics_aggregation_fn provided")
+
+        return parameters_aggregated, metrics_aggregated
diff --git a/baselines/fedavgm/fedavgm/utils.py b/baselines/fedavgm/fedavgm/utils.py
new file mode 100644
index 000000000000..42a3f372e6ad
--- /dev/null
+++ b/baselines/fedavgm/fedavgm/utils.py
@@ -0,0 +1,61 @@
+"""Define any utility function.
+
+They are not directly relevant to  the other (more FL specific) python modules. For
+example, you may define here things like: loading a model from a checkpoint, saving
+results, plotting.
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+from fedavgm.dataset import cifar10, partition
+
+# pylint: disable=too-many-locals
+
+
+def plot_concentrations_cifar10():
+    """Create a plot with different concentrations for dataset using LDA."""
+    x_train, y_train, x_test, y_test, _, num_classes = cifar10(10, (32, 32, 3))
+    x = np.concatenate((x_train, x_test), axis=0)
+    y = np.concatenate((y_train, y_test), axis=0)
+    num_clients = 30
+
+    # Simulated different concentrations for partitioning
+    concentration_values = [np.inf, 100, 1, 0.1, 0.01, 1e-10]
+    color = plt.get_cmap("RdYlGn")(np.linspace(0.15, 0.85, num_classes))
+    num_plots = len(concentration_values)
+    fig, axs = plt.subplots(1, num_plots, figsize=(15, 5), sharey=True)
+
+    pos = axs[0].get_position()
+    pos.x0 += 0.1
+    axs[0].set_position(pos)
+
+    for i, concentration in enumerate(concentration_values):
+        partitions = partition(x, y, num_clients, concentration)
+
+        for client in range(num_clients):
+            _, y_client = partitions[client]
+            lefts = [0]
+            axis = axs[i]
+            class_counts = np.bincount(y_client, minlength=num_classes)
+            np.sum(class_counts > 0)
+
+            class_distribution = class_counts.astype(np.float16) / len(y_client)
+
+            for idx, val in enumerate(class_distribution[:-1]):
+                lefts.append(lefts[idx] + val)
+
+            axis.barh(client, class_distribution, left=lefts, color=color)
+            axis.set_xticks([])
+            axis.set_yticks([])
+            axis.set_xlabel("Class distribution")
+            axis.set_title(f"Concentration = {concentration}")
+
+    fig.text(0, 0.5, "Client", va="center", rotation="vertical")
+    plt.tight_layout()
+    plt.savefig("../_static/concentration_cifar10_v2.png")
+    print(">>> Concentration plot created")
+
+
+if __name__ == "__main__":
+    plot_concentrations_cifar10()
diff --git a/baselines/fedavgm/pyproject.toml b/baselines/fedavgm/pyproject.toml
new file mode 100644
index 000000000000..d222baa65b0e
--- /dev/null
+++ b/baselines/fedavgm/pyproject.toml
@@ -0,0 +1,139 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.masonry.api"
+
+[tool.poetry]
+name = "fedavgm"
+version = "1.0.0"
+description = "FedAvgM: Measuring the effects of non-identical data distribution for federated visual classification"
+license = "Apache-2.0"
+authors = ["Gustavo Bertoli"]
+readme = "README.md"
+homepage = "https://flower.ai"
+repository = "https://github.com/adap/flower"
+documentation = "https://flower.ai"
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: MacOS :: MacOS X",
+    "Operating System :: POSIX :: Linux",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: Implementation :: CPython",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Scientific/Engineering :: Mathematics",
+    "Topic :: Software Development",
+    "Topic :: Software Development :: Libraries",
+    "Topic :: Software Development :: Libraries :: Python Modules",
+    "Typing :: Typed",
+]
+
+[tool.poetry.dependencies]
+python = ">=3.9, <3.12.0"  # changed! original baseline template uses >= 3.8.15
+flwr = { extras = ["simulation"], version = "1.5.0" }
+hydra-core = "1.3.2" # don't change this
+cython = "^3.0.0"
+tensorflow = "2.11.1"
+numpy = "1.25.2"
+matplotlib = "^3.7.2"
+
+[tool.poetry.dev-dependencies]
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
+mypy = "==1.4.1"
+pylint = "==2.8.2"
+flake8 = "==3.9.2"
+pytest = "==6.2.4"
+pytest-watch = "==4.2.0"
+ruff = "==0.0.272"
+types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
+
+[tool.isort]
+line_length = 88
+indent = "    "
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+use_parentheses = true
+
+[tool.black]
+line-length = 88
+target-version = ["py38", "py39", "py310", "py311"]
+
+[tool.pytest.ini_options]
+minversion = "6.2"
+addopts = "-qq"
+testpaths = [
+    "flwr_baselines",
+]
+
+[tool.mypy]
+ignore_missing_imports = true
+strict = false
+plugins = "numpy.typing.mypy_plugin"
+
+[tool.pylint."MESSAGES CONTROL"]
+disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
+good-names = "i,j,k,_,x,y,X,Y"
+signature-mutators = "hydra.main.main"
+
+[[tool.mypy.overrides]]
+module = [
+    "importlib.metadata.*",
+    "importlib_metadata.*",
+]
+follow_imports = "skip"
+follow_imports_for_stubs = true
+disallow_untyped_calls = false
+
+[[tool.mypy.overrides]]
+module = "torch.*"
+follow_imports = "skip"
+follow_imports_for_stubs = true
+
+[tool.docformatter]
+wrap-summaries = 88
+wrap-descriptions = 88
+
+[tool.ruff]
+target-version = "py38"
+line-length = 88
+select = ["D", "E", "F", "W", "B", "ISC", "C4"]
+fixable = ["D", "E", "F", "W", "B", "ISC", "C4"]
+ignore = ["B024", "B027"]
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+    "proto",
+]
+
+[tool.ruff.pydocstyle]
+convention = "numpy"
diff --git a/baselines/fedbn/README.md b/baselines/fedbn/README.md
index 4b271bd49851..37388894ff9d 100644
--- a/baselines/fedbn/README.md
+++ b/baselines/fedbn/README.md
@@ -34,37 +34,37 @@ dataset: [MNIST, MNIST-M, SVHN, USPS, SynthDigits]
 
 **Model:** A six-layer CNN with 14,219,210 parameters following the structure described in appendix D.2.
 
-**Dataset:**  This baseline makes use of the pre-processed partitions created and open source by the authors of the FedBN paper. You can read more about how those were created [here](https://github.com/med-air/FedBN). Follow the steps below in the `Environment Setup` section to download them. 
+**Dataset:**  This baseline makes use of the pre-processed partitions created and open source by the authors of the FedBN paper. You can read more about how those were created [here](https://github.com/med-air/FedBN). Follow the steps below in the `Environment Setup` section to download them.
 
 
 A more detailed explanation of the datasets is given in the following table.
 
-|     | MNIST     | MNIST-M   | SVHN  |  USPS    | SynthDigits |
-|--- |---        |---        |---    |---            |---    |
-| data type| handwritten digits| MNIST modification randomly colored with colored patches| Street view house numbers | handwritten digits from envelopes by the U.S. Postal Service | Syntehtic digits Windows TM font varying the orientation, blur and stroke colors |
-| color | greyscale | RGB | RGB | greyscale | RGB |
-| pixelsize | 28x28 | 28 x 28 | 32 x32 | 16 x16 | 32 x32 |
-| labels | 0-9 | 0-9 | 1-10 | 0-9 | 1-10 |
-| number of trainset | 60.000 | 60.000 | 73.257 | 9,298 | 50.000 |
-| number of testset| 10.000 | 10.000 | 26.032 | - | - |
-| image shape | (28,28) | (28,28,3) | (32,32,3) | (16,16) | (32,32,3) |
+|                    | MNIST              | MNIST-M                                                  | SVHN                      | USPS                                                         | SynthDigits                                                                      |
+| ------------------ | ------------------ | -------------------------------------------------------- | ------------------------- | ------------------------------------------------------------ | -------------------------------------------------------------------------------- |
+| data type          | handwritten digits | MNIST modification randomly colored with colored patches | Street view house numbers | handwritten digits from envelopes by the U.S. Postal Service | Synthetic digits Windows TM font varying the orientation, blur and stroke colors |
+| color              | greyscale          | RGB                                                      | RGB                       | greyscale                                                    | RGB                                                                              |
+| pixelsize          | 28x28              | 28 x 28                                                  | 32 x32                    | 16 x16                                                       | 32 x32                                                                           |
+| labels             | 0-9                | 0-9                                                      | 1-10                      | 0-9                                                          | 1-10                                                                             |
+| number of trainset | 60.000             | 60.000                                                   | 73.257                    | 9,298                                                        | 50.000                                                                           |
+| number of testset  | 10.000             | 10.000                                                   | 26.032                    | -                                                            | -                                                                                |
+| image shape        | (28,28)            | (28,28,3)                                                | (32,32,3)                 | (16,16)                                                      | (32,32,3)                                                                        |
 
 
 **Training Hyperparameters:** By default (i.e. if you don't override anything in the config) these main hyperparameters used are shown in the table below. For a complete list of hyperparameters, please refer to the config files in `fedbn/conf`.
 
-| Description | Value |
-| ----------- | ----- |
-| rounds | 10 |
-| num_clients | 5 |
-| strategy_fraction_fit | 1.0 |
-| strategy.fraction_evaluate | 0.0 |
-| training samples per client| 743 |
-| client.l_r | 10E-2 |
-| local epochs | 1 |
-| loss | cross entropy loss |
-| optimizer | SGD |
-| client_resources.num_cpu | 2 |
-| client_resources.num_gpus | 0.0 |
+| Description                 | Value              |
+| --------------------------- | ------------------ |
+| rounds                      | 10                 |
+| num_clients                 | 5                  |
+| strategy_fraction_fit       | 1.0                |
+| strategy.fraction_evaluate  | 0.0                |
+| training samples per client | 743                |
+| client.l_r                  | 10E-2              |
+| local epochs                | 1                  |
+| loss                        | cross entropy loss |
+| optimizer                   | SGD                |
+| client_resources.num_cpu    | 2                  |
+| client_resources.num_gpus   | 0.0                |
 
 ## Environment Setup
 
@@ -93,7 +93,7 @@ cd data ..
 
 ## Running the Experiments
 
-First, activate your environment via `poetry shell`. The commands below show how to run the experiments and modify some of its key hyperparameters via the cli. Each time you run an experiment, the log and results will be stored inside `outputs/<date>/<time>`. Please refer to [the Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html) to learn more about Flower Simulation.
+First, activate your environment via `poetry shell`. The commands below show how to run the experiments and modify some of its key hyperparameters via the cli. Each time you run an experiment, the log and results will be stored inside `outputs/<date>/<time>`. Please refer to [the Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) to learn more about Flower Simulation.
 
 ```bash
 # run with default arguments
@@ -111,7 +111,7 @@ python -m fedbn.main strategy.fraction_evaluate=1 # your code will run slower
 # adjust hyperparameters like the number of rounds or batch size like this
 python -m fedbn.main num_rounds=100 dataset.batch_size
 
-# increase the number of clients like this (note this should be a multiple 
+# increase the number of clients like this (note this should be a multiple
 # of the number of dataset you involve in the experiment -- 5 by default)
 # this means that without changing other hyperparameters, you can only have
 # either 5,10,15,20,25,30,35,40,45 or 50 clients
@@ -132,7 +132,7 @@ python -m fedbn.main client=fedavg
 
 ## Limitations
 
-The pre-processing of the five datasets provided by the authors, imposes some limitations on the number of clients that can be spawned for the experiment. Naturally, this limitation can be circumvented if you edit the code, and in particular the `dataset.DigitsDataset` constructor. The aforementioned limitation happens because each dataset is partitioned into 10 disjoint sets and a 'DigitsDataset' can only be constructed by concatenating any set of such partitions (at least one, at most all 10). _How does the limitation manifest?_ Given that we have 5 datasets, if a client just takes one partition, a FL setup can accommodate 50 clients, one using a different partition. But, if you want for instance each client have 3 partitions of the same dataset (yes, clients can only hold data of one dataset) then the maximum number of clients gets reduced to 20. Following this logic you can see that if a client wants to use all 10 partitions of a given dataset, then only 5 clients can participate in the experiment. 
+The pre-processing of the five datasets provided by the authors, imposes some limitations on the number of clients that can be spawned for the experiment. Naturally, this limitation can be circumvented if you edit the code, and in particular the `dataset.DigitsDataset` constructor. The aforementioned limitation happens because each dataset is partitioned into 10 disjoint sets and a 'DigitsDataset' can only be constructed by concatenating any set of such partitions (at least one, at most all 10). _How does the limitation manifest?_ Given that we have 5 datasets, if a client just takes one partition, a FL setup can accommodate 50 clients, one using a different partition. But, if you want for instance each client have 3 partitions of the same dataset (yes, clients can only hold data of one dataset) then the maximum number of clients gets reduced to 20. Following this logic you can see that if a client wants to use all 10 partitions of a given dataset, then only 5 clients can participate in the experiment.
 
 Another limitation in the current implementation is that there should be the same number of clients for each dataset. Also, all clients should contain the same number of partitions of their respective datasets. You can remove these constrain my editing `dataset.get_data()`.
 
diff --git a/baselines/fedbn/docs/multirun_plot.ipynb b/baselines/fedbn/docs/multirun_plot.ipynb
index 6314e0033b9d..2660d1cc4e19 100644
--- a/baselines/fedbn/docs/multirun_plot.ipynb
+++ b/baselines/fedbn/docs/multirun_plot.ipynb
@@ -58,12 +58,12 @@
    "source": [
     "def fuse_by_dataset(losses):\n",
     "    \"\"\"Transform per-round history (list of dicts) into\n",
-    "    \n",
+    "\n",
     "    a single dict, with values as lists.\"\"\"\n",
     "    fussed_losses = {}\n",
     "\n",
     "    for _, loss_dict in losses:\n",
-    "        for k,v in loss_dict.items():\n",
+    "        for k, v in loss_dict.items():\n",
     "            if k in fussed_losses:\n",
     "                fussed_losses[k].append(v)\n",
     "            else:\n",
@@ -82,9 +82,14 @@
     "    res_list = []\n",
     "    for results in list(Path(path_multirun).glob(\"**/history.pkl\")):\n",
     "        data, config = read_pickle_and_config(results)\n",
-    "        pre_train_loss = data['history'].metrics_distributed_fit['pre_train_losses']\n",
+    "        pre_train_loss = data[\"history\"].metrics_distributed_fit[\"pre_train_losses\"]\n",
     "        fussed_losses = fuse_by_dataset(pre_train_loss)\n",
-    "        res_list.append({'strategy': config['client']['client_label'], 'train_losses': fussed_losses})\n",
+    "        res_list.append(\n",
+    "            {\n",
+    "                \"strategy\": config[\"client\"][\"client_label\"],\n",
+    "                \"train_losses\": fussed_losses,\n",
+    "            }\n",
+    "        )\n",
     "    return res_list"
    ]
   },
@@ -95,7 +100,7 @@
    "outputs": [],
    "source": [
     "# Here replace with the path of the multi run you just comleted\n",
-    "all_losses = process_multirun_data('../multirun/2023-11-15/22-28-28')"
+    "all_losses = process_multirun_data(\"../multirun/2023-11-15/22-28-28\")"
    ]
   },
   {
@@ -104,30 +109,29 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "def average_by_client_type(all_fused_lossed):\n",
     "    \"\"\"If there are multliple runs for the same strategy add them up,\n",
-    "    \n",
+    "\n",
     "    average them later. This is useful if you run the `--multirun` running\n",
     "    more than one time the same configuration.\"\"\"\n",
-    "    \n",
+    "\n",
     "    # identify how many unique clients were used\n",
     "    to_plot = {}\n",
     "    for res in all_fused_lossed:\n",
-    "        strategy = res['strategy']\n",
+    "        strategy = res[\"strategy\"]\n",
     "        if strategy not in to_plot:\n",
     "            to_plot[strategy] = {}\n",
-    "        \n",
-    "        for dataset, train_loss in res['train_losses'].items():\n",
+    "\n",
+    "        for dataset, train_loss in res[\"train_losses\"].items():\n",
     "            if dataset in to_plot[strategy]:\n",
-    "                to_plot[strategy][dataset]['train_loss'] += np.array(train_loss)\n",
-    "                to_plot[strategy][dataset]['run_count'] += 1\n",
+    "                to_plot[strategy][dataset][\"train_loss\"] += np.array(train_loss)\n",
+    "                to_plot[strategy][dataset][\"run_count\"] += 1\n",
     "            else:\n",
-    "                to_plot[strategy][dataset] = {'train_loss': np.array(train_loss)}\n",
-    "                to_plot[strategy][dataset]['run_count'] = 1\n",
+    "                to_plot[strategy][dataset] = {\"train_loss\": np.array(train_loss)}\n",
+    "                to_plot[strategy][dataset][\"run_count\"] = 1\n",
     "\n",
     "    # print(to_plot)\n",
-    "    return to_plot\n"
+    "    return to_plot"
    ]
   },
   {
@@ -167,25 +171,25 @@
     "print(datasets)\n",
     "\n",
     "num_datasets = len(datasets)\n",
-    "fig, axs = plt.subplots(figsize=(14,4), nrows=1, ncols=num_datasets)\n",
+    "fig, axs = plt.subplots(figsize=(14, 4), nrows=1, ncols=num_datasets)\n",
     "\n",
     "\n",
     "for s_id, (strategy, results) in enumerate(to_plot.items()):\n",
     "    for i, dataset in enumerate(datasets):\n",
-    "        loss = results[dataset]['train_loss']/results[dataset]['run_count']\n",
+    "        loss = results[dataset][\"train_loss\"] / results[dataset][\"run_count\"]\n",
     "        axs[i].plot(range(len(loss)), loss, label=strategy)\n",
-    "        axs[i].set_xlabel('Round')\n",
+    "        axs[i].set_xlabel(\"Round\")\n",
     "        if i == 0:\n",
-    "            axs[i].set_ylabel('Train Loss')\n",
+    "            axs[i].set_ylabel(\"Train Loss\")\n",
     "\n",
     "        axs[i].legend()\n",
     "\n",
-    "        if s_id==0:\n",
+    "        if s_id == 0:\n",
     "            axs[i].grid()\n",
     "            axs[i].set_title(dataset)\n",
-    "            axs[i].set_xticks(np.arange(0,100+1, 25))\n",
+    "            axs[i].set_xticks(np.arange(0, 100 + 1, 25))\n",
     "\n",
-    "saveFig('train_loss.png', fig)"
+    "saveFig(\"train_loss.png\", fig)"
    ]
   },
   {
diff --git a/baselines/fedbn/fedbn/dataset.py b/baselines/fedbn/fedbn/dataset.py
index 8ce3eb520f89..58f3462fe9f7 100644
--- a/baselines/fedbn/fedbn/dataset.py
+++ b/baselines/fedbn/fedbn/dataset.py
@@ -1,4 +1,5 @@
 """Digits dataset."""
+
 import os
 from random import shuffle
 from typing import List, Optional, Tuple
diff --git a/baselines/fedbn/fedbn/strategy.py b/baselines/fedbn/fedbn/strategy.py
index 0f457daedb33..f69be79a7182 100644
--- a/baselines/fedbn/fedbn/strategy.py
+++ b/baselines/fedbn/fedbn/strategy.py
@@ -3,6 +3,7 @@
 Needed only when the strategy is not yet implemented in Flower or because you want to
 extend or modify the functionality of an existing strategy.
 """
+
 from typing import Dict, List, Tuple
 
 from flwr.common.typing import Metrics
diff --git a/baselines/fedbn/pyproject.toml b/baselines/fedbn/pyproject.toml
index 0e46a33360db..97087da700a7 100644
--- a/baselines/fedbn/pyproject.toml
+++ b/baselines/fedbn/pyproject.toml
@@ -7,11 +7,11 @@ name = "fedbn"
 version = "1.0.0"
 description = "Flower Baselines"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -46,9 +46,9 @@ torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2
 tqdm = "^4.66.1"
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
-docformatter = "==1.5.1"
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
@@ -86,10 +86,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/fedmeta/fedmeta/dataset_preparation.py b/baselines/fedmeta/fedmeta/dataset_preparation.py
index c139cdf86d69..6e952306f811 100644
--- a/baselines/fedmeta/fedmeta/dataset_preparation.py
+++ b/baselines/fedmeta/fedmeta/dataset_preparation.py
@@ -6,6 +6,7 @@
 uncomment the lines below and tell us in the README.md (see the "Running the Experiment"
 block) that this file should be executed first.
 """
+
 import json
 import os
 from collections import defaultdict
diff --git a/baselines/fedmeta/fedmeta/main.py b/baselines/fedmeta/fedmeta/main.py
index e43ad94a3089..788286c4fddf 100644
--- a/baselines/fedmeta/fedmeta/main.py
+++ b/baselines/fedmeta/fedmeta/main.py
@@ -4,7 +4,6 @@
 model is going to be evaluated, etc. At the end, this script saves the results.
 """
 
-
 import flwr as fl
 import hydra
 from hydra.core.hydra_config import HydraConfig
diff --git a/baselines/fedmeta/fedmeta/strategy.py b/baselines/fedmeta/fedmeta/strategy.py
index 7947938116e9..a12f556dc8b8 100644
--- a/baselines/fedmeta/fedmeta/strategy.py
+++ b/baselines/fedmeta/fedmeta/strategy.py
@@ -3,6 +3,7 @@
 Needed only when the strategy is not yet implemented in Flower or because you want to
 extend or modify the functionality of an existing strategy.
 """
+
 from collections import OrderedDict
 from logging import WARNING
 from typing import Dict, List, Optional, Tuple, Union
diff --git a/baselines/fedmeta/pyproject.toml b/baselines/fedmeta/pyproject.toml
index cbaa9bb5d110..77279e5e8a5b 100644
--- a/baselines/fedmeta/pyproject.toml
+++ b/baselines/fedmeta/pyproject.toml
@@ -9,9 +9,9 @@ description = "Implementation of FedMeta (Fei Chen et al. 2018)"
 license = "Apache-2.0"
 authors = ["Jinsoo Kim <wlstn25092303@gmail.com>", "Kangyoon Lee <keylee@gachon.ac.kr>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -37,20 +37,19 @@ classifiers = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.10.0, <3.11.0" 
+python = ">=3.10.0, <3.11.0"
 flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 matplotlib = "3.7.1"
 scikit-learn = "1.3.1"
-torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl" }
 pillow = "9.5.0" # needed <10.0.0 for LEAF repo scripts
 
-
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
-docformatter = "==1.5.1"
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
@@ -58,6 +57,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
@@ -86,10 +86,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/fedmlb/README.md b/baselines/fedmlb/README.md
index 47cc69b48e09..0efad9db1d4f 100644
--- a/baselines/fedmlb/README.md
+++ b/baselines/fedmlb/README.md
@@ -222,7 +222,7 @@ You can override settings directly from the command line in this way:
 ```bash
 python -m fedmlb.main clients_per_round=10 # this will run using 10 clients per round instead of 5 clients as the default config 
 
-# this will select the dataset partitioned with 0.6 concentration paramater instead of 0.3 as the default config
+# this will select the dataset partitioned with 0.6 concentration parameter instead of 0.3 as the default config
 python -m fedmlb.main dataset_config.alpha_dirichlet=0.6
 ```
 
@@ -249,7 +249,7 @@ python -m fedmlb.main dataset_config.alpha_dirichlet=0.6 total_clients=500 clien
 ```
 
 #### Tiny-Imagenet
-For Tiny-ImageNet, as in the orginal paper, batch size of local updates should be set 
+For Tiny-ImageNet, as in the original paper, batch size of local updates should be set 
 to 100 in settings with 100 clients and to 20 in settings with 500 clients;
 this is equal to set the amount of local_updates to 50 (as the default) -- 
 so no change to batch size is required --, in fact
diff --git a/baselines/fedmlb/fedmlb/dataset.py b/baselines/fedmlb/fedmlb/dataset.py
index 290f7600332f..6633849a73f7 100644
--- a/baselines/fedmlb/fedmlb/dataset.py
+++ b/baselines/fedmlb/fedmlb/dataset.py
@@ -1,4 +1,5 @@
 """Handle dataset loading and preprocessing utility."""
+
 import os
 from typing import Union
 
diff --git a/baselines/fedmlb/pyproject.toml b/baselines/fedmlb/pyproject.toml
index 005803e1891e..47d115d48834 100644
--- a/baselines/fedmlb/pyproject.toml
+++ b/baselines/fedmlb/pyproject.toml
@@ -9,9 +9,9 @@ description = "FedMLB Flower Baseline"
 license = "Apache-2.0"
 authors = ["Alessio Mora <alessio.mora@unibo.it>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -45,9 +45,9 @@ matplotlib = "3.7.1"
 psutil = "5.9.5" # this is just to log some info about memory usage
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
-docformatter = "==1.5.1"
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
@@ -55,6 +55,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
@@ -83,10 +84,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/fednova/LICENSE b/baselines/fednova/LICENSE
new file mode 100644
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@@ -0,0 +1,202 @@
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diff --git a/baselines/fednova/README.md b/baselines/fednova/README.md
new file mode 100644
index 000000000000..1feb389cc101
--- /dev/null
+++ b/baselines/fednova/README.md
@@ -0,0 +1,191 @@
+---
+title: Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
+url: https://arxiv.org/abs/2007.07481
+labels: [normalized averaging, heterogeneous optimization, image classification]
+dataset: [CIFAR-10]
+---
+
+# Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization
+
+**Paper:** [arxiv.org/abs/2007.07481](https://arxiv.org/abs/2007.07481)
+
+**Authors:** *Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, H. Vincent Poor*
+
+**Abstract:** *In federated learning, heterogeneity in the clients' local datasets and computation speeds results in large variations in the number of local updates performed by each client in each communication round. Naive weighted aggregation of such models causes objective inconsistency, that is, the global model converges to a stationary point of a mismatched objective function which can be arbitrarily different from the true objective. This paper provides a general framework to analyze the convergence of federated heterogeneous optimization algorithms. It subsumes previously proposed methods such as FedAvg and FedProx and provides the first principled understanding of the solution bias and the convergence slowdown due to objective inconsistency. Using insights from this analysis, we propose FedNova, a normalized averaging method that eliminates objective inconsistency while preserving fast error convergence.*
+
+
+## About this baseline
+
+**What’s implemented:** *_The code in this baseline reproduces the results from Table 1 in the paper which corresponds to experiments on Non-IID CIFAR dataset._*
+
+**Datasets:** *_The dataset in the experiment is a Non-IID CIFAR-10 dataset which is partitioned across 16 clients using a Dirichlet distribution with parameter alpha=0.1._*
+
+**Hardware Setup:** *_The experiment setting consists of 16 clients. The training is done on a single workstation consisting of 8 CPU cores, 32 GB of RAM and an Nvidia A100 GPU. 
+The total GPU memory usage for the experiment is ~ 10 GB(1.1 GB per client + main process). Therefore effectively, 8 clients run in parallel in this setup using the default config. 
+The total time for a single experiment in this setup is ~ 50 minutes. (or 30 seconds per communication round). In case of resource constraints, 
+the experiment can be run with 4 clients in parallel by setting client_resources as follows: {num_cpus: 1, num_gpus: 0.25}. This uses ~ 5.4 GB of GPU memory and 4 CPU cores._*
+
+**Contributors:** *_Aasheesh Singh (Github: [@ashdtu](https://github.com/ashdtu)), MILA-Quebec AI Institute_*
+
+
+## Experimental Setup
+
+**Task:** The task is Image classification on CIFAR-10 dataset.
+
+**Model:** The experiment uses the below configuration of VGG-11 model for the image classification task. 
+```
+----------------------------------------------------------------
+        Layer (type)               Output Shape         Param #
+================================================================
+            Conv2d-1           [-1, 64, 32, 32]           1,792
+              ReLU-2           [-1, 64, 32, 32]               0
+         MaxPool2d-3           [-1, 64, 16, 16]               0
+            Conv2d-4          [-1, 128, 16, 16]          73,856
+              ReLU-5          [-1, 128, 16, 16]               0
+         MaxPool2d-6            [-1, 128, 8, 8]               0
+            Conv2d-7            [-1, 256, 8, 8]         295,168
+              ReLU-8            [-1, 256, 8, 8]               0
+            Conv2d-9            [-1, 256, 8, 8]         590,080
+             ReLU-10            [-1, 256, 8, 8]               0
+        MaxPool2d-11            [-1, 256, 4, 4]               0
+           Conv2d-12            [-1, 512, 4, 4]       1,180,160
+             ReLU-13            [-1, 512, 4, 4]               0
+           Conv2d-14            [-1, 512, 4, 4]       2,359,808
+             ReLU-15            [-1, 512, 4, 4]               0
+        MaxPool2d-16            [-1, 512, 2, 2]               0
+           Conv2d-17            [-1, 512, 2, 2]       2,359,808
+             ReLU-18            [-1, 512, 2, 2]               0
+           Conv2d-19            [-1, 512, 2, 2]       2,359,808
+             ReLU-20            [-1, 512, 2, 2]               0
+        MaxPool2d-21            [-1, 512, 1, 1]               0
+          Dropout-22                  [-1, 512]               0
+           Linear-23                  [-1, 512]         262,656
+             ReLU-24                  [-1, 512]               0
+          Dropout-25                  [-1, 512]               0
+           Linear-26                  [-1, 512]         262,656
+             ReLU-27                  [-1, 512]               0
+           Linear-28                   [-1, 10]           5,130
+================================================================
+Total params: 9,750,922
+Trainable params: 9,750,922
+Non-trainable params: 0
+----------------------------------------------------------------
+Input size (MB): 0.01
+Forward/backward pass size (MB): 2.57
+Params size (MB): 37.20
+Estimated Total Size (MB): 39.78
+----------------------------------------------------------------
+```
+
+**Dataset:** The dataset is Non-IID CIFAR-10 dataset which is partitioned across 16 clients using a Dirichlet distribution Dir16(0.1), (alpha=0.1) as done
+in the [paper](https://arxiv.org/abs/2002.06440). Each client gets a different skewed distribution of the class labels following this split.
+
+**Training Hyperparameters:**
+
+| Hyperparameter                  | Description                                                                               | Default value |
+|----------------------------------|-------------------------------------------------------------------------------------------|---------------|
+| optimizer.learning_rate          | Learning rate of local client optimizers                                                  | 0.02          |
+| optimizer.momentum               | Momentum factor                                                                          | 0.9             |
+| optimizer.mu                     | Proximal updates factor                                                                  | 0             |
+| optimizer.weight_decay          | Weight decay for regularization                                                          | 1e-4          |
+| num_epochs                       | Number of local training epochs for clients                                               | 2             |
+| num_rounds                       | Number of server communication rounds                                                    | 100           |
+| var_local_epochs                 | Whether to have variable or fixed local client training epochs. If True, samples num_epochs uniformly in (2,5) | False         |
+| batch size                       | Batch size for training                                                                   | 32            |
+
+
+
+
+## Environment Setup
+
+``` python
+# Navigate to baselines/fednova
+cd baselines/fednova
+
+# Set python version
+pyenv install 3.10.11
+pyenv local 3.10.11
+
+# Tell poetry to use python 3.10
+poetry env use 3.10.11
+
+# install the base Poetry environment, make sure there is no existing poetry.lock file and pyproject.toml file is located in the current directory
+poetry install
+
+# activate the environment
+poetry shell
+```
+
+## Running the Experiments
+Once the poetry environment is active, you can use the following command to run the various experiments in Table 1.
+You would need to specify the below two command line parameters to iterate through various experiment configurations. They are as follows:
+
+1. `optimizer`: This parameter specifies the local optimizer configuration. It can take the following values:
+    - `vanilla`: This corresponds to the vanilla SGD as the client optimizer
+    - `momentum`: This corresponds to the SGD optimizer with momentum. It is the default optimizer setting.
+    - `proximal`: This corresponds to the SGD optimizer with proximal term in the loss.
+    - `server`: This corresponds to momentum updates of global weights on the strategy or server side. The client optimizers do not use momentum in this setting. This setting is only valid for FedNova strategy. 
+    - `hybrid`: This corresponds to hybrid momentum scheme where both the local client optimizer and the server maintain a momentum buffer. This setting is only valid for FedNova strategy. 
+
+2. `strategy`: This specifies the aggregation strategy for the client updates. The default is `fednova`. 
+If you do not specify this parameter, all experiments will run with FedNova as the strategy and reproduce the rightmost columns of Table 1. 
+It can take the following values:
+    - `fednova`: This corresponds to the FedNova (default) aggregation strategy.
+    - `fedavg`: This corresponds to the FedAvg aggregation strategy. The left column of Table 1 can be reproduced by setting this parameter.
+
+3. `var_local_epochs`: Takes value True/False. This parameter specifies whether the number of local training epochs for each client is fixed (Epochs=2) or variable(Uniform sampled from [2,5) ). 
+It takes the following values:
+   - `False`: (default) This corresponds to the fixed number of local epochs for each client. This corresponds to the first part of the Table 1.
+   - `True`: This corresponds to the variable number of local epochs for each client. This corresponds to the second part of the Table 1.
+
+```bash  
+# Run experiments for first 6 rows of Table-1 below. 
+python -m fednova.main --multirun optimizer=vanilla,momentum,proximal strategy=fedavg,fednova var_local_epochs=False,True seed=4,5,6
+
+# Run experiments to compare effect of Momentum (No momentum vs Server vs Hybrid)
+python -m fednova.main --multirun optimizer=server,hybrid strategy=fednova var_local_epochs=True seed=4,5,6
+
+# Plot results and generate Results table. The plots are saved in _static/ directory. 
+python fednova/utils.py
+
+```
+
+> **Note**: Due to instability in weight updates in hetrogeneous learning setting on Non-IID datasets, one or two experiments may show NaNs in the loss for few very unlucky seeds. 
+> It is recommended to run each experiment for at-least 2-3 seeds for best results.
+
+## Expected Results
+
+The results below correspond to Table 1 of the paper on the non-IID CIFAR-10 dataset.
+
+Centralized Evaluation: Accuracy(in %) on centralized Test set. Mean and Confidence Intervals plotted over at least 3 seeds.
+
+
+
+| Local Epochs | Client Optimizer | FedAvg | FedNova |
+| ------------ | ---------------- | ------ | ------- |
+| 2            | Vanilla          | 62.35 ± 1.33 | 67.04 ± 0.94 |
+| 2            | Momentum         | 70.78 ± 1.03 | 73.01 ± 0.32 |
+| 2            | Proximal         | 62.30 ± 0.38 | 67.00 ± 0.47 |
+| Random(2-5)  | Vanilla          | 64.30 ± 1.75 | 70.54 ± 0.43 |
+| Random(2-5)  | Momentum         | 72.75 ± 1.28 | 74.26 ± 0.39 |
+| Random(2-5)  | Proximal         | 64.47 ± 0.28 | 69.25 ± 1.30 |
+| Random(2-5)  | Server           |      N/A     | 73.65 ± 0.51 |
+| Random(2-5)  | Hybrid           |      N/A     | 75.54 ± 1.05 |
+
+## Plots
+
+The plots below correspond to Figure 6 (page 11) and Figure 7 in the Addendum section (page 34) of the full-length [paper](https://arxiv.org/abs/2007.07481).
+
+**FedAvg vs FedNova for various local solvers with fixed local epochs = 2**
+
+<img alt="FedAvg vs FedNova (vanilla)" src="_static/testAccuracy_vanilla_varEpochs_False.png" width="250"/> <img alt="FedAvg vs FedNova (momentum)" src="_static/testAccuracy_momentum_varEpochs_False.png" width="250"/> <img alt="FedAvg vs FedNova (proximal)" src="_static/testAccuracy_proximal_varEpochs_False.png" width="250"/>
+
+
+**FedAvg vs FedNova for various local solvers with variable local epochs ~ U(2,5)**
+
+<img alt="FedAvg vs FedNova (vanilla)" src="_static/testAccuracy_vanilla_varEpochs_True.png" width="250"/> <img alt="FedAvg vs FedNova (momentum)" src="_static/testAccuracy_momentum_varEpochs_True.png" width="250"/> <img alt="FedAvg vs FedNova (proximal)" src="_static/testAccuracy_proximal_varEpochs_True.png" width="250"/>
+
+**Comparison of Momentum schemes for FedNova (both client and strategy side)**
+
+<img alt="Momentum Comparison" src="_static/testAccuracy_momentum_plot_varEpochs_True.png" width="360"/>
+
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diff --git a/baselines/fednova/fednova/__init__.py b/baselines/fednova/fednova/__init__.py
new file mode 100644
index 000000000000..a5e567b59135
--- /dev/null
+++ b/baselines/fednova/fednova/__init__.py
@@ -0,0 +1 @@
+"""Template baseline package."""
diff --git a/baselines/fednova/fednova/baseline_client.py b/baselines/fednova/fednova/baseline_client.py
new file mode 100644
index 000000000000..6029a762183e
--- /dev/null
+++ b/baselines/fednova/fednova/baseline_client.py
@@ -0,0 +1,144 @@
+"""Clients for the baseline comparison strategies: FedAvg, FedProx."""
+
+from collections import OrderedDict
+from typing import Callable, Dict, List, Tuple
+
+import flwr as fl
+import numpy as np
+import torch
+from flwr.common.typing import NDArrays, Scalar
+from hydra.utils import instantiate
+from omegaconf import DictConfig
+from torch.optim import SGD
+from torch.utils.data import DataLoader
+
+from fednova.models import test, train
+
+
+class FedAvgClient(
+    fl.client.NumPyClient
+):  # pylint: disable=too-many-instance-attributes
+    """Standard Flower client for FedAvg."""
+
+    def __init__(  # pylint: disable=too-many-arguments
+        self,
+        net: torch.nn.Module,
+        client_id: str,
+        trainloader: DataLoader,
+        valloader: DataLoader,
+        device: torch.device,
+        num_epochs: int,
+        data_size: int,
+        config: DictConfig,
+    ):
+        self.net = net
+        self.exp_config = config
+        self.optimizer = SGD(
+            self.net.parameters(),
+            lr=config.optimizer.lr,
+            momentum=config.optimizer.momentum,
+            weight_decay=config.optimizer.weight_decay,
+        )
+        self.trainloader = trainloader
+        self.valloader = valloader
+        self.client_id = client_id
+        self.device = device
+        self.num_epochs = num_epochs
+        self.num_data_samples = data_size
+
+    def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays:
+        """Return the parameters of the current net."""
+        return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
+
+    def set_parameters(self, parameters: NDArrays) -> None:
+        """Change the parameters of the model using the given ones."""
+        params_dict = zip(self.net.state_dict().keys(), parameters)
+        state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+        self.net.load_state_dict(state_dict, strict=True)
+
+    def fit(
+        self, parameters: NDArrays, config: Dict[str, Scalar]
+    ) -> Tuple[NDArrays, int, Dict]:
+        """Implement distributed fit function for a given client."""
+        self.set_parameters(parameters)
+        for g in self.optimizer.param_groups:
+            g["lr"] = config["lr"]
+
+        if self.exp_config.var_local_epochs:
+            seed_val = (
+                2023
+                + int(self.client_id)
+                + int(config["server_round"])
+                + int(self.exp_config.seed)
+            )
+            np.random.seed(seed_val)
+            num_epochs = np.random.randint(
+                self.exp_config.var_min_epochs, self.exp_config.var_max_epochs
+            )
+        else:
+            num_epochs = self.num_epochs
+
+        train(
+            self.net,
+            self.optimizer,
+            self.trainloader,
+            self.device,
+            num_epochs,
+            proximal_mu=self.exp_config.optimizer.mu,
+        )
+
+        scaling_factor = self.num_data_samples
+
+        return self.get_parameters({}), int(scaling_factor), {}
+
+    def evaluate(
+        self, parameters: NDArrays, config: Dict[str, Scalar]
+    ) -> Tuple[float, int, Dict]:
+        """Implement distributed evaluation for a given client."""
+        # Evaluation ideally is done on validation set, but because we already know
+        # the best hyper-parameters from the paper and since individual client
+        # datasets are already quite small, we merge the validation set with the
+        # training set and evaluate on the training set with the aggregated global
+        # model parameters. This behaviour can be modified by passing the validation
+        # set in the below test(self.valloader) function and replacing len(
+        # self.valloader) below. Note that we evaluate on the centralized test-set on
+        # server-side in the strategy.
+
+        self.set_parameters(parameters)
+        loss, metrics = test(self.net, self.trainloader, self.device)
+        return float(loss), len(self.trainloader), metrics
+
+
+def gen_clients_fedavg(  # pylint: disable=too-many-arguments
+    num_epochs: int,
+    trainloaders: List[DataLoader],
+    testloader: DataLoader,
+    data_sizes: List,
+    model: DictConfig,
+    exp_config: DictConfig,
+) -> Callable[[str], FedAvgClient]:
+    """Return a generator function to create a FedAvg client."""
+
+    def client_fn(cid: str) -> FedAvgClient:
+        """Create a Flower client representing a single organization."""
+        # Load model
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        net = instantiate(model)
+
+        # Note: each client gets a different trainloader/valloader, so each client
+        # will train and evaluate on their own unique data
+        trainloader = trainloaders[int(cid)]
+        client_dataset_sizes = data_sizes[int(cid)]
+
+        return FedAvgClient(
+            net,
+            cid,
+            trainloader,
+            testloader,
+            device,
+            num_epochs,
+            client_dataset_sizes,
+            exp_config,
+        )
+
+    return client_fn
diff --git a/baselines/fednova/fednova/client.py b/baselines/fednova/fednova/client.py
new file mode 100644
index 000000000000..fa9101b36cae
--- /dev/null
+++ b/baselines/fednova/fednova/client.py
@@ -0,0 +1,149 @@
+"""Client implementation for FedNova."""
+
+from typing import Callable, Dict, List, Tuple
+
+import flwr as fl
+import numpy as np
+import torch
+from flwr.common.typing import NDArrays, Scalar
+from hydra.utils import instantiate
+from omegaconf import DictConfig
+from torch.utils.data import DataLoader
+
+from fednova.models import test, train
+
+
+class FedNovaClient(
+    fl.client.NumPyClient
+):  # pylint: disable=too-many-instance-attributes
+    """Standard Flower client for CNN training."""
+
+    def __init__(  # pylint: disable=too-many-arguments
+        self,
+        net: torch.nn.Module,
+        client_id: str,
+        trainloader: DataLoader,
+        valloader: DataLoader,
+        device: torch.device,
+        num_epochs: int,
+        ratio: float,
+        config: DictConfig,
+    ):
+        self.net = net
+        self.exp_config = config
+
+        if self.exp_config.var_local_epochs and (
+            self.exp_config.exp_name == "proximal"
+        ):
+            # For only FedNova with proximal local solver and variable local epochs,
+            # mu = 0.001 works best.
+            # For other experiments, the default setting of mu = 0.005 works best
+            # Ref: https://arxiv.org/pdf/2007.07481.pdf (Page 33, Section:
+            # More Experiment Details)
+            self.exp_config.optimizer.mu = 0.001
+
+        self.optimizer = instantiate(
+            self.exp_config.optimizer, params=self.net.parameters(), ratio=ratio
+        )
+        self.trainloader = trainloader
+        self.valloader = valloader
+        self.client_id = client_id
+        self.device = device
+        self.num_epochs = num_epochs
+        self.data_ratio = ratio
+
+    def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays:
+        """Return the parameters of the current net."""
+        params = [
+            val["cum_grad"].cpu().numpy()
+            for _, val in self.optimizer.state_dict()["state"].items()
+        ]
+        return params
+
+    def set_parameters(self, parameters: NDArrays) -> None:
+        """Change the parameters of the model using the given ones."""
+        self.optimizer.set_model_params(parameters)
+
+    def fit(
+        self, parameters: NDArrays, config: Dict[str, Scalar]
+    ) -> Tuple[NDArrays, int, Dict]:
+        """Implement distributed fit function for a given client."""
+        self.set_parameters(parameters)
+        self.optimizer.set_lr(config["lr"])
+
+        if self.exp_config.var_local_epochs:
+            seed_val = (
+                2023
+                + int(self.client_id)
+                + int(config["server_round"])
+                + int(self.exp_config.seed)
+            )
+            np.random.seed(seed_val)
+            num_epochs = np.random.randint(
+                self.exp_config.var_min_epochs, self.exp_config.var_max_epochs
+            )
+        else:
+            num_epochs = self.num_epochs
+
+        train(
+            self.net, self.optimizer, self.trainloader, self.device, epochs=num_epochs
+        )
+
+        # Get ratio by which the strategy would scale local gradients from each client
+        # We use this scaling factor to aggregate the gradients on the server
+        grad_scaling_factor: Dict[str, float] = self.optimizer.get_gradient_scaling()
+
+        return self.get_parameters({}), len(self.trainloader), grad_scaling_factor
+
+    def evaluate(
+        self, parameters: NDArrays, config: Dict[str, Scalar]
+    ) -> Tuple[float, int, Dict]:
+        """Implement distributed evaluation for a given client."""
+        # Evaluation ideally is done on validation set, but because we already know
+        # the best hyper-parameters from the paper and since individual client
+        # datasets are already quite small, we merge the validation set with the
+        # training set and evaluate on the training set with the aggregated global
+        # model parameters. This behaviour can be modified by passing the validation
+        # set in the below test(self.valloader) function and replacing len(
+        # self.valloader) below. Note that we evaluate on the centralized test-set on
+        # server-side in the strategy.
+
+        self.set_parameters(parameters)
+        loss, metrics = test(self.net, self.trainloader, self.device)
+        return float(loss), len(self.trainloader), metrics
+
+
+def gen_clients_fednova(  # pylint: disable=too-many-arguments
+    num_epochs: int,
+    trainloaders: List[DataLoader],
+    testloader: DataLoader,
+    data_sizes: List,
+    model: DictConfig,
+    exp_config: DictConfig,
+) -> Callable[[str], FedNovaClient]:
+    """Return a generator function to create a FedNova client."""
+
+    def client_fn(cid: str) -> FedNovaClient:
+        """Create a Flower client representing a single organization."""
+        # Load model
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        net = instantiate(model)
+
+        # Note: each client gets a different trainloader/valloader, so each client
+        # will train and evaluate on their own unique data
+        trainloader = trainloaders[int(cid)]
+        client_dataset_size: int = data_sizes[int(cid)]
+        client_dataset_ratio: float = client_dataset_size / sum(data_sizes)
+
+        return FedNovaClient(
+            net,
+            cid,
+            trainloader,
+            testloader,
+            device,
+            num_epochs,
+            client_dataset_ratio,
+            exp_config,
+        )
+
+    return client_fn
diff --git a/baselines/fednova/fednova/conf/base.yaml b/baselines/fednova/fednova/conf/base.yaml
new file mode 100644
index 000000000000..52a23d0b6d4e
--- /dev/null
+++ b/baselines/fednova/fednova/conf/base.yaml
@@ -0,0 +1,44 @@
+---
+# this is the config that will be loaded as default by main.py
+# Please follow the provided structure (this will ensuring all baseline follow
+# a similar configuration structure and hence be easy to customise)
+
+defaults:
+  - _self_
+  - strategy: fednova
+  - optimizer: momentum
+
+var_local_epochs: False
+datapath: fednova/data/
+checkpoint_path: fednova/checkpoints/
+num_clients: 16
+num_epochs: 2
+num_rounds: 100
+batch_size: 32
+NIID: True
+alpha: 0.1
+var_min_epochs: 2
+var_max_epochs: 5
+lr_scheduling: True
+seed: 1
+mode: train
+
+model:
+  _target_: fednova.models.VGG
+
+client_resources:
+  num_cpus: 1
+  num_gpus: 0.125
+
+hydra:
+  sweep:
+    dir: multirun
+    subdir: ${hydra.job.override_dirname}/${seed}
+  job:
+    config:
+      override_dirname:
+        exclude_keys:
+          - seed
+          - num_rounds
+        kv_sep: _
+        item_sep: _
diff --git a/baselines/fednova/fednova/conf/optimizer/hybrid.yaml b/baselines/fednova/fednova/conf/optimizer/hybrid.yaml
new file mode 100644
index 000000000000..dd5d5c59d6f2
--- /dev/null
+++ b/baselines/fednova/fednova/conf/optimizer/hybrid.yaml
@@ -0,0 +1,12 @@
+
+# @package _global_
+
+exp_name: hybrid
+
+optimizer:
+  _target_: fednova.models.ProxSGD
+  lr: 0.02
+  momentum: 0.9
+  weight_decay: 1e-4
+  gmf: 0.9
+  mu: 0
diff --git a/baselines/fednova/fednova/conf/optimizer/momentum.yaml b/baselines/fednova/fednova/conf/optimizer/momentum.yaml
new file mode 100644
index 000000000000..2c31d851dc67
--- /dev/null
+++ b/baselines/fednova/fednova/conf/optimizer/momentum.yaml
@@ -0,0 +1,12 @@
+
+# @package _global_
+
+exp_name: momentum
+
+optimizer:
+  _target_: fednova.models.ProxSGD
+  lr: 0.02
+  momentum: 0.9
+  weight_decay: 1e-4
+  gmf: 0
+  mu: 0
diff --git a/baselines/fednova/fednova/conf/optimizer/proximal.yaml b/baselines/fednova/fednova/conf/optimizer/proximal.yaml
new file mode 100644
index 000000000000..b6cb9eba4748
--- /dev/null
+++ b/baselines/fednova/fednova/conf/optimizer/proximal.yaml
@@ -0,0 +1,12 @@
+
+# @package _global_
+
+exp_name: proximal
+
+optimizer:
+  _target_: fednova.models.ProxSGD
+  lr: 0.05
+  momentum: 0
+  weight_decay: 1e-4
+  gmf: 0
+  mu: 0.005
diff --git a/baselines/fednova/fednova/conf/optimizer/server.yaml b/baselines/fednova/fednova/conf/optimizer/server.yaml
new file mode 100644
index 000000000000..79de2dd1e9b5
--- /dev/null
+++ b/baselines/fednova/fednova/conf/optimizer/server.yaml
@@ -0,0 +1,12 @@
+
+# @package _global_
+
+exp_name: server
+
+optimizer:
+  _target_: fednova.models.ProxSGD
+  lr: 0.02
+  momentum: 0.0
+  weight_decay: 1e-4
+  gmf: 0.9
+  mu: 0
diff --git a/baselines/fednova/fednova/conf/optimizer/vanilla.yaml b/baselines/fednova/fednova/conf/optimizer/vanilla.yaml
new file mode 100644
index 000000000000..98aabbd70eed
--- /dev/null
+++ b/baselines/fednova/fednova/conf/optimizer/vanilla.yaml
@@ -0,0 +1,12 @@
+
+# @package _global_
+
+exp_name: vanilla
+
+optimizer:
+  _target_: fednova.models.ProxSGD
+  lr: 0.05
+  momentum: 0
+  weight_decay: 1e-4
+  gmf: 0
+  mu: 0
diff --git a/baselines/fednova/fednova/conf/strategy/fedavg.yaml b/baselines/fednova/fednova/conf/strategy/fedavg.yaml
new file mode 100644
index 000000000000..4c90d81ab5f6
--- /dev/null
+++ b/baselines/fednova/fednova/conf/strategy/fedavg.yaml
@@ -0,0 +1,10 @@
+---
+name: fedavg
+strategy:
+  _target_: flwr.server.strategy.FedAvg
+  _recursive_: false
+    
+
+client_fn:
+  _target_: fednova.baseline_client.gen_clients_fedavg
+  _recursive_: false
diff --git a/baselines/fednova/fednova/conf/strategy/fednova.yaml b/baselines/fednova/fednova/conf/strategy/fednova.yaml
new file mode 100644
index 000000000000..b08052ab111e
--- /dev/null
+++ b/baselines/fednova/fednova/conf/strategy/fednova.yaml
@@ -0,0 +1,10 @@
+---
+name: fednova
+strategy:
+  _target_: fednova.strategy.FedNova
+  _recursive_: false
+    
+
+client_fn:
+  _target_: fednova.client.gen_clients_fednova
+  _recursive_: false
\ No newline at end of file
diff --git a/baselines/fednova/fednova/dataset.py b/baselines/fednova/fednova/dataset.py
new file mode 100644
index 000000000000..f8a19cdac474
--- /dev/null
+++ b/baselines/fednova/fednova/dataset.py
@@ -0,0 +1,80 @@
+"""Dataloaders for the CIFAR-10 dataset."""
+
+from typing import List, Tuple
+
+import torch
+from omegaconf import DictConfig
+from torch.utils.data import DataLoader
+from torchvision import datasets, transforms
+
+from fednova.dataset_preparation import DataPartitioner
+
+
+def load_datasets(config: DictConfig) -> Tuple[List[DataLoader], DataLoader, List]:
+    """Create the dataloaders to be fed into the model.
+
+    Parameters
+    ----------
+    config: DictConfig
+        Parameterises the dataset partitioning process
+    num_clients : int
+        The number of clients that hold a part of the data
+    val_ratio : float, optional
+        The ratio of training data that will be used for validation (between 0 and 1),
+        by default 0.1
+    batch_size : int, optional
+        The size of the batches to be fed into the model, by default 32
+    seed : int, optional
+        Used to set a fix seed to replicate experiments, by default 42
+
+    Returns
+    -------
+    Tuple[DataLoader, DataLoader, List]
+        The DataLoader for training, the DataLoader for testing, client dataset sizes.
+    """
+    transform_train = transforms.Compose(
+        [
+            transforms.RandomCrop(32, padding=4),
+            transforms.RandomHorizontalFlip(),
+            transforms.ToTensor(),
+            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+        ]
+    )
+
+    transform_test = transforms.Compose(
+        [
+            transforms.ToTensor(),
+            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
+        ]
+    )
+
+    trainset = datasets.CIFAR10(
+        root=config.datapath, train=True, download=True, transform=transform_train
+    )
+
+    testset = datasets.CIFAR10(
+        root=config.datapath, train=False, download=True, transform=transform_test
+    )
+
+    partition_sizes = [1.0 / config.num_clients for _ in range(config.num_clients)]
+
+    partition_obj = DataPartitioner(
+        trainset, partition_sizes, is_non_iid=config.NIID, alpha=config.alpha
+    )
+    ratio = partition_obj.ratio
+
+    trainloaders = []
+    for data_split in range(config.num_clients):
+        client_partition = partition_obj.use(data_split)
+        trainloaders.append(
+            torch.utils.data.DataLoader(
+                client_partition,
+                batch_size=config.batch_size,
+                shuffle=True,
+                pin_memory=True,
+            )
+        )
+
+    test_loader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)
+
+    return trainloaders, test_loader, ratio
diff --git a/baselines/fednova/fednova/dataset_preparation.py b/baselines/fednova/fednova/dataset_preparation.py
new file mode 100644
index 000000000000..d118d79c4d40
--- /dev/null
+++ b/baselines/fednova/fednova/dataset_preparation.py
@@ -0,0 +1,118 @@
+"""The methodology to create a Dirichlet distributed Non-IID CIFAR dataset.
+
+obtained from source: https://github.com/JYWa/FedNova/blob/master/util_v4.py
+"""
+
+from random import Random
+
+import numpy as np
+
+
+class Partition:
+    """Dataset-like object, but only access a subset of it."""
+
+    def __init__(self, data, index):
+        self.data = data
+        self.index = index
+
+    def __len__(self):
+        """Return the length of the partition."""
+        return len(self.index)
+
+    def __getitem__(self, index):
+        """Return the item at index idx."""
+        data_idx = self.index[index]
+        return self.data[data_idx]
+
+
+class DataPartitioner:
+    """Partitions a dataset into different chuncks."""
+
+    def __init__(  # pylint: disable=too-many-arguments
+        self,
+        data,
+        sizes,
+        seed=2020,
+        is_non_iid=False,
+        alpha=0,
+        dataset=None,
+    ):
+        self.data = data
+        self.dataset = dataset
+        if is_non_iid:
+            self.partitions, self.ratio = self._get_dirichlet_data_(
+                data, sizes, seed, alpha
+            )
+
+        else:
+            self.partitions = []
+            self.ratio = sizes
+            rng = Random()
+            # rng.seed(seed)
+            data_len = len(data)
+            indexes = list(range(0, data_len))
+            rng.shuffle(indexes)
+
+            for frac in sizes:
+                part_len = int(frac * data_len)
+                self.partitions.append(indexes[0:part_len])
+                indexes = indexes[part_len:]
+
+    def use(self, partition):
+        """Return a partition of the dataset."""
+        return Partition(self.data, self.partitions[partition])
+
+    @staticmethod
+    def _get_dirichlet_data_(
+        data, psizes, seed, alpha
+    ):  # pylint: disable=too-many-locals
+        """Return a partition of the dataset based on Dirichlet distribution."""
+        n_nets = len(psizes)
+        K = 10
+        label_list = np.array(data.targets)
+        min_size = 0
+        N = len(label_list)
+        np.random.seed(seed)
+
+        net_dataidx_map = {}
+        while min_size < K:
+            idx_batch = [[] for _ in range(n_nets)]
+            # for each class in the dataset
+            for k in range(K):
+                idx_k = np.where(label_list == k)[0]
+                np.random.shuffle(idx_k)
+                proportions = np.random.dirichlet(np.repeat(alpha, n_nets))
+                # Balance
+                proportions = np.array(
+                    [
+                        p * (len(idx_j) < N / n_nets)
+                        for p, idx_j in zip(proportions, idx_batch)
+                    ]
+                )
+                proportions = proportions / proportions.sum()
+                proportions = (np.cumsum(proportions) * len(idx_k)).astype(int)[:-1]
+                idx_batch = [
+                    idx_j + idx.tolist()
+                    for idx_j, idx in zip(idx_batch, np.split(idx_k, proportions))
+                ]
+                min_size = min((len(idx_j) for idx_j in idx_batch))
+
+        for j in range(n_nets):
+            np.random.shuffle(idx_batch[j])
+            net_dataidx_map[j] = idx_batch[j]
+
+        net_cls_counts = {}
+
+        for net_i, dataidx in net_dataidx_map.items():
+            unq, unq_cnt = np.unique(label_list[dataidx], return_counts=True)
+            tmp = {unq[i]: unq_cnt[i] for i in range(len(unq))}
+            net_cls_counts[net_i] = tmp
+        # print('Data statistics: %s' % str(net_cls_counts))
+
+        local_sizes = []
+        for i in range(n_nets):
+            local_sizes.append(len(net_dataidx_map[i]))
+        local_sizes = np.array(local_sizes)
+        print("Client Dataset sizes: ", local_sizes)
+
+        return idx_batch, local_sizes
diff --git a/baselines/fednova/fednova/main.py b/baselines/fednova/fednova/main.py
new file mode 100644
index 000000000000..750338ff2c6a
--- /dev/null
+++ b/baselines/fednova/fednova/main.py
@@ -0,0 +1,154 @@
+"""Entry script for Federated training on CIFAR-10 using FedNova."""
+
+import os
+import random
+import time
+from collections import OrderedDict
+from functools import partial
+
+import flwr as fl
+import hydra
+import numpy as np
+import pandas as pd
+import torch
+from flwr.common import ndarrays_to_parameters
+from hydra.core.hydra_config import HydraConfig
+from hydra.utils import call, instantiate
+from omegaconf import DictConfig, OmegaConf
+
+from fednova.dataset import load_datasets
+from fednova.models import test
+from fednova.strategy import weighted_average
+from fednova.utils import fit_config
+
+
+@hydra.main(config_path="conf", config_name="base", version_base=None)
+def main(cfg: DictConfig) -> None:  # pylint: disable=too-many-locals
+    """Run the baseline.
+
+    Parameters
+    ----------
+    cfg : DictConfig
+        An omegaconf object that stores the hydra config.
+    """
+    start = time.time()
+
+    # Set seeds for reproduceability
+    torch.manual_seed(cfg.seed)
+    np.random.seed(cfg.seed)
+    random.seed(cfg.seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(cfg.seed)
+    # torch.backends.cudnn.deterministic = True
+
+    # 1. Print parsed config
+    print(OmegaConf.to_yaml(cfg))
+
+    # 2. Prepare your dataset and directories
+
+    if not os.path.exists(cfg.datapath):
+        os.makedirs(cfg.datapath)
+    if not os.path.exists(cfg.checkpoint_path):
+        os.makedirs(cfg.checkpoint_path)
+
+    trainloaders, testloader, data_sizes = load_datasets(cfg)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    if cfg.mode == "test":
+        checkpoint = np.load(
+            f"{cfg.checkpoint_path}bestModel_{cfg.exp_name}_"
+            f"varEpochs_{cfg.var_local_epochs}.npz",
+            allow_pickle=True,
+        )
+        model = instantiate(cfg.model)
+        params_dict = zip(model.state_dict().keys(), checkpoint["arr_0"])
+        state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+        model.load_state_dict(state_dict)
+        loss, metrics = test(model.to(device), testloader, device)
+        print(f"----Loss: {loss}, Accuracy: {metrics['accuracy']} on Test set ------")
+        return None
+
+    # 3. Define your clients
+    client_fn = call(
+        cfg.strategy.client_fn,
+        num_epochs=cfg.num_epochs,
+        trainloaders=trainloaders,
+        testloader=testloader,
+        data_sizes=data_sizes,
+        model=cfg.model,
+        exp_config=cfg,
+    )
+
+    # 4. Define your strategy
+
+    ndarrays = [
+        layer_param.cpu().numpy()
+        for _, layer_param in instantiate(cfg.model).state_dict().items()
+    ]
+    init_parameters = ndarrays_to_parameters(ndarrays)
+
+    eval_fn = partial(test, instantiate(cfg.model), testloader, device)
+    fit_config_fn = partial(fit_config, cfg)
+
+    extra_args = {} if cfg.strategy.name == "fedavg" else {"exp_config": cfg}
+    strategy = instantiate(
+        cfg.strategy.strategy,
+        evaluate_metrics_aggregation_fn=weighted_average,
+        accept_failures=False,
+        on_fit_config_fn=fit_config_fn,
+        initial_parameters=init_parameters,
+        evaluate_fn=eval_fn,
+        fraction_evaluate=0.0,
+        **extra_args,
+    )
+
+    # 5. Start Simulation
+
+    history = fl.simulation.start_simulation(
+        client_fn=client_fn,
+        num_clients=cfg.num_clients,
+        config=fl.server.ServerConfig(num_rounds=cfg.num_rounds),
+        strategy=strategy,
+        client_resources=cfg.client_resources,
+    )
+    # in case if you are running experiment on a slurm cluster, it could be helpful
+    # to add : ray_init_args={"ignore_reinit_error": True, "num_cpus": 8} as an extra
+    # argument in above start_simulation(). This prevents the simulation from using
+    # all cores and limits them to 8.
+
+    # 6. Save your results
+    save_path = HydraConfig.get().runtime.output_dir
+
+    # rounds, train_loss = zip(*history.losses_distributed)
+    # _, train_accuracy = zip(*history.metrics_distributed["accuracy"])
+    rounds, test_loss = zip(*history.losses_centralized)
+    _, test_accuracy = zip(*history.metrics_centralized["accuracy"])
+
+    if len(rounds) != cfg.num_rounds:
+        # drop zeroth evaluation round before start of training
+        test_loss = test_loss[1:]
+        test_accuracy = test_accuracy[1:]
+        rounds = rounds[1:]
+
+    file_name = os.path.join(
+        save_path,
+        f"{cfg.exp_name}_{cfg.strategy.name}_varEpoch_"
+        f"{cfg.var_local_epochs}_seed_{cfg.seed}.csv",
+    )
+
+    # df = pd.DataFrame({"round": rounds, "train_loss": train_loss, "train_accuracy":
+    # train_accuracy, "test_loss": test_loss, "test_accuracy": test_accuracy})
+
+    df = pd.DataFrame(
+        {"round": rounds, "test_loss": test_loss, "test_accuracy": test_accuracy}
+    )
+
+    df.to_csv(file_name, index=False)
+
+    print(f"---------Experiment Completed in : {(time.time()-start)/60} minutes")
+
+    return None
+
+
+if __name__ == "__main__":
+    main()
diff --git a/baselines/fednova/fednova/models.py b/baselines/fednova/fednova/models.py
new file mode 100644
index 000000000000..4de5cf4309f2
--- /dev/null
+++ b/baselines/fednova/fednova/models.py
@@ -0,0 +1,366 @@
+"""VGG Model Architecture: Adapted from https://github.com/pytorch/vision.git .
+
+Contains Train, Test function definitions for local client training
+"""
+
+import math
+from collections import OrderedDict
+from typing import Dict, Tuple
+
+import torch
+from flwr.common.typing import NDArrays
+from torch import nn
+from torch.optim.optimizer import Optimizer, required
+
+from fednova.utils import comp_accuracy
+
+
+class VGG(nn.Module):
+    """VGG model."""
+
+    def __init__(self):
+        super().__init__()
+        self.features = make_layers(cfg["A"])
+        self.classifier = nn.Sequential(
+            nn.Dropout(),
+            nn.Linear(512, 512),
+            nn.ReLU(True),
+            nn.Dropout(),
+            nn.Linear(512, 512),
+            nn.ReLU(True),
+            nn.Linear(512, 10),
+        )
+        # Initialize weights
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2.0 / n))
+                m.bias.data.zero_()
+
+    def forward(self, x):
+        """Forward pass through the network."""
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        x = self.classifier(x)
+        return x
+
+
+def make_layers(network_cfg, batch_norm=False):
+    """Define the layer configuration of the VGG-16 network."""
+    layers = []
+    in_channels = 3
+    for v in network_cfg:
+        if v == "M":
+            layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
+        else:
+            conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
+            if batch_norm:
+                layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
+            else:
+                layers += [conv2d, nn.ReLU(inplace=True)]
+            in_channels = v
+    return nn.Sequential(*layers)
+
+
+cfg = {
+    "A": [64, "M", 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"],
+    "B": [64, 64, "M", 128, 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"],
+    "D": [
+        64,
+        64,
+        "M",
+        128,
+        128,
+        "M",
+        256,
+        256,
+        256,
+        "M",
+        512,
+        512,
+        512,
+        "M",
+        512,
+        512,
+        512,
+        "M",
+    ],
+    "E": [
+        64,
+        64,
+        "M",
+        128,
+        128,
+        "M",
+        256,
+        256,
+        256,
+        256,
+        "M",
+        512,
+        512,
+        512,
+        512,
+        "M",
+        512,
+        512,
+        512,
+        512,
+        "M",
+    ],
+}
+
+
+# pylint: disable=too-many-locals,too-many-arguments
+def train(
+    model, optimizer, trainloader, device, epochs, proximal_mu=0.0
+) -> Tuple[float, float]:
+    """Train the client model for one round of federated learning."""
+    criterion = nn.CrossEntropyLoss()
+    model = model.to(device)
+    if proximal_mu > 0.0:
+        global_params = [val.detach().clone() for val in model.parameters()]
+    else:
+        global_params = None
+    model.train()
+
+    train_losses = []
+    train_accuracy = []
+    for _epoch in range(epochs):
+        for _batch_idx, (data, target) in enumerate(trainloader):
+            # data loading
+            data = data.to(device)
+            target = target.to(device)
+
+            optimizer.zero_grad()
+
+            # forward pass
+            output = model(data)
+
+            if global_params is None:
+                loss = criterion(output, target)
+            else:
+                # Proximal updates for FedProx
+                proximal_term = 0.0
+                for local_weights, global_weights in zip(
+                    model.parameters(), global_params
+                ):
+                    proximal_term += torch.square(
+                        (local_weights - global_weights).norm(2)
+                    )
+                loss = criterion(output, target) + (proximal_mu / 2) * proximal_term
+
+            # backward pass
+            loss.backward()
+
+            # gradient step
+            optimizer.step()
+
+            # write log files
+            acc = comp_accuracy(output, target)
+
+            train_losses.append(loss.item())
+            train_accuracy.append(acc[0].item())
+
+    train_loss = sum(train_losses) / len(train_losses)
+    train_acc = sum(train_accuracy) / len(train_accuracy)
+
+    return train_loss, train_acc
+
+
+def test(model, test_loader, device, *args) -> Tuple[float, Dict[str, float]]:
+    """Evaluate the federated model on a test set.
+
+    The server Strategy(FedNova, FedAvg, FedProx) uses the same method to compute
+    centralized evaluation on test set using the args. args[0]: int = server round
+    args[1]: List[NDArray] = server model parameters args[2]: Dict = {}
+    """
+    criterion = nn.CrossEntropyLoss()
+    if len(args) > 1:
+        # load the model parameters
+        params_dict = zip(model.state_dict().keys(), args[1])
+        state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+        model.load_state_dict(state_dict)
+
+    model = model.to(device)
+    model.eval()
+    accuracy = []
+    total_loss = 0.0
+
+    with torch.no_grad():
+        for data, target in test_loader:
+            data = data.to(device)
+            target = target.to(device)
+            outputs = model(data)
+            total_loss += criterion(outputs, target).item()
+            acc1 = comp_accuracy(outputs, target)
+            accuracy.append(acc1[0].item())
+
+    total_loss /= len(test_loader)
+    return total_loss, {"accuracy": sum(accuracy) / len(accuracy)}
+
+
+class ProxSGD(Optimizer):  # pylint: disable=too-many-instance-attributes
+    """Optimizer class for FedNova that supports Proximal, SGD, and Momentum updates.
+
+    SGD optimizer modified with support for :
+    1. Maintaining a Global momentum buffer, set using : (self.gmf)
+    2. Proximal SGD updates, set using : (self.mu)
+    Args:
+            params (iterable): iterable of parameters to optimize or dicts defining
+                    parameter groups
+            ratio (float): relative sample size of client
+            gmf (float): global/server/slow momentum factor
+            mu (float): parameter for proximal local SGD
+            lr (float): learning rate
+            momentum (float, optional): momentum factor (default: 0)
+            weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+            dampening (float, optional): dampening for momentum (default: 0)
+            nesterov (bool, optional): enables Nesterov momentum (default: False)
+    """
+
+    def __init__(  # pylint: disable=too-many-arguments
+        self,
+        params,
+        ratio: float,
+        gmf=0,
+        mu=0,
+        lr=required,
+        momentum=0,
+        dampening=0,
+        weight_decay=0,
+        nesterov=False,
+        variance=0,
+    ):
+        self.gmf = gmf
+        self.ratio = ratio
+        self.momentum = momentum
+        self.mu = mu
+        self.local_normalizing_vec = 0
+        self.local_counter = 0
+        self.local_steps = 0
+        self.lr = lr
+
+        if lr is not required and lr < 0.0:
+            raise ValueError(f"Invalid learning rate: {lr}")
+        if momentum < 0.0:
+            raise ValueError(f"Invalid momentum value: {momentum}")
+        if weight_decay < 0.0:
+            raise ValueError(f"Invalid weight_decay value: {weight_decay}")
+
+        defaults = {
+            "lr": lr,
+            "momentum": momentum,
+            "dampening": dampening,
+            "weight_decay": weight_decay,
+            "nesterov": nesterov,
+            "variance": variance,
+        }
+        if nesterov and (momentum <= 0 or dampening != 0):
+            raise ValueError("Nesterov momentum requires a momentum and zero dampening")
+        super().__init__(params, defaults)
+
+    def __setstate__(self, state):
+        """Set the optimizer state."""
+        super().__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault("nesterov", False)
+
+    def step(self, closure=None):  # pylint: disable=too-many-branches
+        """Perform a single optimization step."""
+        for group in self.param_groups:
+            weight_decay = group["weight_decay"]
+            momentum = group["momentum"]
+            dampening = group["dampening"]
+            nesterov = group["nesterov"]
+
+            for p in group["params"]:
+                if p.grad is None:
+                    continue
+                d_p = p.grad.data
+
+                if weight_decay != 0:
+                    d_p.add_(p.data, alpha=weight_decay)
+
+                param_state = self.state[p]
+
+                # if 'old_init' not in param_state:
+                # 	param_state['old_init'] = torch.clone(p.data).detach()
+
+                local_lr = group["lr"]
+
+                # apply momentum updates
+                if momentum != 0:
+                    if "momentum_buffer" not in param_state:
+                        buf = param_state["momentum_buffer"] = torch.clone(d_p).detach()
+                    else:
+                        buf = param_state["momentum_buffer"]
+                        buf.mul_(momentum).add_(d_p, alpha=1 - dampening)
+                    if nesterov:
+                        d_p = d_p.add(momentum, buf)
+                    else:
+                        d_p = buf
+
+                # apply proximal updates
+                if self.mu != 0:
+                    if param_state["old_init"].device != p.device:
+                        param_state["old_init"] = param_state["old_init"].to(p.device)
+                    d_p.add_(p.data - param_state["old_init"], alpha=self.mu)
+
+                # update accumalated local updates
+                if "cum_grad" not in param_state:
+                    param_state["cum_grad"] = torch.clone(d_p).detach()
+                    param_state["cum_grad"].mul_(local_lr)
+
+                else:
+                    param_state["cum_grad"].add_(d_p, alpha=local_lr)
+
+                p.data.add_(d_p, alpha=-local_lr)
+
+        # compute local normalizing vector a_i
+        if self.momentum != 0:
+            self.local_counter = self.local_counter * self.momentum + 1
+            self.local_normalizing_vec += self.local_counter
+
+        etamu = local_lr * self.mu
+        if etamu != 0:
+            self.local_normalizing_vec *= 1 - etamu
+            self.local_normalizing_vec += 1
+
+        if self.momentum == 0 and etamu == 0:
+            self.local_normalizing_vec += 1
+
+        self.local_steps += 1
+
+    def get_gradient_scaling(self) -> Dict[str, float]:
+        """Compute the scaling factor for local client gradients.
+
+        Returns: A dictionary containing weight, tau, and local_norm.
+        """
+        if self.mu != 0:
+            local_tau = torch.tensor(self.local_steps * self.ratio)
+        else:
+            local_tau = torch.tensor(self.local_normalizing_vec * self.ratio)
+        local_stats = {
+            "weight": self.ratio,
+            "tau": local_tau.item(),
+            "local_norm": self.local_normalizing_vec,
+        }
+
+        return local_stats
+
+    def set_model_params(self, init_params: NDArrays):
+        """Set the model parameters to the given values."""
+        i = 0
+        for group in self.param_groups:
+            for p in group["params"]:
+                param_state = self.state[p]
+                param_tensor = torch.tensor(init_params[i])
+                p.data.copy_(param_tensor)
+                param_state["old_init"] = param_tensor
+                i += 1
+
+    def set_lr(self, lr: float):
+        """Set the learning rate to the given value."""
+        for param_group in self.param_groups:
+            param_group["lr"] = lr
diff --git a/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_False_seed_1_Dec3.csv b/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_False_seed_1_Dec3.csv
new file mode 100644
index 000000000000..40e533a5a87e
--- /dev/null
+++ b/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_False_seed_1_Dec3.csv
@@ -0,0 +1,101 @@
+round,test_loss,test_accuracy
+1,2.3322750033846327,9.952229299363058
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new file mode 100644
index 000000000000..e28157d36418
--- /dev/null
+++ b/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_False_seed_2_Dec3.csv
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new file mode 100644
index 000000000000..048346094dc7
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diff --git a/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_False_seed_4_Dec3.csv b/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_False_seed_4_Dec3.csv
new file mode 100644
index 000000000000..b1ad132988a0
--- /dev/null
+++ b/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_False_seed_4_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_False_seed_5_Dec3.csv b/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_False_seed_5_Dec3.csv
new file mode 100644
index 000000000000..f6e3f7309537
--- /dev/null
+++ b/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_False_seed_5_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_False_seed_6_Dec3.csv b/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_False_seed_6_Dec3.csv
new file mode 100644
index 000000000000..f0e9a8d4256c
--- /dev/null
+++ b/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_False_seed_6_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_True_seed_1_Dec3.csv b/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_True_seed_1_Dec3.csv
new file mode 100644
index 000000000000..daa0ef02828c
--- /dev/null
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diff --git a/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_True_seed_2_Dec3.csv b/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_True_seed_2_Dec3.csv
new file mode 100644
index 000000000000..8f64a68dfb43
--- /dev/null
+++ b/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_True_seed_2_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_True_seed_3_Dec3.csv b/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_True_seed_3_Dec3.csv
new file mode 100644
index 000000000000..d1c47a706d95
--- /dev/null
+++ b/baselines/fednova/fednova/results/momentum_fedavg_varEpoch_True_seed_3_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_1_Dec3.csv b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_1_Dec3.csv
new file mode 100644
index 000000000000..dad6dde12c48
--- /dev/null
+++ b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_1_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_2_Dec3.csv b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_2_Dec3.csv
new file mode 100644
index 000000000000..5d98e2d27292
--- /dev/null
+++ b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_2_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_3_Dec3.csv b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_3_Dec3.csv
new file mode 100644
index 000000000000..c4e1a0e05a49
--- /dev/null
+++ b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_3_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_4_Dec3.csv b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_4_Dec3.csv
new file mode 100644
index 000000000000..fb64f61174fd
--- /dev/null
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diff --git a/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_5_Dec3.csv b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_5_Dec3.csv
new file mode 100644
index 000000000000..532926c297a1
--- /dev/null
+++ b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_5_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_6_Dec3.csv b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_6_Dec3.csv
new file mode 100644
index 000000000000..c956a9f4f75b
--- /dev/null
+++ b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_False_seed_6_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/momentum_fednova_varEpoch_True_seed_1_Dec3.csv b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_True_seed_1_Dec3.csv
new file mode 100644
index 000000000000..ba8d1d190422
--- /dev/null
+++ b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_True_seed_1_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/momentum_fednova_varEpoch_True_seed_2_Dec3.csv b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_True_seed_2_Dec3.csv
new file mode 100644
index 000000000000..ba5507a6988d
--- /dev/null
+++ b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_True_seed_2_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/momentum_fednova_varEpoch_True_seed_3_Dec3.csv b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_True_seed_3_Dec3.csv
new file mode 100644
index 000000000000..b54307a0c9ba
--- /dev/null
+++ b/baselines/fednova/fednova/results/momentum_fednova_varEpoch_True_seed_3_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_False_seed_1_Dec3.csv b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_False_seed_1_Dec3.csv
new file mode 100644
index 000000000000..a949da087771
--- /dev/null
+++ b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_False_seed_1_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_False_seed_2_Dec3.csv b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_False_seed_2_Dec3.csv
new file mode 100644
index 000000000000..930937eb4325
--- /dev/null
+++ b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_False_seed_2_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_False_seed_3_Dec3.csv b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_False_seed_3_Dec3.csv
new file mode 100644
index 000000000000..07ac22197ee4
--- /dev/null
+++ b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_False_seed_3_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_False_seed_4_Dec3.csv b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_False_seed_4_Dec3.csv
new file mode 100644
index 000000000000..ee67335eb05f
--- /dev/null
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diff --git a/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_False_seed_5_Dec3.csv b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_False_seed_5_Dec3.csv
new file mode 100644
index 000000000000..81021dc08add
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diff --git a/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_False_seed_6_Dec3.csv b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_False_seed_6_Dec3.csv
new file mode 100644
index 000000000000..3bf4dc7cac0b
--- /dev/null
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diff --git a/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_True_seed_1_Dec3.csv b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_True_seed_1_Dec3.csv
new file mode 100644
index 000000000000..00a7208d28b2
--- /dev/null
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diff --git a/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_True_seed_2_Dec3.csv b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_True_seed_2_Dec3.csv
new file mode 100644
index 000000000000..6aa707c3c535
--- /dev/null
+++ b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_True_seed_2_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_True_seed_4_Dec3_mu_0.001.csv b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_True_seed_4_Dec3_mu_0.001.csv
new file mode 100644
index 000000000000..17d9a4808fe4
--- /dev/null
+++ b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_True_seed_4_Dec3_mu_0.001.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_True_seed_5_Dec3_mu_0.001.csv b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_True_seed_5_Dec3_mu_0.001.csv
new file mode 100644
index 000000000000..5f7544190bf6
--- /dev/null
+++ b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_True_seed_5_Dec3_mu_0.001.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_True_seed_6_Dec3_mu_0.001.csv b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_True_seed_6_Dec3_mu_0.001.csv
new file mode 100644
index 000000000000..c5b6e85b1d11
--- /dev/null
+++ b/baselines/fednova/fednova/results/proximal_fedavg_varEpoch_True_seed_6_Dec3_mu_0.001.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_1_Dec3.csv b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_1_Dec3.csv
new file mode 100644
index 000000000000..b7695e993238
--- /dev/null
+++ b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_1_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_2_Dec3.csv b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_2_Dec3.csv
new file mode 100644
index 000000000000..4e0940547c92
--- /dev/null
+++ b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_2_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_3_Dec3.csv b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_3_Dec3.csv
new file mode 100644
index 000000000000..0d9b5db5589d
--- /dev/null
+++ b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_3_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_4_Dec3.csv b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_4_Dec3.csv
new file mode 100644
index 000000000000..6bf88bcba571
--- /dev/null
+++ b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_4_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_5_Dec3.csv b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_5_Dec3.csv
new file mode 100644
index 000000000000..e6834973c770
--- /dev/null
+++ b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_5_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_6_Dec3.csv b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_6_Dec3.csv
new file mode 100644
index 000000000000..a3dc2c945afc
--- /dev/null
+++ b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_False_seed_6_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fednova_varEpoch_True_seed_1_Dec3.csv b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_True_seed_1_Dec3.csv
new file mode 100644
index 000000000000..5462a5e6bc6c
--- /dev/null
+++ b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_True_seed_1_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fednova_varEpoch_True_seed_2_Dec3.csv b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_True_seed_2_Dec3.csv
new file mode 100644
index 000000000000..007c56c25177
--- /dev/null
+++ b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_True_seed_2_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fednova_varEpoch_True_seed_3_Dec3.csv b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_True_seed_3_Dec3.csv
new file mode 100644
index 000000000000..41371b8f17d8
--- /dev/null
+++ b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_True_seed_3_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/proximal_fednova_varEpoch_True_seed_4_Dec3_mu_0.001.csv b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_True_seed_4_Dec3_mu_0.001.csv
new file mode 100644
index 000000000000..125f89524913
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new file mode 100644
index 000000000000..611864a4eb8c
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diff --git a/baselines/fednova/fednova/results/proximal_fednova_varEpoch_True_seed_6_Dec3_mu_0.001.csv b/baselines/fednova/fednova/results/proximal_fednova_varEpoch_True_seed_6_Dec3_mu_0.001.csv
new file mode 100644
index 000000000000..bbb03857900b
--- /dev/null
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diff --git a/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_False_seed_1_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_False_seed_1_Dec3.csv
new file mode 100644
index 000000000000..b9984234f6d0
--- /dev/null
+++ b/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_False_seed_1_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_False_seed_2_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_False_seed_2_Dec3.csv
new file mode 100644
index 000000000000..06bb9c0fdca8
--- /dev/null
+++ b/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_False_seed_2_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_False_seed_3_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_False_seed_3_Dec3.csv
new file mode 100644
index 000000000000..76086321062e
--- /dev/null
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diff --git a/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_False_seed_4_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_False_seed_4_Dec3.csv
new file mode 100644
index 000000000000..a9344b844064
--- /dev/null
+++ b/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_False_seed_4_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_False_seed_5_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_False_seed_5_Dec3.csv
new file mode 100644
index 000000000000..1458e9f3ab66
--- /dev/null
+++ b/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_False_seed_5_Dec3.csv
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new file mode 100644
index 000000000000..a128dca7178e
--- /dev/null
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diff --git a/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_True_seed_1_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_True_seed_1_Dec3.csv
new file mode 100644
index 000000000000..936c9c021db0
--- /dev/null
+++ b/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_True_seed_1_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_True_seed_2_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_True_seed_2_Dec3.csv
new file mode 100644
index 000000000000..2992b820f37e
--- /dev/null
+++ b/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_True_seed_2_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_True_seed_3_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_True_seed_3_Dec3.csv
new file mode 100644
index 000000000000..f5b94ee32bba
--- /dev/null
+++ b/baselines/fednova/fednova/results/vanilla_fedavg_varEpoch_True_seed_3_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_1_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_1_Dec3.csv
new file mode 100644
index 000000000000..7927040090a9
--- /dev/null
+++ b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_1_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_2_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_2_Dec3.csv
new file mode 100644
index 000000000000..f0f8dc0427a5
--- /dev/null
+++ b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_2_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_3_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_3_Dec3.csv
new file mode 100644
index 000000000000..e74d55c43e94
--- /dev/null
+++ b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_3_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_4_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_4_Dec3.csv
new file mode 100644
index 000000000000..2e3771ee303d
--- /dev/null
+++ b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_4_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_5_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_5_Dec3.csv
new file mode 100644
index 000000000000..194e4971654d
--- /dev/null
+++ b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_5_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_6_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_6_Dec3.csv
new file mode 100644
index 000000000000..2be31cf9e27a
--- /dev/null
+++ b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_False_seed_6_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_True_seed_1_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_True_seed_1_Dec3.csv
new file mode 100644
index 000000000000..2e82d3d07c6e
--- /dev/null
+++ b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_True_seed_1_Dec3.csv
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diff --git a/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_True_seed_2_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_True_seed_2_Dec3.csv
new file mode 100644
index 000000000000..fe24a5caea50
--- /dev/null
+++ b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_True_seed_2_Dec3.csv
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+100,0.9318133774836352,70.22292993630573
diff --git a/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_True_seed_3_Dec3.csv b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_True_seed_3_Dec3.csv
new file mode 100644
index 000000000000..a021d1894bdd
--- /dev/null
+++ b/baselines/fednova/fednova/results/vanilla_fednova_varEpoch_True_seed_3_Dec3.csv
@@ -0,0 +1,101 @@
+round,test_loss,test_accuracy
+1,2.322097668981856,11.335589171974522
+2,2.3512930186690797,16.441082802547772
+3,2.296800385614869,15.92356687898089
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+84,,10.011942675159236
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+87,,10.011942675159236
+88,,10.011942675159236
+89,,10.011942675159236
+90,,10.011942675159236
+91,,10.011942675159236
+92,,10.011942675159236
+93,,10.011942675159236
+94,,10.011942675159236
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diff --git a/baselines/fednova/fednova/server.py b/baselines/fednova/fednova/server.py
new file mode 100644
index 000000000000..2fd7d42cde5a
--- /dev/null
+++ b/baselines/fednova/fednova/server.py
@@ -0,0 +1,5 @@
+"""Create global evaluation function.
+
+Optionally, also define a new Server class (please note this is not needed in most
+settings).
+"""
diff --git a/baselines/fednova/fednova/strategy.py b/baselines/fednova/fednova/strategy.py
new file mode 100644
index 000000000000..91adb57b691e
--- /dev/null
+++ b/baselines/fednova/fednova/strategy.py
@@ -0,0 +1,166 @@
+"""FedNova strategy."""
+
+from logging import INFO
+from typing import Dict, List, Optional, Tuple, Union
+
+import numpy as np
+from flwr.common import (
+    Metrics,
+    NDArray,
+    NDArrays,
+    Parameters,
+    Scalar,
+    ndarrays_to_parameters,
+    parameters_to_ndarrays,
+)
+from flwr.common.logger import log
+from flwr.common.typing import FitRes
+from flwr.server.client_proxy import ClientProxy
+from flwr.server.strategy import FedAvg
+from flwr.server.strategy.aggregate import aggregate
+from omegaconf import DictConfig
+
+
+class FedNova(FedAvg):
+    """FedNova."""
+
+    def __init__(self, exp_config: DictConfig, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # Maintain a momentum buffer for the weight updates across rounds of training
+        self.global_momentum_buffer: List[NDArray] = []
+        if self.initial_parameters is not None:
+            self.global_parameters: List[NDArray] = parameters_to_ndarrays(
+                self.initial_parameters
+            )
+
+        self.exp_config = exp_config
+        self.lr = exp_config.optimizer.lr
+
+        # momentum parameter for the server/strategy side momentum buffer
+        self.gmf = exp_config.optimizer.gmf
+        self.best_test_acc = 0.0
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ):
+        """Aggregate the results from the clients."""
+        if not results:
+            return None, {}
+
+        # Do not aggregate if there are failures and failures are not accepted
+        if not self.accept_failures and failures:
+            return None, {}
+
+        # Compute tau_effective from summation of local client tau: Eqn-6: Section 4.1
+        local_tau = [res.metrics["tau"] for _, res in results]
+        tau_eff = np.sum(local_tau)
+
+        aggregate_parameters = []
+
+        for _client, res in results:
+            params = parameters_to_ndarrays(res.parameters)
+            # compute the scale by which to weight each client's gradient
+            # res.metrics["local_norm"] contains total number of local update steps
+            # for each client
+            # res.metrics["weight"] contains the ratio of client dataset size
+            # Below corresponds to Eqn-6: Section 4.1
+            scale = tau_eff / float(res.metrics["local_norm"])
+            scale *= float(res.metrics["weight"])
+
+            aggregate_parameters.append((params, scale))
+
+        # Aggregate all client parameters with a weighted average using the scale
+        # calculated above
+        agg_cum_gradient = aggregate(aggregate_parameters)
+
+        # In case of Server or Hybrid Momentum, we decay the aggregated gradients
+        # with a momentum factor
+        self.update_server_params(agg_cum_gradient)
+
+        return ndarrays_to_parameters(self.global_parameters), {}
+
+    def update_server_params(self, cum_grad: NDArrays):
+        """Update the global server parameters by aggregating client gradients."""
+        for i, layer_cum_grad in enumerate(cum_grad):
+            if self.gmf != 0:
+                # check if it's the first round of aggregation, if so, initialize the
+                # global momentum buffer
+
+                if len(self.global_momentum_buffer) < len(cum_grad):
+                    buf = layer_cum_grad / self.lr
+                    self.global_momentum_buffer.append(buf)
+
+                else:
+                    # momentum updates using the global accumulated weights buffer
+                    # for each layer of network
+                    self.global_momentum_buffer[i] *= self.gmf
+                    self.global_momentum_buffer[i] += layer_cum_grad / self.lr
+
+                self.global_parameters[i] -= self.global_momentum_buffer[i] * self.lr
+
+            else:
+                # weight updated eqn: x_new = x_old - gradient
+                # the layer_cum_grad already has all the learning rate multiple
+                self.global_parameters[i] -= layer_cum_grad
+
+    def evaluate(
+        self, server_round: int, parameters: Parameters
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """Overide default evaluate method to save model parameters."""
+        if self.evaluate_fn is None:
+            # No evaluation function provided
+            return None
+
+        parameters_ndarrays = parameters_to_ndarrays(parameters)
+        eval_res = self.evaluate_fn(server_round, parameters_ndarrays, {})
+
+        if eval_res is None:
+            return None
+
+        loss, metrics = eval_res
+        accuracy = float(metrics["accuracy"])
+
+        if accuracy > self.best_test_acc:
+            self.best_test_acc = accuracy
+
+            # Save model parameters and state
+            if server_round == 0:
+                return None
+
+            np.savez(
+                f"{self.exp_config.checkpoint_path}bestModel_"
+                f"{self.exp_config.exp_name}_{self.exp_config.strategy}_"
+                f"varEpochs_{self.exp_config.var_local_epochs}.npz",
+                self.global_parameters,
+                [loss, self.best_test_acc],
+                self.global_momentum_buffer,
+            )
+
+            log(INFO, "Model saved with Best Test accuracy %.3f: ", self.best_test_acc)
+
+        return loss, metrics
+
+
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+    """Aggregate the client metrics via weighted average for evaluation.
+
+    Parameters
+    ----------
+    metrics : List[Tuple[int, Metrics]]
+        The list of metrics to aggregate.
+
+    Returns
+    -------
+    Metrics
+        The weighted average metric.
+    """
+    # Multiply accuracy of each client by number of examples used
+    accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics]
+    examples = [num_examples for num_examples, _ in metrics]
+
+    # Aggregate and return custom metric (weighted average)
+    return {"accuracy": np.sum(accuracies) / np.sum(examples)}
diff --git a/baselines/fednova/fednova/utils.py b/baselines/fednova/fednova/utils.py
new file mode 100644
index 000000000000..7d16c76fcf79
--- /dev/null
+++ b/baselines/fednova/fednova/utils.py
@@ -0,0 +1,184 @@
+"""Utility functions for FedNova such as computing accuracy, plotting results, etc."""
+
+import glob
+import os
+from typing import List, Tuple
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import torch
+from flwr.common import Metrics
+from omegaconf import DictConfig
+
+
+def comp_accuracy(output, target, topk=(1,)):
+    """Compute accuracy over the k top predictions wrt the target."""
+    with torch.no_grad():
+        maxk = max(topk)
+        batch_size = target.size(0)
+
+        _, pred = output.topk(maxk, 1, True, True)
+        pred = pred.t()
+        correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+        res = []
+        for k in topk:
+            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
+            res.append(correct_k.mul_(100.0 / batch_size))
+        return res
+
+
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+    """Return weighted average of accuracy metrics."""
+    # Multiply accuracy of each client by number of examples used
+    accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics]
+    examples = [num_examples for num_examples, _ in metrics]
+
+    # Aggregate and return custom metric (weighted average)
+    return {"accuracy": np.sum(accuracies) / np.sum(examples)}
+
+
+def fit_config(exp_config: DictConfig, server_round: int):
+    """Return training configuration dict for each round.
+
+    Learning rate is reduced by a factor after set rounds.
+    """
+    config = {}
+
+    lr = exp_config.optimizer.lr
+
+    if exp_config.lr_scheduling:
+        if server_round == int(exp_config.num_rounds / 2):
+            lr = exp_config.optimizer.lr / 10
+
+        elif server_round == int(exp_config.num_rounds * 0.75):
+            lr = exp_config.optimizer.lr / 100
+
+    config["lr"] = lr
+    config["server_round"] = server_round
+    return config
+
+
+# pylint: disable=too-many-locals, too-many-statements
+def generate_plots(
+    local_solvers: List[str], strategy: List[str], var_epochs: bool, momentum_plot=False
+):
+    """Generate plots for all experiments, saved in directory _static."""
+    root_path = "multirun/"
+    save_path = "_static/"
+
+    def load_exp(exp_name: str, strat: str, var_epoch: bool):
+        exp_dirs = os.path.join(
+            root_path,
+            f"optimizer_{exp_name.lower()}_strategy_"
+            f"{strat.lower()}_var_local_epochs_{var_epoch}",
+        )
+        exp_files = glob.glob(f"{exp_dirs}/*/*.csv")
+
+        exp_df = [pd.read_csv(f) for f in exp_files]
+        exp_df = [df for df in exp_df if not df.isna().any().any()]
+
+        assert len(exp_df) >= 1, (
+            f"Atleast one results file must contain non-NaN values. "
+            f"NaN values found in all seed runs of {exp_df}"
+        )
+        return exp_df
+
+    def get_confidence_interval(data):
+        """Return 95% confidence intervals along with mean."""
+        avg = np.mean(data, axis=0)
+        std = np.std(data, axis=0)
+        lower = avg - 1.96 * std / np.sqrt(len(data))
+        upper = avg + 1.96 * std / np.sqrt(len(data))
+        return avg, lower, upper
+
+    # create tuple combination of experiment configuration for plotting
+    # [("vanilla", "fedavg", True), ("vanilla", "fednova", True)]
+    max_exp_len = max([len(local_solvers), len(strategy)])
+    optim_exp_len = int(max_exp_len / len(local_solvers))
+    strategy_exp_len = int(max_exp_len / len(strategy))
+    var_epochs_len = int(max_exp_len)
+    exp_list = list(
+        zip(
+            local_solvers * optim_exp_len,
+            strategy * strategy_exp_len,
+            [var_epochs] * var_epochs_len,
+        )
+    )
+
+    exp_data = [load_exp(*args) for args in exp_list]
+
+    # Iterate over each experiment
+    plt.figure()
+    title = ""
+    for i, data_dfs in enumerate(exp_data):
+        # Iterate over multiple seeds of same experiment
+        combined_data = np.array([df["test_accuracy"].values for df in data_dfs])
+
+        mean, lower_ci, upper_ci = get_confidence_interval(combined_data)
+
+        epochs = np.arange(1, len(mean) + 1)
+
+        optimizer, server_strategy, variable_epoch = exp_list[i]
+
+        # Assign more readable legends for each plot according to paper
+        if optimizer == "proximal" and server_strategy == "FedAvg":
+            label = "FedProx"
+        elif optimizer.lower() in ["server", "hybrid"]:
+            label = optimizer
+        elif optimizer.lower() == "vanilla" and momentum_plot:
+            label = "No Momentum"
+        else:
+            label = server_strategy
+
+        plt.plot(epochs, mean, label=label)
+        plt.fill_between(epochs, lower_ci, upper_ci, alpha=0.3)
+
+        if optimizer == "momentum":
+            optimizer_label = "SGD-M"
+        elif optimizer == "proximal":
+            optimizer_label = "SGD w/ Proximal"
+        else:
+            optimizer_label = "SGD"
+
+        if var_epochs:
+            title = f"Local Solver: {optimizer_label}, Epochs ~ U(2, 5)"
+        else:
+            title = f"Local Solver: {optimizer_label}, Epochs = 2"
+
+        print(
+            f"---------------------Local Solver: {optimizer.upper()}, "
+            f"Strategy: {server_strategy.upper()} Local Epochs Fixed: {variable_epoch}"
+            f"---------------------"
+        )
+        print(f"Number of valid(not NaN) seeds for this experiment: {len(data_dfs)}")
+
+        print(f"Test Accuracy: {mean[-1]:.2f} ± {upper_ci[-1] - mean[-1]:.2f}")
+
+    if momentum_plot:
+        title = "Comparison of Momentum Schemes: FedNova"
+        save_name = "momentum_plot"
+    else:
+        save_name = local_solvers[0]
+
+    plt.ylabel("Test Accuracy %", fontsize=12)
+    plt.xlabel("Communication rounds", fontsize=12)
+    plt.xlim([0, 103])
+    plt.ylim([30, 80])
+    plt.legend(loc="lower right", fontsize=12)
+    plt.grid()
+    plt.title(title, fontsize=15)
+    plt.savefig(f"{save_path}testAccuracy_{save_name}_varEpochs_{var_epochs}.png")
+
+    plt.show()
+
+
+if __name__ == "__main__":
+    for type_epoch_exp in [False, True]:
+        for solver in ["vanilla", "momentum", "proximal"]:
+            generate_plots([solver], ["FedAvg", "FedNova"], type_epoch_exp)
+
+    generate_plots(
+        ["Hybrid", "Server", "Vanilla"], ["FedNova"], True, momentum_plot=True
+    )
diff --git a/baselines/fednova/pyproject.toml b/baselines/fednova/pyproject.toml
new file mode 100644
index 000000000000..69c530e83926
--- /dev/null
+++ b/baselines/fednova/pyproject.toml
@@ -0,0 +1,144 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.masonry.api"
+
+[tool.poetry]
+name = "fednova" # <----- Ensure it matches the name of your baseline directory containing all the source code
+version = "1.0.0"
+description = "Flower Baselines"
+license = "Apache-2.0"
+authors = ["The Flower Authors <hello@flower.ai>"]
+readme = "README.md"
+homepage = "https://flower.ai"
+repository = "https://github.com/adap/flower"
+documentation = "https://flower.ai"
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: MacOS :: MacOS X",
+    "Operating System :: POSIX :: Linux",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: Implementation :: CPython",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Scientific/Engineering :: Mathematics",
+    "Topic :: Software Development",
+    "Topic :: Software Development :: Libraries",
+    "Topic :: Software Development :: Libraries :: Python Modules",
+    "Typing :: Typed",
+]
+
+[tool.poetry.dependencies]
+# tested with python == 3.10.11
+python = ">=3.10.0, <3.11.0"
+flwr = { extras = ["simulation"], version = "1.5.0" }
+hydra-core = "1.3.2" # don't change this
+torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl" }
+numpy = "1.21.6"
+matplotlib = "3.5.3"
+pandas = "1.3.5"
+
+[tool.poetry.dev-dependencies]
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
+mypy = "==1.4.1"
+pylint = "==2.8.2"
+flake8 = "==3.9.2"
+pytest = "==6.2.4"
+pytest-watch = "==4.2.0"
+ruff = "==0.0.272"
+types-requests = "==2.27.7"
+virtualenv = "20.21.0"
+
+[tool.isort]
+line_length = 88
+indent = "    "
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+use_parentheses = true
+
+[tool.black]
+line-length = 88
+target-version = ["py38", "py39", "py310", "py311"]
+
+[tool.pytest.ini_options]
+minversion = "6.2"
+addopts = "-qq"
+testpaths = [
+    "flwr_baselines",
+]
+
+[tool.mypy]
+ignore_missing_imports = true
+strict = false
+plugins = "numpy.typing.mypy_plugin"
+
+[tool.pylint."MESSAGES CONTROL"]
+disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
+good-names = "i,j,k,_,x,y,X,Y,lr,K,N,m,n,v,mu,p,g,df"
+signature-mutators = "hydra.main.main"
+
+[tool.pylint.typecheck]
+generated-members = "numpy.*, torch.*, tensorflow.*"
+
+[[tool.mypy.overrides]]
+module = [
+    "importlib.metadata.*",
+    "importlib_metadata.*",
+]
+follow_imports = "skip"
+follow_imports_for_stubs = true
+disallow_untyped_calls = false
+
+[[tool.mypy.overrides]]
+module = "torch.*"
+follow_imports = "skip"
+follow_imports_for_stubs = true
+
+[tool.docformatter]
+wrap-summaries = 88
+wrap-descriptions = 88
+
+[tool.ruff]
+target-version = "py38"
+line-length = 88
+select = ["D", "E", "F", "W", "B", "ISC", "C4"]
+fixable = ["D", "E", "F", "W", "B", "ISC", "C4"]
+ignore = ["B024", "B027"]
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+    "proto",
+]
+
+[tool.ruff.pydocstyle]
+convention = "numpy"
diff --git a/baselines/fedpara/.gitignore b/baselines/fedpara/.gitignore
new file mode 100644
index 000000000000..6244dfada6ee
--- /dev/null
+++ b/baselines/fedpara/.gitignore
@@ -0,0 +1,4 @@
+outputs/
+multirun/
+client_states/
+data/
\ No newline at end of file
diff --git a/baselines/fedpara/LICENSE b/baselines/fedpara/LICENSE
new file mode 100644
index 000000000000..d64569567334
--- /dev/null
+++ b/baselines/fedpara/LICENSE
@@ -0,0 +1,202 @@
+
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diff --git a/baselines/fedpara/README.md b/baselines/fedpara/README.md
new file mode 100644
index 000000000000..55acd228c4db
--- /dev/null
+++ b/baselines/fedpara/README.md
@@ -0,0 +1,186 @@
+---
+title: "FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning"
+url:  https://openreview.net/forum?id=d71n4ftoCBy
+labels: [image classification, personalization, low-rank training, tensor decomposition]
+dataset: [CIFAR-10, CIFAR-100, MNIST]
+---
+
+# FedPara: Low-rank Hadamard Product for Communication-Efficient Federated Learning
+
+> Note: If you use this baseline in your work, please remember to cite the original authors of the paper as well as the Flower paper.
+
+**Paper:** [openreview.net/forum?id=d71n4ftoCBy](https://openreview.net/forum?id=d71n4ftoCBy)
+
+**Authors:**  Nam Hyeon-Woo, Moon Ye-Bin, Tae-Hyun Oh
+
+**Abstract:** In this work, we propose a communication-efficient parameterization, FedPara,
+for federated learning (FL) to overcome the burdens on frequent model uploads
+and downloads. Our method re-parameterizes weight parameters of layers using
+low-rank weights followed by the Hadamard product. Compared to the conventional low-rank parameterization, our FedPara method is not restricted to lowrank constraints, and thereby it has a far larger capacity. This property enables to
+achieve comparable performance while requiring 3 to 10 times lower communication costs than the model with the original layers, which is not achievable by
+the traditional low-rank methods. The efficiency of our method can be further improved by combining with other efficient FL optimizers. In addition, we extend
+our method to a personalized FL application, pFedPara, which separates parameters into global and local ones. We show that pFedPara outperforms competing
+personalized FL methods with more than three times fewer parameters. Project
+page: https://github.com/South-hw/FedPara_ICLR22
+
+
+
+## About this baseline
+
+**What’s implemented:**  The code in this directory replicates the experiments in FedPara paper implementing the Low-rank scheme for Convolution module.
+
+Specifically, it replicates the results for CIFAR-10  and CIFAR-100  in Figure 3 and the results for MNIST in Figure 5(c).
+
+
+**Datasets:**  CIFAR-10, CIFAR-100, MNIST from PyTorch's Torchvision
+
+**Hardware Setup:** The experiments have been conducted on our server with the following specs:
+
+- **GPU:** 1x RTX A6000 GPU with 48GB VRAM
+- **CPU:** 1x24 cores Intel Xeon(R) 6248R
+- **RAM:** 150 GB
+
+On a machine with RTX 3090Ti (24GB VRAM) it takes approximately 1h to run each CIFAR-10/100 experiment while using < 12GB of VRAM. You can lower the VRAM footprint my reducing the number of clients allowed to run in parallel in your GPU (do this by raising `client_resources.num_gpus`).
+
+
+**Contributors:** Yahia Salaheldin Shaaban, Omar Mokhtar and Roeia Amr
+
+
+## Experimental Setup
+
+**Task:**  Image classification
+
+**Model:**  This baseline implements VGG16 with group normalization.
+
+**Dataset:**
+
+| Dataset   | #classes | #partitions | partitioning method IID |      partitioning method non-IID      |
+| :-------- | :------: | :---------: | :---------------------: | :-----------------------------------: |
+| CIFAR-10  |    10    |     100     |      random split       | Dirichlet distribution ($\alpha=0.5$) |
+| CIFAR-100 |   100    |     50      |      random split       | Dirichlet distribution ($\alpha=0.5$) |
+
+
+**Training Hyperparameters:**
+
+|                                | Cifar10 IID | Cifar10 Non-IID | Cifar100 IID | Cifar100 Non-IID | MNIST    |
+| ------------------------------ | ----------- | --------------- | ------------ | ---------------- | -------- |
+| Fraction of client (K)         | 16          | 16              | 8            | 8                | 10       |
+| Total rounds (T)               | 200         | 200             | 400          | 400              | 100      |
+| Number of SGD epochs (E)       | 10          | 5               | 10           | 5                | 5        |
+| Batch size (B)                 | 64          | 64              | 64           | 64               | 10       |
+| Initial learning rate (η)      | 0.1         | 0.1             | 0.1          | 0.1              | 0.1-0.01 |
+| Learning rate decay (τ)        | 0.992       | 0.992           | 0.992        | 0.992            | 0.999    |
+| Regularization coefficient (λ) | 1           | 1               | 1            | 1                | 0        |
+
+As for the parameters ratio ($\gamma$) we use the following model sizes. As in the paper, $\gamma=0.1$ is used for CIFAR-10 and $\gamma=0.4$ for CIFAR-100:
+
+| Parameters ratio ($\gamma$) | CIFAR-10 | CIFAR-100 |
+| --------------------------- | -------- | --------- |
+| 1.0 (original)              | 15.25M   | 15.30M    |
+| 0.1                         | 1.55M    | -         |
+| 0.4                         | -        | 4.53M     |
+
+
+### Notes:
+- Notably, Fedpara's low-rank training technique heavily relies on initialization, with our experiments revealing that employing a 'Fan-in' He initialization (or Kaiming) renders the model incapable of convergence, resulting in a performance akin to that of a random classifier. We found that only Fan-out initialization yielded the anticipated results, and we postulated that this is attributed to the variance conservation during backward propagation.
+
+- The paper lacks explicit guidance on calculating the rank, aside from the "Rank_min - Rank_max" equation. To address this, we devised an equation aligning with the literature's explanation and constraint, solving a quadratic equation to determine max_rank and utilizing proposition 2 from the paper to establish min_rank.
+
+- The Jacobian correction was not incorporated into our implementation, primarily due to the lack of explicit instructions in the paper regarding the specific implementation of the dual update principle mentioned in the Jacobian correction section.
+
+- It was observed that data generation is crucial for model convergence
+
+## Environment Setup
+To construct the Python environment follow these steps:
+
+It is assumed that `pyenv` is installed, `poetry` is installed and python 3.10.6 is installed using `pyenv`. Refer to this [documentation](https://flower.ai/docs/baselines/how-to-use-baselines.html#setting-up-your-machine) to ensure that your machine is ready.
+
+```bash
+# Set Python 3.10
+pyenv local 3.10.6
+# Tell poetry to use python 3.10
+poetry env use 3.10.6
+
+# Install the base Poetry environment
+poetry install
+
+# Activate the environment
+poetry shell
+```
+
+## Running the Experiments
+
+Running `FedPara` is easy. You can run it with default parameters directly or by tweaking them directly on the command line. Some command examples are shown below.
+
+```bash
+# To run fedpara with default parameters
+python -m fedpara.main
+
+# Run for more rounds and a different number of local epochs
+python -m fedpara.main num_rounds=2024 num_epochs=1
+
+# Choose parameterization scheme: lowrank or original (normal weights)
+python -m fedpara.main model.param_type=standard # or lowrank (default)
+
+# Choosing between non IID and IID scheme
+python -m fedpara.main dataset_config.partition=iid # or non-iid (default)
+
+# Choosing the ratio (lambda) of number of parameters to communicate
+python -m fedpara.main model.ratio=0.1
+
+# Choosing the CIFAR-100 config
+python -m fedpara.main --config-name cifar100 # change settings as shown above if desired
+```
+
+## Expected Results
+
+To reproduce the curves shown below (which correspond to those in Figure 3 in the paper), run the following commands. Experiments running with `model.param_type=lowrank` correspond to those with `-FedPara` in the legend of the figures below. Those with `model.param_type=standard` are labelled with the `-orig` (as original) tag.
+
+```bash
+# To run fedpara for non-iid CIFAR-10 on vgg16 for lowrank and original schemes
+python -m fedpara.main --multirun model.param_type=standard,lowrank
+# To run fedpara for non-iid CIFAR-100 on vgg16 for lowrank and original schemes
+python -m fedpara.main --config-name cifar100 --multirun model.param_type=standard,lowrank
+# To run fedpara for iid CIFAR-10 on vgg16 for lowrank and original schemes
+python -m fedpara.main --multirun model.param_type=standard,lowrank num_epochs=10 dataset_config.partition=iid
+# To run fedpara for iid CIFAR-100 on vgg16 for lowrank and original schemes
+python -m fedpara.main --config-name cifar100 --multirun model.param_type=standard,lowrank num_epochs=10 dataset_config.partition=iid
+# To run fedavg for non-iid MINST on FC
+python -m fedpara.main --config-name mnist_fedavg
+# To run fedper for non-iid MINST on FC
+python -m fedpara.main --config-name mnist_fedper
+# To run pfedpara for non-iid MINST on FC
+python -m fedpara.main --config-name mnist_pfedpara
+```
+
+#### Communication Cost:
+Communication costs as measured as described in the paper:
+*"FL evaluation typically measures the required rounds to achieve the target accuracy as communication costs, but we instead assess total transferred bit sizes, 2 ×
+(#participants)×(model size)×(#rounds)"*
+
+
+### CIFAR-100 (Accuracy vs Communication Cost)
+
+|                    IID                     |                      Non-IID                      |
+| :----------------------------------------: | :-----------------------------------------------: |
+| ![Cifar100 iid](_static/Cifar100_iid.png) | ![Cifar100 non-iid](_static/Cifar100_noniid.png) |
+
+
+### CIFAR-10 (Accuracy vs Communication Cost)
+
+|                   IID                    |                     Non-IID                     |
+| :--------------------------------------: | :---------------------------------------------: |
+| ![CIFAR10 iid](_static/Cifar10_iid.png) | ![CIFAR10 non-iid](_static/Cifar10_noniid.png) |
+
+
+### NON-IID MINST (FedAvg vs FedPer vs pFedPara)
+
+The only federated averaging (FedAvg) implementation replicates the results outlined in the paper. However, challenges with convergence were encountered when applying `pFedPara` and `FedPer` methods.
+
+![Personalization algorithms](_static/non-iid_mnist_personalization.png)
+
+## Code Acknowledgments
+Our code is inspired from these repos:
+- [Fedpara low rank tensor CNN class structure](https://github.com/South-hw/FedPara_ICLR22)
+- [Non-IID mnist data preparation](https://github.com/nimeshagrawal/FedAvg-Pytorch)
+- [Cifar non IID data generation](https://github.com/guobbin/PFL-MoE)
diff --git a/baselines/fedpara/_static/Cifar100_iid.png b/baselines/fedpara/_static/Cifar100_iid.png
new file mode 100644
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new file mode 100644
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diff --git a/baselines/fedpara/_static/Cifar10_iid.png b/baselines/fedpara/_static/Cifar10_iid.png
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diff --git a/baselines/fedpara/_static/Cifar10_noniid.png b/baselines/fedpara/_static/Cifar10_noniid.png
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diff --git a/baselines/fedpara/_static/non-iid_mnist_personalization.png b/baselines/fedpara/_static/non-iid_mnist_personalization.png
new file mode 100644
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diff --git a/baselines/fedpara/fedpara/__init__.py b/baselines/fedpara/fedpara/__init__.py
new file mode 100644
index 000000000000..a5e567b59135
--- /dev/null
+++ b/baselines/fedpara/fedpara/__init__.py
@@ -0,0 +1 @@
+"""Template baseline package."""
diff --git a/baselines/fedpara/fedpara/client.py b/baselines/fedpara/fedpara/client.py
new file mode 100644
index 000000000000..a435e82c6bf8
--- /dev/null
+++ b/baselines/fedpara/fedpara/client.py
@@ -0,0 +1,197 @@
+"""Client for FedPara."""
+
+import copy
+import os
+from collections import OrderedDict
+from typing import Callable, Dict, List, Optional, Tuple
+
+import flwr as fl
+import torch
+from flwr.common import NDArrays, Scalar
+from hydra.utils import instantiate
+from omegaconf import DictConfig
+from torch.utils.data import DataLoader
+
+from fedpara.models import test, train
+from fedpara.utils import get_keys_state_dict
+
+
+class FlowerClient(fl.client.NumPyClient):
+    """Standard Flower client for CNN training."""
+
+    def __init__(
+        self,
+        cid: int,
+        net: torch.nn.Module,
+        train_loader: DataLoader,
+        device: str,
+        num_epochs: int,
+    ):  # pylint: disable=too-many-arguments
+        self.cid = cid
+        self.net = net
+        self.train_loader = train_loader
+        self.device = torch.device(device)
+        self.num_epochs = num_epochs
+
+    def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays:
+        """Return the parameters of the current net."""
+        return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
+
+    def set_parameters(self, parameters: NDArrays) -> None:
+        """Apply parameters to model state dict."""
+        params_dict = zip(self.net.state_dict().keys(), parameters)
+        state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+        self.net.load_state_dict(state_dict, strict=True)
+
+    def fit(
+        self, parameters: NDArrays, config: Dict[str, Scalar]
+    ) -> Tuple[NDArrays, int, Dict]:
+        """Train the network on the training set."""
+        self.set_parameters(parameters)
+
+        train(
+            self.net,
+            self.train_loader,
+            self.device,
+            epochs=self.num_epochs,
+            hyperparams=config,
+            epoch=int(config["curr_round"]),
+        )
+
+        return (
+            self.get_parameters({}),
+            len(self.train_loader),
+            {},
+        )
+
+
+# pylint: disable=too-many-instance-attributes
+class PFlowerClient(fl.client.NumPyClient):
+    """Personalized Flower Client."""
+
+    def __init__(  # pylint: disable=too-many-arguments
+        self,
+        cid: int,
+        net: torch.nn.Module,
+        train_loader: DataLoader,
+        test_loader: DataLoader,
+        device: str,
+        num_epochs: int,
+        state_path: str,
+        algorithm: str,
+    ):
+        self.cid = cid
+        self.net = net
+        self.train_loader = train_loader
+        self.test_loader = test_loader
+        self.device = torch.device(device)
+        self.num_epochs = num_epochs
+        self.state_path = state_path
+        self.algorithm = algorithm
+        self.private_server_param: Dict[str, torch.Tensor] = {}
+
+    def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays:
+        """Return the parameters of the current net."""
+        model_dict = self.net.state_dict().copy()
+        # overwrite the server private parameters
+        for k in self.private_server_param.keys():
+            model_dict[k] = self.private_server_param[k]
+        return [val.cpu().numpy() for _, val in model_dict.items()]
+
+    def set_parameters(self, parameters: NDArrays, evaluate: bool) -> None:
+        """Apply parameters to model state dict."""
+        params_dict = zip(self.net.state_dict().keys(), parameters)
+        server_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+        self.private_server_param = {
+            k: server_dict[k]
+            for k in get_keys_state_dict(
+                model=self.net, algorithm=self.algorithm, mode="local"
+            )
+        }
+
+        if evaluate:
+            client_dict = self.net.state_dict().copy()
+        else:
+            client_dict = copy.deepcopy(server_dict)
+
+        if os.path.isfile(self.state_path):
+            with open(self.state_path, "rb") as f:
+                client_dict = torch.load(f)
+
+        for k in get_keys_state_dict(
+            model=self.net, algorithm=self.algorithm, mode="global"
+        ):
+            client_dict[k] = server_dict[k]
+
+        self.net.load_state_dict(client_dict, strict=False)
+
+    def fit(
+        self, parameters: NDArrays, config: Dict[str, Scalar]
+    ) -> Tuple[NDArrays, int, Dict]:
+        """Train the network on the training set."""
+        self.set_parameters(parameters, evaluate=False)
+
+        train(
+            self.net,
+            self.train_loader,
+            self.device,
+            epochs=self.num_epochs,
+            hyperparams=config,
+            epoch=int(config["curr_round"]),
+        )
+        if self.state_path is not None:
+            with open(self.state_path, "wb") as f:
+                torch.save(self.net.state_dict(), f)
+
+        return (
+            self.get_parameters({}),
+            len(self.train_loader),
+            {},
+        )
+
+    def evaluate(
+        self, parameters: NDArrays, config: Dict[str, Scalar]
+    ) -> Tuple[float, int, Dict]:
+        """Evaluate the network on the test set."""
+        self.set_parameters(parameters, evaluate=True)
+        self.net.to(self.device)
+        loss, accuracy = test(self.net, self.test_loader, device=self.device)
+        return loss, len(self.test_loader), {"accuracy": accuracy}
+
+
+# pylint: disable=too-many-arguments
+def gen_client_fn(
+    train_loaders: List[DataLoader],
+    model: DictConfig,
+    num_epochs: int,
+    args: Dict,
+    test_loader: Optional[List[DataLoader]] = None,
+    state_path: Optional[str] = None,
+) -> Callable[[str], fl.client.NumPyClient]:
+    """Return a function which creates a new FlowerClient for a given cid."""
+
+    def client_fn(cid: str) -> fl.client.NumPyClient:
+        """Create a new FlowerClient for a given cid."""
+        cid_ = int(cid)
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        if args["algorithm"].lower() == "pfedpara" or args["algorithm"] == "fedper":
+            cl_path = f"{state_path}/client_{cid_}.pth"
+            return PFlowerClient(
+                cid=cid_,
+                net=instantiate(model).to(device),
+                train_loader=train_loaders[cid_],
+                test_loader=copy.deepcopy(test_loader),
+                num_epochs=num_epochs,
+                state_path=cl_path,
+                algorithm=args["algorithm"].lower(),
+                device=device,
+            )
+        return FlowerClient(
+            cid=cid_,
+            net=instantiate(model).to(device),
+            train_loader=train_loaders[cid_],
+            num_epochs=num_epochs,
+            device=device,
+        )
+
+    return client_fn
diff --git a/baselines/fedpara/fedpara/conf/cifar10.yaml b/baselines/fedpara/fedpara/conf/cifar10.yaml
new file mode 100644
index 000000000000..97ec2520c3c1
--- /dev/null
+++ b/baselines/fedpara/fedpara/conf/cifar10.yaml
@@ -0,0 +1,43 @@
+---
+seed: 121
+
+num_clients: 100
+num_rounds: 200
+clients_per_round: 16
+num_epochs: 5
+batch_size: 64
+algorithm: FedPara
+
+server_device: cuda
+
+client_resources:
+  num_cpus: 2
+  num_gpus: 0.125
+
+dataset_config:
+  name: CIFAR10
+  partition: non-iid
+  num_classes: 10
+  alpha: 0.5
+
+model:
+  _target_: fedpara.models.VGG
+  num_classes: ${dataset_config.num_classes}
+  param_type: lowrank # lowrank or standard
+  ratio: 0.1 # lowrank ratio
+
+hyperparams:
+  eta_l: 0.1
+  learning_decay: 0.992
+
+
+strategy:
+  _target_: flwr.server.strategy.FedAvg
+  fraction_fit: 0.00001
+  fraction_evaluate: 0.0
+  min_evaluate_clients: 0
+  min_fit_clients: ${clients_per_round}
+  min_available_clients: ${clients_per_round}
+  accept_failures: false
+
+exp_id: ${model.param_type}_${dataset_config.name}_${dataset_config.partition}_alpha${dataset_config.alpha}
diff --git a/baselines/fedpara/fedpara/conf/cifar100.yaml b/baselines/fedpara/fedpara/conf/cifar100.yaml
new file mode 100644
index 000000000000..d1023014605b
--- /dev/null
+++ b/baselines/fedpara/fedpara/conf/cifar100.yaml
@@ -0,0 +1,42 @@
+---
+seed: 342130
+
+num_clients: 50
+num_rounds: 400
+clients_per_round: 8
+num_epochs: 5
+batch_size: 64
+algorithm: FedPara
+
+server_device: cuda
+
+client_resources:
+  num_cpus: 2
+  num_gpus: 0.125
+
+dataset_config:
+  name: CIFAR100
+  partition: non-iid
+  num_classes: 100
+  alpha: 0.5
+
+model:
+  _target_: fedpara.models.VGG
+  num_classes: ${dataset_config.num_classes}
+  param_type: lowrank # lowrank or standard
+  ratio: 0.4 # lowrank ratio
+
+hyperparams:
+  eta_l: 0.1
+  learning_decay: 0.992
+
+strategy:
+  _target_: flwr.server.strategy.FedAvg
+  fraction_fit: 0.00001
+  fraction_evaluate: 0.0
+  min_evaluate_clients: 0
+  min_fit_clients: ${clients_per_round}
+  min_available_clients: ${clients_per_round}
+  accept_failures: false
+
+exp_id: ${model.param_type}_${dataset_config.name}_${dataset_config.partition}_alpha${dataset_config.alpha}
\ No newline at end of file
diff --git a/baselines/fedpara/fedpara/conf/mnist_fedavg.yaml b/baselines/fedpara/fedpara/conf/mnist_fedavg.yaml
new file mode 100644
index 000000000000..d0cda9a62e76
--- /dev/null
+++ b/baselines/fedpara/fedpara/conf/mnist_fedavg.yaml
@@ -0,0 +1,38 @@
+---
+seed: 424
+
+num_clients: 100
+num_rounds: 100
+clients_per_round: 10
+num_epochs: 1
+batch_size: 10
+server_device: cuda
+algorithm: fedavg
+
+client_resources:
+  num_cpus: 2
+  num_gpus: 0.1
+
+dataset_config:
+  name: MNIST
+  num_classes: 10
+  shard_size: 300
+  
+model:
+  _target_: fedpara.models.FC
+  num_classes: ${dataset_config.num_classes}
+  hidden_size: 200
+
+hyperparams:
+  eta_l: 0.05
+  learning_decay: 1
+
+strategy:
+  _target_: flwr.server.strategy.FedAvg
+  fraction_fit: 0.00001
+  fraction_evaluate: 0
+  min_evaluate_clients: 0
+  min_fit_clients: ${clients_per_round}
+  min_available_clients: ${clients_per_round}
+
+exp_id: ${algorithm}_${dataset_config.name}
diff --git a/baselines/fedpara/fedpara/conf/mnist_fedper.yaml b/baselines/fedpara/fedpara/conf/mnist_fedper.yaml
new file mode 100644
index 000000000000..dd9034284459
--- /dev/null
+++ b/baselines/fedpara/fedpara/conf/mnist_fedper.yaml
@@ -0,0 +1,45 @@
+---
+seed: 17
+
+num_clients: 100
+num_rounds: 100
+clients_per_round: 10
+num_epochs: 1
+batch_size: 10
+state_path: ./client_states/
+server_device: cuda
+client_device: cuda
+
+algorithm: fedper
+# fedavg in future
+
+client_resources:
+  num_cpus: 2
+  num_gpus: 0.1
+
+dataset_config:
+  name: MNIST
+  num_classes: 10
+  shard_size: 300
+
+  data_seed: ${seed}
+  
+model:
+  _target_: fedpara.models.FC
+  num_classes: ${dataset_config.num_classes}
+  hidden_size: 200
+
+hyperparams:
+  eta_l: 0.05
+  learning_decay: 0
+
+strategy:
+  _target_: flwr.server.strategy.FedAvg
+  fraction_fit: 0.00001
+  fraction_evaluate: 0.00001
+  min_evaluate_clients: ${clients_per_round}
+  min_fit_clients: ${clients_per_round}
+  min_available_clients: ${clients_per_round}
+
+
+exp_id: ${algorithm}_${dataset_config.name}
diff --git a/baselines/fedpara/fedpara/conf/mnist_pfedpara.yaml b/baselines/fedpara/fedpara/conf/mnist_pfedpara.yaml
new file mode 100644
index 000000000000..5733397def0f
--- /dev/null
+++ b/baselines/fedpara/fedpara/conf/mnist_pfedpara.yaml
@@ -0,0 +1,41 @@
+---
+seed: 17
+
+num_clients: 100
+num_rounds: 100
+clients_per_round: 10
+num_epochs: 5
+batch_size: 10
+state_path: ./client_states/
+server_device: cuda
+algorithm: pFedpara
+
+client_resources:
+  num_cpus: 2
+  num_gpus: 0.1
+
+dataset_config:
+  name: MNIST
+  num_classes: 10
+  shard_size: 300
+  
+model:
+  _target_: fedpara.models.FC
+  num_classes: ${dataset_config.num_classes}
+  param_type: lowrank # lowrank or standard 
+  ratio: 0.5 # lowrank ratio
+  hidden_size: 200
+
+hyperparams:
+  eta_l: 0.01
+  learning_decay: 0.999
+
+strategy:
+  _target_: flwr.server.strategy.FedAvg
+  fraction_fit: 0.00001
+  fraction_evaluate: 0.00001
+  min_evaluate_clients: ${clients_per_round}
+  min_fit_clients: ${clients_per_round}
+  min_available_clients: ${clients_per_round}
+
+exp_id: ${algorithm}_${dataset_config.name}_${model.param_type}_${model.ratio}
diff --git a/baselines/fedpara/fedpara/dataset.py b/baselines/fedpara/fedpara/dataset.py
new file mode 100644
index 000000000000..c76a75d982f6
--- /dev/null
+++ b/baselines/fedpara/fedpara/dataset.py
@@ -0,0 +1,139 @@
+"""Dataset loading and processing utilities."""
+
+import pickle
+from typing import List, Tuple
+
+from torch.utils.data import DataLoader
+from torchvision import datasets, transforms
+
+from fedpara.dataset_preparation import (
+    DatasetSplit,
+    iid,
+    noniid,
+    noniid_partition_loader,
+)
+
+
+def load_datasets(
+    config, num_clients, batch_size
+) -> Tuple[List[DataLoader], DataLoader]:
+    """Load the dataset and return the dataloaders for the clients and the server."""
+    print("Loading data...")
+    match config["name"]:
+        case "CIFAR10":
+            Dataset = datasets.CIFAR10
+        case "CIFAR100":
+            Dataset = datasets.CIFAR100
+        case "MNIST":
+            Dataset = datasets.MNIST
+        case _:
+            raise NotImplementedError
+    data_directory = f"./data/{config['name'].lower()}/"
+    match config["name"]:
+        case "CIFAR10" | "CIFAR100":
+            ds_path = f"{data_directory}train_{num_clients}_{config.alpha:.2f}.pkl"
+            transform_train = transforms.Compose(
+                [
+                    transforms.RandomCrop(32, padding=4),
+                    transforms.RandomHorizontalFlip(),
+                    transforms.ToTensor(),
+                    transforms.Normalize(
+                        (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
+                    ),
+                ]
+            )
+            transform_test = transforms.Compose(
+                [
+                    transforms.ToTensor(),
+                    transforms.Normalize(
+                        (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
+                    ),
+                ]
+            )
+            try:
+                with open(ds_path, "rb") as file:
+                    train_datasets = pickle.load(file).values()
+                dataset_train = Dataset(
+                    data_directory,
+                    train=True,
+                    download=False,
+                    transform=transform_train,
+                )
+                dataset_test = Dataset(
+                    data_directory,
+                    train=False,
+                    download=False,
+                    transform=transform_test,
+                )
+            except FileNotFoundError:
+                dataset_train = Dataset(
+                    data_directory, train=True, download=True, transform=transform_train
+                )
+                if config.partition == "iid":
+                    train_datasets = iid(dataset_train, num_clients)
+                else:
+                    train_datasets, _ = noniid(dataset_train, num_clients, config.alpha)
+                pickle.dump(train_datasets, open(ds_path, "wb"))
+                train_datasets = train_datasets.values()
+                dataset_test = Dataset(
+                    data_directory, train=False, download=True, transform=transform_test
+                )
+
+        case "MNIST":
+            ds_path = f"{data_directory}train_{num_clients}.pkl"
+            transform_train = transforms.Compose(
+                [
+                    transforms.ToTensor(),
+                    transforms.Normalize((0.1307,), (0.3081,)),
+                ]
+            )
+            transform_test = transforms.Compose(
+                [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+            )
+            try:
+                train_datasets = pickle.load(open(ds_path, "rb"))
+                dataset_train = Dataset(
+                    data_directory,
+                    train=True,
+                    download=False,
+                    transform=transform_train,
+                )
+                dataset_test = Dataset(
+                    data_directory,
+                    train=False,
+                    download=False,
+                    transform=transform_test,
+                )
+
+            except FileNotFoundError:
+                dataset_train = Dataset(
+                    data_directory, train=True, download=True, transform=transform_train
+                )
+                train_datasets = noniid_partition_loader(
+                    dataset_train,
+                    m_per_shard=config.shard_size,
+                    n_shards_per_client=len(dataset_train) // (config.shard_size * 100),
+                )
+                pickle.dump(train_datasets, open(ds_path, "wb"))
+                dataset_test = Dataset(
+                    data_directory, train=False, download=True, transform=transform_test
+                )
+            train_loaders = [
+                DataLoader(x, batch_size=batch_size, shuffle=True)
+                for x in train_datasets
+            ]
+            test_loader = DataLoader(dataset_test, batch_size=batch_size, num_workers=2)
+            return train_loaders, test_loader
+
+    test_loader = DataLoader(dataset_test, batch_size=batch_size, num_workers=2)
+    train_loaders = [
+        DataLoader(
+            DatasetSplit(dataset_train, ids),
+            batch_size=batch_size,
+            shuffle=True,
+            num_workers=2,
+        )
+        for ids in train_datasets
+    ]
+
+    return train_loaders, test_loader
diff --git a/baselines/fedpara/fedpara/dataset_preparation.py b/baselines/fedpara/fedpara/dataset_preparation.py
new file mode 100644
index 000000000000..2f3922e97cef
--- /dev/null
+++ b/baselines/fedpara/fedpara/dataset_preparation.py
@@ -0,0 +1,160 @@
+"""Handle the dataset partitioning and (optionally) complex downloads.
+
+Please add here all the necessary logic to either download, uncompress, pre/post-process
+your dataset (or all of the above). If the desired way of running your baseline is to
+first download the dataset and partition it and then run the experiments, please
+uncomment the lines below and tell us in the README.md (see the "Running the Experiment"
+block) that this file should be executed first.
+"""
+
+import random
+from collections import defaultdict
+
+import numpy as np
+import torch
+from torch.utils.data import Dataset
+
+
+class DatasetSplit(Dataset):
+    """An abstract Dataset class wrapped around Pytorch Dataset class."""
+
+    def __init__(self, dataset, idxs):
+        self.dataset = dataset
+        self.targets = dataset.targets
+        self.idxs = list(idxs)
+
+    def __len__(self):
+        """Return number of images."""
+        return len(self.idxs)
+
+    def __getitem__(self, item):
+        """Return a transformed example of the dataset."""
+        image, label = self.dataset[self.idxs[item]]
+        return image, label
+
+
+def iid(dataset, num_users):
+    """Sample I.I.D. clients data from a dataset.
+
+    Args:
+        dataset: dataset object
+        num_users: number of users
+    Returns:
+        dict of image index
+    """
+    num_items = int(len(dataset) / num_users)
+    dict_users, all_idxs = {}, list(range(len(dataset)))
+    for i in range(num_users):
+        dict_users[i] = set(np.random.choice(all_idxs, num_items, replace=False))
+        all_idxs = list(set(all_idxs) - dict_users[i])
+    return dict_users
+
+
+# pylint: disable=too-many-locals
+def noniid(dataset, no_participants, alpha=0.5):
+    """Sample non-I.I.D client data from dataset.
+
+    Args:
+        dataset: dataset object
+        no_participants: number of users
+        alpha: float parameter for dirichlet distribution
+    Returns:
+        dict of image index
+    Requires:
+        cifar_classes, a preprocessed class-indice dictionary.
+        Sample Method: take a uniformly sampled 10/100-dimension vector
+         as parameters for dirichlet distribution to sample number of
+         images in each class.
+    """
+    np.random.seed(666)
+    random.seed(666)
+    cifar_classes = {}
+    for ind, x in enumerate(dataset):
+        _, label = x
+        if label in cifar_classes:
+            cifar_classes[label].append(ind)
+        else:
+            cifar_classes[label] = [ind]
+
+    per_participant_list = defaultdict(list)
+    no_classes = len(cifar_classes.keys())
+    class_size = len(cifar_classes[0])
+    datasize = {}
+    for n in range(no_classes):
+        random.shuffle(cifar_classes[n])
+        sampled_probabilities = class_size * np.random.dirichlet(
+            np.array(no_participants * [alpha])
+        )
+        for user in range(no_participants):
+            no_imgs = int(round(sampled_probabilities[user]))
+            datasize[user, n] = no_imgs
+            sampled_list = cifar_classes[n][: min(len(cifar_classes[n]), no_imgs)]
+            per_participant_list[user].extend(sampled_list)
+            cifar_classes[n] = cifar_classes[n][min(len(cifar_classes[n]), no_imgs) :]
+    train_img_size = np.zeros(no_participants)
+    for i in range(no_participants):
+        train_img_size[i] = sum([datasize[i, j] for j in range(no_classes)])
+    clas_weight = np.zeros((no_participants, no_classes))
+    for i in range(no_participants):
+        for j in range(no_classes):
+            clas_weight[i, j] = float(datasize[i, j]) / float((train_img_size[i]))
+    return per_participant_list, clas_weight
+
+
+def data_to_tensor(data):
+    """Load dataset to memory, applies transform."""
+    loader = torch.utils.data.DataLoader(data, batch_size=len(data))
+    img, label = next(iter(loader))
+    return img, label
+
+
+def noniid_partition_loader(data, m_per_shard=300, n_shards_per_client=2):
+    """Partition in semi-pathological fashion.
+
+    1. sort examples by label, form shards of size 300 by grouping points
+    successively
+    2. each client is 2 random shards
+    most clients will have 2 digits, at most 4.
+    """
+    # load data into memory
+    img, label = data_to_tensor(data)
+
+    # sort
+    idx = torch.argsort(label)
+    img = img[idx]
+    label = label[idx]
+
+    # split into n_shards of size m_per_shard
+    m = len(data)
+    assert m % m_per_shard == 0
+    n_shards = m // m_per_shard
+    shards_idx = [
+        torch.arange(m_per_shard * i, m_per_shard * (i + 1)) for i in range(n_shards)
+    ]
+    random.shuffle(shards_idx)  # shuffle shards
+
+    # pick shards to create a dataset for each client
+    assert n_shards % n_shards_per_client == 0
+    n_clients = n_shards // n_shards_per_client
+    client_data = [
+        torch.utils.data.TensorDataset(
+            torch.cat(
+                [
+                    img[shards_idx[j]]
+                    for j in range(
+                        i * n_shards_per_client, (i + 1) * n_shards_per_client
+                    )
+                ]
+            ),
+            torch.cat(
+                [
+                    label[shards_idx[j]]
+                    for j in range(
+                        i * n_shards_per_client, (i + 1) * n_shards_per_client
+                    )
+                ]
+            ),
+        )
+        for i in range(n_clients)
+    ]
+    return client_data
diff --git a/baselines/fedpara/fedpara/main.py b/baselines/fedpara/fedpara/main.py
new file mode 100644
index 000000000000..2397a20e17ef
--- /dev/null
+++ b/baselines/fedpara/fedpara/main.py
@@ -0,0 +1,124 @@
+"""Main script for running FedPara."""
+
+from typing import Dict
+
+import flwr as fl
+import hydra
+from flwr.common import Scalar
+from hydra.core.hydra_config import HydraConfig
+from hydra.utils import instantiate
+from omegaconf import DictConfig, OmegaConf
+
+from fedpara import client, server, utils
+from fedpara.dataset import load_datasets
+from fedpara.server import weighted_average
+from fedpara.utils import (
+    get_parameters,
+    save_results_as_pickle,
+    seed_everything,
+    set_client_state_save_path,
+)
+
+
+@hydra.main(config_path="conf", config_name="cifar10", version_base=None)
+def main(cfg: DictConfig) -> None:
+    """Run the baseline.
+
+    Parameters
+    ----------
+    cfg : DictConfig
+        An omegaconf object that stores the hydra config.
+    """
+    # 1. Print parsed config
+    print(OmegaConf.to_yaml(cfg))
+    seed_everything(cfg.seed)
+    OmegaConf.to_container(cfg, resolve=True)
+    if "state_path" in cfg:
+        state_path = set_client_state_save_path(cfg.state_path)
+    else:
+        state_path = None
+
+    # 2. Prepare dataset
+    train_loaders, test_loader = load_datasets(
+        config=cfg.dataset_config,
+        num_clients=cfg.num_clients,
+        batch_size=cfg.batch_size,
+    )
+
+    # 3. Define clients
+    # In this scheme the responsability of choosing the client is on the client manager
+
+    client_fn = client.gen_client_fn(
+        train_loaders=train_loaders,
+        test_loader=test_loader,
+        model=cfg.model,
+        num_epochs=cfg.num_epochs,
+        args={"algorithm": cfg.algorithm},
+        state_path=state_path,
+    )
+
+    def get_on_fit_config():
+        def fit_config_fn(server_round: int):
+            fit_config: Dict[str, Scalar] = OmegaConf.to_container(
+                cfg.hyperparams, resolve=True
+            )  # type: ignore
+            fit_config["curr_round"] = server_round
+            return fit_config
+
+        return fit_config_fn
+
+    net_glob = instantiate(cfg.model)
+
+    # 4. Define strategy
+    if cfg.strategy.min_evaluate_clients:
+        strategy = instantiate(
+            cfg.strategy,
+            on_fit_config_fn=get_on_fit_config(),
+            initial_parameters=fl.common.ndarrays_to_parameters(
+                get_parameters(net_glob)
+            ),
+            evaluate_metrics_aggregation_fn=weighted_average,
+        )
+
+    else:
+        evaluate_fn = server.gen_evaluate_fn(
+            num_clients=cfg.num_clients,
+            test_loader=test_loader,
+            model=cfg.model,
+            device=cfg.server_device,
+        )
+        strategy = instantiate(
+            cfg.strategy,
+            evaluate_fn=evaluate_fn,
+            on_fit_config_fn=get_on_fit_config(),
+            initial_parameters=fl.common.ndarrays_to_parameters(
+                get_parameters(net_glob)
+            ),
+        )
+
+    # 5. Start Simulation
+    history = fl.simulation.start_simulation(
+        client_fn=client_fn,
+        num_clients=cfg.num_clients,
+        config=fl.server.ServerConfig(num_rounds=cfg.num_rounds),
+        strategy=strategy,
+        client_resources=cfg.client_resources,
+    )
+    save_path = HydraConfig.get().runtime.output_dir
+
+    save_results_as_pickle(history, file_path=save_path)
+
+    # 6. Save results
+    file_suffix = "_".join([(net_glob).__class__.__name__, f"{cfg.exp_id}"])
+
+    utils.plot_metric_from_history(
+        hist=history,
+        save_plot_path=save_path,
+        suffix=file_suffix,
+        cfg=cfg,
+        model_size=net_glob.model_size[1],
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/baselines/fedpara/fedpara/models.py b/baselines/fedpara/fedpara/models.py
new file mode 100644
index 000000000000..2546b950283f
--- /dev/null
+++ b/baselines/fedpara/fedpara/models.py
@@ -0,0 +1,476 @@
+"""Model definitions for FedPara."""
+
+import math
+from typing import Dict, Tuple
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from flwr.common import Scalar
+from torch import nn
+from torch.nn import init
+from torch.utils.data import DataLoader
+
+
+class LowRankNN(nn.Module):
+    """Fedpara Low-rank weight systhesis for fully connected layer."""
+
+    def __init__(self, input_, output, rank) -> None:
+        super().__init__()
+
+        self.X = nn.Parameter(
+            torch.empty(size=(input_, rank)),
+            requires_grad=True,
+        )
+        self.Y = nn.Parameter(torch.empty(size=(output, rank)), requires_grad=True)
+
+        init.kaiming_normal_(self.X, mode="fan_out", nonlinearity="relu")
+        init.kaiming_normal_(self.Y, mode="fan_out", nonlinearity="relu")
+
+    def forward(self):
+        """Forward pass."""
+        out = torch.einsum("yr,xr->yx", self.Y, self.X)
+        return out
+
+
+class Linear(nn.Module):
+    """Low-rank fully connected layer module for personalized scheme."""
+
+    def __init__(self, input_, output, ratio, bias=True) -> None:
+        super().__init__()
+        rank = self._calc_from_ratio(ratio, input_, output)
+        self.w1 = LowRankNN(input_, output, rank)
+        self.w2 = LowRankNN(input_, output, rank)
+        # make the bias for each layer
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(output))
+
+    @staticmethod
+    def _calc_from_ratio(ratio, input_, output):
+        # Return the low-rank of sub-matrices given the compression ratio
+        # minimum possible parameter
+        r1 = int(np.ceil(np.sqrt(output)))
+        r2 = int(np.ceil(np.sqrt(input_)))
+        r = np.min((r1, r2))
+        # maximum possible rank
+        # To solve it we need to know the roots of quadratic equation: 2*r*(m+n)=m*n
+        r3 = math.floor((output * input_) / (2 * (output + input_)))
+        rank = math.ceil((1 - ratio) * r + ratio * r3)
+        return rank
+
+    def forward(self, x):
+        """Forward pass."""
+        # personalized
+        w = self.w1() * self.w2() + self.w1()
+        out = F.linear(x, w, self.bias)
+        return out
+
+
+class FC(nn.Module):
+    """2NN Fully connected layer as in the paper: https://arxiv.org/abs/1602.05629."""
+
+    def __init__(  # pylint: disable=too-many-arguments
+        self,
+        input_size=28**2,
+        hidden_size=200,
+        num_classes=10,
+        ratio=0.5,
+        param_type="standard",
+    ):
+        super().__init__()
+        self.input_size = input_size
+        if param_type == "standard":
+            self.fc1 = nn.Linear(input_size, hidden_size)
+            self.fc2 = nn.Linear(hidden_size, 256)
+            self.out = nn.Linear(256, num_classes)
+
+        elif param_type == "lowrank":
+            self.fc1 = Linear(input_size, hidden_size, ratio)
+            self.fc2 = Linear(hidden_size, 256, ratio)
+            self.out = Linear(256, num_classes, ratio)
+
+        else:
+            raise ValueError("param_type must be either standard or lowrank")
+
+    @property
+    def model_size(self):
+        """Return the total number of trainable parameters (in million paramaters) and.
+
+        the size of the model in MB.
+        """
+        total_trainable_params = (
+            sum(p.numel() for p in self.parameters() if p.requires_grad) / 1e6
+        )
+        param_size = 0
+        for param in self.parameters():
+            param_size += param.nelement() * param.element_size()
+        buffer_size = 0
+        for buffer in self.buffers():
+            buffer_size += buffer.nelement() * buffer.element_size()
+        size_all_mb = (param_size + buffer_size) / 1024**2
+        return total_trainable_params, size_all_mb
+
+    def forward(self, x):
+        """Forward pass."""
+        x = x.view(-1, self.input_size)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.out(x)
+        return x
+
+
+class LowRank(nn.Module):
+    """Fedpara Low-rank weight systhesis for Convolution layer."""
+
+    def __init__(  # pylint: disable=too-many-arguments
+        self,
+        in_channels: int,
+        out_channels: int,
+        low_rank: int,
+        kernel_size: int,
+    ):
+        super().__init__()
+        self.T = nn.Parameter(
+            torch.empty(size=(low_rank, low_rank, kernel_size, kernel_size)),
+            requires_grad=True,
+        )
+        self.X = nn.Parameter(
+            torch.empty(size=(low_rank, out_channels)), requires_grad=True
+        )
+        self.Y = nn.Parameter(
+            torch.empty(size=(low_rank, in_channels)), requires_grad=True
+        )
+        init.kaiming_normal_(self.T, mode="fan_out", nonlinearity="relu")
+        init.kaiming_normal_(self.X, mode="fan_out", nonlinearity="relu")
+        init.kaiming_normal_(self.Y, mode="fan_out", nonlinearity="relu")
+
+    def forward(self):
+        """Forward pass."""
+        # torch.einsum simplify the tensor produce (matrix multiplication)
+        return torch.einsum("xyzw,xo,yi->oizw", self.T, self.X, self.Y)
+
+
+# pylint: disable=too-many-instance-attributes
+class Conv2d(nn.Module):
+    """Convolutional layer with low-rank weights."""
+
+    def __init__(  # pylint: disable=too-many-arguments
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int = 3,
+        stride: int = 1,
+        padding: int = 0,
+        bias: bool = False,
+        ratio: float = 0.1,
+        add_nonlinear: bool = False,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.padding = padding
+        self.bias = bias
+        self.ratio = ratio
+        self.low_rank = self._calc_from_ratio()
+        self.add_nonlinear = add_nonlinear
+        self.W1 = LowRank(in_channels, out_channels, self.low_rank, kernel_size)
+        self.W2 = LowRank(in_channels, out_channels, self.low_rank, kernel_size)
+        self.bias = nn.Parameter(torch.zeros(out_channels)) if bias else None
+        self.tanh = nn.Tanh()
+
+    def _calc_from_ratio(self):
+        # Return the low-rank of sub-matrices given the compression ratio
+
+        # minimum possible parameter
+        r1 = int(np.ceil(np.sqrt(self.out_channels)))
+        r2 = int(np.ceil(np.sqrt(self.in_channels)))
+        r = np.min((r1, r2))
+
+        # maximum possible rank,
+        # To solve it we need to know the roots of quadratic equation: ax^2+bx+c=0
+        # a = kernel**2
+        # b = out channel+ in channel
+        # c = - num_target_params/2
+        # r3 is floored because we cannot take the ceil as it results a bigger number
+        # of parameters than the original problem
+
+        num_target_params = self.out_channels * self.in_channels * (self.kernel_size**2)
+        a, b, c = (
+            self.kernel_size**2,
+            self.out_channels + self.in_channels,
+            -num_target_params / 2,
+        )
+        discriminant = b**2 - 4 * a * c
+        r3 = math.floor((-b + math.sqrt(discriminant)) / (2 * a))
+        ratio = math.ceil((1 - self.ratio) * r + self.ratio * r3)
+        return ratio
+
+    def forward(self, x):
+        """Forward pass."""
+        # Hadamard product of two submatrices
+        if self.add_nonlinear:
+            W = self.tanh(self.W1()) * self.tanh(self.W2())
+        else:
+            W = self.W1() * self.W2()
+        out = F.conv2d(
+            input=x, weight=W, bias=self.bias, stride=self.stride, padding=self.padding
+        )
+        return out
+
+
+# pylint: disable=too-many-instance-attributes
+class VGG(nn.Module):
+    """VGG16GN model."""
+
+    def __init__(  # pylint: disable=too-many-arguments
+        self,
+        num_classes,
+        num_groups=2,
+        ratio=0.1,
+        param_type="lowrank",
+        add_nonlinear=False,
+    ):
+        super().__init__()
+        self.param_type = param_type
+        self.num_groups = num_groups
+        self.num_classes = num_classes
+        self.ratio = ratio
+        self.add_nonlinear = add_nonlinear
+        self.features = self._make_layers(
+            [
+                64,
+                64,
+                "M",
+                128,
+                128,
+                "M",
+                256,
+                256,
+                256,
+                "M",
+                512,
+                512,
+                512,
+                "M",
+                512,
+                512,
+                512,
+                "M",
+            ]
+        )
+        self.classifier = nn.Sequential(
+            nn.Dropout(),
+            nn.Linear(512, 512),
+            nn.ReLU(inplace=True),
+            nn.Dropout(),
+            nn.Linear(512, 512),
+            nn.ReLU(inplace=True),
+            nn.Linear(512, num_classes),
+        )
+        self._init_weights()
+
+    def _init_weights(self):
+        """Initialize the weights."""
+        for name, module in self.features.named_children():
+            module = getattr(self.features, name)
+            if isinstance(module, nn.Conv2d):
+                if self.param_type == "lowrank":
+                    num_channels = module.in_channels
+                    setattr(
+                        self.features,
+                        name,
+                        Conv2d(
+                            num_channels,
+                            module.out_channels,
+                            module.kernel_size[0],
+                            module.stride[0],
+                            module.padding[0],
+                            module.bias is not None,
+                            ratio=self.ratio,
+                            add_nonlinear=self.add_nonlinear,
+                            # send the activation function to the Conv2d class
+                        ),
+                    )
+                elif self.param_type == "standard":
+                    n = (
+                        module.kernel_size[0]
+                        * module.kernel_size[1]
+                        * module.out_channels
+                    )
+                    module.weight.data.normal_(0, math.sqrt(2.0 / n))
+                    module.bias.data.zero_()
+
+    def _make_layers(self, cfg, group_norm=True):
+        layers = []
+        in_channels = 3
+        for v in cfg:
+            if v == "M":
+                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
+            else:
+                conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
+                if group_norm:
+                    layers += [
+                        conv2d,
+                        nn.GroupNorm(self.num_groups, v),
+                        nn.ReLU(inplace=True),
+                    ]
+                else:
+                    layers += [conv2d, nn.ReLU(inplace=True)]
+                in_channels = v
+        return nn.Sequential(*layers)
+
+    @property
+    def model_size(self):
+        """Return the total number of trainable parameters (in million paramaters) and.
+
+        the size of the model in MB.
+        """
+        total_trainable_params = (
+            sum(p.numel() for p in self.parameters() if p.requires_grad) / 1e6
+        )
+        param_size = 0
+        for param in self.parameters():
+            param_size += param.nelement() * param.element_size()
+        buffer_size = 0
+        for buffer in self.buffers():
+            buffer_size += buffer.nelement() * buffer.element_size()
+        size_all_mb = (param_size + buffer_size) / 1024**2
+        return total_trainable_params, size_all_mb
+
+    def forward(self, x):
+        """Forward pass."""
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        x = self.classifier(x)
+        return x
+
+
+# Create an instance of the VGG16GN model with Group Normalization,
+# custom Conv2d, and modified classifier
+def test(
+    net: nn.Module, test_loader: DataLoader, device: torch.device
+) -> Tuple[float, float]:
+    """Evaluate the network on the entire test set.
+
+    Parameters
+    ----------
+    net : nn.Module
+        The neural network to test.
+    test_loader : DataLoader
+        The DataLoader containing the data to test the network on.
+    device : torch.device
+        The device on which the model should be tested, either 'cpu' or 'cuda'.
+
+    Returns
+    -------
+    Tuple[float, float]
+        The loss and the accuracy of the input model on the given data.
+    """
+    if len(test_loader.dataset) == 0:
+        raise ValueError("Testloader can't be 0, exiting...")
+
+    criterion = torch.nn.CrossEntropyLoss()
+    correct, total, loss = 0, 0, 0.0
+    net.eval()
+    with torch.no_grad():
+        for images, labels in test_loader:
+            images, labels = images.to(device), labels.to(device)
+            outputs = net(images)
+            loss += criterion(outputs, labels).item()
+            _, predicted = torch.max(outputs.data, 1)
+            total += labels.size(0)
+            correct += (predicted == labels).sum().item()
+    loss /= len(test_loader.dataset)
+    accuracy = correct / total
+    return loss, accuracy
+
+
+def train(  # pylint: disable=too-many-arguments
+    net: nn.Module,
+    trainloader: DataLoader,
+    device: torch.device,
+    epochs: int,
+    hyperparams: Dict[str, Scalar],
+    epoch: int,
+) -> None:
+    """Train the network on the training set.
+
+    Parameters
+    ----------
+    net : nn.Module
+        The neural network to train.
+    trainloader : DataLoader
+        The DataLoader containing the data to train the network on.
+    device : torch.device
+        The device on which the model should be trained, either 'cpu' or 'cuda'.
+    epochs : int
+        The number of epochs the model should be trained for.
+    hyperparams : Dict[str, Scalar]
+        The hyperparameters to use for training.
+    """
+    lr = float(hyperparams["eta_l"]) * float(hyperparams["learning_decay"]) ** (
+        epoch - 1
+    )
+    criterion = torch.nn.CrossEntropyLoss()
+    optimizer = torch.optim.SGD(
+        net.parameters(),
+        lr=lr,
+        momentum=0,
+        weight_decay=0,
+    )
+    net.train()
+    for _ in range(epochs):
+        net = _train_one_epoch(
+            net=net,
+            trainloader=trainloader,
+            device=device,
+            criterion=criterion,
+            optimizer=optimizer,
+        )
+
+
+def _train_one_epoch(  # pylint: disable=too-many-arguments
+    net: nn.Module,
+    trainloader: DataLoader,
+    device: torch.device,
+    criterion,
+    optimizer,
+) -> nn.Module:
+    """Train for one epoch.
+
+    Parameters
+    ----------
+    net : nn.Module
+        The neural network to train.
+    trainloader : DataLoader
+        The DataLoader containing the data to train the network on.
+    device : torch.device
+        The device on which the model should be trained, either 'cpu' or 'cuda'.
+    criterion :
+        The loss function to use for training
+    optimizer :
+        The optimizer to use for training
+    hyperparams : Dict[str, Scalar]
+        The hyperparameters to use for training.
+
+    Returns
+    -------
+    nn.Module
+        The model that has been trained for one epoch.
+    """
+    for images, labels in trainloader:
+        images, labels = images.to(device), labels.to(device)
+        net.zero_grad()
+        log_probs = net(images)
+        loss = criterion(log_probs, labels)
+        loss.backward()
+        optimizer.step()
+    return net
+
+
+if __name__ == "__main__":
+    model = VGG(num_classes=10, num_groups=2, param_type="standard", ratio=0.4)
+    # Print the modified VGG16GN model architecture
+    print(model.model_size)
diff --git a/baselines/fedpara/fedpara/server.py b/baselines/fedpara/fedpara/server.py
new file mode 100644
index 000000000000..036efa090c52
--- /dev/null
+++ b/baselines/fedpara/fedpara/server.py
@@ -0,0 +1,77 @@
+"""Global evaluation function."""
+
+from collections import OrderedDict
+from typing import Callable, Dict, List, Optional, Tuple
+
+import torch
+from flwr.common import Metrics, NDArrays, Scalar
+from hydra.utils import instantiate
+from omegaconf import DictConfig
+from torch.utils.data import DataLoader
+
+from fedpara.models import test
+
+
+def get_on_fit_config(hypearparams: Dict):
+    """Generate fit config function."""
+
+    def fit_config_fn(server_round: int):
+        hypearparams["curr_round"] = server_round
+        return hypearparams
+
+    return fit_config_fn
+
+
+def gen_evaluate_fn(
+    num_clients: int,
+    test_loader: DataLoader,
+    model: DictConfig,
+    device,
+) -> Callable[
+    [int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]]
+]:
+    """Generate a centralized evaluation function.
+
+    Parameters
+    ----------
+    model: DictConfig
+        The model details to evaluate.
+    test_loader : DataLoader
+        The dataloader to test the model with.
+    device : torch.device
+        The device to test the model on.
+
+    Returns
+    -------
+    Callable[ [int, NDArrays, Dict[str, Scalar]],
+            Optional[Tuple[float, Dict[str, Scalar]]] ]
+        The centralized evaluation function.
+    """
+
+    # pylint: disable=unused-argument
+    def evaluate(
+        server_round,
+        parameters_ndarrays: NDArrays,
+        __,
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """Use the entire CIFAR-10/100 test set for evaluation."""
+        net = instantiate(model)
+        params_dict = zip(net.state_dict().keys(), parameters_ndarrays)
+        state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+        net.load_state_dict(state_dict, strict=True)
+        net.to(device)
+
+        loss, accuracy = test(net, test_loader, device=device)
+        return loss, {"accuracy": accuracy}
+
+    return evaluate
+
+
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+    """Do weighted average of metrics."""
+    # Multiply accuracy of each client by number of examples used
+    accuracies = [float(num_examples * m["accuracy"]) for num_examples, m in metrics]
+    examples = [num_examples for num_examples, _ in metrics]
+    print(f"accuracies: {sum(accuracies) / sum(examples)}")
+    # Aggregate and return custom metric (weighted average)
+    return {"accuracy": sum(accuracies) / sum(examples)}
diff --git a/baselines/fedpara/fedpara/strategy.py b/baselines/fedpara/fedpara/strategy.py
new file mode 100644
index 000000000000..17436c401c30
--- /dev/null
+++ b/baselines/fedpara/fedpara/strategy.py
@@ -0,0 +1,5 @@
+"""Optionally define a custom strategy.
+
+Needed only when the strategy is not yet implemented in Flower or because you want to
+extend or modify the functionality of an existing strategy.
+"""
diff --git a/baselines/fedpara/fedpara/utils.py b/baselines/fedpara/fedpara/utils.py
new file mode 100644
index 000000000000..fcaf95f50c59
--- /dev/null
+++ b/baselines/fedpara/fedpara/utils.py
@@ -0,0 +1,161 @@
+"""Utility functions for FedPara."""
+
+import os
+import pickle
+import random
+import time
+from pathlib import Path
+from secrets import token_hex
+from typing import List, Optional
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from flwr.common import NDArrays
+from flwr.server import History
+from omegaconf import DictConfig
+from torch.nn import Module
+
+
+def plot_metric_from_history(
+    hist: History,
+    save_plot_path: str,
+    model_size: float,
+    cfg: DictConfig,
+    suffix: str = "",
+) -> None:
+    """Plot the metrics from the history of the server.
+
+    Parameters
+    ----------
+    hist : History
+        Object containing evaluation for all rounds.
+    save_plot_path : str
+        Folder to save the plot to.
+    model_size : float
+        Size of the model in MB.
+    cfg : Optional
+        Optional dictionary containing the configuration of the experiment.
+    suffix: Optional
+        Optional string to add at the end of the filename for the plot.
+    """
+    metric_dict = (
+        hist.metrics_centralized
+        if hist.metrics_centralized
+        else hist.metrics_distributed
+    )
+    _, axs = plt.subplots()
+    rounds, values_accuracy = zip(*metric_dict["accuracy"])
+    r_cc = (i * 2 * model_size * int(cfg.clients_per_round) / 1024 for i in rounds)
+
+    # Set the title
+    # make the suffix space seperated not underscore seperated
+    title = " ".join(suffix.split("_"))
+    axs.set_title(title)
+    axs.grid(True)
+    axs.plot(np.asarray([*r_cc]), np.asarray(values_accuracy))
+    axs.set_ylabel("Accuracy")
+    axs.set_xlabel("Communication Cost (GB)")
+    fig_name = suffix + ".png"
+    plt.savefig(Path(save_plot_path) / Path(fig_name))
+    plt.close()
+
+
+def seed_everything(seed):
+    """Seed everything for reproducibility."""
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    random.seed(seed)
+    torch.backends.cudnn.deterministic = True
+
+
+def get_parameters(net: Module) -> NDArrays:
+    """Get the parameters of the network."""
+    return [val.cpu().numpy() for _, val in net.state_dict().items()]
+
+
+def save_results_as_pickle(
+    history: History,
+    file_path: str,
+    default_filename: Optional[str] = "history.pkl",
+) -> None:
+    """Save results from simulation to pickle.
+
+    Parameters
+    ----------
+    history: History
+        History returned by start_simulation.
+    file_path: Union[str, Path]
+        Path to file to create and store both history and extra_results.
+        If path is a directory, the default_filename will be used.
+        path doesn't exist, it will be created. If file exists, a
+        randomly generated suffix will be added to the file name. This
+        is done to avoid overwritting results.
+    extra_results : Optional[Dict]
+        A dictionary containing additional results you would like
+        to be saved to disk. Default: {} (an empty dictionary)
+    default_filename: Optional[str]
+        File used by default if file_path points to a directory instead
+        to a file. Default: "results.pkl"
+    """
+    path = Path(file_path)
+
+    # ensure path exists
+    path.mkdir(exist_ok=True, parents=True)
+
+    def _add_random_suffix(path_: Path):
+        """Add a random suffix to the file name."""
+        print(f"File `{path_}` exists! ")
+        suffix = token_hex(4)
+        print(f"New results to be saved with suffix: {suffix}")
+        return path_.parent / (path_.stem + "_" + suffix + ".pkl")
+
+    def _complete_path_with_default_name(path_: Path):
+        """Append the default file name to the path."""
+        print("Using default filename")
+        if default_filename is None:
+            return path_
+        return path_ / default_filename
+
+    if path.is_dir():
+        path = _complete_path_with_default_name(path)
+
+    if path.is_file():
+        path = _add_random_suffix(path)
+
+    print(f"Results will be saved into: {path}")
+    # data = {"history": history, **extra_results}
+    data = {"history": history}
+    # save results to pickle
+    with open(str(path), "wb") as handle:
+        pickle.dump(data, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+
+def set_client_state_save_path(path: str) -> str:
+    """Set the client state save path."""
+    client_state_save_path = time.strftime("%Y-%m-%d")
+    client_state_sub_path = time.strftime("%H-%M-%S")
+    client_state_save_path = f"{path}{client_state_save_path}/{client_state_sub_path}"
+    if not os.path.exists(client_state_save_path):
+        os.makedirs(client_state_save_path)
+    return client_state_save_path
+
+
+def get_keys_state_dict(model, algorithm, mode: str = "local") -> List[str]:
+    """."""
+    keys: List[str] = []
+    match algorithm:
+        case "fedper":
+            if mode == "local":
+                keys = list(filter(lambda x: "fc1" not in x, model.state_dict().keys()))
+            elif mode == "global":
+                keys = list(filter(lambda x: "fc1" in x, model.state_dict().keys()))
+        case "pfedpara":
+            if mode == "local":
+                keys = list(filter(lambda x: "w2" in x, model.state_dict().keys()))
+            elif mode == "global":
+                keys = list(filter(lambda x: "w1" in x, model.state_dict().keys()))
+        case _:
+            raise NotImplementedError(f"algorithm {algorithm} not implemented")
+
+    return keys
diff --git a/baselines/fedpara/pyproject.toml b/baselines/fedpara/pyproject.toml
new file mode 100644
index 000000000000..be6b3301872a
--- /dev/null
+++ b/baselines/fedpara/pyproject.toml
@@ -0,0 +1,142 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.masonry.api"
+
+[tool.poetry]
+name = "fedpara" # <----- Ensure it matches the name of your baseline directory containing all the source code
+version = "1.0.0"
+description = "Flower Baselines"
+license = "Apache-2.0"
+authors = ["Yahia Salaheldin Shaaban <yehia.salah.ms@alexu.edu.eg>", "Omar Mokhtar < >", "Roeia Amr < >"]
+readme = "README.md"
+homepage = "https://flower.ai"
+repository = "https://github.com/adap/flower"
+documentation = "https://flower.ai"
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: MacOS :: MacOS X",
+    "Operating System :: POSIX :: Linux",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: Implementation :: CPython",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Scientific/Engineering :: Mathematics",
+    "Topic :: Software Development",
+    "Topic :: Software Development :: Libraries",
+    "Topic :: Software Development :: Libraries :: Python Modules",
+    "Typing :: Typed",
+]
+
+[tool.poetry.dependencies]
+python = ">=3.10, <3.12.0"  # don't change this
+flwr = { extras = ["simulation"], version = "1.5.0" }
+hydra-core = "1.3.2" # don't change this
+matplotlib = "^3.7.2"
+tqdm = "^4.66.1"
+torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl" }
+
+[tool.poetry.dev-dependencies]
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
+mypy = "==1.4.1"
+pylint = "==2.8.2"
+flake8 = "==3.9.2"
+pytest = "==6.2.4"
+pytest-watch = "==4.2.0"
+ruff = "==0.0.272"
+types-requests = "==2.27.7"
+virtualenv = "20.21.0"
+
+[tool.isort]
+line_length = 88
+indent = "    "
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+use_parentheses = true
+
+[tool.black]
+line-length = 88
+target-version = ["py38", "py39", "py310", "py311"]
+
+[tool.pytest.ini_options]
+minversion = "6.2"
+addopts = "-qq"
+testpaths = [
+    "flwr_baselines",
+]
+
+[tool.mypy]
+ignore_missing_imports = true
+strict = false
+plugins = "numpy.typing.mypy_plugin"
+
+[tool.pylint."MESSAGES CONTROL"]
+disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
+good-names = "i,j,k,_,x,y,X,Y,m,w1,w2,W1,W2,n,r1,r2,r3,W,w,v,lr,f,r,T,a,b,c"
+signature-mutators = "hydra.main.main"
+
+[tool.pylint.typecheck]
+generated-members = "numpy.*, torch.*, tensorflow.*"
+
+[[tool.mypy.overrides]]
+module = [
+    "importlib.metadata.*",
+    "importlib_metadata.*",
+]
+follow_imports = "skip"
+follow_imports_for_stubs = true
+disallow_untyped_calls = false
+
+[[tool.mypy.overrides]]
+module = "torch.*"
+follow_imports = "skip"
+follow_imports_for_stubs = true
+
+[tool.docformatter]
+wrap-summaries = 88
+wrap-descriptions = 88
+
+[tool.ruff]
+target-version = "py38"
+line-length = 88
+select = ["D", "E", "F", "W", "B", "ISC", "C4"]
+fixable = ["D", "E", "F", "W", "B", "ISC", "C4"]
+ignore = ["B024", "B027"]
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+    "proto",
+]
+
+[tool.ruff.pydocstyle]
+convention = "numpy"
diff --git a/baselines/fedper/README.md b/baselines/fedper/README.md
index 157bc22d2da5..757c396cc170 100644
--- a/baselines/fedper/README.md
+++ b/baselines/fedper/README.md
@@ -19,7 +19,7 @@ dataset: [CIFAR-10, FLICKR-AES]
 
 **What’s implemented:** The code in this directory replicates the experiments in _Federated Learning with Personalization Layers_ (Arivazhagan et al., 2019) for CIFAR10 and FLICKR-AES datasets, which proposed the `FedPer` model. Specifically, it replicates the results found in figures 2, 4, 7, and 8 in their paper. __Note__ that there is typo in the caption of Figure 4 in the article, it should be CIFAR10 and __not__ CIFAR100. 
 
-**Datasets:** CIFAR10 from PyTorch's Torchvision and FLICKR-AES. FLICKR-AES was proposed as dataset in _Personalized Image Aesthetics_ (Ren et al., 2017) and can be downloaded using a link provided on thier [GitHub](https://github.com/alanspike/personalizedImageAesthetics). One must first download FLICKR-AES-001.zip (5.76GB), extract all inside and place in baseline/FedPer/datasets. To this location, also download the other 2 related files: (1) FLICKR-AES_image_labeled_by_each_worker.csv, and (2) FLICKR-AES_image_score.txt. Images are also scaled to 224x224 for both datasets. This is not explicitly stated in the paper but seems to be boosting performance. Also, for FLICKR dataset, it is stated in the paper that they use data from clients with more than 60 and less than 290 rated images. This amounts to circa 60 clients and we randomly select 30 out of these (as in paper). Therefore, the results might differ somewhat but only slighly. Since the pre-processing steps in the paper are somewhat obscure, the metric values in the plots below may differ slightly, but not the overall results and findings. 
+**Datasets:** CIFAR10 from PyTorch's Torchvision and FLICKR-AES. FLICKR-AES was proposed as dataset in _Personalized Image Aesthetics_ (Ren et al., 2017) and can be downloaded using a link provided on their [GitHub](https://github.com/alanspike/personalizedImageAesthetics). One must first download FLICKR-AES-001.zip (5.76GB), extract all inside and place in baseline/FedPer/datasets. To this location, also download the other 2 related files: (1) FLICKR-AES_image_labeled_by_each_worker.csv, and (2) FLICKR-AES_image_score.txt. Images are also scaled to 224x224 for both datasets. This is not explicitly stated in the paper but seems to be boosting performance. Also, for FLICKR dataset, it is stated in the paper that they use data from clients with more than 60 and less than 290 rated images. This amounts to circa 60 clients and we randomly select 30 out of these (as in paper). Therefore, the results might differ somewhat but only slightly. Since the pre-processing steps in the paper are somewhat obscure, the metric values in the plots below may differ slightly, but not the overall results and findings. 
 
 ```bash
 # These steps are not needed if you are only interested in CIFAR-10
@@ -61,7 +61,7 @@ It's worth mentioning that GPU memory for each client is ~7.5GB. When training o
 
 Please see how models are implemented using a so called model_manager and model_split class since FedPer uses head and base layers in a neural network. These classes are defined in the models.py file and thereafter called when building new models in the directory /implemented_models. Please, extend and add new models as you wish. 
 
-**Dataset:** CIFAR10, FLICKR-AES. CIFAR10 will be partitioned based on number of classes for data that each client shall recieve e.g. 4 allocated classes could be [1, 3, 5, 9]. FLICKR-AES is an unbalanced dataset, so there we only apply random sampling. 
+**Dataset:** CIFAR10, FLICKR-AES. CIFAR10 will be partitioned based on number of classes for data that each client shall receive e.g. 4 allocated classes could be [1, 3, 5, 9]. FLICKR-AES is an unbalanced dataset, so there we only apply random sampling. 
 
 **Training Hyperparameters:** The hyperparameters can be found in conf/base.yaml file which is the configuration file for the main script. 
 
@@ -77,7 +77,7 @@ Please see how models are implemented using a so called model_manager and model_
 | algorithm | fedavg|
 
 **Stateful Clients:**
-In this Baseline (FedPer), we must store the state of the local client head while aggregation of body parameters happen at the server. Flower is currently making this possible but for the time being, we reside to storing client _head_ state in a folder called client_states. We store the values after each fit and evaluate function carried out on each client, and call for the state before executing these funcitons. Moreover, the state of a unique client is accessed using the client ID. 
+In this Baseline (FedPer), we must store the state of the local client head while aggregation of body parameters happen at the server. Flower is currently making this possible but for the time being, we reside to storing client _head_ state in a folder called client_states. We store the values after each fit and evaluate function carried out on each client, and call for the state before executing these functions. Moreover, the state of a unique client is accessed using the client ID. 
 
 > NOTE: This is a work-around so that the local head parameters are not reset before each fit and evaluate. Nevertheless, it can come to change with future releases. 
 
diff --git a/baselines/fedper/fedper/client.py b/baselines/fedper/fedper/client.py
index 83babbd9613f..2f9446a99de9 100644
--- a/baselines/fedper/fedper/client.py
+++ b/baselines/fedper/fedper/client.py
@@ -1,4 +1,5 @@
 """Client implementation - can call FedPer and FedAvg clients."""
+
 import pickle
 from collections import OrderedDict, defaultdict
 from pathlib import Path
@@ -322,9 +323,9 @@ def client_fn(cid: str) -> BaseClient:
         # Create the test loader
         testloader = DataLoader(testset, config.batch_size)
 
-        manager: Union[
-            Type[MobileNetModelManager], Type[ResNetModelManager]
-        ] = MobileNetModelManager
+        manager: Union[Type[MobileNetModelManager], Type[ResNetModelManager]] = (
+            MobileNetModelManager
+        )
         if config.model_name.lower() == "resnet":
             manager = ResNetModelManager
         elif config.model_name.lower() == "mobile":
diff --git a/baselines/fedper/fedper/constants.py b/baselines/fedper/fedper/constants.py
index 3eda77c5134e..b11945296692 100644
--- a/baselines/fedper/fedper/constants.py
+++ b/baselines/fedper/fedper/constants.py
@@ -1,4 +1,5 @@
 """Constants used in machine learning pipeline."""
+
 from enum import Enum
 
 
diff --git a/baselines/fedper/fedper/dataset.py b/baselines/fedper/fedper/dataset.py
index 81a95286b1b8..63724bb90eda 100644
--- a/baselines/fedper/fedper/dataset.py
+++ b/baselines/fedper/fedper/dataset.py
@@ -8,6 +8,7 @@
 `dataset_preparation.py` module. You can use all those functions from functions/methods
 defined here of course.
 """
+
 import os
 import pickle
 import sys
diff --git a/baselines/fedper/fedper/dataset_preparation.py b/baselines/fedper/fedper/dataset_preparation.py
index 0b8b53782aac..fa1492c38eb9 100644
--- a/baselines/fedper/fedper/dataset_preparation.py
+++ b/baselines/fedper/fedper/dataset_preparation.py
@@ -1,4 +1,5 @@
 """Dataset preparation."""
+
 import os
 import random
 from collections import Counter
diff --git a/baselines/fedper/fedper/implemented_models/mobile_model.py b/baselines/fedper/fedper/implemented_models/mobile_model.py
index 57d3210c9511..ea9b79726c09 100644
--- a/baselines/fedper/fedper/implemented_models/mobile_model.py
+++ b/baselines/fedper/fedper/implemented_models/mobile_model.py
@@ -1,4 +1,5 @@
 """MobileNet-v1 model, model manager and model split."""
+
 from typing import Dict, List, Optional, Tuple, Union
 
 import torch
diff --git a/baselines/fedper/fedper/implemented_models/resnet_model.py b/baselines/fedper/fedper/implemented_models/resnet_model.py
index 0d9837b118a3..fc742885fab6 100644
--- a/baselines/fedper/fedper/implemented_models/resnet_model.py
+++ b/baselines/fedper/fedper/implemented_models/resnet_model.py
@@ -1,4 +1,5 @@
 """ResNet model, model manager and split."""
+
 from typing import Dict, List, Optional, Tuple, Union
 
 import torch
diff --git a/baselines/fedper/fedper/models.py b/baselines/fedper/fedper/models.py
index 2a2ebde158f8..be2bda9ae88c 100644
--- a/baselines/fedper/fedper/models.py
+++ b/baselines/fedper/fedper/models.py
@@ -1,4 +1,5 @@
 """Abstract class for splitting a model into body and head."""
+
 from abc import ABC, abstractmethod
 from collections import OrderedDict
 from typing import Any, Dict, List, Tuple, Type, Union
diff --git a/baselines/fedper/fedper/server.py b/baselines/fedper/fedper/server.py
index 93616f50f45a..50a4f8c5d8a8 100644
--- a/baselines/fedper/fedper/server.py
+++ b/baselines/fedper/fedper/server.py
@@ -1,4 +1,5 @@
 """Server strategies pipelines for FedPer."""
+
 from flwr.server.strategy.fedavg import FedAvg
 
 from fedper.strategy import (
diff --git a/baselines/fedper/fedper/strategy.py b/baselines/fedper/fedper/strategy.py
index 5ae55086db2f..527397cf1455 100644
--- a/baselines/fedper/fedper/strategy.py
+++ b/baselines/fedper/fedper/strategy.py
@@ -1,4 +1,5 @@
 """FL server strategies."""
+
 from collections import OrderedDict
 from pathlib import Path
 from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
diff --git a/baselines/fedper/fedper/utils.py b/baselines/fedper/fedper/utils.py
index 00b4c5318729..524b68c2e225 100644
--- a/baselines/fedper/fedper/utils.py
+++ b/baselines/fedper/fedper/utils.py
@@ -1,4 +1,5 @@
 """Utility functions for FedPer."""
+
 import os
 import pickle
 import time
@@ -97,9 +98,9 @@ def get_create_model_fn(
 ]:
     """Get create model function."""
     device = config.server_device
-    split: Union[
-        Type[MobileNetModelSplit], Type[ResNetModelSplit]
-    ] = MobileNetModelSplit
+    split: Union[Type[MobileNetModelSplit], Type[ResNetModelSplit]] = (
+        MobileNetModelSplit
+    )
     if config.model_name.lower() == "mobile":
 
         def create_model() -> Union[Type[MobileNet], Type[ResNet]]:
diff --git a/baselines/fedper/pyproject.toml b/baselines/fedper/pyproject.toml
index efcdf25eface..634ea2898f91 100644
--- a/baselines/fedper/pyproject.toml
+++ b/baselines/fedper/pyproject.toml
@@ -7,11 +7,11 @@ name = "fedper" # <----- Ensure it matches the name of your baseline directory c
 version = "1.0.0"
 description = "Federated Learning with Personalization Layers"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>", "William Lindskog <williamlindskog@protonmail.com>"]
+authors = ["The Flower Authors <hello@flower.ai>", "William Lindskog <williamlindskog@protonmail.com>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -38,19 +38,18 @@ classifiers = [
 
 [tool.poetry.dependencies]
 python = ">=3.10.0, <3.11.0"  # don't change this
-flwr = {extras = ["simulation"], version = "1.5.0" }
+flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 pandas = "^2.0.3"
 matplotlib = "^3.7.2"
 tqdm = "^4.66.1"
-torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl"}
-
+torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
-docformatter = "==1.5.1"
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
@@ -58,6 +57,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
@@ -86,10 +86,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint."TYPECHECK"]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
@@ -140,4 +140,4 @@ exclude = [
 ]
 
 [tool.ruff.pydocstyle]
-convention = "numpy"
\ No newline at end of file
+convention = "numpy"
diff --git a/baselines/fedprox/README.md b/baselines/fedprox/README.md
index a31663746d99..f0abc1451635 100644
--- a/baselines/fedprox/README.md
+++ b/baselines/fedprox/README.md
@@ -91,7 +91,7 @@ To run using FedAvg:
 # This is done so to match the experimental setup in the FedProx paper
 python -m fedprox.main --config-name fedavg
 
-# this config can also be overriden from the CLI
+# this config can also be overridden from the CLI
 ```
 
 ## Expected results
diff --git a/baselines/fedprox/fedprox/client.py b/baselines/fedprox/fedprox/client.py
index e168a9ee24ab..d10a368edae4 100644
--- a/baselines/fedprox/fedprox/client.py
+++ b/baselines/fedprox/fedprox/client.py
@@ -1,6 +1,5 @@
 """Defines the MNIST Flower Client and a function to instantiate it."""
 
-
 from collections import OrderedDict
 from typing import Callable, Dict, List, Tuple
 
diff --git a/baselines/fedprox/fedprox/dataset.py b/baselines/fedprox/fedprox/dataset.py
index e740ef0f67a9..d66aed583623 100644
--- a/baselines/fedprox/fedprox/dataset.py
+++ b/baselines/fedprox/fedprox/dataset.py
@@ -1,6 +1,5 @@
 """MNIST dataset utilities for federated learning."""
 
-
 from typing import Optional, Tuple
 
 import torch
diff --git a/baselines/fedprox/fedprox/dataset_preparation.py b/baselines/fedprox/fedprox/dataset_preparation.py
index 6c44c7411c30..3b9181eaf715 100644
--- a/baselines/fedprox/fedprox/dataset_preparation.py
+++ b/baselines/fedprox/fedprox/dataset_preparation.py
@@ -1,4 +1,5 @@
 """Functions for dataset download and processing."""
+
 from typing import List, Optional, Tuple
 
 import numpy as np
diff --git a/baselines/fedprox/fedprox/models.py b/baselines/fedprox/fedprox/models.py
index 1086f36404aa..b46ce1fdce5a 100644
--- a/baselines/fedprox/fedprox/models.py
+++ b/baselines/fedprox/fedprox/models.py
@@ -1,6 +1,5 @@
 """CNN model architecture, training, and testing functions for MNIST."""
 
-
 from typing import List, Tuple
 
 import torch
diff --git a/baselines/fedprox/fedprox/server.py b/baselines/fedprox/fedprox/server.py
index 3f0f00bea63b..b90b1708d01e 100644
--- a/baselines/fedprox/fedprox/server.py
+++ b/baselines/fedprox/fedprox/server.py
@@ -1,6 +1,5 @@
 """Flower Server."""
 
-
 from collections import OrderedDict
 from typing import Callable, Dict, Optional, Tuple
 
diff --git a/baselines/fedprox/fedprox/strategy.py b/baselines/fedprox/fedprox/strategy.py
index 5086d229da24..bdee4c5e3c3b 100644
--- a/baselines/fedprox/fedprox/strategy.py
+++ b/baselines/fedprox/fedprox/strategy.py
@@ -1,6 +1,5 @@
 """Flower strategy."""
 
-
 from typing import List, Tuple, Union
 
 from flwr.common import Metrics
diff --git a/baselines/fedprox/pyproject.toml b/baselines/fedprox/pyproject.toml
index ee127ac19fe0..1fe9fe4b6cad 100644
--- a/baselines/fedprox/pyproject.toml
+++ b/baselines/fedprox/pyproject.toml
@@ -7,11 +7,11 @@ name = "fedprox"
 version = "1.0.0"
 description = "Implementation of FedProx (Lin et al. 2020)"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -43,13 +43,13 @@ hydra-core = "1.3.2"
 matplotlib = "3.7.1"
 jupyter = "^1.0.0"
 pandas = "^2.0.3"
-torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
-docformatter = "==1.5.1"
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
@@ -57,6 +57,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
@@ -85,10 +86,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/fedstar/.gitignore b/baselines/fedstar/.gitignore
new file mode 100644
index 000000000000..e39a6de1f73d
--- /dev/null
+++ b/baselines/fedstar/.gitignore
@@ -0,0 +1,4 @@
+datasets
+outputs
+data_splits
+multirun
diff --git a/baselines/fedstar/LICENSE b/baselines/fedstar/LICENSE
new file mode 100644
index 000000000000..d64569567334
--- /dev/null
+++ b/baselines/fedstar/LICENSE
@@ -0,0 +1,202 @@
+
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diff --git a/baselines/fedstar/README.md b/baselines/fedstar/README.md
new file mode 100644
index 000000000000..fe782e3e829f
--- /dev/null
+++ b/baselines/fedstar/README.md
@@ -0,0 +1,163 @@
+---
+title: Federated Self-training for Semi-supervised Audio Recognition
+url: https://dl.acm.org/doi/10.1145/3520128
+labels: [Audio Classification, Semi Supervised learning]
+dataset: [Ambient Context, Speech Commands]
+---
+
+# FedStar: Federated Self-training for Semi-supervised Audio Recognition
+
+> Note: If you use this baseline in your work, please remember to cite the original authors of the paper as well as the Flower paper.
+
+**Paper:**  [dl.acm.org/doi/10.1145/3520128](https://dl.acm.org/doi/10.1145/3520128)
+
+**Authors:** Vasileios Tsouvalas, Aaqib Saeed, Tanir Özcelebi
+
+**Abstract:** Federated Learning is a distributed machine learning paradigm dealing with decentralized and personal datasets. Since data reside on devices such as smartphones and virtual assistants, labeling is entrusted to the clients or labels are extracted in an automated way. Specifically, in the case of audio data, acquiring semantic annotations can be prohibitively expensive and time-consuming. As a result, an abundance of audio data remains unlabeled and unexploited on users’ devices. Most existing federated learning approaches focus on supervised learning without harnessing the unlabeled data. In this work, we study the problem of semi-supervised learning of audio models via self-training in conjunction with federated learning. We propose FedSTAR to exploit large-scale on-device unlabeled data to improve the generalization of audio recognition models. We further demonstrate that self-supervised pre-trained models can accelerate the training of on-device models, significantly improving convergence within fewer training rounds. We conduct experiments on diverse public audio classification datasets and investigate the performance of our models under varying percentages of labeled and unlabeled data. Notably, we show that with as little as 3% labeled data available, FedSTAR on average can improve the recognition rate by 13.28% compared to the fully supervised federated model.
+
+
+## About this baseline
+
+**What’s implemented:** The code is structured in such a way that all experiments for ambient context and speech commands can be derived.
+
+**Datasets:** Ambient Context, Speech Commands
+
+**Hardware Setup:** These experiments were run on a linux server with 56 CPU threads with 325 GB Ram with A10 GPU in it. Any machine with 16 CPU cores and 32 GB memory would be able to run experiments with small number of clients in a reasonable amount of time. For context, a machine with 24 cores and a RTX3090Ti ran the Speech Commands experiment in Table 3 with 10 clients in 1h. For this experiment 30GB of RAM was used and clients required ~1.4GB of VRAM each. The same experiment but with the Ambient Context dataset too 13minutes.
+
+**Contributors:** Raj Parekh [GitHub](https://github.com/Raj-Parekh24), [Mail](rajparekhwc@gmail.com)
+
+## Environment Setup
+```bash
+# Set python version
+pyenv local 3.10.6
+# Tell poetry to use python 3.10
+poetry env use 3.10.6
+# Now install the environment
+poetry install
+# Start the shell to activate your environment.
+poetry shell
+```
+
+Next, you'll need to download the datasets. In the case of SpeechCommands, some preprocessing is also required:
+
+```bash
+# Make the shell script executable
+chmod +x setup_datasets.sh
+
+# The below script will download the datasets and create a directory structure require to run this experiment.
+./setup_datasets.sh
+
+# If you want to run the SpeechCommands experiment, pre-process the dataset
+# This will generate a few training example from the _silence_ category
+python -m fedstar.dataset_preparation
+# Please note the above will make following changes:
+#    * Add new files to datasets/speech_commands/Data/Train/_silence_
+#    * Add new entries to data_splits/speech_commands/train_split.txt
+# Therefore the above command should only be run once. If you want to run it again
+# after making modifications to the script, please either revert the changes outlined
+# above or erase the dataset and repeat the download + preprocessing as defined in setup_datasets.sh script.
+```
+
+## Setting up GPU Memory
+
+**Note:** The experiment is designed to run on both GPU and CPU, but runs better on a system with GPU (specially when using the SpeechCommands dataset). If you wish to use GPU, make sure you have installed the [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads). This baseline has been tested with CUDA 12.3. By default, it will run only on the CPU. Please update the value of the list `gpu_total_mem` with the corresponding memory for each GPU in your machine that you want to expose to the experiment. The variable is in the `distribute_gpus` function inside the `clients.py`. Reference is shown below.
+
+```python
+# For Eg:- We have a system with two GPUs with 8GB and 4GB VRAM.
+#          The modified variable will looks like below.
+gpu_free_mem = [8000,4000]
+```
+
+
+
+## Running the Experiments
+
+By default, the `Ambient Context` experiment in Table 3 with 10 clients will be run.
+
+```bash
+python -m fedstar.server
+python -m fedstar.clients
+```
+
+You can change the dataset, number of clients and number of rounds like this:
+
+```bash
+python -m fedstar.server num_clients=5 dataset_name=speech_commands server.rounds=20
+python -m fedstar.clients num_clients=5 dataset_name=speech_commands
+```
+
+To run experiments for Table 4, you should pass a different config file (i.e. that in `fedstar/conf/table4.yaml`). You can do this as follows:
+
+```bash
+# by default will run FedStar with Ambient Context and L=3%
+python -m fedstar.server --config-name table4
+python -m fedstar.clients --config-name table4
+```
+
+To modify the ratio of labelled data do so as follows:
+```bash
+# To use a different L setting
+python -m fedstar.server --config-name table4 L=L5 # {L3, L5, L20, L50}
+# same for fedstar.clients
+```
+
+To run in supervised mode, pass `fedstar=false` to any of the commands above (when launching both the server and clients). Naturally, you can also override any other setting, like `dataset_name` and `num_clients` if desired.
+
+
+## Expected Results
+
+
+This section indicates the commands to execute to obtain the results shown below in Table 3 and Table 4. While both configs fix the number of rounds to 100, in many settings fewer rounds are enough for the model to reach the accuracy shown in the tables. The commands below make use of Hydra's `--multirun` to run multiple experiments. This is better suited when using Flower simulations. Here they work fine but, if you encounter any issues, you can always "unroll" the multirun and run one configuration at a time. If you do this, results won't go into the `multirun/` directory, instead to the default `outputs/` directory.
+
+
+### Table 3
+
+Results will be stored in `multirun/Table3/<dataset_name>/N_<num_clients>/<date>/<time>`. Please note since we are running two Hydra processes, both server and client will generate a log and therefore respective subdirectories in `multirun/`. This is a small compromise of not using Flower simulation. 
+
+```bash
+# For Ambient Context
+python -m fedstar.server --multirun num_clients=5,10,15,30
+python -m fedstar.clients --multirun num_clients=5,10,15,30
+
+# For SpeechCommands
+python -m fedstar.server --multirun num_clients=5,10,15,30 dataset_name=speech_commands
+python -m fedstar.clients --multirun num_clients=5,10,15,30 dataset_name=speech_commands
+```
+
+| Clients | Speech Commands |                | Ambient Context |                |
+|---------|-----------------|----------------|-----------------|----------------|
+|         | Actual          | Implementation | Actual          | Implementation |
+| N=5     | 96.93           | 97.15          | 71.88           | 72.60          |
+| N=10    | 96.78           | 96.42          | 68.01           | 68.43          |
+| N=15    | 96.33           | 96.43          | 66.86           | 66.28          |
+| N=30    | 94.62           | 95.37          | 65.14           | 59.45          |
+
+
+### Table 4
+
+Following the logic presented for obtaining Table 3 results, the larger Table 4 set of results can be obtained by running the `--multirun` commands shown below.
+
+```bash
+# Generate supervised results for Ambient Context (note this will run 4x4=16 experiments)
+python -m fedstar.server --config-name table4 --multirun num_clients=5,10,15,30 L=L3,L5,L20,L50 fedstar=false
+python -m fedstar.clients --config-name table4 --multirun num_clients=5,10,15,30 L=L3,L5,L20,L50 fedstar=false
+
+# Generate supervised results for Speech Commands (note this will run 4x4=16 experiments)
+python -m fedstar.server --config-name table4 --multirun num_clients=5,10,15,30 L=L3,L5,L20,L50 dataset_name=speech_commands fedstar=false
+python -m fedstar.clients --config-name table4 --multirun num_clients=5,10,15,30 L=L3,L5,L20,L50 dataset_name=speech_commands fedstar=false
+
+# Generate FedStar results for Ambient Context
+python -m fedstar.server --config-name table4 --multirun num_clients=5,10,15,30 L=L3,L5,L20,L50
+python -m fedstar.clients --config-name table4 --multirun num_clients=5,10,15,30 L=L3,L5,L20,L50
+
+# Generate FedStar results for Speech Commands
+python -m fedstar.server --config-name table4 --multirun num_clients=5,10,15,30 L=L3,L5,L20,L50 dataset_name=speech_commands
+python -m fedstar.clients --config-name table4 --multirun num_clients=5,10,15,30 L=L3,L5,L20,L50 dataset_name=speech_commands
+```
+
+
+
+|Dataset|Clients|SupervisedFederatedLearning<br>L=3&#8195;&#8195;L=5&#8195;&#8195;L=20&#8195;&#8195;L=50&#8195;&#8195;L=100|FedStar<br>L=3&#8195;&#8195;L=5&#8195;&#8195;L=20&#8195;&#8195;L=50|
+|:--:|:--:|:---:|:---:|
+Ambient<br></p>Context|5<br>10<br>15<br>30|43.75&#8195;&#8195;45.17&#8195;&#8195;63.40&#8195;&#8195;67.57&#8195;75.27<br>41.75&#8195;&#8195;44.90&#8195;&#8195;56.23&#8195;&#8195;60.82&#8195;70.39<br>43.18&#8195;&#8195;42.75&#8195;&#8195;49.60&#8195;&#8195;59.47&#8195;68.41<br>36.44&#8195;&#8195;38.73&#8195;&#8195;48.91&#8195;&#8195;55.83&#8195;67.23|49.60&#8195;&#8195;52.78&#8195;&#8195;66.12&#8195;&#8195;66.71<br>47.84&#8195;&#8195;52.48&#8195;&#8195;61.98&#8195;&#8195;63.46<br>49.05&#8195;&#8195;56.05&#8195;&#8195;64.25&#8195;&#8195;64.05<br>47.34&#8195;&#8195;46.51&#8195;&#8195;60.45&#8195;&#8195;55.47|
+Speech<br></p>Commands|5<br>10<br>15<br>30|80.83&#8195;&#8195;87.97&#8195;&#8195;92.35&#8195;&#8195;94.66&#8195;95.87<br>54.66&#8195;&#8195;78.90&#8195;&#8195;92.13&#8195;&#8195;93.71&#8195;96.55<br>53.44&#8195;&#8195;64.53&#8195;&#8195;91.83&#8195;&#8195;94.23&#8195;96.35<br>36.41&#8195;&#8195;42.87&#8195;&#8195;83.68&#8195;&#8195;93.18&#8195;94.24|87.39&#8195;&#8195;90.11&#8195;&#8195;94.09&#8195;&#8195;94.85<br>87.15&#8195;&#8195;91.06&#8195;&#8195;94.74&#8195;&#8195;96.15<br>86.78&#8195;&#8195;91.01&#8195;&#8195;95.21&#8195;&#8195;95.70<br>80.89&#8195;&#8195;85.62&#8195;&#8195;94.08&#8195;&#8195;94.18|
diff --git a/baselines/fedstar/data_splits.tar b/baselines/fedstar/data_splits.tar
new file mode 100644
index 000000000000..8c155a5b021f
Binary files /dev/null and b/baselines/fedstar/data_splits.tar differ
diff --git a/baselines/fedstar/fedstar/.gitattributes b/baselines/fedstar/fedstar/.gitattributes
new file mode 100644
index 000000000000..dc87e624e315
--- /dev/null
+++ b/baselines/fedstar/fedstar/.gitattributes
@@ -0,0 +1 @@
+setup_project.sh text eol=lf
\ No newline at end of file
diff --git a/baselines/fedstar/fedstar/.gitignore b/baselines/fedstar/fedstar/.gitignore
new file mode 100644
index 000000000000..a844c7eb581e
--- /dev/null
+++ b/baselines/fedstar/fedstar/.gitignore
@@ -0,0 +1,3 @@
+datasets
+outputs
+data_splits
\ No newline at end of file
diff --git a/baselines/fedstar/fedstar/__init__.py b/baselines/fedstar/fedstar/__init__.py
new file mode 100644
index 000000000000..02a650fb44fa
--- /dev/null
+++ b/baselines/fedstar/fedstar/__init__.py
@@ -0,0 +1 @@
+"""Template baseline package."""
diff --git a/baselines/fedstar/fedstar/client.py b/baselines/fedstar/fedstar/client.py
new file mode 100644
index 000000000000..cc6a24c6ff9f
--- /dev/null
+++ b/baselines/fedstar/fedstar/client.py
@@ -0,0 +1,155 @@
+"""Required imports for client.py script."""
+
+import gc
+import logging
+import os
+from typing import Dict, List
+
+import flwr
+import tensorflow as tf
+
+from fedstar.dataset import DataBuilder
+from fedstar.models import Network, PslNetwork
+
+os.environ["GRPC_VERBOSITY"] = "ERROR"
+
+LOG_LEVEL = logging.ERROR
+
+
+class AudioClient(
+    flwr.client.NumPyClient
+):  # pylint: disable=too-many-instance-attributes
+    """AudioClient class act as a client for FedStar implementation.
+
+    It extends NumpyClient feature because flower internally requires a NumpyClient.
+    """
+
+    # pylint: disable=too-many-arguments, too-many-locals, unexpected-keyword-arg
+    def __init__(
+        self,
+        client_id,
+        num_clients,
+        dataset_dir,
+        fedstar,
+        parent_path,
+        variance=0.25,
+        batch_size=64,
+        learning_rate=0.001,
+        l_per=0.2,
+        u_per=1.0,
+        class_distribute: bool = False,
+        balance_dataset: bool = False,
+        mean_class_distribution=3,
+        seed=2021,
+        verbose=2,
+    ):
+        # Client Parameters
+        self.client_id = client_id
+        self.batch_size = batch_size
+        self.verbose = verbose
+        self.fedstar = fedstar
+        self.aux_loss_weight = 0.5
+        self.parent_path = parent_path
+        # Load Clients Data
+        (
+            self.train_labelled,
+            self.train_unlabelled,
+            self.num_classes,
+            self.num_batches,
+        ) = DataBuilder.load_sharded_dataset(
+            parent_path=self.parent_path,
+            data_dir=dataset_dir,
+            num_clients=num_clients,
+            client=client_id,
+            variance=variance,
+            batch_size=batch_size,
+            l_per=l_per,
+            u_per=u_per,
+            fedstar=fedstar,
+            class_distribute=class_distribute,
+            mean_class_distribution=mean_class_distribution,
+            balance_dataset=balance_dataset,
+            seed=seed,
+        )
+        self.num_examples_train = (
+            self.num_batches * batch_size if self.train_labelled else 0
+        )
+        # Local Variables Initialize
+        self.local_train_round = 0
+        self.local_evaluate_round = 0
+        self.weights = Network.get_init_weights(num_classes=self.num_classes)
+        self.history: Dict[str, List[float]] = {"loss": [], "accuracy": []}
+
+        if self.fedstar:
+            self.model = PslNetwork(
+                num_classes=self.num_classes, aux_loss_weight=self.aux_loss_weight
+            )
+            self.model.compile(optimizer=tf.keras.optimizers.Adam(float(learning_rate)))
+        else:
+            self.model = Network(num_classes=self.num_classes).get_network()
+            self.model.compile(
+                optimizer=tf.keras.optimizers.Adam(float(learning_rate)),
+                loss=tf.keras.losses.SparseCategoricalCrossentropy(
+                    from_logits=True, name="loss"
+                ),
+                metrics=[tf.keras.metrics.SparseCategoricalAccuracy(name="accuracy")],
+            )  # type: ignore
+
+        # tf.keras.backend.clear_session()
+
+    def get_parameters(self, config):
+        """Return the current client model weights."""
+        return self.weights
+
+    # pylint: disable=no-value-for-parameter
+    def fit(self, parameters, config):
+        """Update the current client model weights."""
+        self.local_train_round += 1
+        # Run Training Proccess
+        if self.fedstar:
+            self.model.set_weights(parameters)
+            history = self.model.fit(
+                (self.train_labelled, self.train_unlabelled),
+                num_batches=self.num_batches,
+                epochs=int(config["epochs"]),
+                c_round=int(config["c_round"]),
+                rounds=int(config["rounds"]),
+                verbose=self.verbose,
+            )
+        else:
+            self.model.set_weights(parameters)
+            history = self.model.fit(
+                self.train_labelled,
+                batch_size=int(config["batch_size"]),
+                epochs=int(config["epochs"]),
+                verbose=self.verbose,
+            )
+        # Print Results
+        print(
+            f"""Client {self.client_id}
+            finished {self.local_train_round}th round of training with
+            loss {history.history['loss'][0]:.4f} and
+            accuracy {history.history['accuracy'][0]:.4f}"""
+        )
+        # Clear Memory
+        # tf.keras.backend.clear_session()
+        gc.collect()
+        return (
+            self.model.get_weights(),
+            self.num_examples_train,
+            {"local_train_round": self.local_train_round},
+        )
+
+    def evaluate(self, parameters, config):
+        """Essential function for Numpy Client but not required for FedStar."""
+        raise NotImplementedError("Client Side evaluation not required.")
+
+
+def set_logger_level():
+    """Set the logging level for the 'flower' logger."""
+    if "flower" in [
+        repr(logging.getLogger(name))[8:].split(" ")[0]
+        for name in logging.Logger.manager.loggerDict
+    ]:
+        logger = logging.getLogger("flower")
+        logger.setLevel(LOG_LEVEL)
diff --git a/baselines/fedstar/fedstar/clients.py b/baselines/fedstar/fedstar/clients.py
new file mode 100644
index 000000000000..ffcd68a6d5c4
--- /dev/null
+++ b/baselines/fedstar/fedstar/clients.py
@@ -0,0 +1,239 @@
+"""Required imports for clients.py script."""
+
+import multiprocessing
+import os
+import sys
+from multiprocessing import Process
+
+import flwr as fl
+import hydra
+import tensorflow as tf
+from omegaconf import OmegaConf
+
+from fedstar.client import AudioClient, set_logger_level
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+os.environ["GRPC_VERBOSITY"] = "ERROR"
+
+multiprocessing.set_start_method("spawn", force=True)
+
+parent_path = os.getcwd()
+
+
+class Client(Process):
+    """Client class acts as handler for client class.
+
+    It manages the client creation and also manages resource allocation
+    """
+
+    def __init__(self, queue):
+        Process.__init__(self)
+        self.queue = queue
+
+    def run(self):
+        """Run client processes from a configuration queue.
+
+        Continuously retrieves configurations from a queue and initiates an AudioClient
+        for each. Configures GPU settings, creates and starts the client. The loop ends
+        when a `None` configuration is encountered, indicating no more tasks.
+
+        Each client is started with settings from the config, including client ID,
+        server address, dataset directory, and other parameters.
+        The method marks each task as done upon client completion.
+
+        Assumes the existence of `queue`, `AudioClient`, and `Client.setup_gpu`.
+        Designed for threading or multiprocessing.
+        """
+        while True:
+            cfg = self.queue.get()
+            if cfg is None:
+                self.queue.task_done()
+                break
+            # Configure GPU
+            Client.setup_gpu(
+                gpu=cfg["gpu_id"],
+                gpu_memory=cfg["gpu_memory"],
+                client_id=cfg["client_id"],
+            )
+            process_path = (os.sep).join(parent_path.split(os.sep)) + os.sep
+            # Create Client
+            client = AudioClient(
+                client_id=cfg["client_id"],
+                num_clients=cfg["num_clients"],
+                dataset_dir=cfg["dataset_dir"],
+                parent_path=process_path,
+                l_per=cfg["l_per"],
+                u_per=cfg["u_per"],
+                batch_size=cfg["batch_size"],
+                learning_rate=cfg["learning_rate"],
+                verbose=cfg["verbose"],
+                seed=cfg["seed"],
+                fedstar=cfg["fedstar"],
+                class_distribute=cfg["class_distribute"],
+                balance_dataset=cfg["balance_dataset"],
+            )
+            # Start Client
+            try:
+                if bool(cfg["verbose"]):
+                    print(
+                        f"""This is client {client.client_id}
+                        with train dataset of {client.num_examples_train}
+                        elements."""
+                    )
+                set_logger_level()
+                fl.client.start_numpy_client(
+                    server_address=cfg["server_address"], client=client
+                )
+                print("Client Shutdown.")
+            except RuntimeError as runtime_error:
+                print(runtime_error)
+            # Return job done once client is terminated.
+            self.queue.task_done()
+
+    @staticmethod
+    def setup_gpu(gpu, gpu_memory, client_id):
+        """Configure GPU settings for TensorFlow.
+
+        Sets the environment variable for CUDA_VISIBLE_DEVICES based on the provided
+        GPU identifier. If the GPU is specified and available, it configures TensorFlow
+        to limit the GPU memory usage to the specified amount. If no GPUs are available,
+        or if 'gpu' is None, TensorFlow will default to CPU usage.
+
+        Parameters
+        ----------
+        - gpu (str): Identifier for the GPU to be used. Set to None for CPU usage.
+        - gpu_memory (int): The maximum amount of memory (in MB) to be
+                            allocated to the GPU.
+
+        Note:
+        - This method is specific to TensorFlow's handling of GPU resources.
+        - It should be called before initializing any TensorFlow models or operations.
+        """
+        os.environ["CUDA_VISIBLE_DEVICES"] = gpu if gpu is not None else ""
+
+        gpus = tf.config.list_physical_devices("GPU")
+        if not gpus:
+            print(f"No GPU's available. Client {client_id} will run on CPU.")
+        else:
+            try:
+                tf.config.experimental.set_virtual_device_configuration(
+                    gpus[0],
+                    [
+                        tf.config.experimental.VirtualDeviceConfiguration(
+                            memory_limit=gpu_memory
+                        )
+                    ],
+                )
+            except RuntimeError as runtime_error:
+                print(runtime_error)
+
+
+# pylint: disable=unsubscriptable-object
+def distribute_gpus(num_clients, client_memory=1024):
+    """Distribute GPU resources among multiple clients.
+
+    Parameters
+    ----------
+    - num_clients (int): Number of clients to distribute GPUs among.
+    - client_memory (int): The amount of memory (in MB) to allocate per client.
+
+    Returns
+    -------
+    - List of GPU IDs or None for each client, based on memory availability.
+    """
+    gpus = tf.config.experimental.list_physical_devices("GPU")
+    if not gpus:
+        # Scenario 1: No GPUs available, return None for all clients
+        return [None] * num_clients
+
+    # Manually specify the memory you want to expose in each GPU
+    gpu_total_mem = [4000]  # Adjust based on your GPUs
+
+    if gpus and len(gpu_total_mem) == 0:
+        print("If you want to enable GPU training, please set `gpu_total_mem`")
+
+    gpu_free_mem = gpu_total_mem.copy()
+    clients_gpu = [None] * num_clients
+
+    for client_id in range(num_clients):
+        suitable_gpus = [
+            i for i, mem in enumerate(gpu_free_mem) if mem >= client_memory
+        ]
+        if suitable_gpus:
+            # Scenario 2: Allocate GPU if memory is available
+            chosen_gpu = min(
+                suitable_gpus, key=lambda i: gpu_total_mem[i] - gpu_free_mem[i]
+            )
+            gpu_free_mem[chosen_gpu] -= client_memory
+            gpu_id = gpus[chosen_gpu].name.split(":")[-1]
+            clients_gpu[client_id] = gpu_id
+        else:
+            # Scenario 3: No sufficient memory in GPUs, allocate CPU (None)
+            clients_gpu[client_id] = None
+    print("*" * 50)
+    print(clients_gpu)
+    return clients_gpu
+
+
+# pylint: disable=expression-not-assigned
+@hydra.main(config_path="conf", config_name="table3", version_base=None)
+def main(cfg):
+    """Initialize and run a multi-processing client setup using Hydra configuration.
+
+    Loads the configuration using Hydra, distributes GPUs among clients based on
+    the configuration, and sets up data for each client. The function then creates
+    and starts a pool of `Client` objects, each in its own process, and feeds them
+    client-specific configurations. It waits for all clients to complete their tasks.
+
+    Parameters
+    ----------
+    - cfg: The configuration object provided by Hydra, containing settings for
+           the number of clients, server address, dataset information, GPU memory,
+           and other client-related configurations.
+
+    The configuration (`cfg`) is expected to follow a specific structure as defined
+    in the Hydra configuration files under 'conf/table3'.
+    """
+    print(OmegaConf.to_yaml(cfg))
+
+    clients_gpu = distribute_gpus(
+        num_clients=cfg.client.num_clients,
+        client_memory=cfg.client.gpu_memory,
+    )
+    dataset_name = cfg.client.dataset_name
+
+    # Load Configurations of Clients
+    clients_data = [
+        {
+            "client_id": i,
+            "num_clients": cfg.client.num_clients,
+            "server_address": cfg.client.server_address,
+            "dataset_dir": dataset_name,
+            "batch_size": cfg.client.batch_size,
+            "learning_rate": cfg.client.learning_rate,
+            "gpu_id": clients_gpu[i],
+            "gpu_memory": cfg.client.gpu_memory,
+            "seed": cfg.client.seed,
+            "l_per": cfg.client.l_per,
+            "u_per": cfg.client.u_per,
+            "fedstar": cfg.client.fedstar,
+            "class_distribute": cfg.client.class_distribute,
+            "balance_dataset": cfg.dataset_name == "speech_commands",
+            "verbose": cfg.client.verbose,
+        }
+        for i in range(cfg.client.num_clients)
+    ]
+
+    # Start Multi-processing Clients and wait for them to finish
+    clients_queue = multiprocessing.JoinableQueue()
+    clients = [Client(clients_queue) for i in range(cfg.client.num_clients)]
+    [client.start() for client in clients]
+    [clients_queue.put(client) for client in clients_data]
+    [clients_queue.put(None) for i in range(cfg.client.num_clients)]
+    clients_queue.join()
+
+
+if __name__ == "__main__":
+    multiprocessing.freeze_support()
+    main()
+    sys.exit(0)
diff --git a/baselines/fedstar/fedstar/conf/L/L20.yaml b/baselines/fedstar/fedstar/conf/L/L20.yaml
new file mode 100644
index 000000000000..9eca9821d444
--- /dev/null
+++ b/baselines/fedstar/fedstar/conf/L/L20.yaml
@@ -0,0 +1,3 @@
+---
+l_per: 0.2
+u_per: 0.8
\ No newline at end of file
diff --git a/baselines/fedstar/fedstar/conf/L/L3.yaml b/baselines/fedstar/fedstar/conf/L/L3.yaml
new file mode 100644
index 000000000000..76c0be488f71
--- /dev/null
+++ b/baselines/fedstar/fedstar/conf/L/L3.yaml
@@ -0,0 +1,3 @@
+---
+l_per: 0.03
+u_per: 0.97
\ No newline at end of file
diff --git a/baselines/fedstar/fedstar/conf/L/L5.yaml b/baselines/fedstar/fedstar/conf/L/L5.yaml
new file mode 100644
index 000000000000..3ea87ab02553
--- /dev/null
+++ b/baselines/fedstar/fedstar/conf/L/L5.yaml
@@ -0,0 +1,3 @@
+---
+l_per: 0.05
+u_per: 0.95
\ No newline at end of file
diff --git a/baselines/fedstar/fedstar/conf/L/L50.yaml b/baselines/fedstar/fedstar/conf/L/L50.yaml
new file mode 100644
index 000000000000..dd61476bb1ca
--- /dev/null
+++ b/baselines/fedstar/fedstar/conf/L/L50.yaml
@@ -0,0 +1,3 @@
+---
+l_per: 0.5
+u_per: 0.5
\ No newline at end of file
diff --git a/baselines/fedstar/fedstar/conf/table3.yaml b/baselines/fedstar/fedstar/conf/table3.yaml
new file mode 100644
index 000000000000..198e91e7d6a8
--- /dev/null
+++ b/baselines/fedstar/fedstar/conf/table3.yaml
@@ -0,0 +1,39 @@
+---
+# this is the config that will be loaded as default by main.py
+# Please follow the provided structure (this will ensuring all baseline follow
+# a similar configuration structure and hence be easy to customise)
+
+dataset_name: ambient_context # or speech_commands
+num_clients: 10
+
+server:
+  server_address: "localhost:8080"
+  rounds: 100
+  num_clients: ${num_clients}
+  fraction_fit: 0.8
+  gpu_memory: 2048
+  eval_step: 1
+  train_epochs: 1
+  batch_size: 64
+  dataset_name: ${dataset_name}
+  seed: 2021
+  verbose: 1
+
+client:
+  server_address: "localhost:8080"
+  num_clients: ${num_clients}
+  gpu_memory: 1024
+  dataset_name: ${dataset_name}
+  seed: 2021
+  batch_size: 64
+  learning_rate: 0.001
+  l_per: 1.00
+  u_per: 0.00
+  fedstar: false
+  class_distribute: false
+  verbose: 0
+
+hydra:
+  sweep:
+    dir: multirun
+    subdir: Table3/${dataset_name}/N_${num_clients}/${now:%Y-%m-%d}/${now:%H-%M-%S}
\ No newline at end of file
diff --git a/baselines/fedstar/fedstar/conf/table4.yaml b/baselines/fedstar/fedstar/conf/table4.yaml
new file mode 100644
index 000000000000..05f87666debb
--- /dev/null
+++ b/baselines/fedstar/fedstar/conf/table4.yaml
@@ -0,0 +1,40 @@
+
+dataset_name: ambient_context # or speech_commands
+num_clients: 5 # this also fixes min_sample_size (both values are the same always)
+fedstar: true # set false for supervised
+
+defaults:
+  - _self_
+  - L: L3
+
+server:
+  server_address: "localhost:8080"
+  rounds: 100
+  num_clients: ${num_clients}
+  fraction_fit: 0.8
+  gpu_memory: 2048
+  eval_step: 1
+  train_epochs: 1
+  batch_size: 64
+  dataset_name: ${dataset_name}
+  seed: 2021
+  verbose: 1
+
+client:
+  server_address: "localhost:8080"
+  num_clients: ${num_clients}
+  gpu_memory: 1024
+  dataset_name: ${dataset_name}
+  seed: 2021
+  batch_size: 64
+  learning_rate: 0.001
+  l_per: ${L.l_per}
+  u_per: ${L.u_per}
+  fedstar: ${fedstar}
+  class_distribute: false
+  verbose: 0
+
+hydra:
+  sweep:
+    dir: multirun
+    subdir: Table4/${dataset_name}/fedstar_${fedstar}/N_${num_clients}/L_${L}/${now:%Y-%m-%d}/${now:%H-%M-%S}
\ No newline at end of file
diff --git a/baselines/fedstar/fedstar/dataset.py b/baselines/fedstar/fedstar/dataset.py
new file mode 100644
index 000000000000..929507a19b0c
--- /dev/null
+++ b/baselines/fedstar/fedstar/dataset.py
@@ -0,0 +1,507 @@
+"""Required imports for data.py script."""
+
+import os
+
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+
+from fedstar.utils import AudioTools, DataTools
+
+
+def ambient_context_path_extracter(path):
+    """Extract and construct a file path for ambient context from a given path string.
+
+    This function takes a path string, splits it at underscores,
+    removes the last segment, and then reconstructs the path by
+    re-joining the segments and appending the original
+    path at the end, separated by the OS-specific directory separator.
+
+    Parameters
+    ----------
+    - path (str): The original path string to be processed.
+
+    Returns
+    -------
+    - str: The reconstructed file path suitable for ambient context usage.
+
+    Example:
+    Given a path string 'dir_subdir_file_xyz', the function will return
+    'dir_subdir/dir_subdir_file_xyz'.
+    """
+    arr = path.split("_")[:-1]
+    file_path = "_".join(arr) + os.sep + path
+    return file_path
+
+
+class DataBuilder:
+    """A utility class for building and processing datasets for audio analysis.
+
+    This class provides methods to load, split, and prepare datasets for training
+    and testing in audio-related machine learning tasks. It supports operations like
+    loading data from files, splitting datasets into labelled and unlabelled sets,
+    balancing datasets, and converting them into TensorFlow dataset objects.
+
+    Class Attributes:
+    - AUTOTUNE: TensorFlow's setting for optimized parallel data loading.
+    - WORDS: List of specific words used in the audio processing context.
+
+    Methods are designed to work with specific data structures and assume a certain
+    directory and file naming convention.
+    """
+
+    AUTOTUNE = tf.data.experimental.AUTOTUNE
+    WORDS = [
+        "down",
+        "go",
+        "left",
+        "no",
+        "off",
+        "on",
+        "right",
+        "stop",
+        "up",
+        "yes",
+        "_silence_",
+    ]
+
+    # pylint: disable=too-many-arguments, too-many-locals, too-many-statements
+    @staticmethod
+    def get_files(parent_path, data_dir, train=False, raw=False):
+        """Load audio file paths and labels from specified directories.
+
+        Reads training or testing data file paths and their corresponding labels from
+        given directories. Supports different dataset types like 'speech_commands' and
+        'ambient_context'. Can return raw file paths and labels or a TensorFlow dataset.
+
+        Parameters
+        ----------
+        - parent_path (str): Base path for the dataset directories.
+        - data_dir (str): Specific directory name containing the dataset.
+        - train (bool): If True, load training data; otherwise, load testing data.
+        - raw (bool): If True, return raw file paths and labels; otherwise, return a
+        TensorFlow dataset.
+
+        Returns
+        -------
+        - A TensorFlow dataset or a tuple of file paths and labels,
+        along with the number of classes in the dataset.
+        """
+        path = os.path.join(parent_path, "data_splits", data_dir)
+        train_path = os.path.join(path, "train_split.txt")
+        test_path = os.path.join(path, "test_split.txt")
+        path_data_dir = os.path.join(parent_path, "datasets", data_dir)
+        if train:
+            train_files_path, train_labels = [], []
+            if data_dir == "speech_commands":
+                print("Dataset is speech_commands")
+                train_user_files = pd.read_csv(train_path, header=None).values.flatten()
+                for tr_uf in train_user_files:
+                    path_tr_uf = os.path.join(*tr_uf.split("/"))
+                    train_files_path.append(
+                        os.path.join(path_data_dir, "Data", "Train", path_tr_uf)
+                    )
+                    label = tr_uf.split("/")[0]
+                    if label in DataBuilder.WORDS:
+                        train_labels.append(tr_uf.split("/")[0])
+                    else:
+                        train_labels.append("_unknown_")
+            elif data_dir == "ambient_context":
+                print("Dataset is ambient_context")
+                train_user_files = pd.read_csv(train_path, sep="\t", header=None).values
+                for path, label in train_user_files:
+                    file_path = ambient_context_path_extracter(path)
+                    train_files_path.append(
+                        os.path.join(path_data_dir, "Data", file_path)
+                    )
+                    train_labels.append(label)
+            # One-hot labels transformation
+            train_labels = np.array(train_labels, dtype=object)
+            # Map labels to 0-11
+            unique_labels = np.unique(train_labels)
+            num_classes = len(unique_labels)
+            labels_dict = {unique_labels[i]: i for i in range(len(unique_labels))}
+            train_labels = [
+                labels_dict[train_labels[i]] for i in range(len(train_labels))
+            ]
+            # Change from object type to int
+            train_labels = np.array(train_labels, dtype=np.int64)
+            # Convert to dataset (if necessary)
+            dataset = (
+                (train_files_path, train_labels)
+                if raw
+                else tf.data.Dataset.from_tensor_slices(
+                    (train_files_path, train_labels)
+                ).map(AudioTools.read_audio, num_parallel_calls=DataBuilder.AUTOTUNE)
+            )
+        else:
+            # Read files from txt file.
+            test_files_path, test_labels = [], []
+            if data_dir == "speech_commands":
+                print("Dataset is speech_commands")
+                test_user_files = pd.read_csv(test_path, header=None).values.flatten()
+                for ts_uf in test_user_files:
+                    path_ts_uf = os.path.join(*ts_uf.split("/"))
+                    test_files_path.append(
+                        os.path.join(path_data_dir, "Data", "Test", path_ts_uf)
+                    )
+                    test_labels.append(ts_uf.split("/")[0])
+            elif data_dir == "ambient_context":
+                print("Dataset is ambient_context")
+                test_user_files = pd.read_csv(test_path, sep="\t", header=None).values
+                for path, label in test_user_files:
+                    file_path = ambient_context_path_extracter(path)
+                    test_files_path.append(
+                        os.path.join(path_data_dir, "Data", file_path)
+                    )
+                    test_labels.append(label)
+            # One-hot labels transformation
+            test_labels = np.array(test_labels, dtype=object)
+            # Map labels to 0-12
+            unique_labels = np.unique(test_labels)
+            num_classes = len(unique_labels)
+            labels_dict = {unique_labels[i]: i for i in range(len(unique_labels))}
+            test_labels = [labels_dict[test_labels[i]] for i in range(len(test_labels))]
+            # Change from object type to int
+            test_labels = np.array(test_labels, dtype=np.int64)
+            # Convert to dataset (if necessary)
+            dataset = (
+                (test_files_path, test_labels)
+                if raw
+                else tf.data.Dataset.from_tensor_slices(
+                    (test_files_path, test_labels)
+                ).map(AudioTools.read_audio, num_parallel_calls=DataBuilder.AUTOTUNE)
+            )
+        return dataset, num_classes
+
+    # pylint: disable=too-many-arguments, too-many-locals
+    @staticmethod
+    def split_dataset(
+        parent_path,
+        data_dir,
+        num_clients,
+        client,
+        batch_size=64,
+        variance=0.25,
+        l_per=0.1,
+        u_per=1.0,
+        mean_class_distribution=3,
+        class_distribute=False,
+        fedstar=False,
+        seed=2021,
+    ):
+        """Split dataset into labelled and unlabelled sets for federated learning.
+
+        This method divides the dataset into portions suitable for training by multiple
+        clients in a federated learning context. It handles both labelled and unlabelled
+        data, and supports class-based and sample-based distribution strategies.
+
+        Parameters
+        ----------
+        - parent_path (str): Base path for the dataset directories.
+        - data_dir (str): Specific directory name containing the dataset.
+        - num_clients (int): Total number of clients in the federated learning setup.
+        - client (int): Identifier for the current client.
+        - batch_size (int): Size of the batches for training.
+        - variance (float): Variance allowed in the distribution of samples/classes.
+        - l_per (float): Percentage of labelled data to be used.
+        - u_per (float): Percentage of unlabelled data to be used.
+        - mean_class_distribution (int): Average number of classes per client.
+        - class_distribute (bool): If True, distribute data based on classes.
+        - fedstar (bool): If True, apply federated star learning setup.
+        - seed (int): Random seed for reproducibility.
+
+        Returns
+        -------
+        - Labelled and unlabelled TensorFlow datasets, number of classes, and number
+        of batches for the specified client.
+        """
+        # Load data
+        ds_train, num_classes = DataBuilder.get_files(
+            parent_path=parent_path, data_dir=data_dir, train=True, raw=True
+        )
+        (ds_train_labelled, labelled_size), (
+            ds_train_un_labelled,
+            unlabelled_size,
+        ) = DataTools.get_subset(
+            dataset=ds_train,
+            percentage=l_per,
+            u_per=u_per,
+            num_classes=num_classes,
+            seed=seed,
+        )
+        ds_train_un_labelled = (
+            DataTools.convert_to_unlabelled(
+                dataset=ds_train_un_labelled, unlabelled_data_identifier=-1
+            )
+            if fedstar
+            else []
+        )
+        # Split data
+        if not class_distribute:  # Split according to number of samples
+            labelled_sets = list(
+                DataTools.distribute_per_samples(
+                    dataset=ds_train_labelled,
+                    num_clients=num_clients,
+                    variance=variance,
+                    seed=seed,
+                )
+            )
+        else:  # Split according to classes
+            labelled_sets = DataTools.distribute_per_class_with_class_limit(
+                dataset=ds_train_labelled,
+                num_clients=num_clients,
+                num_classes=num_classes,
+                mean_class_distribution=mean_class_distribution,
+                class_variance=variance,
+                seed=seed,
+            )
+        unlabelled_sets = (
+            list(
+                DataTools.distribute_per_samples(
+                    dataset=ds_train_un_labelled,
+                    num_clients=num_clients,
+                    variance=variance,
+                    seed=seed,
+                )
+            )
+            if fedstar
+            else ([], [])
+        )
+        # Convert to tf dataset objects
+        ds_train_labelled = tf.data.Dataset.from_tensor_slices(
+            (labelled_sets[client][0], labelled_sets[client][1])
+        ).map(AudioTools.read_audio, num_parallel_calls=DataBuilder.AUTOTUNE)
+        ds_train_unlabelled = (
+            tf.data.Dataset.from_tensor_slices(
+                (unlabelled_sets[client][0], unlabelled_sets[client][1])
+            ).map(AudioTools.read_audio, num_parallel_calls=DataBuilder.AUTOTUNE)
+            if fedstar
+            else None
+        )
+        # Calculate datasets info for training
+        labelled_size, unlabelled_size = (
+            len(labelled_sets[client][0]),
+            len(unlabelled_sets[client][0]) if fedstar else 0,
+        )
+        num_batches = (
+            (unlabelled_size + batch_size - 1) // batch_size
+            if fedstar
+            else (labelled_size + batch_size - 1) // batch_size
+        )
+        # Print datasets sizes
+        print(
+            f"""Client {client}: Train data {labelled_size+unlabelled_size}
+            (Unlabelled: {unlabelled_size} - Labelled: {labelled_size})"""
+        )
+        return ds_train_labelled, ds_train_unlabelled, num_classes, num_batches
+
+    @staticmethod
+    def get_ds_test(parent_path, data_dir, batch_size, buffer=1024, seed=2021):
+        """Load and prepare the testing dataset.
+
+        Reads testing data from specified paths, converts it into a TensorFlow dataset,
+        and prepares it for model evaluation or testing.
+
+        Parameters
+        ----------
+        - parent_path (str): Base path for the dataset directories.
+        - data_dir (str): Specific directory name containing the testing dataset.
+        - batch_size (int): Size of the batches for testing.
+        - buffer (int): Buffer size for shuffling the dataset.
+        - seed (int): Random seed for reproducibility.
+
+        Returns
+        -------
+        - A TensorFlow dataset for testing and the number of classes in the dataset.
+        """
+        ds_test, num_classes = DataBuilder.get_files(
+            parent_path=parent_path, data_dir=data_dir
+        )
+        _, _, ds_test = DataBuilder.to_dataset(
+            ds_train_labelled=None,
+            ds_train_un_labelled=None,
+            ds_test=ds_test,
+            buffer=buffer,
+            batch_size=batch_size,
+            seed=seed,
+        )
+        return ds_test, num_classes
+
+    # pylint: disable=too-many-arguments, too-many-locals
+    @staticmethod
+    def load_sharded_dataset(
+        parent_path,
+        data_dir,
+        num_clients,
+        client,
+        variance=0.25,
+        batch_size=64,
+        l_per=1.0,
+        u_per=1.0,
+        mean_class_distribution=5,
+        fedstar=False,
+        class_distribute=False,
+        balance_dataset: bool = False,
+        seed=2021,
+    ):
+        """Load and shard the dataset for federated learning scenarios.
+
+        This method prepares a dataset specifically sharded for a given client in a
+        federated learning setup. It supports configurations for balanced and unbalanced
+        datasets, federated star learning, and class-based distribution.
+
+        Parameters
+        ----------
+        - parent_path (str): Base path for the dataset directories.
+        - data_dir (str): Specific directory name containing the dataset.
+        - num_clients (int): Total number of clients in the federated learning setup.
+        - client (int): Identifier for the current client.
+        - variance (float): Variance allowed in the distribution of samples/classes.
+        - batch_size (int): Size of the batches for training.
+        - l_per (float): Percentage of labelled data to be used.
+        - u_per (float): Percentage of unlabelled data to be used.
+        - mean_class_distribution (int): Average number of classes per client.
+        - fedstar (bool): If True, apply federated star learning setup.
+        - class_distribute (bool): If True, distribute data based on classes.
+        - balance_dataset (bool): If True, balance the dataset for each class.
+        - seed (int): Random seed for reproducibility.
+
+        Returns
+        -------
+        - Labelled and unlabelled TensorFlow datasets, number of classes, and number
+        of batches for the specified client.
+        """
+        batch_size = batch_size // 2 if fedstar else batch_size
+        (
+            ds_train_labelled,
+            ds_train_un_labelled,
+            num_classes,
+            num_batches,
+        ) = DataBuilder.split_dataset(
+            parent_path=parent_path,
+            data_dir=data_dir,
+            client=client,
+            num_clients=num_clients,
+            l_per=l_per,
+            u_per=u_per,
+            fedstar=fedstar,
+            class_distribute=class_distribute,
+            mean_class_distribution=mean_class_distribution,
+            batch_size=batch_size,
+            variance=variance,
+            seed=seed,
+        )
+        ds_train_labelled, ds_train_un_labelled, _ = DataBuilder.to_dataset(
+            ds_train_labelled=ds_train_labelled,
+            ds_train_un_labelled=ds_train_un_labelled,
+            ds_test=None,
+            seed=seed,
+            batch_size=batch_size,
+            balance_dataset=balance_dataset,
+        )
+        return ds_train_labelled, ds_train_un_labelled, num_classes, num_batches
+
+    # pylint: disable=too-many-arguments, too-many-locals
+    @staticmethod
+    def to_dataset(
+        ds_train_labelled,
+        ds_train_un_labelled,
+        ds_test,
+        batch_size,
+        buffer=1024,
+        seed=2021,
+        balance_dataset: bool = False,
+    ):
+        """Convert data into TensorFlow datasets for training and testing.
+
+        Transforms raw data into shuffled and batched TensorFlow datasets. It handles
+        both labelled and unlabelled training data, as well as testing data.
+
+        Parameters
+        ----------
+        - ds_train_labelled: Labelled training data.
+        - ds_train_un_labelled: Unlabelled training data.
+        - ds_test: Testing data.
+        - batch_size (int): Size of the batches for the datasets.
+        - buffer (int): Buffer size for shuffling the dataset.
+        - seed (int): Random seed for shuffling reproducibility.
+        - balance_dataset (bool): If True, balance the training dataset.
+
+        Returns
+        -------
+        - Labelled and unlabelled training TensorFlow datasets and a testing dataset.
+        """
+        ds_train_labelled = (
+            ds_train_labelled.shuffle(
+                buffer_size=buffer, seed=seed, reshuffle_each_iteration=True
+            )
+            .map(AudioTools.prepare_example, num_parallel_calls=DataBuilder.AUTOTUNE)
+            .batch(batch_size=batch_size)
+            # .prefetch(DataBuilder.AUTOTUNE)
+            if ds_train_labelled
+            else None
+        )
+        ds_test = (
+            ds_test.map(
+                AudioTools.prepare_test_example, num_parallel_calls=DataBuilder.AUTOTUNE
+            )
+            .batch(1)
+            .prefetch(DataBuilder.AUTOTUNE)
+            if ds_test
+            else None
+        )
+        ds_train_un_labelled = (
+            ds_train_un_labelled.shuffle(
+                buffer_size=buffer, seed=seed + 1, reshuffle_each_iteration=True
+            )
+            .map(AudioTools.prepare_example, num_parallel_calls=DataBuilder.AUTOTUNE)
+            .batch(batch_size=batch_size)
+            # .prefetch(DataBuilder.AUTOTUNE)
+            if ds_train_un_labelled
+            else None
+        )
+
+        # make balanced
+        if balance_dataset:
+            if ds_train_labelled:
+                ds_train_labelled = balance_sampler(ds_train_labelled, batch_size)
+            # re sampling not required for unlabelled data.
+            # if ds_train_un_labelled:
+            #     ds_train_un_labelled=balance_sampler(ds_train_un_labelled, batch_size)
+        return ds_train_labelled, ds_train_un_labelled, ds_test
+
+
+def balance_sampler(dataset, batch_size):
+    """Address the skew in the speechcommands dataset.
+
+    Here we resample it so we see the same number of instances for all classes. Here we
+    use TF's rejection_resample functionality
+    https://www.tensorflow.org/guide/data#rejection_resampling.
+    """
+
+    # pylint: disable=unused-argument
+    def class_func(features, label):
+        return label
+
+    # Measure initial distribution over labels
+    ddd = np.concatenate([lbl for _, lbl in list(dataset.as_numpy_iterator())])
+    initial_dist = np.bincount(ddd) / len(ddd)
+
+    # target distribution is uniform
+    target_dist = np.ones_like(initial_dist) / len(initial_dist)
+
+    # resample
+    dataset_resampled = (
+        dataset.unbatch()
+        .rejection_resample(
+            class_func, target_dist=target_dist, initial_dist=initial_dist
+        )
+        .batch(batch_size)
+    )
+
+    dataset_balanced = dataset_resampled.map(
+        lambda extra_label, features_and_label: features_and_label
+    )
+
+    return dataset_balanced
diff --git a/baselines/fedstar/fedstar/dataset_preparation.py b/baselines/fedstar/fedstar/dataset_preparation.py
new file mode 100644
index 000000000000..c41abbadbc16
--- /dev/null
+++ b/baselines/fedstar/fedstar/dataset_preparation.py
@@ -0,0 +1,44 @@
+"""Generate 1-second audio clip from background sounds in speechcommands."""
+
+import os
+import random
+
+import scipy.io.wavfile as wav
+from tqdm import tqdm
+
+# Folder path where your audio files are stored
+FOLDER_PATH = "datasets/speech_commands/Data/Train/_silence_"
+# Path of the text file where you want to append the filenames
+TEXT_FILE_PATH = "data_splits/speech_commands/train_split.txt"
+
+
+def split_audio_v2(read_file_path, samples_per_clip=1000):
+    """Split an audio file into a specified number of clips.
+
+    Parameters
+    ----------
+    read_file_path : str
+        The file path of the audio file to be split.
+    samples_per_clip : int, optional
+        The number of one-second clips to extract from the audio file. Defaults to 1000.
+    """
+    sample_rate, data = wav.read(read_file_path)
+
+    # extract `samples_per_clip` 1-second long clips at random
+    for i in tqdm(range(samples_per_clip)):
+        start = random.randint(0, len(data) - sample_rate)
+        segment = data[start : start + sample_rate]
+        new_filename = file_path.replace(".wav", "") + f"_{i:05d}.wav"
+
+        wav.write(new_filename, sample_rate, segment)
+        sound_name = new_filename.split("/")[-1]
+        # Append the new filename to the text file
+        with open(TEXT_FILE_PATH, "a", encoding="utf-8") as write_file:
+            write_file.write(f"\n_silence_/{sound_name}")
+
+
+# Iterate over files in the folder
+for filename in os.listdir(FOLDER_PATH):
+    if filename.endswith(".wav"):  # Check if the file is a WAV file
+        file_path = os.path.join(FOLDER_PATH, filename)
+        split_audio_v2(file_path)
diff --git a/baselines/fedstar/fedstar/main.py b/baselines/fedstar/fedstar/main.py
new file mode 100644
index 000000000000..f5f425272fdb
--- /dev/null
+++ b/baselines/fedstar/fedstar/main.py
@@ -0,0 +1 @@
+"""Run server.py and client.py files separately as directed in the Readme.md."""
diff --git a/baselines/fedstar/fedstar/models.py b/baselines/fedstar/fedstar/models.py
new file mode 100644
index 000000000000..a8f6d0333fea
--- /dev/null
+++ b/baselines/fedstar/fedstar/models.py
@@ -0,0 +1,428 @@
+"""Required imports for data.py script."""
+
+import itertools
+import time
+
+import numpy as np
+import tensorflow as tf
+
+
+class History(tf.keras.callbacks.History):
+    """A custom implementation of the Keras History callback for logging history.
+
+    This class extends tf.keras.callbacks.History to provide custom functionalities for
+    tracking the training process. It captures and stores metrics at the end of each
+    epoch.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self.history = {}
+
+    def on_epoch_end(self, epoch, logs=None):
+        """Handle the end of an epoch during model training.
+
+        Updates the history attribute with information about the completed epoch,
+        including any provided logs such as loss and accuracy metrics.
+
+        Parameters
+        ----------
+        - epoch (int): The index of the recently completed epoch.
+        - logs (dict, optional): A dictionary containing metrics and their values
+        for the completed epoch.
+        """
+        logs = logs or {}
+        self.epoch.append(epoch)
+        for key, value in logs.items():
+            self.history.setdefault(key, []).append(value)
+
+
+class Network:
+    """A network class for building a custom audio classification model.
+
+    This class constructs a convolutional neural network for audio data classification.
+    It includes methods to define convolutional blocks and assemble the full model.
+
+    Attributes
+    ----------
+    - num_classes (int): Number of classes for classification.
+    - dropout_rate (float): Dropout rate for regularization.
+    - l2_rate (float): L2 regularization rate.
+    - input_shape (tuple): Shape of the input data.
+    - sequence_length (int): Length of the input sequences.
+    """
+
+    # pylint: disable=too-many-arguments, too-many-locals
+    def __init__(
+        self,
+        num_classes,
+        dropout_rate=0.1,
+        l2_rate=0.0001,
+        input_shape=(None, 64, 1),
+        sequence_length=16000,
+    ):
+        self._num_classes = num_classes
+        self._dropout_rate = dropout_rate
+        self._l2_rate = l2_rate
+        self._input_shape = input_shape
+        self._sequence_length = sequence_length
+
+    @staticmethod
+    def conv_block(
+        input_shape, num_features, l2_rate, dropout_rate=0.1, add_max_pool=True
+    ):
+        """Define a convolutional block for the neural network.
+
+        Constructs a block with convolutional layers, normalization, activation,
+        and optional pooling and dropout, based on the specified parameters.
+
+        Parameters
+        ----------
+        - input_shape (tuple): Shape of the input to the convolutional block.
+        - num_features (int): Number of filters in the convolutional layers.
+        - l2_rate (float): L2 regularization rate.
+        - dropout_rate (float, optional): Dropout rate for regularization.
+        - add_max_pool (bool, optional): Whether to include max pooling in the block.
+
+        Returns
+        -------
+        - tf.keras.Model: A Keras model representing the convolutional block.
+        """
+        inputs = tf.keras.layers.Input(shape=input_shape)
+        x_t = tf.keras.layers.Conv2D(
+            num_features,
+            (1, 4),
+            padding="same",
+            kernel_regularizer=tf.keras.regularizers.l2(l2_rate),
+        )(inputs)
+        x_t = tf.keras.layers.GroupNormalization(groups=4)(x_t)
+        x_t = tf.keras.layers.Activation("relu")(x_t)
+        x_f = tf.keras.layers.Conv2D(
+            num_features,
+            (4, 1),
+            padding="same",
+            kernel_regularizer=tf.keras.regularizers.l2(l2_rate),
+        )(inputs)
+        x_f = tf.keras.layers.GroupNormalization(groups=4)(x_f)
+        x_f = tf.keras.layers.Activation("relu")(x_f)
+        x = tf.keras.layers.Concatenate(axis=-1)([x_t, x_f])
+        x = tf.keras.layers.Conv2D(
+            num_features,
+            (1, 1),
+            padding="same",
+            kernel_regularizer=tf.keras.regularizers.l2(l2_rate),
+        )(x)
+        x = tf.keras.layers.GroupNormalization(groups=4)(x)
+        x = tf.keras.layers.Activation("relu")(x)
+        if add_max_pool:
+            x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)
+        x = tf.keras.layers.SpatialDropout2D(dropout_rate)(x)
+        return tf.keras.Model(inputs, x)
+
+    def get_network(self):
+        """Assembles and returns the custom audio classification model.
+
+        Constructs the neural network layer by layer using the class attributes and
+        predefined convolutional blocks.
+
+        Returns
+        -------
+        - tf.keras.Model: The assembled TensorFlow Keras model
+          ready for training or inference.
+        """
+        inputs = tf.keras.layers.Input(shape=self._input_shape, name="audio_input")
+        x = __class__.conv_block(
+            self._input_shape, 24, self._l2_rate, dropout_rate=self._dropout_rate
+        )(inputs)
+        x = __class__.conv_block(
+            x.shape[1:], 32, self._l2_rate, dropout_rate=self._dropout_rate
+        )(x)
+        x = __class__.conv_block(
+            x.shape[1:], 64, self._l2_rate, dropout_rate=self._dropout_rate
+        )(x)
+        x = __class__.conv_block(
+            x.shape[1:], 128, self._l2_rate, dropout_rate=self._dropout_rate
+        )(x)
+        x = tf.keras.layers.GlobalMaxPool2D()(x)
+        outputs = tf.keras.layers.Dense(self._num_classes)(x)
+        model = tf.keras.Model(inputs, outputs, name="audio_classifier")
+        return model
+
+    def get_evaluation_network(self, input_shape=(None, None, 64, 1)):
+        """Create an evaluation model for the network.
+
+        Constructs a model for evaluation which applies the primary network to a
+        time-distributed input and computes the mean output.
+
+        Parameters
+        ----------
+        - input_shape (tuple, optional):
+          Shape of the input data for the evaluation model.
+
+        Returns
+        -------
+        - tf.keras.Model: The evaluation model based on the primary network.
+        """
+        model = self.get_network()
+        input_layer = tf.keras.layers.Input(shape=input_shape, name="audio_input")
+        output = tf.reduce_mean(
+            tf.keras.layers.TimeDistributed(model)(input_layer), axis=1
+        )
+        return tf.keras.Model(input_layer, output)
+
+    @staticmethod
+    def get_init_weights(num_classes, input_shape=(None, 64, 1), l2_rate=0.0001):
+        """Generate and return initial weights for the network.
+
+        Constructs a temporary model for the sole purpose of initializing weights,
+        based on the specified number of classes, input shape, and regularization rate.
+
+        Parameters
+        ----------
+        - num_classes (int): Number of classes for the classification task.
+        - input_shape (tuple, optional): Shape of the input data.
+        - l2_rate (float, optional): L2 regularization rate.
+
+        Returns
+        -------
+        - List: A list of numpy arrays representing the initial weights of the model.
+        """
+        inputs = tf.keras.layers.Input(shape=input_shape, name="audio_input")
+        x = Network.conv_block(input_shape, 24, l2_rate)(inputs)
+        x = Network.conv_block(x.shape[1:], 32, l2_rate)(x)
+        x = Network.conv_block(x.shape[1:], 64, l2_rate)(x)
+        x = Network.conv_block(x.shape[1:], 128, l2_rate)(x)
+        x = tf.keras.layers.GlobalMaxPool2D()(x)
+        outputs = tf.keras.layers.Dense(num_classes)(x)
+        model = tf.keras.Model(inputs, outputs, name="audio_classifier")
+        return model.get_weights()
+
+
+class PslNetwork(tf.keras.Model):  # pylint: disable=abstract-method
+    """A TensorFlow Keras model for Pseudo-Label Semi-supervised Learning (PSL).
+
+    This class extends tf.keras.Model to implement a specific architecture and
+    training procedure for semi-supervised learning using pseudo-labeling.
+
+    Attributes
+    ----------
+    - num_classes (int): Number of classes for classification.
+    - aux_loss_weight (float): Weight for the auxiliary loss in training.
+    - dropout_rate (float): Dropout rate for regularization.
+    - model (tf.keras.Model): The underlying audio classification model.
+    - confidence (float): Confidence threshold for pseudo-labeling.
+    """
+
+    def __init__(self, num_classes, aux_loss_weight=0.5, dropout_rate=0.1):
+        super().__init__()
+        self.model = Network(
+            num_classes=num_classes, dropout_rate=dropout_rate
+        ).get_network()
+        self.confidence = 0.5
+        self.aux_loss_weight = aux_loss_weight
+        self.history = History()
+        self.optimizer = None
+        self.loss_fn = None
+
+    # pylint: disable=arguments-differ
+    def compile(self, optimizer):
+        """Configure the model for training.
+
+        Sets the optimizer and loss function for the model.
+
+        Parameters
+        ----------
+        - optimizer: The optimizer to use for training the model.
+        """
+        super().compile()
+        self.optimizer = optimizer
+        self.loss_fn = tf.nn.sparse_softmax_cross_entropy_with_logits
+
+    @staticmethod
+    def cosine_confidence_scheduler(
+        step, total_steps, confidence_min=0.5, confidence_max=0.9
+    ):
+        """Determine the confidence level for pseudo-labeling using a cosine schedule.
+
+        Gradually adjusts the confidence threshold over the course of training based
+        on the cosine function.
+
+        Parameters
+        ----------
+        - step (int): The current training step or round.
+        - total_steps (int): The total number of steps or rounds
+                             in the training process.
+        - confidence_min (float, optional): Minimum confidence threshold.
+        - confidence_max (float, optional): Maximum confidence threshold.
+
+        Returns
+        -------
+        - float: The computed confidence threshold for the current step.
+        """
+        return confidence_max - (confidence_max - confidence_min) * 0.5 * (
+            1 + np.cos(step / total_steps * np.pi)
+        )
+
+    # pylint: disable=too-many-arguments, too-many-locals, arguments-differ
+    def fit(
+        self, train_datasets, epochs, c_round, rounds, num_batches=None, T=1, verbose=0
+    ):
+        """Trains the model on given datasets for a specified number of epochs.
+
+        This custom training loop handles both labeled and unlabeled data, applying
+        pseudo-labeling for semi-supervised learning.
+
+        Parameters
+        ----------
+        - train_datasets (tuple): A tuple containing labeled
+                                  and unlabeled training datasets.
+        - epochs (int): Number of epochs to train the model.
+        - c_round (int): Current round of training in a federated learning scenario.
+        - rounds (int): Total number of rounds in the federated learning scenario.
+        - num_batches (int, optional): Number of batches per epoch.
+        - T (float, optional): Temperature parameter for softmax in pseudo-labeling.
+        - verbose (int, optional): Verbosity mode.
+
+        Returns
+        -------
+        - History: An object containing training history information.
+        """
+        l_dataset, u_dataset = train_datasets
+        self.history.on_train_begin()
+        for epoch in range(epochs):
+            start, epoch_loss = time.time(), 0.0
+            # Threshold value to get good psuedo labels, part of Eq-5
+            self.confidence = __class__.cosine_confidence_scheduler(
+                step=c_round, total_steps=rounds
+            )
+            progbar = tf.keras.utils.Progbar(
+                target=num_batches - 1,
+                width=30,
+                verbose=verbose if verbose != 2 else 0,
+                interval=0.05,
+                stateful_metrics=None,
+                unit_name="step",
+            )
+            if verbose == 1:
+                tf.print(f"Epoch {epoch+1}/{epochs}")
+            step = 0
+            # Run train step on each batch
+            # tf.data.Dataset.repeat
+            # (https://www.tensorflow.org/api_docs/python/tf/data/Dataset#repeat)
+            # can also be used instead of itertools.cycle
+            for step, ((l_batch_x, l_batch_y), (u_batch_x, u_batch_y)) in enumerate(
+                zip(itertools.cycle(l_dataset), u_dataset)
+            ):
+                batch_x, batch_y = tf.concat([l_batch_x, u_batch_x], 0), tf.concat(
+                    [l_batch_y, u_batch_y], 0
+                )
+                loss = self.train_step(x=batch_x, y=batch_y, T=T)
+                progbar.update(current=step, values=[("loss", float(loss["loss"]))])
+                epoch_loss += float(loss["loss"])
+            # Store result in history
+            self.history.on_epoch_end(
+                epoch=epoch,
+                logs={"loss": epoch_loss / num_batches, "accuracy": float(0.0)},
+            )
+            if verbose == 2:
+                tf.print(
+                    f"Epoch {epoch + 1}/{epochs}\n"
+                    f"{step}/{step} - {time.time() - start:.1f}s - "
+                    f"loss: {epoch_loss / num_batches:.4f} - "
+                    f"accuracy: {float(0.0):.4f}"
+                )
+        return self.history
+
+    # pylint: disable=too-many-arguments, too-many-locals, arguments-differ
+    @tf.function
+    def train_step(self, x, y, T=4):
+        """Perform a single training step on a batch of data.
+
+        Applies the forward pass, loss computation, and backpropagation to update
+        the model weights using both labeled and unlabeled data.
+
+        Parameters
+        ----------
+        - x (Tensor): Input data.
+        - y (Tensor): Corresponding labels (or -1 for unlabeled data).
+        - T (float, optional): Temperature parameter for softmax in pseudo-labeling.
+
+        Returns
+        -------
+        - dict: A dictionary containing the computed losses for the training step.
+        """
+        with tf.GradientTape() as tape:
+            logits, labels = self.model(x, training=True), y
+            labeled_mask, unlabeled_mask = tf.not_equal(-1, labels), tf.cast(
+                tf.equal(-1, labels), dtype=tf.float32
+            )
+            num_labeled, num_unlabeled = tf.math.reduce_sum(
+                tf.cast(labeled_mask, dtype=tf.float32)
+            ), tf.math.reduce_sum(unlabeled_mask)
+            masked_logits, masked_labels = tf.boolean_mask(
+                logits, labeled_mask
+            ), tf.boolean_mask(labels, labeled_mask)
+            # Equation 4: Loss function for labelled images.
+            loss_labeled = (
+                tf.reduce_sum(self.loss_fn(labels=masked_labels, logits=masked_logits))
+                / num_labeled
+            )
+            # Equation 5: Psuedo labels for the unlabelled images.
+            pseudo_labels = tf.stop_gradient(input=tf.nn.softmax(logits / T))
+            pseudo_mask = tf.cast(
+                tf.reduce_max(pseudo_labels, axis=1) >= self.confidence,
+                dtype=tf.float32,
+            )
+            # Equation 6: Loss function for unlabelled images.
+            loss_unlabeled = (
+                tf.reduce_sum(
+                    (
+                        tf.cast(
+                            self.loss_fn(
+                                labels=tf.argmax(pseudo_labels, axis=1), logits=logits
+                            ),
+                            dtype=tf.float32,
+                        )
+                        * pseudo_mask
+                    )
+                    * unlabeled_mask
+                )
+                / num_unlabeled
+            )
+            # Equation 7: loss of kth client in rth round (Eq-4 + Eq-6)
+            total_loss = tf.add_n(
+                [
+                    ((1 - self.aux_loss_weight) * loss_labeled)
+                    + (self.aux_loss_weight * loss_unlabeled)
+                ]
+                + self.model.losses
+            )
+        trainable_vars = self.trainable_variables
+        gradients = tape.gradient(total_loss, trainable_vars)
+        self.optimizer.apply_gradients(zip(gradients, trainable_vars))
+        return {
+            "loss": total_loss,
+            "labelled_loss": loss_labeled,
+            "unlabelled_loss": loss_unlabeled,
+        }
+
+    def get_evaluation_model(self, input_shape=(None, None, 64, 1)):
+        """Create an evaluation model based on the trained PSL network.
+
+        Constructs a model for evaluation by applying the trained network in a
+        time-distributed manner to process variable-length inputs.
+
+        Parameters
+        ----------
+        - input_shape (tuple, optional): Shape of the input
+                                         data for the evaluation model.
+
+        Returns
+        -------
+        - tf.keras.Model: The evaluation model based on the PSL network.
+        """
+        input_layer = tf.keras.layers.Input(shape=input_shape)
+        output = tf.reduce_mean(
+            tf.keras.layers.TimeDistributed(self.model)(input_layer), axis=1
+        )
+        return tf.keras.Model(input_layer, output)
diff --git a/baselines/fedstar/fedstar/server.py b/baselines/fedstar/fedstar/server.py
new file mode 100644
index 000000000000..7247973d65fa
--- /dev/null
+++ b/baselines/fedstar/fedstar/server.py
@@ -0,0 +1,292 @@
+"""Required imports for server.py script."""
+
+import gc
+import logging
+import os
+import sys
+import time
+import uuid
+
+import flwr
+import hydra
+import tensorflow as tf
+from omegaconf import DictConfig, OmegaConf
+
+from fedstar.dataset import DataBuilder
+from fedstar.models import Network
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+os.environ["GRPC_VERBOSITY"] = "ERROR"
+GRPC_MAX_MESSAGE_LENGTH: int = 536_870_912
+
+parent_path = os.getcwd()
+
+
+def get_eval_fn(ds_test, evaluation_step, num_classes, verbose):
+    """Return a function to evaluate the model during federated learning.
+
+    This function is used by the Flower framework to evaluate the model at specified
+    intervals using the provided test dataset.
+
+    Parameters
+    ----------
+    - ds_test (tf.data.Dataset): The dataset used for evaluation.
+    - evaluation_step: After this rounds of aggregartion server evaluates the model.
+    - num_classes: Number of classes/labels model has to classify.
+    - verbose: How much details should be displayed.
+
+    Returns
+    -------
+    - Function: An evaluation function that takes server_round, weights, and configs
+    as parameters and returns the loss and accuracy.
+    """
+
+    # pylint: disable=unused-argument
+    def evaluate(server_round, weights, configs):
+        # Clear session
+        # https://www.tensorflow.org/api_docs/python/tf/keras/backend/clear_session
+        tf.keras.backend.clear_session()
+        loss, acc = 0, 0
+        if (server_round - 1) % evaluation_step == 0:
+            model = Network(num_classes=num_classes).get_evaluation_network()
+            model.compile(
+                optimizer=tf.keras.optimizers.Adam(),
+                loss=tf.keras.losses.SparseCategoricalCrossentropy(
+                    from_logits=True, name="loss"
+                ),
+                metrics=[tf.keras.metrics.SparseCategoricalAccuracy(name="accuracy")],
+            )
+            model.set_weights(weights)
+            loss, acc = model.evaluate(ds_test, verbose=verbose)
+        gc.collect()
+        return float(loss), {"accuracy": float(acc)}
+
+    return evaluate
+
+
+def get_on_fit_config_fn(total_rounds: int, epochs: int, batch_size: int):
+    """Return a function to configure the federated learning fit process.
+
+    This function is called by the Flower framework to obtain configuration
+    parameters for each training round.
+
+    Parameters
+    ----------
+    - total_rounds: Number of coummunication rounds between client and server.
+    - epochs: Number of epochs/ iterations
+    - batch_size: The number of training instances in the batch
+
+    Returns
+    -------
+    - Function: A function that takes rounds, epochs, and batch_size
+    as parameters and returns a configuration dictionary.
+    """
+
+    # pylint: disable=unused-argument
+    def fit_config(server_round: int):
+        print(
+            f"\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n"
+            f"Server started {server_round}th round of training.\n"
+            f"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n"
+        )
+        return {
+            "rounds": str(total_rounds),
+            "c_round": str(server_round),
+            "epochs": str(epochs),
+            "batch_size": str(batch_size),
+        }
+
+    return fit_config
+
+
+class AudioServer:  # pylint: disable=too-many-instance-attributes
+    """A server class for federated learning using Flower framework.
+
+    This class sets up and runs a federated learning server using the Flower framework,
+    handling the orchestration of training across multiple clients. It manages model
+    training rounds, evaluation steps, and tracks overall progress and performance.
+
+    Attributes
+    ----------
+    - flwr_evalution_step (int): After this many rounds the server evaluates the model.
+    - flwr_min_sample_size (int): Minimum number of clients participate in a FL round.
+    - flwr_num_clients (int): Total number of clients for federated learning.
+    - flwr_rounds (int): Number of coummunication rounds between client and server.
+    - model_num_classes (int): Number of classes/labels model has to classify.
+    - model_batch_size (int): Batch size for model training.
+    - model_epochs (int): Number of epochs for model training.
+    - model_ds_test (tf.data.Dataset): Dataset for testing the model.
+    - model_verbose (int): Verbosity level for model training.
+    """
+
+    # pylint: disable=too-many-arguments, too-many-locals
+    def __init__(
+        self,
+        flwr_evalution_step,
+        fraction_fit,
+        flwr_num_clients,
+        flwr_rounds,
+        model_num_classes,
+        model_batch_size,
+        model_epochs,
+        model_ds_test,
+        model_verbose,
+    ):
+        # Flower Parameters
+        self.rounds = flwr_rounds
+        # Local Variables Counters and Variables
+        self.current_round = 0
+        self.final_accuracy = 0.0
+        self.round_time = time.time()
+        self.strategy = flwr.server.strategy.FedAvg(
+            fraction_fit=float(fraction_fit),
+            min_available_clients=flwr_num_clients,
+            on_fit_config_fn=get_on_fit_config_fn(
+                self.rounds, model_epochs, model_batch_size
+            ),
+            fraction_evaluate=0,
+            min_evaluate_clients=0,
+            on_evaluate_config_fn=None,
+            evaluate_fn=get_eval_fn(
+                model_ds_test,
+                flwr_evalution_step,
+                num_classes=model_num_classes,
+                verbose=model_verbose,
+            ),
+            accept_failures=True,
+        )
+        self.client_manager = flwr.server.client_manager.SimpleClientManager()
+
+    def server_start(self, server_address):
+        """Start the federated learning server with the given address.
+
+        Initializes and runs the Flower federated learning server using the specified
+        server address and pre-configured strategy and client manager.
+
+        Parameters
+        ----------
+        - server_address (str): The address at which the server will be accessible.
+        """
+        # print("|" * 50)
+        # print(server_address)
+        flwr.server.start_server(
+            server_address=server_address,
+            server=flwr.server.Server(
+                client_manager=self.client_manager, strategy=self.strategy
+            ),
+            config=flwr.server.ServerConfig(num_rounds=self.rounds),
+            strategy=self.strategy,
+            grpc_max_message_length=GRPC_MAX_MESSAGE_LENGTH,
+        )
+
+
+def set_logger_level():
+    """Set the logging level for the 'flower' logger.
+
+    Adjusts the logging level to INFO for the 'flower' logger if it is present in the
+    logging manager's dictionary.
+    """
+    if "flower" in [
+        repr(logging.getLogger(name))[8:].split(" ")[0]
+        for name in logging.Logger.manager.loggerDict
+    ]:
+        logger = logging.getLogger("flower")
+        logger.setLevel(logging.INFO)
+
+
+def set_gpu_limits(gpu_id, gpu_memory):
+    """Configure GPU settings for TensorFlow.
+
+    Sets the CUDA_VISIBLE_DEVICES environment variable and configures TensorFlow's
+    virtual device settings for GPU memory limits.
+
+    Parameters
+    ----------
+    - gpu_id (str): Identifier for the GPU to be used.
+    - gpu_memory (int): The maximum amount of memory (in MB) to be allocated to the GPU.
+    """
+    os.environ["CUDA_VISIBLE_DEVICES"] = gpu_id
+
+    gpus = tf.config.list_physical_devices("GPU")
+    if not gpus:
+        print("No GPU's available. Server will run on CPU.")
+    else:
+        print("GPU is available trying to configure memory.")
+        try:
+            tf.config.experimental.set_virtual_device_configuration(
+                gpus[0],
+                [
+                    tf.config.experimental.VirtualDeviceConfiguration(
+                        memory_limit=gpu_memory
+                    )
+                ],
+            )
+        except RuntimeError as runtime_error:
+            print(runtime_error)
+
+
+# Change the config path and yaml file name based on expermients you want to run.
+# The current configs will call the basic config file which runs the small
+# experiment to check integrity.
+# Eg:-
+# To carry out results for experiment in row 3 row 1 col 1. The parameters will be
+# config_path = conf/table_3/row_2_clients_10
+# config_name = speech_commands
+@hydra.main(config_path="conf", config_name="table3", version_base=None)
+def main(cfg: DictConfig):
+    """Run the federated learning server with Hydra configuration.
+
+    Initializes and starts the federated learning server based on configurations
+    provided by Hydra. It sets up the environment, loads the test dataset, and
+    initiates the AudioServer instance to manage the federated learning process.
+
+    Parameters
+    ----------
+    - cfg (DictConfig): The configuration object provided by Hydra.
+    """
+    # Set Experiment Parameters
+    unique_id = str(uuid.uuid1())
+
+    # print config structured as YAML
+    print(OmegaConf.to_yaml(cfg))
+
+    dataset_name = cfg.server.dataset_name
+    # Notify Experiment ID to console.
+    print(
+        f"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n"
+        f" Experiment ID : {unique_id}\n"
+        f"%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n"
+    )
+
+    # GPU Setup
+    gpus = tf.config.experimental.list_physical_devices("GPU")
+    if gpus:
+        set_gpu_limits(gpu_id="0", gpu_memory=cfg.server.gpu_memory)
+    # Load Test Dataset
+    ds_test, num_classes = DataBuilder.get_ds_test(
+        parent_path=parent_path,
+        data_dir=dataset_name,
+        batch_size=cfg.server.batch_size,
+        buffer=1024,
+        seed=cfg.server.seed,
+    )
+    # Create Server Object
+    audio_server = AudioServer(
+        flwr_evalution_step=cfg.server.eval_step,
+        fraction_fit=cfg.server.fraction_fit,
+        flwr_num_clients=cfg.server.num_clients,
+        flwr_rounds=cfg.server.rounds,
+        model_num_classes=num_classes,
+        model_batch_size=cfg.server.batch_size,
+        model_epochs=cfg.server.train_epochs,
+        model_ds_test=ds_test,
+        model_verbose=cfg.server.verbose,
+    )
+    # Run server
+    set_logger_level()
+    audio_server.server_start(cfg.server.server_address)
+
+
+if __name__ == "__main__":
+    main()
+    sys.exit(0)
diff --git a/baselines/fedstar/fedstar/strategy.py b/baselines/fedstar/fedstar/strategy.py
new file mode 100644
index 000000000000..17436c401c30
--- /dev/null
+++ b/baselines/fedstar/fedstar/strategy.py
@@ -0,0 +1,5 @@
+"""Optionally define a custom strategy.
+
+Needed only when the strategy is not yet implemented in Flower or because you want to
+extend or modify the functionality of an existing strategy.
+"""
diff --git a/baselines/fedstar/fedstar/utils.py b/baselines/fedstar/fedstar/utils.py
new file mode 100644
index 000000000000..89d2ec3303b5
--- /dev/null
+++ b/baselines/fedstar/fedstar/utils.py
@@ -0,0 +1,671 @@
+"""Required imports for utils.py script."""
+
+import collections
+import itertools
+import random
+
+import numpy as np
+import tensorflow as tf
+
+
+class AudioTools:  # pylint: disable=too-many-instance-attributes
+    """Provide static methods for audio data processing in a machine learning context.
+
+    This class includes methods for reading, padding, extracting, and transforming audio
+    waveforms into features suitable for machine learning models.
+    """
+
+    @staticmethod
+    def read_audio(path, label):
+        """Read an audio file and return its waveform along with the label.
+
+        Parameters
+        ----------
+        - path: The file path of the audio file.
+        - label: The associated label of the audio file.
+
+        Returns
+        -------
+        - Tuple containing the waveform of the audio file and its label.
+        """
+        raw_audio = tf.io.read_file(path)
+        waveform, _ = tf.audio.decode_wav(raw_audio)
+        waveform = waveform[Ellipsis, 0]
+        return waveform, label
+
+    @staticmethod
+    def pad(waveform, sequence_length=16000):
+        """Pad the given waveform to a specified sequence length.
+
+        Parameters
+        ----------
+        - waveform: The waveform to be padded.
+        - sequence_length: The length to which the waveform should be padded.
+
+        Returns
+        -------
+        - The padded waveform.
+        """
+        padding = tf.maximum(sequence_length - tf.shape(waveform)[0], 0)
+        left_pad = padding // 2
+        right_pad = padding - left_pad
+        return tf.pad(waveform, paddings=[[left_pad, right_pad]])
+
+    @staticmethod
+    def extract_window(waveform, seg_length=15690):
+        """Extract a window of specified length from the given waveform.
+
+        Parameters
+        ----------
+        - waveform: The input waveform.
+        - seg_length: The length of the segment to extract.
+
+        Returns
+        -------
+        - A segment of the waveform of the specified length.
+        """
+        waveform = AudioTools.pad(waveform)
+        return tf.image.random_crop(waveform, [seg_length])
+
+    # pylint: disable=too-many-arguments, too-many-locals
+    @staticmethod
+    def extract_spectrogram(
+        waveform,
+        sample_rate=16000,
+        frame_length=400,
+        frame_step=160,
+        fft_length=1024,
+        n_mels=64,
+        fmin=60.0,
+        fmax=7800.0,
+    ):
+        """Convert a waveform to a log-mel spectrogram.
+
+        Parameters
+        ----------
+        - waveform: The input waveform.
+        - sample_rate: Sampling rate of the audio.
+        - frame_length: Length of each audio frame.
+        - frame_step: Step size between frames.
+        - fft_length: Length of the Fast Fourier Transform.
+        - n_mels: Number of Mel bands.
+        - fmin: Minimum frequency.
+        - fmax: Maximum frequency.
+
+        Returns
+        -------
+        - Log-mel spectrogram of the waveform.
+        """
+        stfts = tf.signal.stft(
+            waveform,
+            frame_length=frame_length,
+            frame_step=frame_step,
+            fft_length=fft_length,
+        )
+        spectrograms = tf.abs(stfts)
+        num_spectrogram_bins = stfts.shape[-1]
+        lower_edge_hertz, upper_edge_hertz, num_mel_bins = fmin, fmax, n_mels
+        linear_to_mel_weight_matrix = tf.signal.linear_to_mel_weight_matrix(
+            num_mel_bins,
+            num_spectrogram_bins,
+            sample_rate,
+            lower_edge_hertz,
+            upper_edge_hertz,
+        )
+        mel_spectrograms = tf.tensordot(spectrograms, linear_to_mel_weight_matrix, 1)
+        mel_spectrograms.set_shape(
+            spectrograms.shape[:-1].concatenate(linear_to_mel_weight_matrix.shape[-1:])
+        )
+        mel_spectrograms = tf.clip_by_value(
+            mel_spectrograms, clip_value_min=1e-5, clip_value_max=1e8
+        )
+        log_mel_spectrograms = tf.math.log(mel_spectrograms)
+        return log_mel_spectrograms[Ellipsis, tf.newaxis]
+
+    @staticmethod
+    def prepare_example(waveform, label):
+        """Prepare an audio example for model training or evaluation.
+
+        Parameters
+        ----------
+        - waveform: The input waveform.
+        - label: The label of the audio example.
+
+        Returns
+        -------
+        - Prepared log-mel spectrogram feature and the corresponding label.
+        """
+        waveform = AudioTools.pad(waveform)
+        waveform = tf.math.l2_normalize(waveform, epsilon=1e-9)
+        waveform = AudioTools.extract_window(waveform)
+        log_mel_spectrogram = AudioTools.extract_spectrogram(waveform)
+        return log_mel_spectrogram, label
+
+    @staticmethod
+    def prepare_test_example(waveform, label):
+        """Prepare an audio example specifically for testing.
+
+        Parameters
+        ----------
+        - waveform: The input waveform.
+        - label: The label of the audio example.
+
+        Returns
+        -------
+        - Prepared log-mel spectrogram feature for testing and the corresponding label.
+        """
+        waveform = AudioTools.pad(waveform)
+        waveform = tf.signal.frame(
+            waveform, frame_length=98 * 160, frame_step=98 * 160, pad_end=False
+        )
+        waveform = tf.math.l2_normalize(waveform, axis=-1, epsilon=1e-9)
+        log_mel_spectrogram = AudioTools.extract_spectrogram(waveform)
+        return log_mel_spectrogram, label
+
+
+class DataTools:  # pylint: disable=too-many-instance-attributes
+    """Provide methods for dataset manipulation and distribution in federated learning.
+
+    Includes methods for calculating statistics, distributing datasets among clients,
+    and transforming datasets for semi-supervised learning.
+    """
+
+    @staticmethod
+    def get_num_samples(dataset):
+        """Get the number of samples in a dataset.
+
+        Parameters
+        ----------
+        - dataset: The dataset to evaluate.
+
+        Returns
+        -------
+        - The number of samples in the dataset.
+        """
+        return len(dataset[0])
+
+    @staticmethod
+    def get_statistics(num_samples, num_clients, var):
+        """Calculate statistical parameters for dataset distribution.
+
+        Parameters
+        ----------
+        - num_samples: Total number of samples in the dataset.
+        - num_clients: Number of clients in federated learning.
+        - var: Variance factor for distribution.
+
+        Returns
+        -------
+        - Calculated mean, variance, limits, and error values for distribution.
+        """
+        mean = num_samples / num_clients
+        var = int(var * mean)
+        minimum, maximum = mean - var, int(mean + var)
+        error = num_samples - minimum * num_clients  # values to be distributed
+        round_error = (error - int(error)) + ((mean - int(mean)) * num_clients)
+        return mean, var, (int(minimum), maximum), (int(error), round_error)
+
+    @staticmethod
+    def get_class_distribution(dataset):
+        """Determine the class distribution in a dataset.
+
+        Parameters
+        ----------
+        - dataset: The dataset whose class distribution is to be analyzed.
+
+        Returns
+        -------
+        - A counter object representing the class distribution.
+        """
+        return collections.Counter(dataset[1])
+
+    @staticmethod
+    def split_class_samples(dataset, partitions=1):
+        """Split class samples in a dataset into specified number of partitions.
+
+        Parameters
+        ----------
+        - dataset: The dataset to be split.
+        - partitions: The number of partitions.
+
+        Returns
+        -------
+        - A list of dataset partitions.
+        """
+        total = len(dataset[0])
+        size = total // partitions
+        rest = total % partitions
+        ranges = []
+        assert size != 0 and partitions > 0
+        if rest:
+            index = list(range(0, total, size))
+            extra = [index[i] + i for i in range(rest + 1)] + [
+                x + rest for x in index[rest + 1 :][: partitions - rest]
+            ]
+            ranges = [(extra[i], extra[i + 1]) for i in range(len(extra) - 1)]
+        else:
+            index = list(range(0, total + 1, size))
+            ranges = [(index[i], index[i + 1]) for i in range(len(index) - 1)]
+        return [(dataset[0][i:j], dataset[1][i:j]) for i, j in ranges]
+
+    @staticmethod
+    def get_dataset_class_samples(dataset, class_number):
+        """Extract and return samples of a specific class from the dataset.
+
+        Parameters
+        ----------
+        - dataset: The dataset from which to extract samples.
+        - class_number: The class number to filter by.
+
+        Returns
+        -------
+        - Tuple of lists: (samples, labels) of the specified class.
+        """
+        dataset = list(
+            zip(
+                *[
+                    (dataset[0][idx], label)
+                    for idx, label in enumerate(dataset[1])
+                    if label == class_number
+                ]
+            )
+        )
+        return list(dataset[0]), list(dataset[1])
+
+    @staticmethod
+    def get_subset(dataset, percentage, num_classes, u_per=1.0, seed=2021):
+        """Generate labeled and unlabeled subsets based on the given percentage.
+
+        Parameters
+        ----------
+        - dataset: The dataset to split into subsets.
+        - percentage: The percentage of data to include in the labeled subset.
+        - num_classes: The number of classes in the dataset.
+        - u_per: The percentage of data to include in the unlabeled subset.
+        - seed: The seed for random operations to ensure reproducibility.
+
+        Returns
+        -------
+        - Two tuples: The labeled subset and the unlabeled subset.
+        """
+        # num_samples = DataTools.get_num_samples(dataset=dataset)
+        class_distribution = DataTools.get_class_distribution(dataset=dataset)
+        class_distribution = dict(sorted(class_distribution.items()))
+        class_samples = {k: int(percentage * v) for k, v in class_distribution.items()}
+        class_samples = dict(sorted(class_samples.items()))
+        dataset = list(zip(dataset[0], dataset[1]))
+        dataset_per_class = [
+            [s for s in dataset if s[1] == i] for i in range(num_classes)
+        ]
+        class_num_samples = list(class_samples.values())
+        # Get Subset of labelled samples
+        subset = list(
+            itertools.chain(
+                *[
+                    [
+                        dataset_per_class[i].pop(
+                            random.Random(seed).randrange(len(dataset_per_class[i]))
+                        )
+                        for _ in range(class_num_samples[i])
+                    ]
+                    for i in range(num_classes)
+                ]
+            )
+        )
+        subset_u = list(itertools.chain(*dataset_per_class))
+        random.Random(seed).shuffle(subset)
+        random.Random(seed).shuffle(subset_u)
+        subset_u = (
+            subset_u[0 : int((len(subset_u) - 1) * u_per)] if u_per < 1.0 else subset_u
+        )
+        return (subset, len(subset)), (subset_u, len(subset_u))
+
+    @staticmethod
+    def distribute_per_samples(dataset, num_clients, variance=0.25, seed=2021):
+        """Distribute dataset samples evenly among a specified number of clients.
+
+        Parameters
+        ----------
+        - dataset: The dataset to be distributed.
+        - num_clients: The number of clients among whom to distribute the dataset.
+        - variance: The variance allowed in the distribution of samples.
+        - seed: The seed for random operations to ensure reproducibility.
+
+        Returns
+        -------
+        - Generator yielding dataset subsets for each client.
+        """
+        dataset = tuple(list(t) for t in zip(*dataset))
+        num_samples = DataTools.get_num_samples(dataset=dataset)
+        _, _, limits, errors = DataTools.get_statistics(
+            num_samples=num_samples, num_clients=num_clients, var=variance
+        )
+        distribution = DataTools.create_distribution(
+            num_clients=num_clients,
+            num_samples=num_samples,
+            remain_samples=errors[0],
+            minimum=limits[0],
+            maximum=limits[1],
+            round_error=errors[1],
+            seed=seed,
+        )
+        samples, labels = dataset
+        dataset = DataTools.shuffle_dataset(dataset=dataset, seed=seed)
+        iter_samples, iter_labels = iter(samples), iter(labels)
+        for i in range(num_clients):
+            yield list(itertools.islice(iter_samples, distribution[i])), list(
+                itertools.islice(iter_labels, distribution[i])
+            )
+
+    # pylint: disable=too-many-locals
+    @staticmethod
+    def distribute_per_class(
+        dataset, num_clients, num_classes, class_variance=0.0, seed=2021
+    ):
+        """Distribute dataset based on class distribution among clients.
+
+        Parameters
+        ----------
+        - dataset: The dataset to be distributed.
+        - num_clients: The number of clients.
+        - num_classes: The number of classes in the dataset.
+        - class_variance: The variance in the number of classes per client.
+        - seed: The seed for random operations to ensure reproducibility.
+
+        Returns
+        -------
+        - A list of datasets for each client, distributed based on class.
+        """
+        dataset = tuple(list(t) for t in zip(*dataset))
+        (overlap, _num_classes_, _num_clients_) = (
+            (True, num_clients, num_classes)
+            if num_classes < num_clients
+            else (False, num_classes, num_clients)
+        )
+        _, _, limits, errors = DataTools.get_statistics(
+            num_samples=_num_classes_, num_clients=_num_clients_, var=class_variance
+        )
+        distribution = DataTools.create_distribution(
+            num_clients=_num_clients_,
+            num_samples=_num_classes_,
+            remain_samples=errors[0],
+            minimum=limits[0],
+            maximum=limits[1],
+            round_error=errors[1],
+            seed=seed,
+        )
+
+        if (
+            overlap
+        ):  # Clients will contain only one class and clients classes will overlap
+            distribution = list(
+                itertools.chain(
+                    *[[idx] * clients for idx, clients in enumerate(distribution)]
+                )
+            )
+            random.Random(seed).shuffle(distribution)
+        else:
+            classes = list(range(_num_classes_))
+            random.Random(seed).shuffle(classes)
+            _iter_ = iter(classes)
+
+            def distribute_fun():
+                return (
+                    yield from (
+                        list(itertools.islice(_iter_, distribution[i]))
+                        for i in range(_num_clients_)
+                    )
+                )
+
+            distribution = list(distribute_fun())
+        class_datasets = [
+            DataTools.get_dataset_class_samples(
+                dataset=dataset, class_number=class_number
+            )
+            for class_number in range(num_classes)
+        ]
+
+        # Distribute dataset to clients according to distribution
+        if overlap:
+            dataset_use = dict(sorted(collections.Counter(distribution).items()))
+            class_datasets = [
+                (
+                    class_datasets[c]
+                    if dataset_use[c] == 1
+                    else DataTools.split_class_samples(
+                        class_datasets[c], dataset_use[c]
+                    )
+                )
+                for c in range(num_classes)
+            ]
+            parts = list(
+                itertools.chain.from_iterable(
+                    [list(range(len(c))) for c in class_datasets if isinstance(c, list)]
+                )
+            )
+            _iter_ = iter(parts)
+            datasets = [
+                (
+                    class_datasets[i]
+                    if dataset_use[i] == 1
+                    else class_datasets[i][next(_iter_)]
+                )
+                for i in distribution
+            ]
+            datasets = [DataTools.shuffle_dataset(dataset) for dataset in datasets]
+        else:
+            datasets = [
+                [class_datasets[class_number] for class_number in i]
+                for i in distribution
+            ]
+            datasets = [np.hstack((datasets[i])).tolist() for i in range(len(datasets))]
+            datasets = [tuple(data) for data in datasets]
+            datasets = [DataTools.shuffle_dataset(dataset) for dataset in datasets]
+
+        datasets = [
+            [dataset[0], [int(label) for label in dataset[1]]] for dataset in datasets
+        ]
+        return datasets
+
+    @staticmethod
+    def distribute_per_speaker(dataset, num_speakers, seed=2021):
+        """Distribute the dataset based on speaker identity.
+
+        Parameters
+        ----------
+        - dataset: The dataset to be distributed.
+        - num_speakers: The number of distinct speakers in the dataset.
+        - seed: The seed for random operations to ensure reproducibility.
+
+        Returns
+        -------
+        - A list of datasets, each corresponding to a different speaker.
+        """
+        random.seed(seed)
+        np.random.seed(seed)
+        dataset = tuple(list(t) for t in zip(*dataset))
+        datasets = [
+            [
+                (file, label)
+                for file, label in zip(dataset[0], dataset[1])
+                if int(((file.split("/")[-1]).split(".")[0]).split("-")[-1]) == i + 1
+            ]
+            for i in range(num_speakers)
+        ]
+        datasets = [list(zip(*dataset)) for dataset in datasets]
+        datasets = [(list(dataset[0]), list(dataset[1])) for dataset in datasets]
+        return datasets
+
+    # pylint: disable=too-many-arguments
+    @staticmethod
+    def distribute_per_class_with_class_limit(
+        dataset,
+        num_clients,
+        num_classes,
+        mean_class_distribution=3,
+        class_variance=0.0,
+        seed=2021,
+    ):
+        """Distribute the dataset with a limit on the number of classes per client.
+
+        Parameters
+        ----------
+        - dataset: The dataset to be distributed.
+        - num_clients: The number of clients.
+        - num_classes: The number of classes in the dataset.
+        - mean_class_distribution: The mean number of classes per client.
+        - class_variance: The variance in the number of classes per client.
+        - seed: The seed for random operations to ensure reproducibility.
+
+        Returns
+        -------
+        - A list of datasets for each client, with a controlled class distribution.
+        """
+        dataset = tuple(list(t) for t in zip(*dataset))
+        random.seed(seed)
+        np.random.seed(seed)
+        probability_distribution = np.random.normal(
+            mean_class_distribution, np.sqrt(class_variance), num_clients
+        )
+        probability_distribution = [int(p) for p in probability_distribution]
+        # Create class distribution across clients
+        distributions = [
+            list(np.random.choice(a=num_classes, size=p, replace=False))
+            for p in probability_distribution
+        ]
+        # Find number of clients that share dataset.
+        dataset_use = dict(
+            sorted(collections.Counter(list(itertools.chain(*distributions))).items())
+        )
+        # Make sure that all classes are used. Otherwise change seed.
+        assert (
+            len(dataset_use) == num_classes
+        ), "Not all classes were selected. Choose a different seed!"
+        # Split classes into clients partitions
+        class_datasets = [
+            DataTools.get_dataset_class_samples(
+                dataset=dataset, class_number=class_number
+            )
+            for class_number in range(num_classes)
+        ]
+        class_datasets = [
+            (
+                class_datasets[c]
+                if dataset_use[c] == 1
+                else DataTools.split_class_samples(class_datasets[c], dataset_use[c])
+            )
+            for c in range(num_classes)
+        ]
+        # Keep track of splitted partitions
+        parts = [list(np.arange(i)) for i in list(dataset_use.values())]
+        # Fill datasets with examples from class distribution.
+        datasets = [
+            [
+                (
+                    class_datasets[j]
+                    if (len(parts[j]) == 1 and isinstance(class_datasets[j], tuple))
+                    else class_datasets[j][parts[j].pop()]
+                )
+                for j in dis
+            ]
+            for dis in distributions
+        ]
+        # Flatten clients datasets to ([samples],[labels]) format.
+        datasets = [
+            [
+                list(itertools.chain(*list(zip(*d))[0])),
+                list(itertools.chain(*list(zip(*d))[1])),
+            ]
+            for d in datasets
+        ]
+        # Shuffle each dataset.
+        datasets = [DataTools.shuffle_dataset(d) for d in datasets]
+        return datasets
+
+    # pylint: disable=too-many-arguments
+    @staticmethod
+    def create_distribution(
+        num_clients,
+        num_samples,
+        remain_samples,
+        minimum,
+        maximum,
+        round_error,
+        seed=2021,
+    ):
+        """Create a distribution plan for distributing samples among clients.
+
+        Parameters
+        ----------
+        - num_clients: The number of clients.
+        - num_samples: The total number of samples to distribute.
+        - remain_samples: Remaining samples to distribute after initial division.
+        - minimum: The minimum number of samples per client.
+        - maximum: The maximum number of samples per client.
+        - round_error: The error due to rounding in distribution.
+        - seed: The seed for random operations to ensure reproducibility.
+
+        Returns
+        -------
+        - A list representing the distribution of samples among clients.
+        """
+        distribution = [minimum] * num_clients
+        random.seed(seed)
+        while remain_samples > 0:
+            idx = random.randint(0, len(distribution) - 1)
+            distribution[idx], remain_samples = (
+                (distribution[idx] + 1, remain_samples - 1)
+                if distribution[idx] < maximum
+                else (distribution[idx], remain_samples)
+            )
+        # Add error due to rounding to random positions.
+        for _i in range(round(round_error)):
+            idx = random.randint(0, len(distribution) - 1)
+            distribution[idx] = (
+                distribution[idx] + 1
+                if sum(distribution) + 1 <= num_samples
+                else distribution[idx]
+            )
+        return distribution
+
+    @staticmethod
+    def convert_to_unlabelled(dataset, unlabelled_data_identifier=-1):
+        """Convert a labeled dataset to an unlabeled one by replacing labels.
+
+        Replaces the labels in the dataset with a specified identifier, effectively
+        converting it to an unlabeled dataset. This is useful in semi-supervised
+        learning scenarios.
+
+        Parameters
+        ----------
+        - dataset: The dataset to be converted, consisting of data and labels.
+        - unlabelled_data_identifier: The identifier to replace the labels with,
+        defaulting to -1.
+
+        Returns
+        -------
+        - A list of tuples, where each tuple contains data and unlabelled identifier.
+        """
+        return [(data[0], unlabelled_data_identifier) for data in dataset]
+
+    @staticmethod
+    def shuffle_dataset(dataset, seed=2021):
+        """Shuffle a dataset in a reproducible manner.
+
+        Randomly shuffles the dataset using a specified seed to ensure the shuffle
+        can be reproduced.
+
+        Parameters
+        ----------
+        - dataset: The dataset to shuffle, consisting of data and labels.
+        - seed: The seed for the random number generator to ensure reproducibility,
+        defaulting to 2021.
+
+        Returns
+        -------
+        - The shuffled dataset as a tuple of two lists, one for data and one for labels.
+        """
+        random.seed(seed)
+        dataset = list(zip(dataset[0], dataset[1]))
+        random.shuffle(dataset)
+        return tuple(list(t) for t in zip(*dataset))
diff --git a/baselines/fedstar/pyproject.toml b/baselines/fedstar/pyproject.toml
new file mode 100644
index 000000000000..fbcaa666ba30
--- /dev/null
+++ b/baselines/fedstar/pyproject.toml
@@ -0,0 +1,141 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.masonry.api"
+
+[tool.poetry]
+name = "fedstar" # <----- Ensure it matches the name of your baseline directory containing all the source code
+version = "1.0.0"
+description = "Federated Self-training for Semi-supervised Audio Recognition"
+license = "Apache-2.0"
+authors = ["Raj Parekh <rajparekhwc@gmail.com>", "The Flower Authors <hello@flower.ai>"]
+readme = "README.md"
+homepage = "https://flower.ai"
+repository = "https://github.com/adap/flower"
+documentation = "https://flower.ai"
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: MacOS :: MacOS X",
+    "Operating System :: POSIX :: Linux",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: Implementation :: CPython",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Scientific/Engineering :: Mathematics",
+    "Topic :: Software Development",
+    "Topic :: Software Development :: Libraries",
+    "Topic :: Software Development :: Libraries :: Python Modules",
+    "Typing :: Typed",
+]
+
+[tool.poetry.dependencies]
+python = ">=3.10.0, <3.11.0"  # don't change this
+flwr = "1.5.0" # don't change this
+hydra-core = "1.3.2" # don't change this
+tensorflow = { extras = ["and-cuda"], version = "2.14.0"}
+pandas = "2.1.1"
+scipy = "^1.11.4"
+tqdm = "^4.66.1"
+
+[tool.poetry.dev-dependencies]
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.5.1"
+mypy = "==1.4.1"
+pylint = "==3.0.3"
+flake8 = "==3.9.2"
+pytest = "==6.2.4"
+pytest-watch = "==4.2.0"
+ruff = "==0.0.272"
+types-requests = "==2.27.7"
+
+[tool.isort]
+line_length = 88
+indent = "    "
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+use_parentheses = true
+
+[tool.black]
+line-length = 88
+target-version = ["py38", "py39", "py310", "py311"]
+
+[tool.pytest.ini_options]
+minversion = "6.2"
+addopts = "-qq"
+testpaths = [
+    "flwr_baselines",
+]
+
+[tool.mypy]
+ignore_missing_imports = true
+strict = false
+plugins = "numpy.typing.mypy_plugin"
+
+[tool.pylint."MESSAGES CONTROL"]
+disable = "duplicate-code,too-few-public-methods,useless-import-alias"
+good-names = "i,j,k,_,x,y,X,Y"
+signature-mutators="hydra.main.main"
+
+[tool.pylint.typecheck]
+generated-members="numpy.*, torch.*, tensorflow.*"
+
+[[tool.mypy.overrides]]
+module = [
+    "importlib.metadata.*",
+    "importlib_metadata.*",
+]
+follow_imports = "skip"
+follow_imports_for_stubs = true
+disallow_untyped_calls = false
+
+[[tool.mypy.overrides]]
+module = "torch.*"
+follow_imports = "skip"
+follow_imports_for_stubs = true
+
+[tool.docformatter]
+wrap-summaries = 88
+wrap-descriptions = 88
+
+[tool.ruff]
+target-version = "py38"
+line-length = 88
+select = ["D", "E", "F", "W", "B", "ISC", "C4"]
+fixable = ["D", "E", "F", "W", "B", "ISC", "C4"]
+ignore = ["B024", "B027"]
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+    "proto",
+]
+
+[tool.ruff.pydocstyle]
+convention = "numpy"
diff --git a/baselines/fedstar/setup_datasets.sh b/baselines/fedstar/setup_datasets.sh
new file mode 100644
index 000000000000..cd9583983eb3
--- /dev/null
+++ b/baselines/fedstar/setup_datasets.sh
@@ -0,0 +1,42 @@
+#!/bin/bash
+
+# Extract the data_splits.tar file
+tar -xvf data_splits.tar
+
+# Create datasets directory and navigate into it
+mkdir datasets
+cd datasets
+
+# Create speech_commands directory and navigate into it
+mkdir speech_commands
+cd speech_commands
+
+# Download and extract the training data
+wget http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz -O train.tar.gz
+mkdir -p ./Data/Train
+tar -xvzf ./train.tar.gz -C ./Data/Train
+
+# Download and extract the testing data
+wget http://download.tensorflow.org/data/speech_commands_test_set_v0.02.tar.gz -O test.tar.gz
+mkdir -p ./Data/Test
+tar -xvzf ./test.tar.gz -C ./Data/Test
+
+# # Rename the _background_noise_ directory to _silence_
+mv ./Data/Train/_background_noise_ ./Data/Train/_silence_
+
+cd ..
+# Create speech_commands directory and navigate into it
+mkdir ambient_context
+cd ambient_context
+
+wget http://sensix.tech/datasets/ambientacousticcontext/audioset_1sec_v1.tar.gz -O ambient_data.tar.gz
+mkdir -p ./Data
+mkdir -p ./temp_data
+tar -xvzf ./ambient_data.tar.gz -C ./temp_data
+mv ./temp_data/audioset_1sec/* ./Data
+rm -r ./temp_data
+cd ..
+cd ..
+
+# Print completion message
+echo "Dataset setup completed."
\ No newline at end of file
diff --git a/baselines/fedvssl/.gitignore b/baselines/fedvssl/.gitignore
new file mode 100644
index 000000000000..a31a594130ab
--- /dev/null
+++ b/baselines/fedvssl/.gitignore
@@ -0,0 +1,2 @@
+outputs/
+*.__pycache__
diff --git a/baselines/fedvssl/LICENSE b/baselines/fedvssl/LICENSE
new file mode 100644
index 000000000000..d64569567334
--- /dev/null
+++ b/baselines/fedvssl/LICENSE
@@ -0,0 +1,202 @@
+
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diff --git a/baselines/fedvssl/README.md b/baselines/fedvssl/README.md
new file mode 100644
index 000000000000..15bf66ab6a21
--- /dev/null
+++ b/baselines/fedvssl/README.md
@@ -0,0 +1,227 @@
+---
+title: Federated Self-supervised Learning for Video Understanding
+url: https://arxiv.org/abs/2207.01975
+labels: [action recognition, cross-device, ssl, video, videossl]
+dataset: [UCF-101, Kinectics-400]
+---
+
+# Federated Self-supervised Learning for Video Understanding
+> Note: If you use this baseline in your work, please remember to cite the original authors of the paper as well as the Flower paper.
+
+
+**paper:** [arxiv.org/abs/2207.01975](https://arxiv.org/abs/2207.01975)
+
+
+**Authors:** Yasar Abbas Ur Rehman, Yan Gao, Jiajun Shen, Pedro Porto Buarque de Gusmao, Nicholas Lane
+
+
+**Abstract:** The ubiquity of camera-enabled mobile devices has lead to large amounts of unlabelled video data being produced at the edge. Although various self-supervised learning (SSL) methods have been proposed to harvest their latent spatio-temporal representations for task-specific training, practical challenges including privacy concerns and communication costs prevent SSL from being deployed at large scales. To mitigate these issues, we propose the use of Federated Learning (FL) to the task of video SSL. In this work, we evaluate the performance of current state-of-the-art (SOTA) video-SSL techniques and identify their shortcomings when integrated into the large-scale FL setting simulated with kinetics-400 dataset. We follow by proposing a novel federated SSL framework for video, dubbed FedVSSL, that integrates different aggregation strategies and partial weight updating. Extensive experiments demonstrate the effectiveness and significance of FedVSSL as it outperforms the centralized SOTA for the downstream retrieval task by 6.66% on UCF-101 and 5.13% on HMDB-51. 
+
+![](_static/FVSSL.png)
+
+## About this baseline
+
+**Whats's implemented:** The code in this directory replicates the experiments in *Federated Self-supervised Learning for Video Understanding* (Rehman et al., 2022) for UCF-101, which proposed the FedVSSL algorithm. Specifically, it replicates the results for UCF-101 in Table 4 in the paper.
+As common SSL training pipeline, this code has two parts: SSL pre-training in FL and downstream fine-tuning.
+
+**Dataset:** [UCF-101](https://www.crcv.ucf.edu/data/UCF101.php)
+
+**Hardware Setup:** These experiments (SSL pre-train + downstream fine-tuning) were on a server with 6x RTX-3090 GPU and 128 CPU threads. Assuming a single client runs per GPU, on a RTX 3090 it takes 34s for a client to complete its local training (1 epoch); on an A100 this goes down to 17s. In this baseline 5 clients participate in each round for a total of 20 rounds. On a single A100 you can run the federated pre-training in under 1h.
+
+**Contributors:** Yasar Abbas Ur Rehman and Yan Gao
+
+## Experimental Setup
+
+**Task:** Action Recognition
+
+**Model:** 
+* We first pre-train the R3D-18 model using the Catch-the-Patch (CtP [paper](https://arxiv.org/abs/2105.02545)) SSL technique in FL settings. The details of the CtP SSL technique can be found at `fedvssl/CtP/pyvrl/models/pretraining/ctp`, and the details of the R3D-18 model can be found at `fedvssl/CtP/pyvrl/models/backbones/r3d.py`. 
+* After pre-training, we fine-tune the pre-trained R3D-18 model on the UCF-101 dataset.
+
+**Dataset:** The pre-training in the paper was conducted on Kinectics-400 dataset, and we provide the script to generate the partitions for FL training.
+However, the original dataset source as well as the pre-processing scripts they offered are not available currently.
+Alternately, one can download this dataset from PyTorch ([torchvision-datasets-Kinetics](https://pytorch.org/vision/stable/generated/torchvision.datasets.Kinetics.html)).
+
+In this baseline, we demonstrate SSL pre-training and supervised fine-tuning with UCF-101 dataset. 
+For UCF-101 dataset, one can simply follow the dataset preparation instruction below to download and generate the FL partitions.
+
+| Dataset | #classes | #partitions | partitioning method |  partition settings  |
+|:--------|:--------:|:-----------:| :---: |:--------------------:|
+| UCF101  |   101    |     10      | randomly partitioned |       uniform        |
+| Kinectics-400    |   400    |     100     | randomly partitioned | 8 classes per client |
+
+**Training Hyperparameters:** The following table shows the main hyperparameters for this baseline with their default value (i.e. the value used if you run `python main.py` directly)
+
+| Description        |            Default Value            |
+|:-------------------|:-----------------------------------:|
+| total clients      |                  5                  |
+| clients per round  |                  5                  | 
+| number of rounds	  |                 20                  | 
+| client resources	  | {'num_cpus': 2.0, 'num_gpus': 1.0 } | 
+| optimizer	  |                 SGD                 | 
+| alpha coefficient	 |                 0.9                 | 
+| beta coefficient	  |                  1                  | 
+
+## Environment Setup
+Please make sure you have installed CUDA 11.7 on your machine 
+(see [NVIDIA CUDA Toolkit website](https://developer.nvidia.com/cuda-11-7-0-download-archive)).
+To construct the Python environment follow these steps (assuming you arelady have `pyenv` and `Poetry` in your system):
+
+```bash
+# Set directory to use python 3.10 (install with `pyenv install <version>` if you don't have it)
+pyenv local 3.10.12
+poetry env use 3.10.12
+
+# Install the base Poetry environment
+poetry install
+
+# Activate the environment
+poetry shell
+```
+
+## Running the Experiments
+To run FedVSSL with UCF-101 baseline, please ensure you have activated your Poetry environment (execute `poetry shell` from this directory). 
+Then, download the `CtP` repo and install required packages:
+
+```bash
+# Clone CtP repo
+git clone https://github.com/yan-gao-GY/CtP.git fedvssl/CtP
+
+# Additional packages to decompress the dataset
+sudo apt install unrar unzip
+```
+
+### Dataset preparation
+Let's first download UCF-101 dataset and related annotation files:
+
+```bash
+cd fedvssl
+mkdir -p data/ucf101/
+
+# Downloading
+wget https://www.crcv.ucf.edu/data/UCF101/UCF101.rar -O data/ucf101/UCF101.rar --no-check-certificate
+
+# Unzipping
+unrar e data/ucf101/UCF101.rar data/ucf101/UCF101_raw/
+
+# Downloading the train/test split
+wget https://www.crcv.ucf.edu/data/UCF101/UCF101TrainTestSplits-RecognitionTask.zip -O data/ucf101/UCF101TrainTestSplits-RecognitionTask.zip --no-check-certificate
+
+# Unzipping
+unzip data/ucf101/UCF101TrainTestSplits-RecognitionTask.zip -d data/ucf101/
+```
+
+Then, we use the scripts to pre-process the dataset:
+
+```bash
+# Pre-processing the dataset
+python CtP/scripts/process_ucf101.py --raw_dir data/ucf101/UCF101_raw/ --ann_dir data/ucf101/ucfTrainTestlist/ --out_dir data/ucf101/
+
+# Covert to .json files
+python dataset_convert_to_json.py
+
+# (optional)
+rm data/ucf101/UCF101.rar
+rm -r data/ucf101/UCF101_raw/
+```
+
+Then, we perform data partitioning for FL:
+
+```bash
+python data_partitioning_ucf.py --json_path data/ucf101/annotations --output_path data/ucf101/annotations/client_distribution/ --num_clients 5
+
+cd ..
+```
+
+
+### Federated SSL pre-training
+
+Finally, we can launch the training. To run SSL pretraining using `FedVSSL`:
+```bash
+# Run federated SSL training with FedVSSL
+python -m fedvssl.main # this will run using the default settings.
+
+# You can override settings directly from the command line
+python -m fedvssl.main strategy.mix_coeff=1 rounds=100 # will set hyper-parameter alpha to 1 and the number of rounds to 100
+```
+
+To run using `FedAvg`:
+
+```bash
+# This will run FedAvg baseline
+# This is done so to match the experimental setup in the paper
+python -m fedvssl.main strategy.fedavg=true
+
+# This config can also be overridden.
+```
+
+Running any of the above will create a directory structure in the form of `outputs/<DATE>/<TIME>/fedvssl_results` to save the global checkpoints and the local clients' training logs.
+
+
+### Downstream fine-tuning
+
+The downstream fine-tuning does not involve Flower because it's done in centralised fashion. Let's fine-tune the model we just pre-trained with `FedVSSL` using UCF-101. First,
+we need to transform model format:
+
+```bash
+python -m fedvssl.finetune_preprocess --pretrained_model_path=<CHECKPOINT>.npz
+# check for checkpoints in the output directory of your experiment (outputs/<DATE>/<TIMES>/fedvssl_results)
+# Where <DATE>/<TIME> are the date and time when you launched the experiment
+```
+
+Then, launch the fine-tuning using `CtP` script. Results will be stored in a new directory named `finetune_results`.
+
+```bash
+bash fedvssl/CtP/tools/dist_train.sh fedvssl/conf/mmcv_conf/finetuning/r3d_18_ucf101/finetune_ucf101.py 1 --work_dir=./finetune_results --data_dir=fedvssl/data
+```
+
+Note that the first parameter of this script is the path of config file, while the second is the number of GPUs used for fine-tuning.
+
+After that, we perform the test process:
+
+```bash
+bash fedvssl/CtP/tools/dist_test.sh fedvssl/conf/mmcv_conf/finetuning/r3d_18_ucf101/test_ucf101.py 1 --work_dir=./finetune_results --data_dir=fedvssl/data --progress
+```
+
+## Expected results
+
+### Pre-training and fine-tuning on UCF-101
+The pre-training in the paper was conducted on Kinectics-400, which takes a considerable amount of time without access to server-grade hardware (~1 day with 6x RTX 3090).
+As a result, we provide the following command to do pre-training on UCF-101, in order to validate FedVSSL.
+
+In this experiment, we simulate the cross-silo scenario with UCF-101 video data that distributed among 5 clients. We did the FL pre-training of CtP SSL for 20 rounds followed by fine-tuning the whole network on UCF-101 video data. The `conf/base.yaml` file contains all the details about the pre-training setup. 
+
+To start the pre-training one can use the following command:
+```bash
+python -m fedvssl.main  # this will run using the default settings.
+```
+
+
+After pre-training one can use the provided commands in the section above to run the fine-tuning. 
+
+The fine-tuning lasts for 150 epochs.
+
+| Method                        | FL-Setup| Clients| Pretrain Dataset | Finetune Dataset| Top-1% Acc. | Top 5% Acc.|
+|-------------------------------| --------|--------|------------------|-----------------|-------------|------------|
+| FedVSSL(`alpha=0.9`,`beta=1`) | Cross-Silo| 5 | UCF-101| UCF-101| 66.32%| 86.57%|
+
+
+### Pre-training on Kinectics-400 and fine-tuning on UCF-101
+
+We provide the checkpoints of the pre-trained SSL models on Kinectics-400.
+With them as starting points, one can run downstream fine-tuning on UCF-101 to obtain the expected results in the paper.
+
+
+| Method                    | Checkpoint file                                                                                     | UCF Top-1 |
+|---------------------------|-----------------------------------------------------------------------------------------------------|-----------|
+| FedVSSL(`alpha=0`, `beta=0`) | [round-540.npz](https://drive.google.com/file/d/15EEIQay5FRBMloEzt1SQ8l8VjZFzpVNt/view?usp=sharing) | 79.91     |
+| FedVSSL(`alpha=1`, `beta=0`) | [round-540.npz](https://drive.google.com/file/d/1OUj8kb0ahJSKAZEB-ES94pOG5-fB-28-/view?usp=sharing) | 79.14     |
+| FedVSSL(`alpha=0`, `beta=1`) | [round-540.npz](https://drive.google.com/file/d/1N62kXPcLQ_tM45yd2kBYjNOskdHclwLM/view?usp=sharing) | 79.43     |
+| FedVSSL(`alpha=1`, `beta=1`) | [round-540.npz](https://drive.google.com/file/d/1SKb5aXjpVAeWbzTKMFN9rjHW_LQsmUXj/view?usp=sharing) | 78.99     |
+| FedVSSL(`alpha=0.9`, `beta=0`) | [round-540.npz](https://drive.google.com/file/d/1W1oCnLXX0UJhQ4MlmRw-r7z5DTCeO75b/view?usp=sharing) | 80.62     |
+| FedVSSL(`alpha=0.9`, `beta=1`) | [round-540.npz](https://drive.google.com/file/d/1BK-bbyunxTWNqs-QyOYiohaNv-t3-hYe/view?usp=sharing) | 79.41     |
+
+
+
+
+
diff --git a/baselines/fedvssl/_static/FVSSL.png b/baselines/fedvssl/_static/FVSSL.png
new file mode 100644
index 000000000000..600419e3064e
Binary files /dev/null and b/baselines/fedvssl/_static/FVSSL.png differ
diff --git a/baselines/fedvssl/fedvssl/__init__.py b/baselines/fedvssl/fedvssl/__init__.py
new file mode 100644
index 000000000000..a5e567b59135
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/__init__.py
@@ -0,0 +1 @@
+"""Template baseline package."""
diff --git a/baselines/fedvssl/fedvssl/client.py b/baselines/fedvssl/fedvssl/client.py
new file mode 100644
index 000000000000..6f0d3b372097
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/client.py
@@ -0,0 +1,142 @@
+"""Define your client class and a function to construct such clients.
+
+Please overwrite `flwr.client.NumPyClient` or `flwr.client.Client` and create a function
+to instantiate your client.
+"""
+
+import os
+import re
+from collections import OrderedDict
+from math import exp
+
+import flwr as fl
+import torch
+
+from .utils import train_model_cl
+
+
+# pylint: disable=too-many-instance-attributes
+class SslClient(fl.client.NumPyClient):
+    """Flower client implementing video SSL w/ PyTorch."""
+
+    def __init__(  # pylint: disable=too-many-arguments
+        self,
+        model,
+        train_dataset,
+        test_dataset,
+        cfg,
+        args,
+        distributed,
+        logger,
+    ):
+        self.model = model
+        self.train_dataset = train_dataset
+        self.test_dataset = test_dataset
+        self.args = args
+        self.cfg = cfg
+        self.distributed = distributed
+        self.logger = logger
+        self.mu = 0.3
+
+    def get_parameters(self, config):
+        """Return local model parameters as a list of NumPy ndarrays."""
+        return [val.cpu().numpy() for _, val in self.model.state_dict().items()]
+
+    def set_parameters(self, parameters):
+        """Set local model parameters (only backbone)."""
+        state_dict = OrderedDict()
+        params_dict = zip(self.model.state_dict().keys(), parameters)
+
+        # define a new state_dict
+        # only update the backbone weights but keep the cls weights
+        for k, v in params_dict:
+            if (
+                k.strip().split(".")[0] == "backbone"
+            ):  # if there is any term backbone update its weights
+                state_dict[k] = torch.from_numpy(v)
+            else:
+                state_dict[k] = self.model.state_dict()[k]  # keep the previous weights
+
+        self.model.load_state_dict(state_dict, strict=True)
+
+    def fit(self, parameters, config):  # pylint: disable=too-many-locals
+        """Customise the training function."""
+        int(config["epoch_global"])
+        self.cfg.lr_config = {"policy": "step", "step": [100, 200]}
+
+        chk_name_list = [
+            fn for fn in os.listdir(self.cfg.work_dir) if fn.endswith(".pth")
+        ]
+        chk_epoch_list = [
+            int(re.findall(r"\d+", fn)[0])
+            for fn in chk_name_list
+            if fn.startswith("epoch")
+        ]
+
+        # Gathering  local epochs if there are any
+        if chk_epoch_list:
+            chk_epoch_list.sort()
+            checkpoint = os.path.join(
+                self.cfg.work_dir, f"epoch_{chk_epoch_list[-1]}.pth"
+            )
+            pr_model = torch.load(checkpoint)
+            state_dict_pr = pr_model["state_dict"]
+            # load the model with previous state_dictionary
+            self.model.load_state_dict(state_dict_pr, strict=True)
+
+        # Update local model w/ global parameters
+        self.set_parameters(parameters)
+
+        train_model_cl(
+            model=self.model,
+            train_dataset=self.train_dataset,
+            cfg=self.cfg,
+            distributed=self.distributed,
+            logger=self.logger,
+        )
+
+        # Return updated model parameters to the server
+        num_examples = len(self.train_dataset)
+
+        # fetch loss from log file
+        work_dir = self.args.work_dir
+        log_f_list = []
+        for file_item in os.listdir(work_dir):
+            if file_item.endswith("log.json"):
+                num = int("".join(file_item.split(".")[0].split("_")))
+                log_f_list.append((file_item, num))
+
+        # take the last log file
+        log_f_list.sort(key=lambda x: x[1])
+        log_f_name = work_dir + "/" + log_f_list[-1][0]
+        loss_list = []
+        with open(log_f_name, "r") as f_r:
+            for line in f_r.readlines():
+                line_dict = eval(line.strip())  # pylint: disable=eval-used
+                loss = float(line_dict["loss"])
+                loss_list.append(loss)
+
+        avg_loss = sum(loss_list) / len(loss_list)
+        exp_loss = exp(-avg_loss)
+        metrics = {"loss": exp_loss}
+
+        # get the model keys
+        metrics["state_dict_key"] = list(self.model.state_dict().keys())
+
+        return self.get_parameters(config=None), num_examples, metrics
+
+    def evaluate(self, parameters, config):
+        """Local evaluation, but we do not perform client evaluation in this project."""
+        # for completion
+        result = 0
+
+        return (
+            float(0),
+            int(1),
+            {"accuracy": float(result)},
+        )
+
+
+def _temp_get_parameters(model):
+    # Return local model parameters as a list of NumPy ndarrays
+    return [val.cpu().numpy() for _, val in model.state_dict().items()]
diff --git a/baselines/fedvssl/fedvssl/conf/base.yaml b/baselines/fedvssl/fedvssl/conf/base.yaml
new file mode 100644
index 000000000000..3c9ff23c6320
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/conf/base.yaml
@@ -0,0 +1,32 @@
+---
+# this is the config that will be loaded as default by main.py
+# Please follow the provided structure (this will ensuring all baseline follow
+# a similar configuration structure and hence be easy to customise)
+
+
+### hyper-parameters for FL pre-training ###
+exp_name: fedvssl_results
+data_dir: fedvssl/data
+partition_dir: annotations/client_distribution
+cfg_path_pretrain: fedvssl/conf/mmcv_conf/pretraining/r3d_18_ucf101/pretraining_for_ucf.py
+
+# FL settings
+pool_size: 5
+rounds: 20
+
+# FedVSSL strategy
+strategy:
+  _target_: fedvssl.strategy.FedVSSL # need to change after path modification
+  mix_coeff: 0.9
+  swbeta: 1
+  base_work_dir: ??? # will be set at runtime
+  fraction_fit: 1.0
+  min_fit_clients: 5
+  min_available_clients: 5
+  fedavg: false
+
+# ray config
+client_resources:
+  num_gpus: 1.0
+  num_cpus: 2
+  include_dashboard: false
diff --git a/baselines/fedvssl/fedvssl/conf/mmcv_conf/finetuning/model_r3d18.py b/baselines/fedvssl/fedvssl/conf/mmcv_conf/finetuning/model_r3d18.py
new file mode 100644
index 000000000000..bf85634b2c24
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/conf/mmcv_conf/finetuning/model_r3d18.py
@@ -0,0 +1,32 @@
+"""Config file used for fine-tuning on UCF-101 dataset."""
+
+model = {
+    "type": "TSN",
+    "backbone": {
+        "type": "R3D",
+        "depth": 18,
+        "num_stages": 4,
+        "stem": {
+            "temporal_kernel_size": 3,
+            "temporal_stride": 1,
+            "in_channels": 3,
+            "with_pool": False,
+        },
+        "down_sampling": [False, True, True, True],
+        "channel_multiplier": 1.0,
+        "bottleneck_multiplier": 1.0,
+        "with_bn": True,
+        "zero_init_residual": False,
+        "pretrained": None,
+    },
+    "st_module": {"spatial_type": "avg", "temporal_size": 2, "spatial_size": 7},
+    "cls_head": {
+        "with_avg_pool": False,
+        "temporal_feature_size": 1,
+        "spatial_feature_size": 1,
+        "dropout_ratio": 0.5,
+        "in_channels": 512,
+        "init_std": 0.001,
+        "num_classes": 101,
+    },
+}
diff --git a/baselines/fedvssl/fedvssl/conf/mmcv_conf/finetuning/r3d_18_ucf101/finetune_ucf101.py b/baselines/fedvssl/fedvssl/conf/mmcv_conf/finetuning/r3d_18_ucf101/finetune_ucf101.py
new file mode 100644
index 000000000000..f3e98229e599
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/conf/mmcv_conf/finetuning/r3d_18_ucf101/finetune_ucf101.py
@@ -0,0 +1,11 @@
+"""Config file used for fine-tuning on UCF-101 dataset."""
+
+_base_ = ["../model_r3d18.py", "../runtime_ucf101.py"]
+
+work_dir = "./finetune_results/"
+
+model = {
+    "backbone": {
+        "pretrained": "./model_pretrained.pth",
+    },
+}
diff --git a/baselines/fedvssl/fedvssl/conf/mmcv_conf/finetuning/r3d_18_ucf101/test_ucf101.py b/baselines/fedvssl/fedvssl/conf/mmcv_conf/finetuning/r3d_18_ucf101/test_ucf101.py
new file mode 100644
index 000000000000..2db8ed728a5f
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/conf/mmcv_conf/finetuning/r3d_18_ucf101/test_ucf101.py
@@ -0,0 +1,11 @@
+"""Config file used for fine-tuning on UCF-101 dataset."""
+
+_base_ = ["../model_r3d18.py", "../runtime_ucf101.py"]
+
+work_dir = "./finetune_results/"
+
+model = {
+    "backbone": {
+        "pretrained": "finetune_results/epoch_150.pth",
+    },
+}
diff --git a/baselines/fedvssl/fedvssl/conf/mmcv_conf/finetuning/runtime_ucf101.py b/baselines/fedvssl/fedvssl/conf/mmcv_conf/finetuning/runtime_ucf101.py
new file mode 100644
index 000000000000..2f1918cf103d
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/conf/mmcv_conf/finetuning/runtime_ucf101.py
@@ -0,0 +1,137 @@
+"""Config file used for fine-tuning on UCF-101 dataset."""
+
+dist_params = {"backend": "nccl"}
+log_level = "INFO"
+load_from = None
+resume_from = None
+syncbn = True
+
+train_cfg = None
+test_cfg = None
+evaluation = {"interval": 10}
+
+data = {
+    "videos_per_gpu": 4,  # total batch size 8*4 == 32
+    "workers_per_gpu": 4,
+    "train": {
+        "type": "TSNDataset",
+        "name": "ucf101_train_split1",
+        "data_source": {
+            "type": "JsonClsDataSource",
+            "ann_file": "ucf101/annotations/train_split_1.json",
+        },
+        "backend": {
+            "type": "ZipBackend",
+            "zip_fmt": "ucf101/zips/{}.zip",
+            "frame_fmt": "img_{:05d}.jpg",
+        },
+        "frame_sampler": {
+            "type": "RandomFrameSampler",
+            "num_clips": 1,
+            "clip_len": 16,
+            "strides": 2,
+            "temporal_jitter": False,
+        },
+        "test_mode": False,
+        "transform_cfg": [
+            {"type": "GroupScale", "scales": [(149, 112), (171, 128), (192, 144)]},
+            {"type": "GroupFlip", "flip_prob": 0.35},
+            {"type": "RandomBrightness", "prob": 0.20, "delta": 32},
+            {"type": "RandomContrast", "prob": 0.20, "delta": 0.20},
+            {
+                "type": "RandomHueSaturation",
+                "prob": 0.20,
+                "hue_delta": 12,
+                "saturation_delta": 0.1,
+            },
+            {"type": "GroupRandomCrop", "out_size": 112},
+            {
+                "type": "GroupToTensor",
+                "switch_rgb_channels": True,
+                "div255": True,
+                "mean": (0.485, 0.456, 0.406),
+                "std": (0.229, 0.224, 0.225),
+            },
+        ],
+    },
+    "val": {
+        "type": "TSNDataset",
+        "name": "ucf101_test_split1",
+        "data_source": {
+            "type": "JsonClsDataSource",
+            "ann_file": "ucf101/annotations/test_split_1.json",
+        },
+        "backend": {
+            "type": "ZipBackend",
+            "zip_fmt": "ucf101/zips/{}.zip",
+            "frame_fmt": "img_{:05d}.jpg",
+        },
+        "frame_sampler": {
+            "type": "UniformFrameSampler",
+            "num_clips": 10,
+            "clip_len": 16,
+            "strides": 2,
+            "temporal_jitter": False,
+        },
+        "test_mode": True,
+        "transform_cfg": [
+            {"type": "GroupScale", "scales": [(171, 128)]},
+            {"type": "GroupCenterCrop", "out_size": 112},
+            {
+                "type": "GroupToTensor",
+                "switch_rgb_channels": True,
+                "div255": True,
+                "mean": (0.485, 0.456, 0.406),
+                "std": (0.229, 0.224, 0.225),
+            },
+        ],
+    },
+    "test": {
+        "type": "TSNDataset",
+        "name": "ucf101_test_split1",
+        "data_source": {
+            "type": "JsonClsDataSource",
+            "ann_file": "ucf101/annotations/test_split_1.json",
+        },
+        "backend": {
+            "type": "ZipBackend",
+            "zip_fmt": "ucf101/zips/{}.zip",
+            "frame_fmt": "img_{:05d}.jpg",
+        },
+        "frame_sampler": {
+            "type": "UniformFrameSampler",
+            "num_clips": 10,
+            "clip_len": 16,
+            "strides": 2,
+            "temporal_jitter": False,
+        },
+        "test_mode": True,
+        "transform_cfg": [
+            {"type": "GroupScale", "scales": [(171, 128)]},
+            {"type": "GroupCenterCrop", "out_size": 112},
+            {
+                "type": "GroupToTensor",
+                "switch_rgb_channels": True,
+                "div255": True,
+                "mean": (0.485, 0.456, 0.406),
+                "std": (0.229, 0.224, 0.225),
+            },
+        ],
+    },
+}
+
+# optimizer
+total_epochs = 150
+optimizer = {"type": "SGD", "lr": 0.01, "momentum": 0.9, "weight_decay": 5e-4}
+optimizer_config = {"grad_clip": {"max_norm": 40, "norm_type": 2}}
+# learning policy
+lr_config = {"policy": "step", "step": [60, 120]}
+checkpoint_config = {"interval": 1, "max_keep_ckpts": 1, "create_symlink": False}
+workflow = [("train", 50)]
+log_config = {
+    "interval": 10,
+    "hooks": [
+        {"type": "TextLoggerHook"},
+        {"type": "TensorboardLoggerHook"},
+    ],
+}
diff --git a/baselines/fedvssl/fedvssl/conf/mmcv_conf/pretraining/pretraining_runtime_kinetics.py b/baselines/fedvssl/fedvssl/conf/mmcv_conf/pretraining/pretraining_runtime_kinetics.py
new file mode 100644
index 000000000000..1f376dae0a58
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/conf/mmcv_conf/pretraining/pretraining_runtime_kinetics.py
@@ -0,0 +1,34 @@
+"""Config file used for pre-training on K-400 dataset."""
+
+_base_ = "./pretraining_runtime_ucf.py"
+
+data = {
+    "train": {
+        "type": "CtPDataset",
+        "data_source": {
+            "type": "JsonClsDataSource",
+            "ann_file": "",
+        },
+        "backend": {
+            "type": "ZipBackend",
+            "zip_fmt": "zips/{}.zip",
+            "frame_fmt": "img_{:05d}.jpg",
+        },
+    }
+}
+
+# optimizer
+total_epochs = 1
+optimizer = {"type": "SGD", "lr": 0.01, "momentum": 0.0, "weight_decay": 0.0001}
+optimizer_config = {"grad_clip": {"max_norm": 40, "norm_type": 2}}
+# learning policy
+lr_config = {"policy": "step", "step": [30, 60]}
+checkpoint_config = {"interval": 1, "max_keep_ckpts": 1, "create_symlink": False}
+workflow = [("train", 1)]
+log_config = {
+    "interval": 50,
+    "hooks": [
+        {"type": "TextLoggerHook"},
+        {"type": "TensorboardLoggerHook"},
+    ],
+}
diff --git a/baselines/fedvssl/fedvssl/conf/mmcv_conf/pretraining/pretraining_runtime_ucf.py b/baselines/fedvssl/fedvssl/conf/mmcv_conf/pretraining/pretraining_runtime_ucf.py
new file mode 100644
index 000000000000..a7314c8fcf74
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/conf/mmcv_conf/pretraining/pretraining_runtime_ucf.py
@@ -0,0 +1,134 @@
+"""Config file used for pre-training on UCF-101 dataset."""
+
+dist_params = {"backend": "nccl"}
+log_level = "INFO"
+load_from = None
+resume_from = None
+syncbn = True
+# CUDA_VISIBLE_DEVICES='4,5'
+
+data = {
+    "videos_per_gpu": 4,  # total batch size is 8Gpus*4 == 32
+    "workers_per_gpu": 4,
+    "train": {
+        "type": "CtPDataset",
+        "data_source": {
+            "type": "JsonClsDataSource",
+            "ann_file": "",
+        },
+        "backend": {
+            "type": "ZipBackend",
+            "zip_fmt": "zips/{}.zip",
+            "frame_fmt": "img_{:05d}.jpg",
+        },
+        "frame_sampler": {
+            "type": "RandomFrameSampler",
+            "num_clips": 1,
+            "clip_len": 16,
+            "strides": [1, 2, 3, 4, 5],
+            "temporal_jitter": True,
+        },
+        "transform_cfg": [
+            {"type": "GroupScale", "scales": [112, 128, 144]},
+            {"type": "GroupRandomCrop", "out_size": 112},
+            {"type": "GroupFlip", "flip_prob": 0.50},
+            {
+                "type": "PatchMask",
+                "region_sampler": {
+                    "scales": [16, 24, 28, 32, 48, 64],
+                    "ratios": [0.5, 0.67, 0.75, 1.0, 1.33, 1.50, 2.0],
+                    "scale_jitter": 0.18,
+                    "num_rois": 3,
+                },
+                "key_frame_probs": [0.5, 0.3, 0.2],
+                "loc_velocity": 3,
+                "size_velocity": 0.025,
+                "label_prob": 0.8,
+            },
+            {
+                "type": "RandomHueSaturation",
+                "prob": 0.25,
+                "hue_delta": 12,
+                "saturation_delta": 0.1,
+            },
+            {
+                "type": "DynamicBrightness",
+                "prob": 0.5,
+                "delta": 30,
+                "num_key_frame_probs": (0.7, 0.3),
+            },
+            {
+                "type": "DynamicContrast",
+                "prob": 0.5,
+                "delta": 0.12,
+                "num_key_frame_probs": (0.7, 0.3),
+            },
+            {
+                "type": "GroupToTensor",
+                "switch_rgb_channels": True,
+                "div255": True,
+                "mean": (0.485, 0.456, 0.406),
+                "std": (0.229, 0.224, 0.225),
+            },
+        ],
+    },
+    "val": {
+        "type": "CtPDataset",
+        "data_source": {
+            "type": "JsonClsDataSource",
+            "ann_file": "annotations/test_split_1.json",
+        },
+        "backend": {
+            "type": "ZipBackend",
+            "zip_fmt": "zips/{}.zip",
+            "frame_fmt": "img_{:05d}.jpg",
+        },
+        "frame_sampler": {
+            "type": "RandomFrameSampler",
+            "num_clips": 1,
+            "clip_len": 16,
+            "strides": [1, 2, 3, 4, 5],
+            "temporal_jitter": True,
+        },
+        "transform_cfg": [
+            {"type": "GroupScale", "scales": [112, 128, 144]},
+            {"type": "GroupRandomCrop", "out_size": 112},
+            {
+                "type": "PatchMask",
+                "region_sampler": {
+                    "scales": [16, 24, 28, 32, 48, 64],
+                    "ratios": [0.5, 0.67, 0.75, 1.0, 1.33, 1.50, 2.0],
+                    "scale_jitter": 0.18,
+                    "num_rois": 3,
+                },
+                "key_frame_probs": [0.5, 0.3, 0.2],
+                "loc_velocity": 3,
+                "size_velocity": 0.025,
+                "label_prob": 0.8,
+            },
+            {
+                "type": "GroupToTensor",
+                "switch_rgb_channels": True,
+                "div255": True,
+                "mean": (0.485, 0.456, 0.406),
+                "std": (0.229, 0.224, 0.225),
+            },
+        ],
+    },
+}
+
+# optimizer
+total_epochs = 1
+optimizer = {"type": "SGD", "lr": 0.01, "momentum": 0.9, "weight_decay": 0.0001}
+optimizer_config = {"grad_clip": {"max_norm": 40, "norm_type": 2}}
+# learning policy
+lr_config = {"policy": "step", "step": [100, 200]}
+checkpoint_config = {"interval": 1, "max_keep_ckpts": 1, "create_symlink": False}
+workflow = [("train", 1)]
+log_config = {
+    "interval": 10,
+    "hooks": [
+        {"type": "TextLoggerHook"},
+        {"type": "TensorboardLoggerHook"},
+    ],
+}
diff --git a/baselines/fedvssl/fedvssl/conf/mmcv_conf/pretraining/r3d_18_kinetics/pretraining_for_kinetics.py b/baselines/fedvssl/fedvssl/conf/mmcv_conf/pretraining/r3d_18_kinetics/pretraining_for_kinetics.py
new file mode 100644
index 000000000000..d40f43d70b84
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/conf/mmcv_conf/pretraining/r3d_18_kinetics/pretraining_for_kinetics.py
@@ -0,0 +1,36 @@
+"""Config file used for pre-training on K-400 dataset."""
+
+_base_ = "../pretraining_runtime_kinetics.py"
+
+work_dir = "./output/ctp/r3d_18_kinetics/pretraining/"
+
+model = {
+    "type": "CtP",
+    "backbone": {
+        "type": "R3D",
+        "depth": 18,
+        "num_stages": 4,
+        "stem": {
+            "temporal_kernel_size": 3,
+            "temporal_stride": 1,
+            "in_channels": 3,
+            "with_pool": False,
+        },
+        "down_sampling": [False, True, True, False],
+        "down_sampling_temporal": [False, True, True, True],
+        "channel_multiplier": 1.0,
+        "bottleneck_multiplier": 1.0,
+        "with_bn": True,
+        "zero_init_residual": False,
+    },
+    "head": {
+        "in_channels": 512,
+        "in_temporal_size": 2,
+        "hidden_channels": 512,
+        "roi_feat_size": 5,
+        "spatial_stride": 8,
+        "num_pred_frames": 16,
+        "target_means": (0.0, 0.0, 0.0, 0.0),
+        "target_stds": (0.8, 0.8, 0.04, 0.04),
+    },
+}
diff --git a/baselines/fedvssl/fedvssl/conf/mmcv_conf/pretraining/r3d_18_ucf101/pretraining_for_ucf.py b/baselines/fedvssl/fedvssl/conf/mmcv_conf/pretraining/r3d_18_ucf101/pretraining_for_ucf.py
new file mode 100644
index 000000000000..1a532d0e74de
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/conf/mmcv_conf/pretraining/r3d_18_ucf101/pretraining_for_ucf.py
@@ -0,0 +1,34 @@
+"""Config file used for pre-training on UCF-101 dataset."""
+
+_base_ = "../pretraining_runtime_ucf.py"
+
+model = {
+    "type": "CtP",
+    "backbone": {
+        "type": "R3D",
+        "depth": 18,
+        "num_stages": 4,
+        "stem": {
+            "temporal_kernel_size": 3,
+            "temporal_stride": 1,
+            "in_channels": 3,
+            "with_pool": False,
+        },
+        "down_sampling": [False, True, True, False],
+        "down_sampling_temporal": [False, True, True, True],
+        "channel_multiplier": 1.0,
+        "bottleneck_multiplier": 1.0,
+        "with_bn": True,
+        "zero_init_residual": False,
+    },
+    "head": {
+        "in_channels": 512,
+        "in_temporal_size": 2,
+        "hidden_channels": 512,
+        "roi_feat_size": 5,
+        "spatial_stride": 8,
+        "num_pred_frames": 16,
+        "target_means": (0.0, 0.0, 0.0, 0.0),
+        "target_stds": (0.8, 0.8, 0.04, 0.04),
+    },
+}
diff --git a/baselines/fedvssl/fedvssl/data_partitioning_k400.py b/baselines/fedvssl/fedvssl/data_partitioning_k400.py
new file mode 100644
index 000000000000..20e356dd4565
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/data_partitioning_k400.py
@@ -0,0 +1,113 @@
+"""Data partitioning script for K-400 dataset."""
+
+import argparse
+import json
+import os
+import random
+
+
+def parse_args():
+    """Parse argument to the main function."""
+    parser = argparse.ArgumentParser(
+        description="Build non-iid partition Kinetics-400 dataset"
+    )
+    parser.add_argument(
+        "--out_path", default="./non_iid/", type=str, help="output directory path."
+    )
+    parser.add_argument(
+        "--input_path",
+        default="/path/to/Kinetics-processed/annotations/train_in_official.txt",
+        type=str,
+        help="input index file",
+    )
+    args = parser.parse_args()
+
+    return args
+
+
+# pylint: disable=too-many-locals
+def main():
+    """Define the main function for data partitioning."""
+    args = parse_args()
+    out_path = args.out_path
+    input_path = args.input_path
+
+    if not os.path.exists(out_path):
+        os.mkdir(out_path)
+
+    # initialise label_name_dict
+    label_name_dict = {}
+    for i in range(1, 401):
+        label_name_dict[i] = []
+
+    # generate label_name_dict.
+    # key is label index while value is list of sample names.
+    with open(input_path, "r") as f_r:
+        for line in f_r.readlines():
+            cls_ind = int(line.split(" ")[1])
+            name = line.split(" ")[0]
+            label_name_dict[cls_ind].append(name)
+
+    # generate a list where elements are tuple (label_ind, num_samples)
+    label_len_list = []
+    for i in range(1, 401):
+        length = len(label_name_dict[i])
+        label_len_list.append((i, length))
+
+    # order classes by number of samples
+    label_len_list.sort(key=lambda x: x[1])
+
+    for i in range(50):
+        cid1 = i + 1
+        cid2 = i + 51
+        client_list1 = []
+        client_list2 = []
+        print("partition: ", i)
+
+        # select the first and last 4 classes seperately.
+        partition_1 = label_len_list[i : i + 4]
+        partition_2 = label_len_list[-i - 5 : -i - 1]
+
+        for j in range(4):
+            lab1, length1 = partition_1[j]
+            lab2, length2 = partition_2[j]
+
+            # randomly shuffle samples
+            random.seed(1234)
+            random.shuffle(label_name_dict[lab1])
+            random.seed(4321)
+            random.shuffle(label_name_dict[lab2])
+
+            # halve samples for this class.
+            first_half1 = label_name_dict[lab1][: length1 // 2]
+            second_half1 = label_name_dict[lab1][length1 // 2 :]
+
+            first_half2 = label_name_dict[lab2][: length2 // 2]
+            second_half2 = label_name_dict[lab2][length2 // 2 :]
+
+            # adding elements for the first client.
+            for ele in first_half1:
+                client_list1.append({"name": ele, "label": int(lab1)})
+            for ele in first_half2:
+                client_list1.append({"name": ele, "label": int(lab2)})
+
+            # adding elements for the second client.
+            for ele in second_half1:
+                client_list2.append({"name": ele, "label": int(lab1)})
+            for ele in second_half2:
+                client_list2.append({"name": ele, "label": int(lab2)})
+
+        # generate json file for each client.
+        with open(
+            os.path.join(out_path, "client_dist{}.json".format(cid1)), "w"
+        ) as f_w:
+            json.dump(client_list1, f_w, indent=2)
+
+        with open(
+            os.path.join(out_path, "client_dist{}.json".format(cid2)), "w"
+        ) as f_w:
+            json.dump(client_list2, f_w, indent=2)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/baselines/fedvssl/fedvssl/data_partitioning_ucf.py b/baselines/fedvssl/fedvssl/data_partitioning_ucf.py
new file mode 100644
index 000000000000..bd79c44d6703
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/data_partitioning_ucf.py
@@ -0,0 +1,74 @@
+"""Data partitioning script for UCF-101 dataset."""
+
+import argparse
+import json
+import os
+import random
+
+
+# Use this script file to create data partitions
+# on UCF101 dataset for federated learning from .json files.
+def parse_args():
+    """Parse argument to the main function."""
+    parser = argparse.ArgumentParser(description="Build partition UCF101 dataset")
+    parser.add_argument(
+        "--json_path",
+        default="/path/to/ucf101/annotations",
+        type=str,
+        help="directory path to training .json files.",
+    )
+    parser.add_argument(
+        "--output_path",
+        default="/path/to/ucf101/annotations/annotations_fed",
+        type=str,
+        help="output path for generated clients .json files.",
+    )
+    parser.add_argument(
+        "--num_clients",
+        default=10,
+        type=int,
+        help="number of clients for partitioning.",
+    )
+    parser.add_argument(
+        "--seed", default=7, type=int, help="random seed for partitioning."
+    )
+    args = parser.parse_args()
+
+    return args
+
+
+def main():
+    """Define the main function for data partitioning."""
+    args = parse_args()
+    json_path = args.json_path
+    output_path = args.output_path
+    num_clients = args.num_clients
+    seed = args.seed
+
+    random.seed(seed)
+
+    if not os.path.exists(output_path):
+        os.mkdir(output_path)
+
+    # load .json files and concatenate
+    data_list = []
+    # for i in range(1, 4):
+    with open(json_path + "/train_split_1.json", "r") as f_r:
+        json_object = json.load(f_r)
+        data_list += json_object
+
+    # data splitting to each client
+    random.shuffle(data_list)
+    num_data = len(data_list)
+    for i in range(0, num_clients):
+        client_data = data_list[
+            int(i * num_data / num_clients) : int((i + 1) * num_data / num_clients)
+        ]
+        with open(
+            os.path.join(output_path, "client_dist" + str(i + 1) + ".json"), "w"
+        ) as f:
+            json.dump(client_data, f, indent=2)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/baselines/fedvssl/fedvssl/dataset.py b/baselines/fedvssl/fedvssl/dataset.py
new file mode 100644
index 000000000000..5e436abe12fb
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/dataset.py
@@ -0,0 +1,10 @@
+"""Handle basic dataset creation.
+
+In case of PyTorch it should return dataloaders for your dataset (for both the clients
+and the server). If you are using a custom dataset class, this module is the place to
+define it. If your dataset requires to be downloaded (and this is not done
+automatically -- e.g. as it is the case for many dataset in TorchVision) and
+partitioned, please include all those functions and logic in the
+`dataset_preparation.py` module. You can use all those functions from functions/methods
+defined here of course.
+"""
diff --git a/baselines/fedvssl/fedvssl/dataset_convert_to_json.py b/baselines/fedvssl/fedvssl/dataset_convert_to_json.py
new file mode 100644
index 000000000000..b82f11f165c4
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/dataset_convert_to_json.py
@@ -0,0 +1,30 @@
+"""Dataset pre-processing: convert .txt files to .json files."""
+
+import json
+
+# We use the .json files for the annotations.
+# One can convert the train_split_1.txt to train_split_1.json
+# by using the following code:
+
+ann_path = [
+    "data/ucf101/annotations/train_split_1.txt",
+    "data/ucf101/annotations/test_split_1.txt",
+]
+out_path = [
+    "data/ucf101/annotations/train_split_1.json",
+    "data/ucf101/annotations/test_split_1.json",
+]
+
+assert len(ann_path) == len(out_path)
+
+for i, _ in enumerate(ann_path):
+    with open(ann_path[i], "r") as f:
+        lines = f.read().splitlines()
+    anns = []
+    for line in lines:
+        if line.strip() == "":
+            continue
+        name, label = line.split(" ")
+        anns.append({"name": name, "label": int(label)})
+    with open(out_path[i], "w") as f:
+        json.dump(anns, f, indent=2)
diff --git a/baselines/fedvssl/fedvssl/dataset_preparation.py b/baselines/fedvssl/fedvssl/dataset_preparation.py
new file mode 100644
index 000000000000..7d64d5852793
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/dataset_preparation.py
@@ -0,0 +1,4 @@
+"""Please refer to the README.md to learn more about how to download and partition the.
+
+UCF-101 dataset.
+"""
diff --git a/baselines/fedvssl/fedvssl/finetune_preprocess.py b/baselines/fedvssl/fedvssl/finetune_preprocess.py
new file mode 100644
index 000000000000..785fb1bef6d4
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/finetune_preprocess.py
@@ -0,0 +1,59 @@
+"""Pre-process pre-trained SSL model for downstream fine-tuning."""
+
+import argparse
+from collections import OrderedDict
+
+import numpy as np
+import torch
+from flwr.common import parameters_to_ndarrays
+from mmengine.config import Config
+
+# pylint: disable=import-error,no-name-in-module
+from .CtP.pyvrl.builder import build_model
+
+
+def args_parser():
+    """Parse arguments to pre-process pre-trained SSL model for fine-tuning."""
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--cfg_path",
+        default="fedvssl/conf/mmcv_conf/finetuning/r3d_18_ucf101/finetune_ucf101.py",
+        type=str,
+        help="Path of config file for fine-tuning.",
+    )
+    parser.add_argument(
+        "--pretrained_model_path",
+        default="",
+        type=str,
+        help="Path of pre-trained SSL model.",
+    )
+
+    return parser.parse_args()
+
+
+args = args_parser()
+
+# Load config file
+cfg = Config.fromfile(args.cfg_path)
+cfg.model.backbone["pretrained"] = None
+
+# Build a model using the config file
+model = build_model(cfg.model)
+
+# Conversion of the format of pre-trained SSL model from .npz files to .pth format.
+params = np.load(args.pretrained_model_path, allow_pickle=True)
+
+if params["arr_0"].shape == ():
+    # For the cases where weights are stored as Parameters
+    params = params["arr_0"].item()
+    params = parameters_to_ndarrays(params)
+else:
+    # For the cases where weights are stored as NumPy arrays
+    params = [np.array(v) for v in list(params["arr_0"])]
+
+params_dict = zip(model.state_dict().keys(), params)
+state_dict = {
+    "state_dict": OrderedDict({k: torch.from_numpy(v) for k, v in params_dict})
+}
+torch.save(state_dict, "./model_pretrained.pth")
diff --git a/baselines/fedvssl/fedvssl/main.py b/baselines/fedvssl/fedvssl/main.py
new file mode 100644
index 000000000000..6087a82e9706
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/main.py
@@ -0,0 +1,154 @@
+"""Create and connect the building blocks for your experiments; start the simulation.
+
+It includes processioning the dataset, instantiate strategy, specify how the global
+model is going to be evaluated, etc. At the end, this script saves the results.
+"""
+
+# These are the basic packages you'll need here
+
+from argparse import Namespace
+from typing import Dict
+
+import flwr as fl
+import hydra
+import numpy as np
+from hydra.core.hydra_config import HydraConfig
+from hydra.utils import instantiate
+from mmengine.config import Config
+from omegaconf import DictConfig, OmegaConf
+
+from .client import SslClient
+from .utils import init_p_paths, load_data, load_model, set_config_mmcv
+
+
+# pylint: disable=too-many-arguments
+def initial_setup(
+    cid, base_work_dir, rounds, data_dir, num_gpus, partition_dir, cfg_path
+):
+    """Initialise setup for instantiating client class."""
+    cid_plus_one = str(int(cid) + 1)
+    args = Namespace(
+        cfg=cfg_path,  # Path to the pretraining configuration file
+        checkpoint=None,
+        cid=int(cid),
+        data_dir=data_dir,
+        gpus=num_gpus,
+        launcher="none",
+        local_rank=0,
+        progress=False,
+        resume_from=None,
+        rounds=rounds,
+        seed=7,
+        validate=False,
+        work_dir=base_work_dir + "/client" + cid_plus_one,
+    )
+
+    print("Starting client", args.cid)  # pylint: disable=no-member
+    # Fetch the configuration file
+    cfg = Config.fromfile(args.cfg)  # pylint: disable=no-member
+    # Define the client data files;
+    # Usually contains paths to the samples and annotations
+    cfg.data.train.data_source.ann_file = (
+        partition_dir + "/client_dist" + cid_plus_one + ".json"
+    )
+
+    distributed, logger = set_config_mmcv(args, cfg)
+    # These two arguments needs to be false during federated pretraining
+    # Otherwise the client will load the previously saved checkpoint
+
+    cfg.resume_from = False
+    cfg.load_from = False
+
+    # Load the model
+    model = load_model(cfg)
+    # Load the training data
+    train_dataset = load_data(cfg)
+
+    # Since during pretraining of FedVSSL,
+    # we don't need any testing data, we can left it empty.
+    test_dataset = " "
+
+    return args, cfg, distributed, logger, model, train_dataset, test_dataset
+
+
+def fit_config(rnd: int) -> Dict[str, str]:
+    """Return a configuration with global epochs."""
+    config = {
+        "epoch_global": str(rnd),
+    }
+    return config
+
+
+@hydra.main(config_path="conf", config_name="base", version_base=None)
+def main(cfg: DictConfig) -> None:
+    """Define the main function for FL pre-training or fine-tuning."""
+    # Print config structured as YAML
+    print(OmegaConf.to_yaml(cfg))
+
+    save_path = HydraConfig.get().runtime.output_dir
+
+    # First the paths needs to be defined
+    # Otherwise the program may not be able to locate the files of the ctp
+    init_p_paths("fedvssl")
+
+    client_resources = {
+        "num_cpus": cfg.client_resources.num_cpus,
+        "num_gpus": cfg.client_resources.num_gpus,
+    }
+    base_work_dir = save_path + f"/{cfg.exp_name}"
+    cfg.strategy.base_work_dir = base_work_dir
+    rounds = cfg.rounds
+    data_dir = cfg.data_dir + "/ucf101"
+    partition_dir = cfg.partition_dir
+    num_gpus = int(np.ceil(cfg.client_resources.num_gpus))
+
+    def client_fn(cid: str):
+        (
+            args,
+            config,
+            distributed,
+            logger,
+            model,
+            train_dataset,
+            test_dataset,
+        ) = initial_setup(
+            cid,
+            base_work_dir,
+            rounds,
+            data_dir,
+            num_gpus,
+            partition_dir,
+            cfg.cfg_path_pretrain,
+        )
+        return SslClient(
+            model,
+            train_dataset,
+            test_dataset,
+            config,
+            args,
+            distributed,
+            logger,
+        )
+
+    # Configure the strategy
+    strategy = instantiate(
+        cfg.strategy,
+        on_fit_config_fn=fit_config,
+    )
+
+    # (Optional) Specify ray config
+    ray_config = {"include_dashboard": cfg.client_resources.include_dashboard}
+
+    # Start simulation
+    fl.simulation.start_simulation(
+        client_fn=client_fn,
+        num_clients=cfg.pool_size,
+        client_resources=client_resources,
+        config=fl.server.ServerConfig(num_rounds=cfg.rounds),
+        strategy=strategy,
+        ray_init_args=ray_config,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/baselines/fedvssl/fedvssl/models.py b/baselines/fedvssl/fedvssl/models.py
new file mode 100644
index 000000000000..71fa553d1f59
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/models.py
@@ -0,0 +1,7 @@
+"""Define our models, and training and eval functions.
+
+If your model is 100% off-the-shelf (e.g. directly from torchvision without requiring
+modifications) you might be better off instantiating your  model directly from the Hydra
+config. In this way, swapping your model for  another one can be done without changing
+the python code at all
+"""
diff --git a/baselines/fedvssl/fedvssl/server.py b/baselines/fedvssl/fedvssl/server.py
new file mode 100644
index 000000000000..2fd7d42cde5a
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/server.py
@@ -0,0 +1,5 @@
+"""Create global evaluation function.
+
+Optionally, also define a new Server class (please note this is not needed in most
+settings).
+"""
diff --git a/baselines/fedvssl/fedvssl/strategy.py b/baselines/fedvssl/fedvssl/strategy.py
new file mode 100644
index 000000000000..69e786d71e41
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/strategy.py
@@ -0,0 +1,160 @@
+"""Optionally define a custom strategy.
+
+Needed only when the strategy is not yet implemented in Flower or because you want to
+extend or modify the functionality of an existing strategy.
+"""
+
+# import all the necessary libraries
+import os
+import re
+from functools import reduce
+from typing import Dict, List, Optional, Tuple, Union
+
+import flwr as fl
+import mmcv
+import numpy as np
+from flwr.common import (
+    FitRes,
+    NDArrays,
+    Parameters,
+    Scalar,
+    ndarrays_to_parameters,
+    parameters_to_ndarrays,
+)
+from flwr.server.client_proxy import ClientProxy
+
+
+class FedVSSL(fl.server.strategy.FedAvg):
+    """Define the custom strategy for FedVSSL."""
+
+    def __init__(  # pylint: disable=too-many-arguments
+        self,
+        num_rounds: int = 1,
+        mix_coeff: float = 0.2,
+        swbeta: int = 0,
+        base_work_dir: str = "ucf_FedVSSL",
+        fedavg: bool = False,
+        **kwargs,
+    ):
+        assert isinstance(swbeta, int) and swbeta in [
+            0,
+            1,
+        ], "the value must be an integer and either 0 or 1 "
+        self.num_rounds = (num_rounds,)
+        self.mix_coeff = (
+            mix_coeff  # coefficient for mixing the loss and fedavg aggregation methods
+        )
+        self.swbeta = swbeta  # 0: SWA off; 1:SWA on
+        self.base_work_dir = base_work_dir
+        self.fedavg = fedavg
+        super().__init__(**kwargs)
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        """Aggregate fit results using weighted average."""
+        if not results:
+            return None, {}
+        # Do not aggregate if there are failures and failures are not accepted
+        if not self.accept_failures and failures:
+            return None, {}
+
+        # Divide the weights based on the backbone and classification head
+        ###################################################################
+
+        # Aggregate all the weights and the number of examples
+        weight_results = [
+            (parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples)
+            for client, fit_res in results
+        ]
+
+        # aggregate the weights of the backbone
+        weights_avg = aggregate(weight_results)  # Fedavg
+
+        if self.fedavg:
+            weights_avg = ndarrays_to_parameters(weights_avg)
+            glb_dir = self.base_work_dir
+            mmcv.mkdir_or_exist(os.path.abspath(glb_dir))
+        else:
+            # Aggregate all the weights and the loss
+            loss_results = [
+                (parameters_to_ndarrays(fit_res.parameters), fit_res.metrics["loss"])
+                for client, fit_res in results
+            ]
+
+            # aggregate the weights of the backbone
+            weights_loss = aggregate(loss_results)  # loss-based
+
+            weights: NDArrays = [v * self.mix_coeff for v in weights_avg] + [
+                (1 - self.mix_coeff) * v for v in weights_loss
+            ]  # Equation 3 in FedVSSL paper
+
+            # # aggregate the weights of the backbone
+            # weights = aggregate(weight_results) # loss-based
+
+            # create a directory to save the global checkpoints
+            glb_dir = self.base_work_dir
+            mmcv.mkdir_or_exist(os.path.abspath(glb_dir))
+
+            # load the previous weights if there are any
+
+            if server_round > 1 and self.swbeta == 1:
+                chk_name_list = [
+                    fn for fn in os.listdir(glb_dir) if fn.endswith(".npz")
+                ]
+                chk_epoch_list = [
+                    int(re.findall(r"\d+", fn)[0])
+                    for fn in chk_name_list
+                    if fn.startswith("round")
+                ]
+                if chk_epoch_list:
+                    chk_epoch_list.sort()
+                    print(chk_epoch_list)
+                    # select the most recent epoch
+                    checkpoint = os.path.join(
+                        glb_dir, f"round-{chk_epoch_list[-1]}-weights.array.npz"
+                    )
+                    # load the previous model weights
+                    params = np.load(checkpoint, allow_pickle=True)
+                    params = params["arr_0"].item()
+                    print("The weights has been loaded")
+                    params = parameters_to_ndarrays(params)  # return a list
+                    weights_avg = [
+                        np.asarray((0.5 * B + 0.5 * A)) for A, B in zip(weights, params)
+                    ]  # perform SWA
+                    weights_avg = ndarrays_to_parameters(weights_avg)
+            else:
+                print("The results are saved without performing SWA")
+                weights_avg = ndarrays_to_parameters(weights)
+
+        if weights_avg is not None:
+            # save weights
+            print(
+                f"round-{server_round}-weights...",
+            )
+            np.savez(
+                os.path.join(glb_dir, f"round-{server_round}-weights.array"),
+                weights_avg,
+            )
+
+        return weights_avg, {}
+
+
+def aggregate(results):
+    """Compute weighted average."""
+    # Calculate the total number of examples used during training
+    num_examples_total = sum([num_examples for _, num_examples in results])
+
+    # Create a list of weights, each multiplied by the related number of examples
+    weighted_weights = [
+        [layer * num_examples for layer in weights] for weights, num_examples in results
+    ]
+
+    weights_prime: NDArrays = [
+        reduce(np.add, layer_updates) / num_examples_total
+        for layer_updates in zip(*weighted_weights)
+    ]
+    return weights_prime
diff --git a/baselines/fedvssl/fedvssl/utils.py b/baselines/fedvssl/fedvssl/utils.py
new file mode 100644
index 000000000000..74b3955acef7
--- /dev/null
+++ b/baselines/fedvssl/fedvssl/utils.py
@@ -0,0 +1,144 @@
+"""Define any utility function.
+
+They are not directly relevant to  the other (more FL specific) python modules. For
+example, you may define here things like: loading a model from a checkpoint, saving
+results, plotting.
+"""
+
+import os
+import re
+import sys
+
+import mmcv
+import torch
+from mmcv.runner import init_dist
+from mmcv.utils import collect_env
+
+from fedvssl.CtP.pyvrl.apis import (  # pylint: disable=E0611,E0401
+    get_root_logger,
+    set_random_seed,
+    train_network,
+)
+from fedvssl.CtP.pyvrl.builder import (  # pylint: disable=E0611,E0401
+    build_dataset,
+    build_model,
+)
+
+
+def init_p_paths(folder_name):
+    """Initialise the correct working directory."""
+    if os.path.basename(os.path.abspath(os.getcwd())) == f"{folder_name}":
+        # if require change the current working directory.
+        # The CtP folder is in the fedvssl/fedvssl.
+        # Therefore, the current working directory
+        # is required to be fedvssl/fedvssl
+        path_dir = os.getcwd()
+
+        os.chdir(path_dir)
+        # if the CtP folder is outside the current working directory,
+        # the path could be the defined as below:
+        # os.chdir("..")
+
+    root_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), f"{path_dir}"))
+    print(root_dir)
+    sys.path.insert(0, os.path.join(root_dir))
+
+
+def set_config_mmcv(args, cfg):
+    """Set MMCV configs."""
+    # set cudnn_benchmark
+    if cfg.get("cudnn_benchmark", True):
+        torch.backends.cudnn.benchmark = True
+
+    # update configs according to CLI args
+    # os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpus)
+    cfg.gpus = args.gpus
+    if args.work_dir is not None:
+        cfg.work_dir = args.work_dir
+
+    # if the CLI args has already specified a resume_from path,
+    # we will recover training process from it.
+    # otherwise, if there exists a trained model in work directory,
+    # we will resume training from it
+
+    if args.resume_from is not None:
+        cfg.resume_from = args.resume_from
+    else:
+        if os.path.isdir(cfg.work_dir):
+            chk_name_list = [
+                fn for fn in os.listdir(cfg.work_dir) if fn.endswith(".pth")
+            ]
+            # if there may exists multiple checkpoint,
+            # we will select the latest one (with the highest epoch number)
+            if len(chk_name_list) > 0:
+                chk_epoch_list = [
+                    int(re.findall(r"\d+", fn)[0])
+                    for fn in chk_name_list
+                    if fn.startswith("epoch")
+                ]
+                chk_epoch_list.sort()
+                cfg.resume_from = os.path.join(
+                    cfg.work_dir, f"epoch_{chk_epoch_list[-1]}.pth"
+                )
+
+    # setup data root directory
+    if args.data_dir is not None:
+        if "train" in cfg.data:
+            cfg.data.train.data_dir = args.data_dir
+        if "val" in cfg.data:
+            cfg.data.val.data_dir = args.data_dir
+        if "test" in cfg.data:
+            cfg.data.test.data_dir = args.data_dir
+
+    # init distributed env first, since logger depends on the dist info.
+    if args.launcher == "none":
+        distributed = False
+    else:
+        distributed = True
+        init_dist(args.launcher, **cfg.dist_params)
+
+    # create work_dir
+    mmcv.mkdir_or_exist(os.path.abspath(cfg.work_dir))
+    # dump config/save the config in the working directory
+    cfg.dump(os.path.join(cfg.work_dir, os.path.basename(args.cfg)))
+
+    # init logger before other steps
+    logger = get_root_logger(log_level=cfg.log_level)
+    env_info_dict = collect_env()
+    env_info = "\n".join([f"{k}: {v}" for k, v in env_info_dict.items()])
+    dash_line = "-" * 60 + "\n"
+    logger.info("Environment info:\n" + dash_line + env_info + "\n" + dash_line)
+    logger.info(f"Distributed training: {distributed}")
+    logger.info(f"Config:\n{cfg.pretty_text}")
+
+    # set random seeds
+    if args.seed is not None:
+        logger.info("Set random seed to {}".format(args.seed))
+        set_random_seed(args.seed)
+
+    return distributed, logger
+
+
+def load_model(cfg):
+    """Load SSL model."""
+    model = build_model(cfg.model)
+    # replace with videoSSL model
+    return model
+
+
+def load_data(cfg):
+    """Load the data partition for a single client ID."""
+    train_dataset = build_dataset(cfg.data.train)
+    return train_dataset
+
+
+def load_test_data(cfg):
+    """Load test data."""
+    test_dataset = build_dataset(cfg.data.val)
+    return test_dataset
+
+
+def train_model_cl(model, train_dataset, cfg, distributed, logger):
+    """Train downstream model."""
+    # model code
+    train_network(model, train_dataset, cfg, distributed=distributed, logger=logger)
diff --git a/baselines/fedvssl/pyproject.toml b/baselines/fedvssl/pyproject.toml
new file mode 100644
index 000000000000..293388079802
--- /dev/null
+++ b/baselines/fedvssl/pyproject.toml
@@ -0,0 +1,146 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.masonry.api"
+
+[tool.poetry]
+name = "fedvssl" # <----- Ensure it matches the name of your baseline directory containing all the source code
+version = "1.0.0"
+description = "Federated Self-supervised Learning for Video Understanding"
+license = "Apache-2.0"
+authors = ["The Flower Authors <hello@flower.ai>"]
+readme = "README.md"
+homepage = "https://flower.ai"
+repository = "https://github.com/adap/flower"
+documentation = "https://flower.ai"
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: MacOS :: MacOS X",
+    "Operating System :: POSIX :: Linux",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: Implementation :: CPython",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Scientific/Engineering :: Mathematics",
+    "Topic :: Software Development",
+    "Topic :: Software Development :: Libraries",
+    "Topic :: Software Development :: Libraries :: Python Modules",
+    "Typing :: Typed",
+]
+
+[tool.poetry.dependencies]
+python = ">=3.10, <3.12.0"  # don't change this
+flwr = { extras = ["simulation"], version = "1.5.0" }
+hydra-core = "1.3.2" # don't change this
+torch = { url = "https://download.pytorch.org/whl/cu117/torch-1.13.1%2Bcu117-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.14.1%2Bcu117-cp310-cp310-linux_x86_64.whl" }
+mmengine = "0.10.0"
+yapf = "0.40.1"
+tensorboard = "2.15.1"
+numpy = "1.23.0"
+mmcv-full = { url = "https://download.openmmlab.com/mmcv/dist/cu117/torch1.13.0/mmcv_full-1.7.1-cp310-cp310-manylinux1_x86_64.whl" }
+
+[tool.poetry.dev-dependencies]
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
+mypy = "==1.4.1"
+pylint = "==2.8.2"
+flake8 = "==3.9.2"
+pytest = "==6.2.4"
+pytest-watch = "==4.2.0"
+ruff = "==0.0.272"
+types-requests = "==2.27.7"
+virtualenv = "20.21.0"
+
+[tool.isort]
+line_length = 88
+indent = "    "
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+use_parentheses = true
+
+[tool.black]
+line-length = 88
+target-version = ["py38", "py39", "py310", "py311"]
+
+[tool.pytest.ini_options]
+minversion = "6.2"
+addopts = "-qq"
+testpaths = [
+    "flwr_baselines",
+]
+
+[tool.mypy]
+ignore_missing_imports = true
+strict = false
+plugins = "numpy.typing.mypy_plugin"
+
+[tool.pylint."MESSAGES CONTROL"]
+disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
+good-names = "i,j,k,f,_,x,y,X,Y,mu,v"
+signature-mutators = "hydra.main.main"
+
+[[tool.mypy.overrides]]
+module = [
+    "importlib.metadata.*",
+    "importlib_metadata.*",
+]
+follow_imports = "skip"
+follow_imports_for_stubs = true
+disallow_untyped_calls = false
+
+[tool.pylint.typecheck]
+generated-members = "numpy.*, torch.*, tensorflow.*"
+# ignore-paths="fedvssl/CtP/"
+
+[[tool.mypy.overrides]]
+module = "torch.*"
+follow_imports = "skip"
+follow_imports_for_stubs = true
+
+[tool.docformatter]
+wrap-summaries = 88
+wrap-descriptions = 88
+
+[tool.ruff]
+target-version = "py38"
+line-length = 88
+select = ["D", "E", "F", "W", "B", "ISC", "C4"]
+fixable = ["D", "E", "F", "W", "B", "ISC", "C4"]
+ignore = ["B024", "B027"]
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+    "proto",
+]
+
+[tool.ruff.pydocstyle]
+convention = "numpy"
diff --git a/baselines/fedwav2vec2/README.md b/baselines/fedwav2vec2/README.md
index 0b41c6172976..c7191cd50855 100644
--- a/baselines/fedwav2vec2/README.md
+++ b/baselines/fedwav2vec2/README.md
@@ -59,7 +59,7 @@ For more details, please refer to the relevant section in the paper.
 | `sb_config` | `fedwav2vec2/conf/sb_config/w2v2.yaml` | Speechbrain config file for architecture model. Please refer to [SpeechBrain](https://github.com/speechbrain/speechbrain) for more information |
 | `rounds` | `100` | Indicate the number of Federated Learning (FL) rounds|
 | `local_epochs` | `20` | Specify the number of training epochs at the client side |
-| `total_clients` | `1943` | Size of client pool, with a maxium set at 1943 clients|
+| `total_clients` | `1943` | Size of client pool, with a maximum set at 1943 clients|
 | `server_cid` | `19999` | ID of the server to distinguish from the client's ID |
 | `server_device` | `cuda` | You can choose between `cpu` or `cuda` for centralised evaluation, but it is recommended to use `cuda`|
 | `parallel_backend` | `false` | Multi-gpus training. Only active if you have more than 1 gpu per client | 
@@ -109,7 +109,7 @@ python -m fedwav2vec2.dataset_preparation
 # Run with default arguments (one client per GPU)
 python -m fedwav2vec2.main
 
-# if you have a large GPU (32GB+) you migth want to fit two per GPU
+# if you have a large GPU (32GB+) you might want to fit two per GPU
 python -m fedwav2vec2.main client_resources.num_gpus=0.5
 
 # the global model can be saved at the end of each round if you specify a checkpoint path
diff --git a/baselines/fedwav2vec2/fedwav2vec2/client.py b/baselines/fedwav2vec2/fedwav2vec2/client.py
index 319580a83845..eb276ab07ce8 100644
--- a/baselines/fedwav2vec2/fedwav2vec2/client.py
+++ b/baselines/fedwav2vec2/fedwav2vec2/client.py
@@ -4,7 +4,6 @@
 to instantiate your client.
 """
 
-
 import gc
 import logging
 from math import exp
diff --git a/baselines/fedwav2vec2/fedwav2vec2/dataset.py b/baselines/fedwav2vec2/fedwav2vec2/dataset.py
index 65fee90faf38..b09fd9b24472 100644
--- a/baselines/fedwav2vec2/fedwav2vec2/dataset.py
+++ b/baselines/fedwav2vec2/fedwav2vec2/dataset.py
@@ -9,7 +9,6 @@
 defined here of course.
 """
 
-
 import speechbrain as sb
 import torchaudio
 
diff --git a/baselines/fedwav2vec2/fedwav2vec2/dataset_preparation.py b/baselines/fedwav2vec2/fedwav2vec2/dataset_preparation.py
index 255664d3d2c3..7fca743ac7dd 100644
--- a/baselines/fedwav2vec2/fedwav2vec2/dataset_preparation.py
+++ b/baselines/fedwav2vec2/fedwav2vec2/dataset_preparation.py
@@ -7,7 +7,6 @@
 block) that this file should be executed first.
 """
 
-
 import os
 import ssl
 import tarfile
diff --git a/baselines/fedwav2vec2/fedwav2vec2/models.py b/baselines/fedwav2vec2/fedwav2vec2/models.py
index 38916aaac555..02877ed1594a 100644
--- a/baselines/fedwav2vec2/fedwav2vec2/models.py
+++ b/baselines/fedwav2vec2/fedwav2vec2/models.py
@@ -6,7 +6,6 @@
 the python code at all
 """
 
-
 import gc
 import os
 
diff --git a/baselines/fedwav2vec2/fedwav2vec2/sb_recipe.py b/baselines/fedwav2vec2/fedwav2vec2/sb_recipe.py
index 390edfe246d3..40bd5a1742eb 100644
--- a/baselines/fedwav2vec2/fedwav2vec2/sb_recipe.py
+++ b/baselines/fedwav2vec2/fedwav2vec2/sb_recipe.py
@@ -1,6 +1,5 @@
 """Main SpeechBrain training and testing logic."""
 
-
 import gc
 import time
 from collections import OrderedDict
diff --git a/baselines/fedwav2vec2/fedwav2vec2/strategy.py b/baselines/fedwav2vec2/fedwav2vec2/strategy.py
index 6aa6007e4ccd..cee3ca0f8d8d 100644
--- a/baselines/fedwav2vec2/fedwav2vec2/strategy.py
+++ b/baselines/fedwav2vec2/fedwav2vec2/strategy.py
@@ -4,7 +4,6 @@
 extend or modify the functionality of an existing strategy.
 """
 
-
 import gc
 from typing import Dict, List, Optional, Tuple, Union
 
diff --git a/baselines/fedwav2vec2/pyproject.toml b/baselines/fedwav2vec2/pyproject.toml
index 1e7dbf55154b..d9b0c8090cd8 100644
--- a/baselines/fedwav2vec2/pyproject.toml
+++ b/baselines/fedwav2vec2/pyproject.toml
@@ -7,11 +7,11 @@ name = "fedwav2vec2" # <----- Ensure it matches the name of your baseline direct
 version = "1.0.0"
 description = "Federated Learning for ASR Based on wav2vec 2.0"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>", "Tuan Nguyen <tuanmanh.nguyen167@gmail.com>"]
+authors = ["The Flower Authors <hello@flower.ai>", "Tuan Nguyen <tuanmanh.nguyen167@gmail.com>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -42,14 +42,14 @@ flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 speechbrain = "0.5.15"
 pandas = "2.1.1"
-torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.13.1%2Bcu116-cp310-cp310-linux_x86_64.whl"}
-torchaudio = { url = "https://download.pytorch.org/whl/cu116/torchaudio-0.13.1%2Bcu116-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.13.1%2Bcu116-cp310-cp310-linux_x86_64.whl" }
+torchaudio = { url = "https://download.pytorch.org/whl/cu116/torchaudio-0.13.1%2Bcu116-cp310-cp310-linux_x86_64.whl" }
 transformers = "4.33.2"
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
-docformatter = "==1.5.1"
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
@@ -57,6 +57,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
@@ -85,10 +86,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/fjord/fjord/client.py b/baselines/fjord/fjord/client.py
index 2b18d9547086..845a1cec9815 100644
--- a/baselines/fjord/fjord/client.py
+++ b/baselines/fjord/fjord/client.py
@@ -1,4 +1,5 @@
 """Flower client implementing FjORD."""
+
 from collections import OrderedDict
 from copy import deepcopy
 from types import SimpleNamespace
diff --git a/baselines/fjord/fjord/dataset.py b/baselines/fjord/fjord/dataset.py
index 478826c2cf64..7809ec27f2e9 100644
--- a/baselines/fjord/fjord/dataset.py
+++ b/baselines/fjord/fjord/dataset.py
@@ -1,4 +1,5 @@
 """Dataset for CIFAR10."""
+
 import random
 from typing import Optional, Tuple
 
diff --git a/baselines/fjord/fjord/main.py b/baselines/fjord/fjord/main.py
index f85fb9ccf158..0863c12f0212 100644
--- a/baselines/fjord/fjord/main.py
+++ b/baselines/fjord/fjord/main.py
@@ -1,4 +1,5 @@
 """Main script for FjORD."""
+
 import math
 import os
 import random
diff --git a/baselines/fjord/fjord/models.py b/baselines/fjord/fjord/models.py
index 0f3fc276decf..a7821b09f7cd 100644
--- a/baselines/fjord/fjord/models.py
+++ b/baselines/fjord/fjord/models.py
@@ -1,4 +1,5 @@
 """ResNet model for Fjord."""
+
 from types import SimpleNamespace
 from typing import List, Optional, Tuple
 
diff --git a/baselines/fjord/fjord/od/layers/__init__.py b/baselines/fjord/fjord/od/layers/__init__.py
index a87c70401d4c..3ff9d4543f06 100644
--- a/baselines/fjord/fjord/od/layers/__init__.py
+++ b/baselines/fjord/fjord/od/layers/__init__.py
@@ -1,4 +1,5 @@
 """Ordered Dropout layers."""
+
 from .batch_norm import ODBatchNorm2d
 from .conv import ODConv2d
 from .linear import ODLinear
diff --git a/baselines/fjord/fjord/od/layers/batch_norm.py b/baselines/fjord/fjord/od/layers/batch_norm.py
index 5fce4dff0910..b02e2d9f638a 100644
--- a/baselines/fjord/fjord/od/layers/batch_norm.py
+++ b/baselines/fjord/fjord/od/layers/batch_norm.py
@@ -1,4 +1,5 @@
 """BatchNorm using Ordered Dropout."""
+
 from typing import List, Optional
 
 import numpy as np
diff --git a/baselines/fjord/fjord/od/layers/conv.py b/baselines/fjord/fjord/od/layers/conv.py
index 544f3a578418..d68e263c2abb 100644
--- a/baselines/fjord/fjord/od/layers/conv.py
+++ b/baselines/fjord/fjord/od/layers/conv.py
@@ -1,4 +1,5 @@
 """Convolutional layer using Ordered Dropout."""
+
 from typing import Optional, Tuple, Union
 
 import numpy as np
diff --git a/baselines/fjord/fjord/od/layers/linear.py b/baselines/fjord/fjord/od/layers/linear.py
index 927ae4c8d516..3ec4dce3651e 100644
--- a/baselines/fjord/fjord/od/layers/linear.py
+++ b/baselines/fjord/fjord/od/layers/linear.py
@@ -1,4 +1,5 @@
 """Liner layer using Ordered Dropout."""
+
 from typing import Optional, Tuple, Union
 
 import numpy as np
diff --git a/baselines/fjord/fjord/od/layers/utils.py b/baselines/fjord/fjord/od/layers/utils.py
index 46649a51de96..7fc39abad712 100644
--- a/baselines/fjord/fjord/od/layers/utils.py
+++ b/baselines/fjord/fjord/od/layers/utils.py
@@ -1,4 +1,5 @@
 """Utils function for Ordered Dropout layers."""
+
 from typing import Optional, Tuple, Union
 
 from torch.nn import Module
diff --git a/baselines/fjord/fjord/od/models/utils.py b/baselines/fjord/fjord/od/models/utils.py
index 4a1707587ef4..7e121c32964b 100644
--- a/baselines/fjord/fjord/od/models/utils.py
+++ b/baselines/fjord/fjord/od/models/utils.py
@@ -1,4 +1,5 @@
 """Utility functions for models."""
+
 from torch import nn
 
 from ..layers import ODBatchNorm2d, ODConv2d, ODLinear
diff --git a/baselines/fjord/fjord/od/samplers/__init__.py b/baselines/fjord/fjord/od/samplers/__init__.py
index dad08b4236c4..d94b2d49ac61 100644
--- a/baselines/fjord/fjord/od/samplers/__init__.py
+++ b/baselines/fjord/fjord/od/samplers/__init__.py
@@ -1,4 +1,5 @@
 """OD samplers."""
+
 from .base_sampler import BaseSampler
 from .fixed_od import ODSampler
 
diff --git a/baselines/fjord/fjord/od/samplers/base_sampler.py b/baselines/fjord/fjord/od/samplers/base_sampler.py
index 28eac929df81..fd67a7d986ba 100644
--- a/baselines/fjord/fjord/od/samplers/base_sampler.py
+++ b/baselines/fjord/fjord/od/samplers/base_sampler.py
@@ -1,4 +1,5 @@
 """Base sampler class."""
+
 from collections.abc import Generator
 
 from torch.nn import Module
diff --git a/baselines/fjord/fjord/od/samplers/fixed_od.py b/baselines/fjord/fjord/od/samplers/fixed_od.py
index b90912a7b5c2..51aa5a343cd6 100644
--- a/baselines/fjord/fjord/od/samplers/fixed_od.py
+++ b/baselines/fjord/fjord/od/samplers/fixed_od.py
@@ -1,4 +1,5 @@
 """Ordered Dropout stochastic sampler."""
+
 from collections.abc import Generator
 from typing import List
 
diff --git a/baselines/fjord/fjord/server.py b/baselines/fjord/fjord/server.py
index d25e8f17156a..7c1d10f5752d 100644
--- a/baselines/fjord/fjord/server.py
+++ b/baselines/fjord/fjord/server.py
@@ -1,4 +1,5 @@
 """Global evaluation function."""
+
 from typing import Any, Dict, Optional, Tuple
 
 import flwr as fl
diff --git a/baselines/fjord/fjord/strategy.py b/baselines/fjord/fjord/strategy.py
index d3ec99a419bd..fa2fd6bfa7c0 100644
--- a/baselines/fjord/fjord/strategy.py
+++ b/baselines/fjord/fjord/strategy.py
@@ -1,4 +1,5 @@
 """FjORD strategy."""
+
 from copy import deepcopy
 from functools import reduce
 from typing import Dict, List, Optional, Tuple, Union
diff --git a/baselines/fjord/fjord/utils/logger.py b/baselines/fjord/fjord/utils/logger.py
index b0eb2194bfef..183eff4c0daa 100644
--- a/baselines/fjord/fjord/utils/logger.py
+++ b/baselines/fjord/fjord/utils/logger.py
@@ -1,4 +1,5 @@
 """Logger functionality."""
+
 import logging
 
 import coloredlogs
diff --git a/baselines/fjord/fjord/utils/utils.py b/baselines/fjord/fjord/utils/utils.py
index 3a1a327dd555..16f58e8e8df2 100644
--- a/baselines/fjord/fjord/utils/utils.py
+++ b/baselines/fjord/fjord/utils/utils.py
@@ -1,4 +1,5 @@
 """Utility functions for fjord."""
+
 import os
 from typing import List, Optional, OrderedDict
 
diff --git a/baselines/fjord/pyproject.toml b/baselines/fjord/pyproject.toml
index d8a9ae307d7c..86eab6b12dd9 100644
--- a/baselines/fjord/pyproject.toml
+++ b/baselines/fjord/pyproject.toml
@@ -9,9 +9,9 @@ description = "FjORD implementation of Federated Ordered Dropout in Flower"
 license = "Apache-2.0"
 authors = ["Steve Laskaridis <stevelaskaridis@gmail.com>", "Samuel Horvath <samohorvath11@gmail.com>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -44,15 +44,13 @@ matplotlib = "3.7.1"
 coloredlogs = "15.0.1"
 omegaconf = "2.3.0"
 tqdm = "4.65.0"
-torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl"}
-
-
+torch = { url = "https://download.pytorch.org/whl/cu117/torch-2.0.1%2Bcu117-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu117/torchvision-0.15.2%2Bcu117-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
-docformatter = "==1.5.1"
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
@@ -60,6 +58,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
@@ -88,10 +87,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y,fl,lr,p,p_,bn,NUM_CLIENTS,od,m,g,ResNet18,FJORD_CONFIG_TYPE"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 
 [[tool.mypy.overrides]]
diff --git a/baselines/fjord/requirements.txt b/baselines/fjord/requirements.txt
index 35583b1a45c4..28f700c27537 100644
--- a/baselines/fjord/requirements.txt
+++ b/baselines/fjord/requirements.txt
@@ -1,8 +1,7 @@
 coloredlogs==15.0.1
 hydra-core==1.3.2
-flwr==1.5.0
+flwr[simulation]==1.5.0
 omegaconf==2.3.0
-ray==2.6.3
 torch==2.0.1
 torchvision==0.15.2
 tqdm==4.65.0
diff --git a/baselines/fjord/setup.py b/baselines/fjord/setup.py
index aa09948c34fc..aa338c0aaea7 100644
--- a/baselines/fjord/setup.py
+++ b/baselines/fjord/setup.py
@@ -1,4 +1,5 @@
 """Setup fjord package."""
+
 from setuptools import find_packages, setup
 
 VERSION = "0.1.0"
diff --git a/baselines/flanders/README.md b/baselines/flanders/README.md
index 381a65209e95..af9a52907fac 100644
--- a/baselines/flanders/README.md
+++ b/baselines/flanders/README.md
@@ -5,31 +5,31 @@ labels: [robustness, model poisoning, anomaly detection, autoregressive model, r
 dataset: [MNIST, Income, CIFAR-10, California Housing]
 ---
 
-****Paper:**** [arxiv.org/abs/2303.16668](https://arxiv.org/abs/2303.16668)
+**Paper:** [arxiv.org/abs/2303.16668](https://arxiv.org/abs/2303.16668)
 
-****Authors:**** Gabriele Tolomei, Edoardo Gabrielli, Dimitri Belli, Vittorio Miori
+**Authors:** Gabriele Tolomei, Edoardo Gabrielli, Dimitri Belli, Vittorio Miori
 
-****Abstract:**** In this work, we propose FLANDERS, a novel federated learning (FL) aggregation scheme robust to Byzantine attacks. FLANDERS considers the local model updates sent by clients at each FL round as a matrix-valued time series. Then, it identifies malicious clients as outliers of this time series by comparing actual observations with those estimated by a matrix autoregressive forecasting model. Experiments conducted on several datasets under different FL settings demonstrate that FLANDERS matches the robustness of the most powerful baselines against Byzantine clients. Furthermore, FLANDERS remains highly effective even under extremely severe attack scenarios, as opposed to existing defense strategies. 
+**Abstract:** In this work, we propose FLANDERS, a novel federated learning (FL) aggregation scheme robust to Byzantine attacks. FLANDERS considers the local model updates sent by clients at each FL round as a matrix-valued time series. Then, it identifies malicious clients as outliers of this time series by comparing actual observations with those estimated by a matrix autoregressive forecasting model. Experiments conducted on several datasets under different FL settings demonstrate that FLANDERS matches the robustness of the most powerful baselines against Byzantine clients. Furthermore, FLANDERS remains highly effective even under extremely severe attack scenarios, as opposed to existing defense strategies. 
 
 
 ## About this baseline
 
-****What’s implemented:**** The code in this directory replicates the results on MNIST and Income datasets under all attack settings (Gaussian, LIE, OPT and AGR-MM), but I've also implemented the code for California Housing and CIFAR-10. More specifically, you can reproduce part of Fig. 3 (a) and (b), and part of Fig. 4 and Fig. 5 in Appendix G.2 (there's no division between $b\lt50%$, and $b\ge50%$.). Since here I do not compare FLANDERS with other defence baselines, all attacks are grouped into the same plot.
+**What’s implemented:** The code in this directory replicates the results on MNIST and Income datasets under all attack settings (Gaussian, LIE, OPT and AGR-MM), but I've also implemented the code for California Housing and CIFAR-10. More specifically, you can reproduce part of Fig. 3 (a) and (b), and part of Fig. 4 and Fig. 5 in Appendix G.2 (there's no division between $b\lt50%$, and $b\ge50%$.). Since here I do not compare FLANDERS with other defence baselines, all attacks are grouped into the same plot.
 
-****Datasets:**** MNIST, Income
+**Datasets:** MNIST, Income
 
-****Hardware Setup:**** Apple M2 Pro, 16gb RAM
+**Hardware Setup:** Apple M2 Pro, 16gb RAM
 
-****Estimated time to run:**** For **Income**, 117 seconds. For **MNIST**, 24 minutes. This is the time estimated for a *single experiment* (i.e. 50 rounds, 10 clients sampled each round, 1 local epoch).
+**Estimated time to run:** For **Income**, 117 seconds. For **MNIST**, 24 minutes. This is the time estimated for a *single experiment* (i.e. 50 rounds, 10 clients sampled each round, 1 local epoch).
 
-****Contributors:**** Edoardo Gabrielli, University of Rome "La Sapienza" ([GitHub](https://github.com/edogab33), [LinkedIn](https://www.linkedin.com/in/edoardog/), [Scholar](https://scholar.google.com/citations?user=b3bePdYAAAAJ))
+**Contributors:** Edoardo Gabrielli, University of Rome "La Sapienza" ([GitHub](https://github.com/edogab33), [LinkedIn](https://www.linkedin.com/in/edoardog/), [Scholar](https://scholar.google.com/citations?user=b3bePdYAAAAJ))
 
 
 ## Experimental Setup
 
-****Task:**** Image classification, logistic regression, linear regression
+**Task:** Image classification, logistic regression, linear regression
 
-****Models:**** Appendix C of the paper describes the models, but here's a summary.
+**Models:** Appendix C of the paper describes the models, but here's a summary.
 
 Income (binary classification):
 - cyclic coordinate descent (CCD)
@@ -43,7 +43,7 @@ MNIST (multilabel classification, fully connected, feed forward NN):
 - hidden layer 2: 256
 
 
-****Dataset:**** Every dataset is partitioned into two disjoint sets: 80% for training and 20% for testing. The training set is distributed uniformly across all clients (100), while the testing set is held by the server to evaluate the global model.
+**Dataset:** Every dataset is partitioned into two disjoint sets: 80% for training and 20% for testing. The training set is distributed uniformly across all clients (100), while the testing set is held by the server to evaluate the global model.
 
 | Description | Default Value |
 | ----------- | ----- |
@@ -52,7 +52,7 @@ MNIST (multilabel classification, fully connected, feed forward NN):
 | Training set | 80% |
 | Testing set | 20% |
 
-****Training Hyperparameters:****
+**Training Hyperparameters:**
 
 | Dataset | # of clients  | Clients per round | # of rounds | $L$ | Batch size | Learning rate | $\lambda_1$ | $\lambda_2$ | Optimizer | Dropout | Alpha | Beta | # of clients to keep | Sampling |
 | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- |
@@ -74,9 +74,6 @@ poetry install
 
 # Activate the env
 poetry shell
-
-# Manually install natsort
-pip install natsort
 ```
 
 
diff --git a/baselines/flanders/pyproject.toml b/baselines/flanders/pyproject.toml
index 04711e71bf8a..416247f9c7bb 100644
--- a/baselines/flanders/pyproject.toml
+++ b/baselines/flanders/pyproject.toml
@@ -37,13 +37,19 @@ classifiers = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.9, <3.12.0"  # pandas requires >=3.9
+python = ">=3.10, <3.12.0"
 hydra-core = "1.3.2" # don't change this
 flwr = {extras = ["simulation"], version = "1.6.0" }
-torch = "2.1.1"
+torch = [
+    { platform = "darwin", version = "2.1.1" },
+    { platform = "linux", url = "https://download.pytorch.org/whl/cu118/torch-2.1.1%2Bcu118-cp310-cp310-linux_x86_64.whl" }
+    ]
+torchvision = [
+    { platform = "darwin", version = "0.16.1"},
+    { platform = "linux", url = "https://download.pytorch.org/whl/cu118/torchvision-0.16.1%2Bcu118-cp310-cp310-linux_x86_64.whl" }
+    ]
 pandas = "^2.1.3"
 scikit-learn = "1.3.2"
-torchvision = "0.16.1"
 ipykernel = "^6.27.1"
 natsort = "^8.4.0"
 seaborn = "^0.13.0"
diff --git a/baselines/flwr_baselines/dev/bootstrap.sh b/baselines/flwr_baselines/dev/bootstrap.sh
index 0bc322edc0de..a2f3080a82f7 100755
--- a/baselines/flwr_baselines/dev/bootstrap.sh
+++ b/baselines/flwr_baselines/dev/bootstrap.sh
@@ -8,7 +8,7 @@ cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
 # 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 poetry==1.5.1
+python -m pip install -U poetry==1.7.1
 
 # Use `poetry` to install project dependencies
 python -m poetry install
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/cifar/client.py b/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/cifar/client.py
index 509587f9762e..62e44cd92192 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/cifar/client.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/cifar/client.py
@@ -1,6 +1,5 @@
 """Flower Client for CIFAR10/100."""
 
-
 from collections import OrderedDict
 from pathlib import Path
 from typing import Callable, Dict, Tuple
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/cifar/utils.py b/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/cifar/utils.py
index 5a53314e5cf0..bd65005628ff 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/cifar/utils.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/cifar/utils.py
@@ -1,6 +1,5 @@
 """Util functions for CIFAR10/100."""
 
-
 from collections import OrderedDict
 from pathlib import Path
 from typing import Callable, Dict, List, Optional, Tuple
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/main.py b/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/main.py
index a4e29e6ac8a7..cab398a4c76d 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/main.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/adaptive_federated_optimization/main.py
@@ -1,6 +1,5 @@
 """Runs AdaptiveFederated Optimization for CIFAR10/100."""
 
-
 from os import chdir
 from pathlib import Path
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/example_test.py b/baselines/flwr_baselines/flwr_baselines/publications/example_test.py
index f40423c9fae0..8108a1ae0538 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/example_test.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/example_test.py
@@ -3,7 +3,6 @@
 Should be removed when first real code has tests.
 """
 
-
 import unittest
 
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/client.py b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/client.py
index b7ce7cad7fac..023cfa8d4716 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/client.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/client.py
@@ -80,7 +80,7 @@ def gen_client_fn(
     Parameters
     ----------
     device : torch.device
-        The device on which the the client will train on and test on.
+        The device on which the client will train on and test on.
     iid : bool
         The way to partition the data for each client, i.e. whether the data
         should be independent and identically distributed between the clients
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/dataset.py b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/dataset.py
index 4010c3ba679d..e63ad55382e3 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/dataset.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/dataset.py
@@ -1,6 +1,5 @@
 """MNIST dataset utilities for federated learning."""
 
-
 from typing import List, Optional, Tuple
 
 import numpy as np
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/main.py b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/main.py
index 378b3f42816b..f90f608b65f3 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/main.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/main.py
@@ -1,6 +1,5 @@
 """Runs CNN federated learning for MNIST dataset."""
 
-
 from pathlib import Path
 
 import flwr as fl
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/model.py b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/model.py
index ea7864c38057..46e0738cd464 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/model.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/model.py
@@ -1,6 +1,5 @@
 """CNN model architecutre, training and testing functions for MNIST."""
 
-
 from typing import Tuple
 
 import torch
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/tests/data_test.py b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/tests/data_test.py
index 06b2782530f8..4f0e3a8e92eb 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/tests/data_test.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/tests/data_test.py
@@ -1,6 +1,5 @@
 """Used to test the model and the data partitionning."""
 
-
 from flwr_baselines.publications.fedavg_mnist import dataset
 
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/tests/model_test.py b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/tests/model_test.py
index f934a74cda9e..9f33c5db43e3 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/tests/model_test.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/tests/model_test.py
@@ -1,6 +1,5 @@
 """Used to test the model and the data partitionning."""
 
-
 from flwr_baselines.publications.fedavg_mnist import model
 
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/utils.py b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/utils.py
index 3f992f50f202..63d37849bee9 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/utils.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/fedavg_mnist/utils.py
@@ -1,6 +1,5 @@
 """Contains utility functions for CNN FL on MNIST."""
 
-
 from collections import OrderedDict
 from pathlib import Path
 from typing import Callable, Dict, List, Optional, Tuple
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/client.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/client.py
index b67169c1c9bb..3a5450f33e80 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/client.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/client.py
@@ -1,6 +1,5 @@
 """Client implementation for federated learning."""
 
-
 from typing import Dict, List, Tuple
 
 import flwr as fl
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset.py
index 9e22d7326ce7..ca2c5868efca 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset.py
@@ -1,6 +1,5 @@
 """FEMNIST dataset creation module."""
 
-
 import pathlib
 from logging import INFO
 from typing import List, Tuple
@@ -69,8 +68,8 @@ def create_dataset(df_info: pd.DataFrame, labels: np.ndarray) -> NISTLikeDataset
 
 
 def create_partition_list(df_info: pd.DataFrame) -> List[List[int]]:
-    """
-    Create list of list with int masks identifying writers.
+    """Create list of list with int masks identifying writers.
+
     Parameters
     ----------
     df_info: pd.DataFrame
@@ -174,8 +173,8 @@ def train_valid_test_partition(
 def transform_datasets_into_dataloaders(
     datasets: List[Dataset], **dataloader_kwargs
 ) -> List[DataLoader]:
-    """
-    Transform datasets into dataloaders.
+    """Transform datasets into dataloaders.
+
     Parameters
     ----------
     datasets: List[Dataset]
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset_test.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset_test.py
index 4a6d52e92787..6043d77b5fb2 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset_test.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/dataset_test.py
@@ -1,4 +1,5 @@
 """Dataset tests."""
+
 # pylint: disable=no-self-use, protected-access
 import tempfile
 import unittest
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor.py
index e1e71788eff4..9143f6720758 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor.py
@@ -1,6 +1,5 @@
 """Module to preprocess the NIST Special Database 19 into FeMNIST/FEMNIST."""
 
-
 import pathlib
 from logging import DEBUG, WARN
 from typing import Dict, Union
@@ -21,10 +20,12 @@ class NISTPreprocessor:
     """Preprocess NIST images from two directories divided by class and by
     writer.
 
-    The preprocessing procedure include different step for FEMNIST and FeMNIST.
+    The preprocessing procedure include different step for FEMNIST and
+    FeMNIST.
 
-    Using two datasets (by_class and by_write, actually one dataset but divided differently) is
-    required to merge information about the writer_id and label.
+    Using two datasets (by_class and by_write, actually one dataset but
+    divided differently) is required to merge information about the
+    writer_id and label.
     """
 
     def __init__(
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor_test.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor_test.py
index d13f3d45b8b9..9e5266347f6b 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor_test.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_preprocessor_test.py
@@ -1,4 +1,5 @@
 """NIST preprocessor test."""
+
 # pylint: disable=no-self-use
 import unittest
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_sampler.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_sampler.py
index 9521353eea82..db3e028ee5bb 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_sampler.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_sampler.py
@@ -1,6 +1,5 @@
 """Module for sampling the reference data of FEMNIST."""
 
-
 import math
 from logging import WARN
 from typing import Optional
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_sampler_test.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_sampler_test.py
index 55970fb85fac..290914b58167 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_sampler_test.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/nist_sampler_test.py
@@ -1,4 +1,5 @@
 """NIST sampler test."""
+
 # pylint: disable=no-self-use, protected-access
 import unittest
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/utils.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/utils.py
index 20ea32cc648e..e55e08c37801 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/utils.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/utils.py
@@ -1,6 +1,5 @@
 """Utilities used for the FEMNIST dataset creation."""
 
-
 import hashlib
 import pathlib
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/zip_downloader.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/zip_downloader.py
index 8aa223fc1d49..687fd7be825b 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/zip_downloader.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/dataset/zip_downloader.py
@@ -1,7 +1,6 @@
 """Module for downloading the ZIP files, extracting them and removing the
 downloaded zip file."""
 
-
 import os
 import pathlib
 import shutil
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/main.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/main.py
index 1aa5e18bbb6c..701db72748a9 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/main.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/main.py
@@ -1,6 +1,5 @@
 """Main module for running FEMNIST experiments."""
 
-
 import pathlib
 from functools import partial
 from typing import Type, Union
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model.py
index 318661784b61..678dce843f62 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model.py
@@ -1,6 +1,5 @@
 """Implementation of the model used for the FEMNIST experiments."""
 
-
 from logging import INFO
 from typing import Optional, Tuple
 
@@ -54,8 +53,9 @@ def train(
     n_batches: Optional[int] = None,
     verbose: bool = False,
 ) -> Tuple[float, float, Optional[float], Optional[float]]:
-    """Train a given model with CrossEntropy and SGD (or some version of it
-    like batch-SGD).
+    """Train a given model with CrossEntropy and SGD.
+
+    Alternatively, some version of it, like batch-SGD
 
     n_batches is an alternative way of specifying the training length
     (instead of epochs)
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model_test.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model_test.py
index fd71ddbc52b7..6d289b36bc98 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model_test.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/model_test.py
@@ -1,4 +1,5 @@
 """FEMNIST model test."""
+
 # pylint: disable=no-self-use
 import unittest
 
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/strategy.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/strategy.py
index c8cb8a4f419d..afa6a5cdd6ad 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/strategy.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/strategy.py
@@ -1,6 +1,5 @@
 """FedAvg with the same clients used for both training and evaluation."""
 
-
 from typing import Callable, Dict, List, Optional, Tuple
 
 from flwr.common import (
diff --git a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/utils.py b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/utils.py
index 22d010c9601c..4ce95743599e 100644
--- a/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/utils.py
+++ b/baselines/flwr_baselines/flwr_baselines/publications/leaf/femnist/utils.py
@@ -1,6 +1,5 @@
 """Utils used for the FEMNIST project."""
 
-
 from typing import Dict, List, Tuple
 
 import numpy as np
@@ -40,8 +39,8 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Dict[str, Scalar]:
 
 
 def setup_seed(seed: int):
-    """
-    Set up seed for numpy and torch.
+    """Set up seed for numpy and torch.
+
     Parameters
     ----------
     seed: int
diff --git a/baselines/flwr_baselines/pyproject.toml b/baselines/flwr_baselines/pyproject.toml
index f5fb64744ff8..f0b2ac84e66e 100644
--- a/baselines/flwr_baselines/pyproject.toml
+++ b/baselines/flwr_baselines/pyproject.toml
@@ -7,11 +7,11 @@ name = "flwr_baselines"
 version = "1.0.0"
 description = "Flower Baselines"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -53,9 +53,9 @@ pandas = "^1.5.3"
 pyhamcrest = "^2.0.4"
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
-docformatter = "==1.5.1"
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
 mypy = "==0.961"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
@@ -103,8 +103,8 @@ follow_imports = "skip"
 follow_imports_for_stubs = true
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*"
+generated-members = "numpy.*, torch.*"
 
 [tool.pylint.messages_control]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
diff --git a/baselines/flwr_baselines/requirements.txt b/baselines/flwr_baselines/requirements.txt
index 7b90b8a9bf1f..50655dd5a4fe 100644
--- a/baselines/flwr_baselines/requirements.txt
+++ b/baselines/flwr_baselines/requirements.txt
@@ -1,9 +1,9 @@
 ##### Flower Baseline Dependencies
 # Under Apache-2.0 license
-# Authors: The Flower Authors <hello@flower.dev>
-# Website: https://flower.dev
+# Authors: The Flower Authors <hello@flower.ai>
+# Website: https://flower.ai
 # Repository: https://github.com/adap/flower
-# Documentation: https://flower.dev
+# Documentation: https://flower.ai
 
 ##### dependencies
 flwr >= 0.18.0
@@ -18,9 +18,9 @@ wget >= 3.2
 virtualenv >= 20.24.6
 
 ##### dev-dependencies
-isort == 5.11.5
-black == 23.1.0
-docformatter == 1.5.1
+isort == 5.13.2
+black == 24.2.0
+docformatter == 1.7.5
 mypy == 0.961
 pylint == 2.8.2
 flake8 == 3.9.2
diff --git a/baselines/heterofl/LICENSE b/baselines/heterofl/LICENSE
new file mode 100644
index 000000000000..d64569567334
--- /dev/null
+++ b/baselines/heterofl/LICENSE
@@ -0,0 +1,202 @@
+
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diff --git a/baselines/heterofl/README.md b/baselines/heterofl/README.md
new file mode 100644
index 000000000000..dcb54900878f
--- /dev/null
+++ b/baselines/heterofl/README.md
@@ -0,0 +1,200 @@
+---
+title: "HeteroFL: Computation And Communication Efficient Federated Learning For Heterogeneous Clients"
+url: https://openreview.net/forum?id=TNkPBBYFkXg
+labels: [system heterogeneity, image classification]
+dataset: [MNIST, CIFAR-10]
+---
+
+# HeteroFL: Computation And Communication Efficient Federated Learning For Heterogeneous Clients
+
+**Paper:** [openreview.net/forum?id=TNkPBBYFkXg](https://openreview.net/forum?id=TNkPBBYFkXg)
+
+**Authors:** Enmao Diao, Jie Ding, Vahid Tarokh
+
+**Abstract:** Federated Learning (FL) is a method of training machine learning models on private data distributed over a large number of possibly heterogeneous clients such as mobile phones and IoT devices. In this work, we propose a new federated learning framework named HeteroFL to address heterogeneous clients equipped with very different computation and communication capabilities. Our solution can enable the training of heterogeneous local models with varying computation complexities and still produce a single global inference model. For the first time, our method challenges the underlying assumption of existing work that local models have to share the same architecture as the global model. We demonstrate several strategies to enhance FL training and conduct extensive empirical evaluations, including five computation complexity levels of three model architecture on three datasets. We show that adaptively distributing subnetworks according to clients’ capabilities is both computation and communication efficient.
+
+
+## About this baseline
+
+**What’s implemented:**  The code in this directory is an implementation of HeteroFL in PyTorch using Flower. The code incorporates references from the authors' implementation. Implementation of custom model split and aggregation as suggested by [@negedng](https://github.com/negedng), is available [here](https://github.com/msck72/heterofl_custom_aggregation). By modifying the configuration in the `base.yaml`, the results in the paper can be replicated, with both fixed and dynamic computational complexities among clients.
+
+**Key Terminology:**
++ *Model rate* defines the computational complexity of a client. Authors have defined five different computation complexity levels {a, b, c, d, e} with the hidden channel shrinkage ratio r = 0.5. 
+
++ *Model split mode* specifies whether the computational complexities of clients are fixed (throughout the experiment), or whether they are dynamic (change their mode_rate/computational-complexity every-round). 
+
++ *Model mode* determines the proportionality of clients with various computation complexity levels, for example, a4-b2-e4 determines at each round, proportion of clients with computational complexity level a = 4 / (4 + 2 + 4) * num_clients, similarly, proportion of clients with computational complexity level b = 2 / (4 + 2 + 4) * num_clients and so on.
+
+**Implementation Insights:**
+*ModelRateManager* manages the model rate of client in simulation, which changes the model rate based on the model mode of the setup and *ClientManagerHeterofl* keeps track of model rates of the clients, so configure fit knows which/how-much subset of the model that needs to be sent to the client.
+
+**Datasets:** The code utilized benchmark MNIST and CIFAR-10 datasets from Pytorch's torchvision for its experimentation.
+
+**Hardware Setup:**  The experiments were run on Google colab pro with 50GB RAM and T4 TPU. For MNIST dataset & CNN model, it approximately takes 1.5 hours to complete 200 rounds while for CIFAR10 dataset & ResNet18 model it takes around 3-4 hours to complete 400 rounds (may vary based on the model-mode of the setup).
+
+**Contributors:** M S Chaitanya Kumar [(github.com/msck72)](https://github.com/msck72)
+
+
+## Experimental Setup
+
+**Task:** Image Classification.
+**Model:** This baseline uses two models:
++ Convolutional Neural Network(CNN) model is used for MNIST dataset.
++ PreResNet (preactivated ResNet) model is used for CIFAR10 dataset.
+
+These models use static batch normalization (sBN) and they incorporate a Scaler module following each convolutional layer.
+
+**Dataset:** This baseline includes MNIST and CIFAR10 datasets. 
+
+| Dataset | #Classes | IID Partition | non-IID Partition |
+| :---: | :---: | :---: | :---: | 
+| MNIST<br>CIFAR10 |  10| Distribution of equal number of data examples among n clients | Distribution of data examples such that each client has at most 2 (customizable) classes |
+
+
+**Training Hyperparameters:** 
+
+| Description | Data Setting | MNIST | CIFAR-10 |
+| :---: | :---: |  :---:| :---: |
+Total Clients  | both | 100 | 100 |
+Clients Per Round | both | 100 | 100
+Local Epcohs | both | 5 | 5
+Num. ROunds | IID <br> non-IID| 200<br>400 | 400<br>800
+Optimizer | both | SGD | SGD
+Momentum | both | 0.9 | 0.9
+Weight-decay | both | 5.00e-04 | 5.00e-04
+Learning Rate | both | 0.01 | 0.1
+Decay Schedule | IID <br> non-IID| [100]<br>[150, 250] | [200]<br>[300,500]
+Hidden Layers | both | [64 , 128 , 256 , 512] | [64 , 128 , 256 , 512]
+
+
+The hyperparameters of Fedavg baseline are available in [Liang et al (2020)](https://arxiv.org/abs/2001.01523).
+
+## Environment Setup
+
+To construct the Python environment, simply run:
+
+```bash
+# Set python version
+pyenv install 3.10.6
+pyenv local 3.10.6
+
+# Tell poetry to use python 3.10
+poetry env use 3.10.6
+
+# install the base Poetry environment
+poetry install
+
+# activate the environment
+poetry shell
+```
+
+
+## Running the Experiments
+To run HeteroFL experiments in poetry activated environment:
+```bash  
+# The main experiment implemented in your baseline using default hyperparameters (that should be setup in the Hydra configs)
+# should run (including dataset download and necessary partitioning) by executing the command:
+
+python -m heterofl.main  # Which runs the heterofl with arguments available in heterfl/conf/base.yaml
+
+# We could override the settings that were specified in base.yaml using the command-line-arguments
+# Here's an example for changing the dataset name, non-iid and model
+python -m heterofl.main dataset.dataset_name='CIFAR10' dataset.iid=False model.model_name='resnet18'
+
+# Similarly, another example for changing num_rounds, model_split_mode, and model_mode
+python -m heterofl.main num_rounds=400 control.model_split_mode='dynamic' control.model_mode='a1-b1'
+
+# Similarly, another example for changing num_rounds, model_split_mode, and model_mode
+python -m heterofl.main num_rounds=400 control.model_split_mode='dynamic' control.model_mode='a1-b1'
+
+```
+To run FedAvg experiments:
+```bash
+python -m heterofl.main --config-name fedavg
+# Similarly to the commands illustrated above, we can modify the default settings in the fedavg.yaml file.
+```
+
+## Expected Results
+
+```bash
+# running the multirun for IID-MNIST with various model-modes using default config
+python -m heterofl.main --multirun control.model_mode='a1','a1-e1','a1-b1-c1-d1-e1'
+
+# running the multirun for IID-CIFAR10 dataset with various model-modes by modifying default config
+python -m heterofl.main --multirun control.model_mode='a1','a1-e1','a1-b1-c1-d1-e1' dataset.dataset_name='CIFAR10' model.model_name='resnet18' num_rounds=400 optim_scheduler.lr=0.1 strategy.milestones=[150, 250]
+
+# running the multirun for non-IID-MNIST with various model-modes by modifying default config
+python -m heterofl.main --multirun control.model_mode='a1','a1-e1','a1-b1-c1-d1-e1' dataset.iid=False num_rounds=400 optim_scheduler.milestones=[200]
+
+# similarly, we can perform for various model-modes, datasets. But we cannot multirun with both non-iid and iid at once for reproducing the tables below, since the number of rounds and milestones for MultiStepLR are different for non-iid and iid. The tables below are the reproduced results of various multiruns.
+
+#To reproduce the fedavg results
+#for MNIST dataset
+python -m heterofl.main --config-name fedavg --multirun dataset.iid=True,False
+# for CIFAR10 dataset
+python -m heterofl.main --config-name fedavg --multirun num_rounds=1800 dataset.dataset_name='CIFAR10' dataset.iid=True,False dataset.batch_size.train=50 dataset.batch_size.test=128 model.model_name='CNNCifar' optim_scheduler.lr=0.1
+```
+<br>
+ 
+Results of the combination of various computation complexity levels for **MNIST** dataset with **dynamic** scenario(where a client does not belong to a fixed computational complexity level):
+
+| Model | Ratio | Parameters | FLOPS | Space(MB) | IID-accuracy | non-IId local-acc | non-IID global-acc |
+| :--: | :----: | :-----: | :-------: | :-------: | :----------: | :---------------: | :----------------: |
+| a | 1  | 1556.874  K | 80.504  M | 5.939 | 99.47 | 99.82 | 98.87 |
+| a-e | 0.502 | 781.734  K | 40.452  M | 2.982 | 99.49 | 99.86 | 98.9 |
+| a-b-c-d-e | 0.267 | 415.807  K | 21.625  M | 1.586 | 99.23 | 99.84 | 98.5 |
+| b | 1  | 391.37  K | 20.493  M | 1.493 | 99.54 | 99.81 | 98.81 |
+| b-e | 0.508  | 198.982  K | 10.447  M | 0.759 | 99.48 | 99.87 | 98.98 |
+| b-c-d-e | 0.334  | 130.54  K | 6.905  M | 0.498 | 99.34 | 99.81 | 98.73 |
+| c | 1 | 98.922  K | 5.307  M | 0.377 | 99.37 | 99.64 | 97.14 |
+| c-e |  0.628  | 62.098  K | 3.363  M | 0.237 | 99.16 | 99.72 | 97.68 |
+| c-d-e | 0.441  | 43.5965  K | 2.375  M | 0.166 | 99.28 | 99.69 | 97.27 |
+| d | 1 | 25.274  K | 1.418 M | 0.096 | 99.07 | 99.77 | 97.58 |
+| d-e | 0.63 | 15.934  K | 0.909  M | 0.0608 | 99.12 | 99.65 | 97.33 |
+| e | 1 | 6.594  K | 0.4005  M | 0.025 | 98.46 | 99.53 | 96.5 |
+| FedAvg | 1 | 633.226  K | 1.264128  M | 2.416 | 97.85 | 97.76 | 97.74 |
+
+
+<br>
+
+Results of the combination of various computation complexity levels for **CIFAR10** dataset with **dynamic** scenario(where a client does not belong to a fixed computational complexity level):
+> *The HeteroFL paper reports a model with 1.8M parameters for their FedAvg baseline. However, as stated by the paper authors, those results are borrowed from [Liang et al (2020)](https://arxiv.org/abs/2001.01523), which uses a small CNN with fewer parameters (~64K as shown in this table below). We believe the HeteroFL authors made a mistake when reporting the number of parameters. We borrowed the model from Liang et al (2020)'s [repo](https://github.com/pliang279/LG-FedAvg/blob/master/models/Nets.py). As in the paper, FedAvg was run for 1800 rounds.*
+
+
+| Model | Ratio | Parameters | FLOPS | Space(MB) | IID-acc | non-IId  local-acc <br> Final &#8195;&#8195;Best| non-IID  global-acc <br> Final &#8195;&#8195; Best|
+| :--: | :----: | :-----: | :-------: | :-------: | :----------: | :-----: | :------: |
+ a		| 1     | 9622  K	| 330.2 M  	 | 36.705	| 90.83 | 89.04   &#8195;&#8195;  92.41 | 48.72  &#8195;&#8195;  59.29	|
+ a-e		| 0.502 | 4830  K	| 165.9 M  	 | 18.426	| 89.98 | 87.98   &#8195;&#8195;  91.25	| 50.16  &#8195;&#8195;  57.66	|
+ a-b-c-d-e	| 0.267 | 2565  K	| 88.4 M	 | 9.785	| 87.46 | 89.75   &#8195;&#8195;  91.19	| 46.96  &#8195;&#8195;  55.6	|
+ b		| 1     | 2409  K	| 83.3 M	 | 9.189	| 88.59 | 89.31   &#8195;&#8195;  92.07	| 49.85  &#8195;&#8195;  60.79	|
+ b-e		| 0.508 | 1224  K	| 42.4 M	 | 4.667	| 89.23 | 90.93   &#8195;&#8195;  92.3	| 55.46  &#8195;&#8195;  61.98	|
+ b-c-d-e	| 0.332 | 801  K	| 27.9 M	 | 3.054	| 87.61 | 89.23   &#8195;&#8195;  91.83	| 51.59  &#8195;&#8195;  59.4	| 
+ c		| 1     | 604  K	| 21.2 M	 | 2.303	| 85.74 | 89.83   &#8195;&#8195;  91.75	| 44.03  &#8195;&#8195;  58.26	|
+ c-e		| 0.532 | 321  K	| 11.4 M	 | 1.225	| 87.32 | 89.28   &#8195;&#8195;  91.56	| 53.43  &#8195;&#8195;  59.5	| 
+ c-d-e		| 0.438 | 265  K	| 9.4 M	 | 1.010	| 85.59 | 91.48   &#8195;&#8195;  92.05	| 58.26  &#8195;&#8195;  61.79	|
+ d		| 1     | 152  K	| 5.5 M	 | 0.579	| 82.91 | 90.81   &#8195;&#8195;  91.47	| 55.95  &#8195;&#8195;  58.34	|
+ d-e		| 0.626 | 95  K	| 3.5 M	 | 0.363	| 82.77 | 88.79   &#8195;&#8195;  90.13	| 48.49  &#8195;&#8195;  54.18	|
+ e		| 1     | 38  K	| 1.5 M	 | 0.146	| 76.53 | 90.05   &#8195;&#8195;  90.91	| 54.68  &#8195;&#8195;  57.05	|
+|FedAvg	| 1 | 64  K| 1.3  M | 0.2446 | 70.65 | 53.12 &#8195;&#8195; 58.6 | 52.93 &#8195;&#8195; 58.47 |
+
+
+<!--
+| Model | Ratio | Parameters | FLOPS | Space(MB) | IID-accuracy | non-IId-|-local-acc | non-IID-|-global-acc | 
+| :--: | :----: | :-----: | :-------: | :-------: | :----------: | :-----: | :------: | :-----: | :-----: |
+|  |  |  |  |  |  | **800th** | **Best of 800**|  **800th** | **Best of 800**|
+ a		| 1     | 9622.09  K	| 330.246 M  	 | 36.705	| 90.83 | 89.04   |  92.41 	| 48.72  |  59.29	|
+ a-e		| 0.502 | 4830.22  K	| 165.859 M  	 | 18.426	| 89.98 | 87.98   |  91.25	| 50.16  |  57.66	|
+ a-b-c-d-e	| 0.267 | 2564.95  K	| 88.354 M	 | 9.785	| 87.46 | 89.75   |  91.19	| 46.96  |  55.6	|
+ b		| 1     | 2408.746  K	| 83.334 M	 | 9.189	| 88.59 | 89.31   |  92.07	| 49.85  |  60.79	|
+ b-e		| 0.508 | 1223.548  K	| 42.403 M	 | 4.667	| 89.23 | 90.93   |  92.3	| 55.46  |  61.98	|
+ b-c-d-e	| 0.332 | 800.665  K	| 27.881 M	 | 3.054	| 87.61 | 89.23   |  91.83	| 51.59  |  59.4	| 
+ c		| 1     | 603.802  K	| 21.220 M	 | 2.303	| 85.74 | 89.83   |  91.75	| 44.03  |  58.26	|
+ c-e		| 0.532 | 321.076  K	| 11.345 M	 | 1.225	| 87.32 | 89.28   |  91.56	| 53.43  |  59.5	| 
+ c-d-e		| 0.438 | 264.638  K	| 9.396 M	 | 1.010	| 85.59 | 91.48   |  92.05	| 58.26  |  61.79	|
+ d		| 1     | 151.762  K	| 5.498 M	 | 0.579	| 82.91 | 90.81   |  91.47	| 55.95  |  58.34	|
+ d-e		| 0.626 | 95.056  K	| 3.485 M	 | 0.363	| 82.77 | 88.79   |  90.13	| 48.49  |  54.18	|
+ e		| 1     | 38.35  K	| 1.471 M	 | 0.146	| 76.53 | 90.05   |  90.91	| 54.68  |  57.05	|
+| 		| 	|		|**FedAvg** |		|	|<sub>**1800th**</sub>|<sub>**Best of 1800**</sub>|<sub>**1800th**</sub>|<sub>**Best of 1800**</sub>|
+|FedAvg		| 1 	| 64.102  K	| 1.3202  M 	| 0.2446 	| 70.65 | 56.69   | 58.72 	| 56.76  | 58.64 	|
+
+-->
diff --git a/baselines/heterofl/heterofl/__init__.py b/baselines/heterofl/heterofl/__init__.py
new file mode 100644
index 000000000000..a5e567b59135
--- /dev/null
+++ b/baselines/heterofl/heterofl/__init__.py
@@ -0,0 +1 @@
+"""Template baseline package."""
diff --git a/baselines/heterofl/heterofl/client.py b/baselines/heterofl/heterofl/client.py
new file mode 100644
index 000000000000..cf325cb7e85b
--- /dev/null
+++ b/baselines/heterofl/heterofl/client.py
@@ -0,0 +1,133 @@
+"""Defines the MNIST Flower Client and a function to instantiate it."""
+
+from typing import Callable, Dict, List, Optional, Tuple
+
+import flwr as fl
+import torch
+from flwr.common.typing import NDArrays
+
+from heterofl.models import create_model, get_parameters, set_parameters, test, train
+
+# from torch.utils.data import DataLoader
+
+
+class FlowerNumPyClient(fl.client.NumPyClient):
+    """Standard Flower client for training."""
+
+    def __init__(
+        self,
+        # cid: str,
+        net: torch.nn.Module,
+        dataloader,
+        model_rate: Optional[float],
+        client_train_settings: Dict,
+    ):
+        # self.cid = cid
+        self.net = net
+        self.trainloader = dataloader["trainloader"]
+        self.label_split = dataloader["label_split"]
+        self.valloader = dataloader["valloader"]
+        self.model_rate = model_rate
+        self.client_train_settings = client_train_settings
+        self.client_train_settings["device"] = torch.device(
+            "cuda:0" if torch.cuda.is_available() else "cpu"
+        )
+        # print(
+        #     "Client_with model rate = {} , cid of client = {}".format(
+        #         self.model_rate, self.cid
+        #     )
+        # )
+
+    def get_parameters(self, config) -> NDArrays:
+        """Return the parameters of the current net."""
+        # print(f"[Client {self.cid}] get_parameters")
+        return get_parameters(self.net)
+
+    def fit(self, parameters, config) -> Tuple[NDArrays, int, Dict]:
+        """Implement distributed fit function for a given client."""
+        # print(f"cid = {self.cid}")
+        set_parameters(self.net, parameters)
+        if "lr" in config:
+            self.client_train_settings["lr"] = config["lr"]
+        train(
+            self.net,
+            self.trainloader,
+            self.label_split,
+            self.client_train_settings,
+        )
+        return get_parameters(self.net), len(self.trainloader), {}
+
+    def evaluate(self, parameters, config) -> Tuple[float, int, Dict]:
+        """Implement distributed evaluation for a given client."""
+        set_parameters(self.net, parameters)
+        loss, accuracy = test(
+            self.net, self.valloader, device=self.client_train_settings["device"]
+        )
+        return float(loss), len(self.valloader), {"accuracy": float(accuracy)}
+
+
+def gen_client_fn(
+    model_config: Dict,
+    client_to_model_rate_mapping: Optional[List[float]],
+    client_train_settings: Dict,
+    data_loaders,
+) -> Callable[[str], FlowerNumPyClient]:  # pylint: disable=too-many-arguments
+    """Generate the client function that creates the Flower Clients.
+
+    Parameters
+    ----------
+    model_config : Dict
+        Dict that contains all the information required to
+        create a model (data_shape , hidden_layers , classes_size...)
+    client_to_model_rate: List[float]
+        List tha contains model_rates of clients.
+        model_rate of client with cid i = client_to_model_rate_mapping[i]
+    client_train_settings : Dict
+        Dict that contains information regarding optimizer , lr ,
+        momentum , device required by the client to train
+    trainloaders: List[DataLoader]
+        A list of DataLoaders, each pointing to the dataset training partition
+        belonging to a particular client.
+    label_split: torch.tensor
+        A Tensor of tensors that conatins the labels of the partitioned dataset.
+        label_split of client with cid i = label_split[i]
+    valloaders: List[DataLoader]
+        A list of DataLoaders, each pointing to the dataset validation partition
+        belonging to a particular client.
+
+    Returns
+    -------
+    Callable[[str], FlowerClient]
+        A tuple containing the client function that creates Flower Clients
+    """
+
+    def client_fn(cid: str) -> FlowerNumPyClient:
+        """Create a Flower client representing a single organization."""
+        # Note: each client gets a different trainloader/valloader, so each client
+        # will train and evaluate on their own unique data
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+        client_dataloader = {
+            "trainloader": data_loaders["trainloaders"][int(cid)],
+            "valloader": data_loaders["valloaders"][int(cid)],
+            "label_split": data_loaders["label_split"][int(cid)],
+        }
+        # trainloader = data_loaders["trainloaders"][int(cid)]
+        # valloader = data_loaders["valloaders"][int(cid)]
+        model_rate = None
+        if client_to_model_rate_mapping is not None:
+            model_rate = client_to_model_rate_mapping[int(cid)]
+
+        return FlowerNumPyClient(
+            # cid=cid,
+            net=create_model(
+                model_config,
+                model_rate=model_rate,
+                device=device,
+            ),
+            dataloader=client_dataloader,
+            model_rate=model_rate,
+            client_train_settings=client_train_settings,
+        )
+
+    return client_fn
diff --git a/baselines/heterofl/heterofl/client_manager_heterofl.py b/baselines/heterofl/heterofl/client_manager_heterofl.py
new file mode 100644
index 000000000000..be5b2227a159
--- /dev/null
+++ b/baselines/heterofl/heterofl/client_manager_heterofl.py
@@ -0,0 +1,207 @@
+"""HeteroFL ClientManager."""
+
+import random
+import threading
+from logging import INFO
+from typing import Dict, List, Optional
+
+import flwr as fl
+import torch
+from flwr.common.logger import log
+from flwr.server.client_proxy import ClientProxy
+from flwr.server.criterion import Criterion
+
+# from heterofl.utils import ModelRateManager
+
+
+class ClientManagerHeteroFL(fl.server.ClientManager):
+    """Provides a pool of available clients."""
+
+    def __init__(
+        self,
+        model_rate_manager=None,
+        clients_to_model_rate_mapping=None,
+        client_label_split: Optional[list[torch.tensor]] = None,
+    ) -> None:
+        super().__init__()
+        self.clients: Dict[str, ClientProxy] = {}
+
+        self.is_simulation = False
+        if model_rate_manager is not None and clients_to_model_rate_mapping is not None:
+            self.is_simulation = True
+
+        self.model_rate_manager = model_rate_manager
+
+        # have a common array in simulation to access in the client_fn and server side
+        if self.is_simulation is True:
+            self.clients_to_model_rate_mapping = clients_to_model_rate_mapping
+            ans = self.model_rate_manager.create_model_rate_mapping(
+                len(clients_to_model_rate_mapping)
+            )
+            # copy self.clients_to_model_rate_mapping , ans
+            for i, model_rate in enumerate(ans):
+                self.clients_to_model_rate_mapping[i] = model_rate
+
+        # shall handle in case of not_simulation...
+        self.client_label_split = client_label_split
+
+        self._cv = threading.Condition()
+
+    def __len__(self) -> int:
+        """Return the length of clients Dict.
+
+        Returns
+        -------
+        len : int
+            Length of Dict (self.clients).
+        """
+        return len(self.clients)
+
+    def num_available(self) -> int:
+        """Return the number of available clients.
+
+        Returns
+        -------
+        num_available : int
+            The number of currently available clients.
+        """
+        return len(self)
+
+    def wait_for(self, num_clients: int, timeout: int = 86400) -> bool:
+        """Wait until at least `num_clients` are available.
+
+        Blocks until the requested number of clients is available or until a
+        timeout is reached. Current timeout default: 1 day.
+
+        Parameters
+        ----------
+        num_clients : int
+            The number of clients to wait for.
+        timeout : int
+            The time in seconds to wait for, defaults to 86400 (24h).
+
+        Returns
+        -------
+        success : bool
+        """
+        with self._cv:
+            return self._cv.wait_for(
+                lambda: len(self.clients) >= num_clients, timeout=timeout
+            )
+
+    def register(self, client: ClientProxy) -> bool:
+        """Register Flower ClientProxy instance.
+
+        Parameters
+        ----------
+        client : flwr.server.client_proxy.ClientProxy
+
+        Returns
+        -------
+        success : bool
+            Indicating if registration was successful. False if ClientProxy is
+            already registered or can not be registered for any reason.
+        """
+        if client.cid in self.clients:
+            return False
+
+        self.clients[client.cid] = client
+
+        # in case of not a simulation, this type of method can be used
+        # if self.is_simulation is False:
+        #     prop = client.get_properties(None, timeout=86400)
+        #     self.clients_to_model_rate_mapping[int(client.cid)] = prop["model_rate"]
+        #     self.client_label_split[int(client.cid)] = prop["label_split"]
+
+        with self._cv:
+            self._cv.notify_all()
+
+        return True
+
+    def unregister(self, client: ClientProxy) -> None:
+        """Unregister Flower ClientProxy instance.
+
+        This method is idempotent.
+
+        Parameters
+        ----------
+        client : flwr.server.client_proxy.ClientProxy
+        """
+        if client.cid in self.clients:
+            del self.clients[client.cid]
+
+            with self._cv:
+                self._cv.notify_all()
+
+    def all(self) -> Dict[str, ClientProxy]:
+        """Return all available clients."""
+        return self.clients
+
+    def get_client_to_model_mapping(self, cid) -> float:
+        """Return model rate of client with cid."""
+        return self.clients_to_model_rate_mapping[int(cid)]
+
+    def get_all_clients_to_model_mapping(self) -> List[float]:
+        """Return all available clients to model rate mapping."""
+        return self.clients_to_model_rate_mapping.copy()
+
+    def update(self, server_round: int) -> None:
+        """Update the client to model rate mapping."""
+        if self.is_simulation is True:
+            if (
+                server_round == 1 and self.model_rate_manager.model_split_mode == "fix"
+            ) or (self.model_rate_manager.model_split_mode == "dynamic"):
+                ans = self.model_rate_manager.create_model_rate_mapping(
+                    self.num_available()
+                )
+                # copy self.clients_to_model_rate_mapping , ans
+                for i, model_rate in enumerate(ans):
+                    self.clients_to_model_rate_mapping[i] = model_rate
+                print(
+                    "clients to model rate mapping ", self.clients_to_model_rate_mapping
+                )
+            return
+
+        # to be handled in case of not a simulation, i.e. to get the properties
+        # again from the clients as they can change the model_rate
+        # for i in range(self.num_available):
+        #     # need to test this , accumilates the
+        #     # changing model rate of the client
+        #     self.clients_to_model_rate_mapping[i] =
+        #     self.clients[str(i)].get_properties['model_rate']
+        # return
+
+    def sample(
+        self,
+        num_clients: int,
+        min_num_clients: Optional[int] = None,
+        criterion: Optional[Criterion] = None,
+    ) -> List[ClientProxy]:
+        """Sample a number of Flower ClientProxy instances."""
+        # Block until at least num_clients are connected.
+        if min_num_clients is None:
+            min_num_clients = num_clients
+        self.wait_for(min_num_clients)
+        # Sample clients which meet the criterion
+        available_cids = list(self.clients)
+        if criterion is not None:
+            available_cids = [
+                cid for cid in available_cids if criterion.select(self.clients[cid])
+            ]
+
+        if num_clients > len(available_cids):
+            log(
+                INFO,
+                "Sampling failed: number of available clients"
+                " (%s) is less than number of requested clients (%s).",
+                len(available_cids),
+                num_clients,
+            )
+            return []
+
+        random_indices = torch.randperm(len(available_cids))[:num_clients]
+        # Use the random indices to select clients
+        sampled_cids = [available_cids[i] for i in random_indices]
+        sampled_cids = random.sample(available_cids, num_clients)
+        print(f"Sampled CIDS =  {sampled_cids}")
+        return [self.clients[cid] for cid in sampled_cids]
diff --git a/baselines/heterofl/heterofl/conf/base.yaml b/baselines/heterofl/heterofl/conf/base.yaml
new file mode 100644
index 000000000000..42edf419cc38
--- /dev/null
+++ b/baselines/heterofl/heterofl/conf/base.yaml
@@ -0,0 +1,47 @@
+num_clients: 100
+num_epochs: 5
+num_rounds: 800
+seed: 0
+client_resources:
+    num_cpus: 1
+    num_gpus: 0.08
+
+control:
+  model_split_mode: 'dynamic'
+  model_mode: 'a1-b1-c1-d1-e1'
+
+dataset:
+  dataset_name: 'CIFAR10'
+  iid: False
+  shard_per_user : 2 # only used in case of non-iid (i.e. iid = false)
+  balance: false
+  batch_size:
+    train: 10
+    test: 50
+  shuffle:
+    train: true
+    test: false
+
+
+model:
+  model_name: resnet18  # use 'conv' for MNIST
+  hidden_layers: [64 , 128 , 256 , 512]
+  norm: bn
+  scale: 1
+  mask: 1
+
+
+optim_scheduler:
+  optimizer: SGD
+  lr: 0.1
+  momentum: 0.9
+  weight_decay: 5.00e-04
+  scheduler: MultiStepLR
+  milestones: [300, 500]
+
+strategy:
+  _target_: heterofl.strategy.HeteroFL
+  fraction_fit: 0.1
+  fraction_evaluate: 0.1
+  min_fit_clients: 10
+  min_evaluate_clients: 10
diff --git a/baselines/heterofl/heterofl/conf/fedavg.yaml b/baselines/heterofl/heterofl/conf/fedavg.yaml
new file mode 100644
index 000000000000..d67d0950654a
--- /dev/null
+++ b/baselines/heterofl/heterofl/conf/fedavg.yaml
@@ -0,0 +1,41 @@
+num_clients: 100
+num_epochs: 1
+num_rounds: 800
+seed: 0
+clip: False
+enable_train_on_train_data_while_testing: False
+client_resources:
+    num_cpus: 1
+    num_gpus: 0.4
+
+dataset:
+  dataset_name: 'MNIST'
+  iid: False
+  shard_per_user : 2
+  balance: False
+  batch_size:
+    train: 10
+    test: 10
+  shuffle:
+    train: true
+    test: false
+
+
+model:
+  model_name: MLP #use CNNCifar for CIFAR10
+
+optim_scheduler:
+  optimizer: SGD
+  lr: 0.05
+  lr_decay_rate: 1.0
+  momentum: 0.5
+  weight_decay: 0
+  scheduler: MultiStepLR
+  milestones: []
+
+strategy:
+  _target_: flwr.server.strategy.FedAvg
+  fraction_fit: 0.1
+  fraction_evaluate: 0.1
+  min_fit_clients: 10
+  min_evaluate_clients: 10
diff --git a/baselines/heterofl/heterofl/dataset.py b/baselines/heterofl/heterofl/dataset.py
new file mode 100644
index 000000000000..0e0f4b726842
--- /dev/null
+++ b/baselines/heterofl/heterofl/dataset.py
@@ -0,0 +1,83 @@
+"""Utilities for creation of DataLoaders for clients and server."""
+
+from typing import List, Optional, Tuple
+
+import torch
+from omegaconf import DictConfig
+from torch.utils.data import DataLoader
+
+from heterofl.dataset_preparation import _partition_data
+
+
+def load_datasets(  # pylint: disable=too-many-arguments
+    strategy_name: str,
+    config: DictConfig,
+    num_clients: int,
+    seed: Optional[int] = 42,
+) -> Tuple[
+    DataLoader, List[DataLoader], List[torch.tensor], List[DataLoader], DataLoader
+]:
+    """Create the dataloaders to be fed into the model.
+
+    Parameters
+    ----------
+    config: DictConfig
+        Parameterises the dataset partitioning process
+    num_clients : int
+        The number of clients that hold a part of the data
+    seed : int, optional
+        Used to set a fix seed to replicate experiments, by default 42
+
+    Returns
+    -------
+    Tuple[DataLoader, DataLoader, DataLoader, DataLoader]
+        The entire trainset Dataloader for testing purposes,
+        The DataLoader for training, the DataLoader for validation,
+        the DataLoader for testing.
+    """
+    print(f"Dataset partitioning config: {config}")
+    trainset, datasets, label_split, client_testsets, testset = _partition_data(
+        num_clients,
+        dataset_name=config.dataset_name,
+        strategy_name=strategy_name,
+        iid=config.iid,
+        dataset_division={
+            "shard_per_user": config.shard_per_user,
+            "balance": config.balance,
+        },
+        seed=seed,
+    )
+    # Split each partition into train/val and create DataLoader
+    entire_trainloader = DataLoader(
+        trainset, batch_size=config.batch_size.train, shuffle=config.shuffle.train
+    )
+
+    trainloaders = []
+    valloaders = []
+    for dataset in datasets:
+        trainloaders.append(
+            DataLoader(
+                dataset,
+                batch_size=config.batch_size.train,
+                shuffle=config.shuffle.train,
+            )
+        )
+
+    for client_testset in client_testsets:
+        valloaders.append(
+            DataLoader(
+                client_testset,
+                batch_size=config.batch_size.test,
+                shuffle=config.shuffle.test,
+            )
+        )
+
+    return (
+        entire_trainloader,
+        trainloaders,
+        label_split,
+        valloaders,
+        DataLoader(
+            testset, batch_size=config.batch_size.test, shuffle=config.shuffle.test
+        ),
+    )
diff --git a/baselines/heterofl/heterofl/dataset_preparation.py b/baselines/heterofl/heterofl/dataset_preparation.py
new file mode 100644
index 000000000000..525e815e9e98
--- /dev/null
+++ b/baselines/heterofl/heterofl/dataset_preparation.py
@@ -0,0 +1,357 @@
+"""Functions for dataset download and processing."""
+
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+import torch
+from torch.utils.data import ConcatDataset, Dataset, Subset, random_split
+from torchvision import transforms
+
+import heterofl.datasets as dt
+
+
+def _download_data(dataset_name: str, strategy_name: str) -> Tuple[Dataset, Dataset]:
+    root = "./data/{}".format(dataset_name)
+    if dataset_name == "MNIST":
+        trainset = dt.MNIST(
+            root=root,
+            split="train",
+            subset="label",
+            transform=dt.Compose(
+                [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+            ),
+        )
+        testset = dt.MNIST(
+            root=root,
+            split="test",
+            subset="label",
+            transform=dt.Compose(
+                [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
+            ),
+        )
+    elif dataset_name == "CIFAR10":
+        normalize = transforms.Normalize(
+            mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+        )
+        if strategy_name == "heterofl":
+            normalize = transforms.Normalize(
+                (0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)
+            )
+        trainset = dt.CIFAR10(
+            root=root,
+            split="train",
+            subset="label",
+            transform=dt.Compose(
+                [
+                    transforms.RandomCrop(32, padding=4),
+                    transforms.RandomHorizontalFlip(),
+                    transforms.ToTensor(),
+                    normalize,
+                ]
+            ),
+        )
+        testset = dt.CIFAR10(
+            root=root,
+            split="test",
+            subset="label",
+            transform=dt.Compose(
+                [
+                    transforms.ToTensor(),
+                    normalize,
+                ]
+            ),
+        )
+    else:
+        raise ValueError(f"{dataset_name} is not valid")
+
+    return trainset, testset
+
+
+# pylint: disable=too-many-arguments
+def _partition_data(
+    num_clients: int,
+    dataset_name: str,
+    strategy_name: str,
+    iid: Optional[bool] = False,
+    dataset_division=None,
+    seed: Optional[int] = 42,
+) -> Tuple[Dataset, List[Dataset], List[torch.tensor], List[Dataset], Dataset]:
+    trainset, testset = _download_data(dataset_name, strategy_name)
+
+    if dataset_name in ("MNIST", "CIFAR10"):
+        classes_size = 10
+
+    if dataset_division["balance"]:
+        trainset = _balance_classes(trainset, seed)
+
+    if iid:
+        datasets, label_split = iid_partition(trainset, num_clients, seed=seed)
+        client_testsets, _ = iid_partition(testset, num_clients, seed=seed)
+    else:
+        datasets, label_split = non_iid(
+            {"dataset": trainset, "classes_size": classes_size},
+            num_clients,
+            dataset_division["shard_per_user"],
+        )
+        client_testsets, _ = non_iid(
+            {
+                "dataset": testset,
+                "classes_size": classes_size,
+            },
+            num_clients,
+            dataset_division["shard_per_user"],
+            label_split,
+        )
+
+        tensor_label_split = []
+        for i in label_split:
+            tensor_label_split.append(torch.Tensor(i))
+        label_split = tensor_label_split
+
+    return trainset, datasets, label_split, client_testsets, testset
+
+
+def iid_partition(
+    dataset: Dataset, num_clients: int, seed: Optional[int] = 42
+) -> Tuple[List[Dataset], List[torch.tensor]]:
+    """IID partition of dataset among clients."""
+    partition_size = int(len(dataset) / num_clients)
+    lengths = [partition_size] * num_clients
+
+    divided_dataset = random_split(
+        dataset, lengths, torch.Generator().manual_seed(seed)
+    )
+    label_split = []
+    for i in range(num_clients):
+        label_split.append(
+            torch.unique(torch.Tensor([target for _, target in divided_dataset[i]]))
+        )
+
+    return divided_dataset, label_split
+
+
+def non_iid(
+    dataset_info,
+    num_clients: int,
+    shard_per_user: int,
+    label_split=None,
+    seed=42,
+) -> Tuple[List[Dataset], List]:
+    """Non-IID partition of dataset among clients.
+
+    Adopted from authors (of heterofl) implementation.
+    """
+    data_split: Dict[int, List] = {i: [] for i in range(num_clients)}
+
+    label_idx_split, shard_per_class = _split_dataset_targets_idx(
+        dataset_info["dataset"],
+        shard_per_user,
+        num_clients,
+        dataset_info["classes_size"],
+    )
+
+    if label_split is None:
+        label_split = list(range(dataset_info["classes_size"])) * shard_per_class
+        label_split = torch.tensor(label_split)[
+            torch.randperm(
+                len(label_split), generator=torch.Generator().manual_seed(seed)
+            )
+        ].tolist()
+        label_split = np.array(label_split).reshape((num_clients, -1)).tolist()
+
+        for i, _ in enumerate(label_split):
+            label_split[i] = np.unique(label_split[i]).tolist()
+
+    for i in range(num_clients):
+        for label_i in label_split[i]:
+            idx = torch.arange(len(label_idx_split[label_i]))[
+                torch.randperm(
+                    len(label_idx_split[label_i]),
+                    generator=torch.Generator().manual_seed(seed),
+                )[0]
+            ].item()
+            data_split[i].extend(label_idx_split[label_i].pop(idx))
+
+    return (
+        _get_dataset_from_idx(dataset_info["dataset"], data_split, num_clients),
+        label_split,
+    )
+
+
+def _split_dataset_targets_idx(dataset, shard_per_user, num_clients, classes_size):
+    label = np.array(dataset.target) if hasattr(dataset, "target") else dataset.targets
+    label_idx_split: Dict = {}
+    for i, _ in enumerate(label):
+        label_i = label[i].item()
+        if label_i not in label_idx_split:
+            label_idx_split[label_i] = []
+        label_idx_split[label_i].append(i)
+
+    shard_per_class = int(shard_per_user * num_clients / classes_size)
+
+    for label_i in label_idx_split:
+        label_idx = label_idx_split[label_i]
+        num_leftover = len(label_idx) % shard_per_class
+        leftover = label_idx[-num_leftover:] if num_leftover > 0 else []
+        new_label_idx = (
+            np.array(label_idx[:-num_leftover])
+            if num_leftover > 0
+            else np.array(label_idx)
+        )
+        new_label_idx = new_label_idx.reshape((shard_per_class, -1)).tolist()
+
+        for i, leftover_label_idx in enumerate(leftover):
+            new_label_idx[i] = np.concatenate([new_label_idx[i], [leftover_label_idx]])
+        label_idx_split[label_i] = new_label_idx
+    return label_idx_split, shard_per_class
+
+
+def _get_dataset_from_idx(dataset, data_split, num_clients):
+    divided_dataset = [None for i in range(num_clients)]
+    for i in range(num_clients):
+        divided_dataset[i] = Subset(dataset, data_split[i])
+    return divided_dataset
+
+
+def _balance_classes(
+    trainset: Dataset,
+    seed: Optional[int] = 42,
+) -> Dataset:
+    class_counts = np.bincount(trainset.target)
+    targets = torch.Tensor(trainset.target)
+    smallest = np.min(class_counts)
+    idxs = targets.argsort()
+    tmp = [Subset(trainset, idxs[: int(smallest)])]
+    tmp_targets = [targets[idxs[: int(smallest)]]]
+    for count in np.cumsum(class_counts):
+        tmp.append(Subset(trainset, idxs[int(count) : int(count + smallest)]))
+        tmp_targets.append(targets[idxs[int(count) : int(count + smallest)]])
+    unshuffled = ConcatDataset(tmp)
+    unshuffled_targets = torch.cat(tmp_targets)
+    shuffled_idxs = torch.randperm(
+        len(unshuffled), generator=torch.Generator().manual_seed(seed)
+    )
+    shuffled = Subset(unshuffled, shuffled_idxs)
+    shuffled.targets = unshuffled_targets[shuffled_idxs]
+
+    return shuffled
+
+
+def _sort_by_class(
+    trainset: Dataset,
+) -> Dataset:
+    class_counts = np.bincount(trainset.targets)
+    idxs = trainset.targets.argsort()  # sort targets in ascending order
+
+    tmp = []  # create subset of smallest class
+    tmp_targets = []  # same for targets
+
+    start = 0
+    for count in np.cumsum(class_counts):
+        tmp.append(
+            Subset(trainset, idxs[start : int(count + start)])
+        )  # add rest of classes
+        tmp_targets.append(trainset.targets[idxs[start : int(count + start)]])
+        start += count
+    sorted_dataset = ConcatDataset(tmp)  # concat dataset
+    sorted_dataset.targets = torch.cat(tmp_targets)  # concat targets
+    return sorted_dataset
+
+
+# pylint: disable=too-many-locals, too-many-arguments
+def _power_law_split(
+    sorted_trainset: Dataset,
+    num_partitions: int,
+    num_labels_per_partition: int = 2,
+    min_data_per_partition: int = 10,
+    mean: float = 0.0,
+    sigma: float = 2.0,
+) -> Dataset:
+    """Partition the dataset following a power-law distribution. It follows the.
+
+    implementation of Li et al 2020: https://arxiv.org/abs/1812.06127 with default
+    values set accordingly.
+
+    Parameters
+    ----------
+    sorted_trainset : Dataset
+        The training dataset sorted by label/class.
+    num_partitions: int
+        Number of partitions to create
+    num_labels_per_partition: int
+        Number of labels to have in each dataset partition. For
+        example if set to two, this means all training examples in
+        a given partition will be long to the same two classes. default 2
+    min_data_per_partition: int
+        Minimum number of datapoints included in each partition, default 10
+    mean: float
+        Mean value for LogNormal distribution to construct power-law, default 0.0
+    sigma: float
+        Sigma value for LogNormal distribution to construct power-law, default 2.0
+
+    Returns
+    -------
+    Dataset
+        The partitioned training dataset.
+    """
+    targets = sorted_trainset.targets
+    full_idx = list(range(len(targets)))
+
+    class_counts = np.bincount(sorted_trainset.targets)
+    labels_cs = np.cumsum(class_counts)
+    labels_cs = [0] + labels_cs[:-1].tolist()
+
+    partitions_idx: List[List[int]] = []
+    num_classes = len(np.bincount(targets))
+    hist = np.zeros(num_classes, dtype=np.int32)
+
+    # assign min_data_per_partition
+    min_data_per_class = int(min_data_per_partition / num_labels_per_partition)
+    for u_id in range(num_partitions):
+        partitions_idx.append([])
+        for cls_idx in range(num_labels_per_partition):
+            # label for the u_id-th client
+            cls = (u_id + cls_idx) % num_classes
+            # record minimum data
+            indices = list(
+                full_idx[
+                    labels_cs[cls]
+                    + hist[cls] : labels_cs[cls]
+                    + hist[cls]
+                    + min_data_per_class
+                ]
+            )
+            partitions_idx[-1].extend(indices)
+            hist[cls] += min_data_per_class
+
+    # add remaining images following power-law
+    probs = np.random.lognormal(
+        mean,
+        sigma,
+        (num_classes, int(num_partitions / num_classes), num_labels_per_partition),
+    )
+    remaining_per_class = class_counts - hist
+    # obtain how many samples each partition should be assigned for each of the
+    # labels it contains
+    # pylint: disable=too-many-function-args
+    probs = (
+        remaining_per_class.reshape(-1, 1, 1)
+        * probs
+        / np.sum(probs, (1, 2), keepdims=True)
+    )
+
+    for u_id in range(num_partitions):
+        for cls_idx in range(num_labels_per_partition):
+            cls = (u_id + cls_idx) % num_classes
+            count = int(probs[cls, u_id // num_classes, cls_idx])
+
+            # add count of specific class to partition
+            indices = full_idx[
+                labels_cs[cls] + hist[cls] : labels_cs[cls] + hist[cls] + count
+            ]
+            partitions_idx[u_id].extend(indices)
+            hist[cls] += count
+
+    # construct subsets
+    partitions = [Subset(sorted_trainset, p) for p in partitions_idx]
+    return partitions
diff --git a/baselines/heterofl/heterofl/datasets/__init__.py b/baselines/heterofl/heterofl/datasets/__init__.py
new file mode 100644
index 000000000000..7a6f06836b69
--- /dev/null
+++ b/baselines/heterofl/heterofl/datasets/__init__.py
@@ -0,0 +1,10 @@
+"""Dataset module.
+
+The entire datasets module is adopted from authors implementation.
+"""
+
+from .cifar import CIFAR10
+from .mnist import MNIST
+from .utils import Compose
+
+__all__ = ("MNIST", "CIFAR10", "Compose")
diff --git a/baselines/heterofl/heterofl/datasets/cifar.py b/baselines/heterofl/heterofl/datasets/cifar.py
new file mode 100644
index 000000000000..fd07135875b2
--- /dev/null
+++ b/baselines/heterofl/heterofl/datasets/cifar.py
@@ -0,0 +1,151 @@
+"""CIFAR10 dataset class, adopted from authors implementation."""
+
+import os
+import pickle
+
+import anytree
+import numpy as np
+import torch
+from PIL import Image
+from torch.utils.data import Dataset
+
+from heterofl.datasets.utils import (
+    download_url,
+    extract_file,
+    make_classes_counts,
+    make_flat_index,
+    make_tree,
+)
+from heterofl.utils import check_exists, load, makedir_exist_ok, save
+
+
+# pylint: disable=too-many-instance-attributes
+class CIFAR10(Dataset):
+    """CIFAR10 dataset."""
+
+    data_name = "CIFAR10"
+    file = [
+        (
+            "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz",
+            "c58f30108f718f92721af3b95e74349a",
+        )
+    ]
+
+    def __init__(self, root, split, subset, transform=None):
+        self.root = os.path.expanduser(root)
+        self.split = split
+        self.subset = subset
+        self.transform = transform
+        if not check_exists(self.processed_folder):
+            self.process()
+        self.img, self.target = load(
+            os.path.join(self.processed_folder, "{}.pt".format(self.split))
+        )
+        self.target = self.target[self.subset]
+        self.classes_counts = make_classes_counts(self.target)
+        self.classes_to_labels, self.classes_size = load(
+            os.path.join(self.processed_folder, "meta.pt")
+        )
+        self.classes_to_labels, self.classes_size = (
+            self.classes_to_labels[self.subset],
+            self.classes_size[self.subset],
+        )
+
+    def __getitem__(self, index):
+        """Get the item with index."""
+        img, target = Image.fromarray(self.img[index]), torch.tensor(self.target[index])
+        inp = {"img": img, self.subset: target}
+        if self.transform is not None:
+            inp = self.transform(inp)
+        return inp["img"], inp["label"]
+
+    def __len__(self):
+        """Length of the dataset."""
+        return len(self.img)
+
+    @property
+    def processed_folder(self):
+        """Return path of processed folder."""
+        return os.path.join(self.root, "processed")
+
+    @property
+    def raw_folder(self):
+        """Return path of raw folder."""
+        return os.path.join(self.root, "raw")
+
+    def process(self):
+        """Save the dataset accordingly."""
+        if not check_exists(self.raw_folder):
+            self.download()
+        train_set, test_set, meta = self.make_data()
+        save(train_set, os.path.join(self.processed_folder, "train.pt"))
+        save(test_set, os.path.join(self.processed_folder, "test.pt"))
+        save(meta, os.path.join(self.processed_folder, "meta.pt"))
+
+    def download(self):
+        """Download dataset from the url."""
+        makedir_exist_ok(self.raw_folder)
+        for url, md5 in self.file:
+            filename = os.path.basename(url)
+            download_url(url, self.raw_folder, filename, md5)
+            extract_file(os.path.join(self.raw_folder, filename))
+
+    def __repr__(self):
+        """Represent CIFAR10 as string."""
+        fmt_str = (
+            f"Dataset {self.__class__.__name__}\nSize: {self.__len__()}\n"
+            f"Root: {self.root}\nSplit: {self.split}\nSubset: {self.subset}\n"
+            f"Transforms: {self.transform.__repr__()}"
+        )
+        return fmt_str
+
+    def make_data(self):
+        """Make data."""
+        train_filenames = [
+            "data_batch_1",
+            "data_batch_2",
+            "data_batch_3",
+            "data_batch_4",
+            "data_batch_5",
+        ]
+        test_filenames = ["test_batch"]
+        train_img, train_label = _read_pickle_file(
+            os.path.join(self.raw_folder, "cifar-10-batches-py"), train_filenames
+        )
+        test_img, test_label = _read_pickle_file(
+            os.path.join(self.raw_folder, "cifar-10-batches-py"), test_filenames
+        )
+        train_target, test_target = {"label": train_label}, {"label": test_label}
+        with open(
+            os.path.join(self.raw_folder, "cifar-10-batches-py", "batches.meta"), "rb"
+        ) as fle:
+            data = pickle.load(fle, encoding="latin1")
+            classes = data["label_names"]
+        classes_to_labels = {"label": anytree.Node("U", index=[])}
+        for cls in classes:
+            make_tree(classes_to_labels["label"], [cls])
+        classes_size = {"label": make_flat_index(classes_to_labels["label"])}
+        return (
+            (train_img, train_target),
+            (test_img, test_target),
+            (classes_to_labels, classes_size),
+        )
+
+
+def _read_pickle_file(path, filenames):
+    img, label = [], []
+    for filename in filenames:
+        file_path = os.path.join(path, filename)
+        with open(file_path, "rb") as file:
+            entry = pickle.load(file, encoding="latin1")
+            img.append(entry["data"])
+            if "labels" in entry:
+                label.extend(entry["labels"])
+            else:
+                label.extend(entry["fine_labels"])
+            # label.extend(entry["labels"]) if "labels" in entry else label.extend(
+            #     entry["fine_labels"]
+            # )
+    img = np.vstack(img).reshape(-1, 3, 32, 32)
+    img = img.transpose((0, 2, 3, 1))
+    return img, label
diff --git a/baselines/heterofl/heterofl/datasets/mnist.py b/baselines/heterofl/heterofl/datasets/mnist.py
new file mode 100644
index 000000000000..1fc744f60bb5
--- /dev/null
+++ b/baselines/heterofl/heterofl/datasets/mnist.py
@@ -0,0 +1,168 @@
+"""MNIST dataset class, adopted from authors implementation."""
+
+import codecs
+import os
+
+import anytree
+import numpy as np
+import torch
+from PIL import Image
+from torch.utils.data import Dataset
+
+from heterofl.datasets.utils import (
+    download_url,
+    extract_file,
+    make_classes_counts,
+    make_flat_index,
+    make_tree,
+)
+from heterofl.utils import check_exists, load, makedir_exist_ok, save
+
+
+# pylint: disable=too-many-instance-attributes
+class MNIST(Dataset):
+    """MNIST dataset."""
+
+    data_name = "MNIST"
+    file = [
+        (
+            "http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz",
+            "f68b3c2dcbeaaa9fbdd348bbdeb94873",
+        ),
+        (
+            "http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz",
+            "9fb629c4189551a2d022fa330f9573f3",
+        ),
+        (
+            "http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz",
+            "d53e105ee54ea40749a09fcbcd1e9432",
+        ),
+        (
+            "http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz",
+            "ec29112dd5afa0611ce80d1b7f02629c",
+        ),
+    ]
+
+    def __init__(self, root, split, subset, transform=None):
+        self.root = os.path.expanduser(root)
+        self.split = split
+        self.subset = subset
+        self.transform = transform
+        if not check_exists(self.processed_folder):
+            self.process()
+        self.img, self.target = load(
+            os.path.join(self.processed_folder, "{}.pt".format(self.split))
+        )
+        self.target = self.target[self.subset]
+        self.classes_counts = make_classes_counts(self.target)
+        self.classes_to_labels, self.classes_size = load(
+            os.path.join(self.processed_folder, "meta.pt")
+        )
+        self.classes_to_labels, self.classes_size = (
+            self.classes_to_labels[self.subset],
+            self.classes_size[self.subset],
+        )
+
+    def __getitem__(self, index):
+        """Get the item with index."""
+        img, target = Image.fromarray(self.img[index]), torch.tensor(self.target[index])
+        inp = {"img": img, self.subset: target}
+        if self.transform is not None:
+            inp = self.transform(inp)
+        return inp["img"], inp["label"]
+
+    def __len__(self):
+        """Length of the dataset."""
+        return len(self.img)
+
+    @property
+    def processed_folder(self):
+        """Return path of processed folder."""
+        return os.path.join(self.root, "processed")
+
+    @property
+    def raw_folder(self):
+        """Return path of raw folder."""
+        return os.path.join(self.root, "raw")
+
+    def process(self):
+        """Save the dataset accordingly."""
+        if not check_exists(self.raw_folder):
+            self.download()
+        train_set, test_set, meta = self.make_data()
+        save(train_set, os.path.join(self.processed_folder, "train.pt"))
+        save(test_set, os.path.join(self.processed_folder, "test.pt"))
+        save(meta, os.path.join(self.processed_folder, "meta.pt"))
+
+    def download(self):
+        """Download and save the dataset accordingly."""
+        makedir_exist_ok(self.raw_folder)
+        for url, md5 in self.file:
+            filename = os.path.basename(url)
+            download_url(url, self.raw_folder, filename, md5)
+            extract_file(os.path.join(self.raw_folder, filename))
+
+    def __repr__(self):
+        """Represent CIFAR10 as string."""
+        fmt_str = (
+            f"Dataset {self.__class__.__name__}\nSize: {self.__len__()}\n"
+            f"Root: {self.root}\nSplit: {self.split}\nSubset: {self.subset}\n"
+            f"Transforms: {self.transform.__repr__()}"
+        )
+        return fmt_str
+
+    def make_data(self):
+        """Make data."""
+        train_img = _read_image_file(
+            os.path.join(self.raw_folder, "train-images-idx3-ubyte")
+        )
+        test_img = _read_image_file(
+            os.path.join(self.raw_folder, "t10k-images-idx3-ubyte")
+        )
+        train_label = _read_label_file(
+            os.path.join(self.raw_folder, "train-labels-idx1-ubyte")
+        )
+        test_label = _read_label_file(
+            os.path.join(self.raw_folder, "t10k-labels-idx1-ubyte")
+        )
+        train_target, test_target = {"label": train_label}, {"label": test_label}
+        classes_to_labels = {"label": anytree.Node("U", index=[])}
+        classes = list(map(str, list(range(10))))
+        for cls in classes:
+            make_tree(classes_to_labels["label"], [cls])
+        classes_size = {"label": make_flat_index(classes_to_labels["label"])}
+        return (
+            (train_img, train_target),
+            (test_img, test_target),
+            (classes_to_labels, classes_size),
+        )
+
+
+def _get_int(num):
+    return int(codecs.encode(num, "hex"), 16)
+
+
+def _read_image_file(path):
+    with open(path, "rb") as file:
+        data = file.read()
+        assert _get_int(data[:4]) == 2051
+        length = _get_int(data[4:8])
+        num_rows = _get_int(data[8:12])
+        num_cols = _get_int(data[12:16])
+        parsed = np.frombuffer(data, dtype=np.uint8, offset=16).reshape(
+            (length, num_rows, num_cols)
+        )
+        return parsed
+
+
+def _read_label_file(path):
+    with open(path, "rb") as file:
+        data = file.read()
+        assert _get_int(data[:4]) == 2049
+        length = _get_int(data[4:8])
+        parsed = (
+            np.frombuffer(data, dtype=np.uint8, offset=8)
+            .reshape(length)
+            .astype(np.int64)
+        )
+        return parsed
diff --git a/baselines/heterofl/heterofl/datasets/utils.py b/baselines/heterofl/heterofl/datasets/utils.py
new file mode 100644
index 000000000000..e6cc9f128827
--- /dev/null
+++ b/baselines/heterofl/heterofl/datasets/utils.py
@@ -0,0 +1,245 @@
+"""Contains utility functions required for datasests.
+
+Adopted from authors implementation.
+"""
+
+import glob
+import gzip
+import hashlib
+import os
+import tarfile
+import zipfile
+from collections import Counter
+
+import anytree
+import numpy as np
+from PIL import Image
+from six.moves import urllib
+from tqdm import tqdm
+
+from heterofl.utils import makedir_exist_ok
+
+IMG_EXTENSIONS = [".jpg", ".jpeg", ".png", ".ppm", ".bmp", ".pgm", ".tif"]
+
+
+def find_classes(drctry):
+    """Find the classes in a directory."""
+    classes = [d.name for d in os.scandir(drctry) if d.is_dir()]
+    classes.sort()
+    classes_to_labels = {classes[i]: i for i in range(len(classes))}
+    return classes_to_labels
+
+
+def pil_loader(path):
+    """Load image from path using PIL."""
+    with open(path, "rb") as file:
+        img = Image.open(file)
+        return img.convert("RGB")
+
+
+# def accimage_loader(path):
+#     """Load image from path using accimage_loader."""
+#     import accimage
+
+#     try:
+#         return accimage.Image(path)
+#     except IOError:
+#         return pil_loader(path)
+
+
+def default_loader(path):
+    """Load image from path using default loader."""
+    # if get_image_backend() == "accimage":
+    #     return accimage_loader(path)
+
+    return pil_loader(path)
+
+
+def has_file_allowed_extension(filename, extensions):
+    """Check whether file possesses any of the extensions listed."""
+    filename_lower = filename.lower()
+    return any(filename_lower.endswith(ext) for ext in extensions)
+
+
+def make_classes_counts(label):
+    """Count number of classes."""
+    label = np.array(label)
+    if label.ndim > 1:
+        label = label.sum(axis=tuple(range(1, label.ndim)))
+    classes_counts = Counter(label)
+    return classes_counts
+
+
+def _make_bar_updater(pbar):
+    def bar_update(count, block_size, total_size):
+        if pbar.total is None and total_size:
+            pbar.total = total_size
+        progress_bytes = count * block_size
+        pbar.update(progress_bytes - pbar.n)
+
+    return bar_update
+
+
+def _calculate_md5(path, chunk_size=1024 * 1024):
+    md5 = hashlib.md5()
+    with open(path, "rb") as file:
+        for chunk in iter(lambda: file.read(chunk_size), b""):
+            md5.update(chunk)
+    return md5.hexdigest()
+
+
+def _check_md5(path, md5, **kwargs):
+    return md5 == _calculate_md5(path, **kwargs)
+
+
+def _check_integrity(path, md5=None):
+    if not os.path.isfile(path):
+        return False
+    if md5 is None:
+        return True
+    return _check_md5(path, md5)
+
+
+def download_url(url, root, filename, md5):
+    """Download files from the url."""
+    path = os.path.join(root, filename)
+    makedir_exist_ok(root)
+    if os.path.isfile(path) and _check_integrity(path, md5):
+        print("Using downloaded and verified file: " + path)
+    else:
+        try:
+            print("Downloading " + url + " to " + path)
+            urllib.request.urlretrieve(
+                url, path, reporthook=_make_bar_updater(tqdm(unit="B", unit_scale=True))
+            )
+        except OSError:
+            if url[:5] == "https":
+                url = url.replace("https:", "http:")
+                print(
+                    "Failed download. Trying https -> http instead."
+                    " Downloading " + url + " to " + path
+                )
+                urllib.request.urlretrieve(
+                    url,
+                    path,
+                    reporthook=_make_bar_updater(tqdm(unit="B", unit_scale=True)),
+                )
+        if not _check_integrity(path, md5):
+            raise RuntimeError("Not valid downloaded file")
+
+
+def extract_file(src, dest=None, delete=False):
+    """Extract the file."""
+    print("Extracting {}".format(src))
+    dest = os.path.dirname(src) if dest is None else dest
+    filename = os.path.basename(src)
+    if filename.endswith(".zip"):
+        with zipfile.ZipFile(src, "r") as zip_f:
+            zip_f.extractall(dest)
+    elif filename.endswith(".tar"):
+        with tarfile.open(src) as tar_f:
+            tar_f.extractall(dest)
+    elif filename.endswith(".tar.gz") or filename.endswith(".tgz"):
+        with tarfile.open(src, "r:gz") as tar_f:
+            tar_f.extractall(dest)
+    elif filename.endswith(".gz"):
+        with open(src.replace(".gz", ""), "wb") as out_f, gzip.GzipFile(src) as zip_f:
+            out_f.write(zip_f.read())
+    if delete:
+        os.remove(src)
+
+
+def make_data(root, extensions):
+    """Get all the files in the root directory that follows the given extensions."""
+    path = []
+    files = glob.glob("{}/**/*".format(root), recursive=True)
+    for file in files:
+        if has_file_allowed_extension(file, extensions):
+            path.append(os.path.normpath(file))
+    return path
+
+
+# pylint: disable=dangerous-default-value
+def make_img(path, classes_to_labels, extensions=IMG_EXTENSIONS):
+    """Make image."""
+    img, label = [], []
+    classes = []
+    leaf_nodes = classes_to_labels.leaves
+    for node in leaf_nodes:
+        classes.append(node.name)
+    for cls in sorted(classes):
+        folder = os.path.join(path, cls)
+        if not os.path.isdir(folder):
+            continue
+        for root, _, filenames in sorted(os.walk(folder)):
+            for filename in sorted(filenames):
+                if has_file_allowed_extension(filename, extensions):
+                    cur_path = os.path.join(root, filename)
+                    img.append(cur_path)
+                    label.append(
+                        anytree.find_by_attr(classes_to_labels, cls).flat_index
+                    )
+    return img, label
+
+
+def make_tree(root, name, attribute=None):
+    """Create a tree of name."""
+    if len(name) == 0:
+        return
+    if attribute is None:
+        attribute = {}
+    this_name = name[0]
+    next_name = name[1:]
+    this_attribute = {k: attribute[k][0] for k in attribute}
+    next_attribute = {k: attribute[k][1:] for k in attribute}
+    this_node = anytree.find_by_attr(root, this_name)
+    this_index = root.index + [len(root.children)]
+    if this_node is None:
+        this_node = anytree.Node(
+            this_name, parent=root, index=this_index, **this_attribute
+        )
+    make_tree(this_node, next_name, next_attribute)
+    return
+
+
+def make_flat_index(root, given=None):
+    """Make flat index for each leaf node in the tree."""
+    if given:
+        classes_size = 0
+        for node in anytree.PreOrderIter(root):
+            if len(node.children) == 0:
+                node.flat_index = given.index(node.name)
+                classes_size = (
+                    given.index(node.name) + 1
+                    if given.index(node.name) + 1 > classes_size
+                    else classes_size
+                )
+    else:
+        classes_size = 0
+        for node in anytree.PreOrderIter(root):
+            if len(node.children) == 0:
+                node.flat_index = classes_size
+                classes_size += 1
+    return classes_size
+
+
+class Compose:
+    """Custom Compose class."""
+
+    def __init__(self, transforms):
+        self.transforms = transforms
+
+    def __call__(self, inp):
+        """Apply transforms when called."""
+        for transform in self.transforms:
+            inp["img"] = transform(inp["img"])
+        return inp
+
+    def __repr__(self):
+        """Represent Compose as string."""
+        format_string = self.__class__.__name__ + "("
+        for transform in self.transforms:
+            format_string += "\n"
+            format_string += "    {0}".format(transform)
+        format_string += "\n)"
+        return format_string
diff --git a/baselines/heterofl/heterofl/main.py b/baselines/heterofl/heterofl/main.py
new file mode 100644
index 000000000000..6315c0615873
--- /dev/null
+++ b/baselines/heterofl/heterofl/main.py
@@ -0,0 +1,215 @@
+"""Runs federated learning for given configuration in base.yaml."""
+
+import pickle
+from pathlib import Path
+
+import flwr as fl
+import hydra
+import torch
+from hydra.core.hydra_config import HydraConfig
+from hydra.utils import instantiate
+from omegaconf import DictConfig, OmegaConf
+
+from heterofl import client, models, server
+from heterofl.client_manager_heterofl import ClientManagerHeteroFL
+from heterofl.dataset import load_datasets
+from heterofl.model_properties import get_model_properties
+from heterofl.utils import ModelRateManager, get_global_model_rate, preprocess_input
+
+
+# pylint: disable=too-many-locals,protected-access
+@hydra.main(config_path="conf", config_name="base.yaml", version_base=None)
+def main(cfg: DictConfig) -> None:
+    """Run the baseline.
+
+    Parameters
+    ----------
+    cfg : DictConfig
+        An omegaconf object that stores the hydra config.
+    """
+    # print config structured as YAML
+    print(OmegaConf.to_yaml(cfg))
+    torch.manual_seed(cfg.seed)
+
+    data_loaders = {}
+
+    (
+        data_loaders["entire_trainloader"],
+        data_loaders["trainloaders"],
+        data_loaders["label_split"],
+        data_loaders["valloaders"],
+        data_loaders["testloader"],
+    ) = load_datasets(
+        "heterofl" if "heterofl" in cfg.strategy._target_ else "fedavg",
+        config=cfg.dataset,
+        num_clients=cfg.num_clients,
+        seed=cfg.seed,
+    )
+
+    model_config = preprocess_input(cfg.model, cfg.dataset)
+
+    model_split_rate = None
+    model_mode = None
+    client_to_model_rate_mapping = None
+    model_rate_manager = None
+    history = None
+
+    if "HeteroFL" in cfg.strategy._target_:
+        # send this array(client_model_rate_mapping) as
+        # an argument to client_manager and client
+        model_split_rate = {"a": 1, "b": 0.5, "c": 0.25, "d": 0.125, "e": 0.0625}
+        # model_split_mode = cfg.control.model_split_mode
+        model_mode = cfg.control.model_mode
+
+        client_to_model_rate_mapping = [float(0) for _ in range(cfg.num_clients)]
+        model_rate_manager = ModelRateManager(
+            cfg.control.model_split_mode, model_split_rate, model_mode
+        )
+
+        model_config["global_model_rate"] = model_split_rate[
+            get_global_model_rate(model_mode)
+        ]
+
+    test_model = models.create_model(
+        model_config,
+        model_rate=(
+            model_split_rate[get_global_model_rate(model_mode)]
+            if model_split_rate is not None
+            else None
+        ),
+        track=True,
+        device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"),
+    )
+
+    get_model_properties(
+        model_config,
+        model_split_rate,
+        model_mode + "" if model_mode is not None else None,
+        data_loaders["entire_trainloader"],
+        cfg.dataset.batch_size.train,
+    )
+
+    # prepare function that will be used to spawn each client
+    client_train_settings = {
+        "epochs": cfg.num_epochs,
+        "optimizer": cfg.optim_scheduler.optimizer,
+        "lr": cfg.optim_scheduler.lr,
+        "momentum": cfg.optim_scheduler.momentum,
+        "weight_decay": cfg.optim_scheduler.weight_decay,
+        "scheduler": cfg.optim_scheduler.scheduler,
+        "milestones": cfg.optim_scheduler.milestones,
+    }
+
+    if "clip" in cfg:
+        client_train_settings["clip"] = cfg.clip
+
+    optim_scheduler_settings = {
+        "optimizer": cfg.optim_scheduler.optimizer,
+        "lr": cfg.optim_scheduler.lr,
+        "momentum": cfg.optim_scheduler.momentum,
+        "weight_decay": cfg.optim_scheduler.weight_decay,
+        "scheduler": cfg.optim_scheduler.scheduler,
+        "milestones": cfg.optim_scheduler.milestones,
+    }
+
+    client_fn = client.gen_client_fn(
+        model_config=model_config,
+        client_to_model_rate_mapping=client_to_model_rate_mapping,
+        client_train_settings=client_train_settings,
+        data_loaders=data_loaders,
+    )
+
+    evaluate_fn = server.gen_evaluate_fn(
+        data_loaders,
+        torch.device("cuda:0" if torch.cuda.is_available() else "cpu"),
+        test_model,
+        models.create_model(
+            model_config,
+            model_rate=(
+                model_split_rate[get_global_model_rate(model_mode)]
+                if model_split_rate is not None
+                else None
+            ),
+            track=False,
+            device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"),
+        )
+        .state_dict()
+        .keys(),
+        enable_train_on_train_data=(
+            cfg.enable_train_on_train_data_while_testing
+            if "enable_train_on_train_data_while_testing" in cfg
+            else True
+        ),
+    )
+    client_resources = {
+        "num_cpus": cfg.client_resources.num_cpus,
+        "num_gpus": cfg.client_resources.num_gpus if torch.cuda.is_available() else 0,
+    }
+
+    if "HeteroFL" in cfg.strategy._target_:
+        strategy_heterofl = instantiate(
+            cfg.strategy,
+            model_name=cfg.model.model_name,
+            net=models.create_model(
+                model_config,
+                model_rate=(
+                    model_split_rate[get_global_model_rate(model_mode)]
+                    if model_split_rate is not None
+                    else None
+                ),
+                device="cpu",
+            ),
+            optim_scheduler_settings=optim_scheduler_settings,
+            global_model_rate=(
+                model_split_rate[get_global_model_rate(model_mode)]
+                if model_split_rate is not None
+                else 1.0
+            ),
+            evaluate_fn=evaluate_fn,
+            min_available_clients=cfg.num_clients,
+        )
+
+        history = fl.simulation.start_simulation(
+            client_fn=client_fn,
+            num_clients=cfg.num_clients,
+            config=fl.server.ServerConfig(num_rounds=cfg.num_rounds),
+            client_resources=client_resources,
+            client_manager=ClientManagerHeteroFL(
+                model_rate_manager,
+                client_to_model_rate_mapping,
+                client_label_split=data_loaders["label_split"],
+            ),
+            strategy=strategy_heterofl,
+        )
+    else:
+        strategy_fedavg = instantiate(
+            cfg.strategy,
+            # on_fit_config_fn=lambda server_round: {
+            #     "lr": cfg.optim_scheduler.lr
+            #     * pow(cfg.optim_scheduler.lr_decay_rate, server_round)
+            # },
+            evaluate_fn=evaluate_fn,
+            min_available_clients=cfg.num_clients,
+        )
+
+        history = fl.simulation.start_simulation(
+            client_fn=client_fn,
+            num_clients=cfg.num_clients,
+            config=fl.server.ServerConfig(num_rounds=cfg.num_rounds),
+            client_resources=client_resources,
+            strategy=strategy_fedavg,
+        )
+
+    # save the results
+    save_path = HydraConfig.get().runtime.output_dir
+
+    # save the results as a python pickle
+    with open(str(Path(save_path) / "results.pkl"), "wb") as file_handle:
+        pickle.dump({"history": history}, file_handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+    # save the model
+    torch.save(test_model.state_dict(), str(Path(save_path) / "model.pth"))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/baselines/heterofl/heterofl/model_properties.py b/baselines/heterofl/heterofl/model_properties.py
new file mode 100644
index 000000000000..53b09bc75ff3
--- /dev/null
+++ b/baselines/heterofl/heterofl/model_properties.py
@@ -0,0 +1,124 @@
+"""Determine number of model parameters, space it requires."""
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from heterofl.models import create_model
+
+
+def get_model_properties(
+    model_config, model_split_rate, model_mode, data_loader, batch_size
+):
+    """Calculate space occupied & number of parameters of model."""
+    model_mode = model_mode.split("-") if model_mode is not None else None
+    # model = create_model(model_config, model_rate=model_split_rate(i[0]))
+
+    total_flops = 0
+    total_model_parameters = 0
+    ttl_prcntg = 0
+    if model_mode is None:
+        total_flops = _calculate_model_memory(create_model(model_config), data_loader)
+        total_model_parameters = _count_parameters(create_model(model_config))
+    else:
+        for i in model_mode:
+            total_flops += _calculate_model_memory(
+                create_model(model_config, model_rate=model_split_rate[i[0]]),
+                data_loader,
+            ) * int(i[1])
+            total_model_parameters += _count_parameters(
+                create_model(model_config, model_rate=model_split_rate[i[0]])
+            ) * int(i[1])
+            ttl_prcntg += int(i[1])
+
+    total_flops = total_flops / ttl_prcntg if ttl_prcntg != 0 else total_flops
+    total_flops /= batch_size
+    total_model_parameters = (
+        total_model_parameters / ttl_prcntg
+        if ttl_prcntg != 0
+        else total_model_parameters
+    )
+
+    space = total_model_parameters * 32.0 / 8 / (1024**2.0)
+    print("num_of_parameters = ", total_model_parameters / 1000, " K")
+    print("total_flops = ", total_flops / 1000000, " M")
+    print("space = ", space)
+
+    return total_model_parameters, total_flops, space
+
+
+def _calculate_model_memory(model, data_loader):
+    def register_hook(module):
+        def hook(module, inp, output):
+            # temp = _make_flops(module, inp, output)
+            # print(temp)
+            for _ in module.named_parameters():
+                flops.append(_make_flops(module, inp, output))
+
+        if (
+            not isinstance(module, nn.Sequential)
+            and not isinstance(module, nn.ModuleList)
+            and not isinstance(module, nn.ModuleDict)
+            and module != model
+        ):
+            hooks.append(module.register_forward_hook(hook))
+
+    hooks = []
+    flops = []
+    model.apply(register_hook)
+
+    one_dl = next(iter(data_loader))
+    input_dict = {"img": one_dl[0], "label": one_dl[1]}
+    with torch.no_grad():
+        model(input_dict)
+
+    for hook in hooks:
+        hook.remove()
+
+    return sum(fl for fl in flops)
+
+
+def _count_parameters(model):
+    return sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+
+def _make_flops(module, inp, output):
+    if isinstance(inp, tuple):
+        return _make_flops(module, inp[0], output)
+    if isinstance(output, tuple):
+        return _make_flops(module, inp, output[0])
+    flops = _compute_flops(module, inp, output)
+    return flops
+
+
+def _compute_flops(module, inp, out):
+    flops = 0
+    if isinstance(module, nn.Conv2d):
+        flops = _compute_conv2d_flops(module, inp, out)
+    elif isinstance(module, (nn.BatchNorm2d, nn.InstanceNorm2d)):
+        flops = np.prod(inp.shape).item()
+        if isinstance(module, (nn.BatchNorm2d, nn.InstanceNorm2d)) and module.affine:
+            flops *= 2
+    elif isinstance(module, nn.Linear):
+        flops = np.prod(inp.size()[:-1]).item() * inp.size()[-1] * out.size()[-1]
+    # else:
+    #     print(f"[Flops]: {type(module).__name__} is not supported!")
+    return flops
+
+
+def _compute_conv2d_flops(module, inp, out):
+    batch_size = inp.size()[0]
+    in_c = inp.size()[1]
+    out_c, out_h, out_w = out.size()[1:]
+    groups = module.groups
+    filters_per_channel = out_c // groups
+    conv_per_position_flops = (
+        module.kernel_size[0] * module.kernel_size[1] * in_c * filters_per_channel
+    )
+    active_elements_count = batch_size * out_h * out_w
+    total_conv_flops = conv_per_position_flops * active_elements_count
+    bias_flops = 0
+    if module.bias is not None:
+        bias_flops = out_c * active_elements_count
+    total_flops = total_conv_flops + bias_flops
+    return total_flops
diff --git a/baselines/heterofl/heterofl/models.py b/baselines/heterofl/heterofl/models.py
new file mode 100644
index 000000000000..02173069bead
--- /dev/null
+++ b/baselines/heterofl/heterofl/models.py
@@ -0,0 +1,840 @@
+"""Conv & resnet18 model architecture, training, testing functions.
+
+Classes Conv, Block, Resnet18 are adopted from authors implementation.
+"""
+
+import copy
+from typing import List, OrderedDict
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from flwr.common import parameters_to_ndarrays
+from torch import nn
+
+from heterofl.utils import make_optimizer
+
+
+class Conv(nn.Module):
+    """Convolutional Neural Network architecture with sBN."""
+
+    def __init__(
+        self,
+        model_config,
+    ):
+        super().__init__()
+        self.model_config = model_config
+
+        blocks = [
+            nn.Conv2d(
+                model_config["data_shape"][0], model_config["hidden_size"][0], 3, 1, 1
+            ),
+            self._get_scale(),
+            self._get_norm(0),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(2),
+        ]
+        for i in range(len(model_config["hidden_size"]) - 1):
+            blocks.extend(
+                [
+                    nn.Conv2d(
+                        model_config["hidden_size"][i],
+                        model_config["hidden_size"][i + 1],
+                        3,
+                        1,
+                        1,
+                    ),
+                    self._get_scale(),
+                    self._get_norm(i + 1),
+                    nn.ReLU(inplace=True),
+                    nn.MaxPool2d(2),
+                ]
+            )
+        blocks = blocks[:-1]
+        blocks.extend(
+            [
+                nn.AdaptiveAvgPool2d(1),
+                nn.Flatten(),
+                nn.Linear(
+                    model_config["hidden_size"][-1], model_config["classes_size"]
+                ),
+            ]
+        )
+        self.blocks = nn.Sequential(*blocks)
+
+    def _get_norm(self, j: int):
+        """Return the relevant norm."""
+        if self.model_config["norm"] == "bn":
+            norm = nn.BatchNorm2d(
+                self.model_config["hidden_size"][j],
+                momentum=None,
+                track_running_stats=self.model_config["track"],
+            )
+        elif self.model_config["norm"] == "in":
+            norm = nn.GroupNorm(
+                self.model_config["hidden_size"][j], self.model_config["hidden_size"][j]
+            )
+        elif self.model_config["norm"] == "ln":
+            norm = nn.GroupNorm(1, self.model_config["hidden_size"][j])
+        elif self.model_config["norm"] == "gn":
+            norm = nn.GroupNorm(4, self.model_config["hidden_size"][j])
+        elif self.model_config["norm"] == "none":
+            norm = nn.Identity()
+        else:
+            raise ValueError("Not valid norm")
+
+        return norm
+
+    def _get_scale(self):
+        """Return the relevant scaler."""
+        if self.model_config["scale"]:
+            scaler = _Scaler(self.model_config["rate"])
+        else:
+            scaler = nn.Identity()
+        return scaler
+
+    def forward(self, input_dict):
+        """Forward pass of the Conv.
+
+        Parameters
+        ----------
+        input_dict : Dict
+            Conatins input Tensor that will pass through the network.
+            label of that input to calculate loss.
+            label_split if masking is required.
+
+        Returns
+        -------
+        Dict
+            The resulting Tensor after it has passed through the network and the loss.
+        """
+        # output = {"loss": torch.tensor(0, device=self.device, dtype=torch.float32)}
+        output = {}
+        out = self.blocks(input_dict["img"])
+        if "label_split" in input_dict and self.model_config["mask"]:
+            label_mask = torch.zeros(
+                self.model_config["classes_size"], device=out.device
+            )
+            label_mask[input_dict["label_split"]] = 1
+            out = out.masked_fill(label_mask == 0, 0)
+        output["score"] = out
+        output["loss"] = F.cross_entropy(out, input_dict["label"], reduction="mean")
+        return output
+
+
+def conv(
+    model_rate,
+    model_config,
+    device="cpu",
+):
+    """Create the Conv model."""
+    model_config["hidden_size"] = [
+        int(np.ceil(model_rate * x)) for x in model_config["hidden_layers"]
+    ]
+    scaler_rate = model_rate / model_config["global_model_rate"]
+    model_config["rate"] = scaler_rate
+    model = Conv(model_config)
+    model.apply(_init_param)
+    return model.to(device)
+
+
+class Block(nn.Module):
+    """Block."""
+
+    expansion = 1
+
+    def __init__(self, in_planes, planes, stride, model_config):
+        super().__init__()
+        if model_config["norm"] == "bn":
+            n_1 = nn.BatchNorm2d(
+                in_planes, momentum=None, track_running_stats=model_config["track"]
+            )
+            n_2 = nn.BatchNorm2d(
+                planes, momentum=None, track_running_stats=model_config["track"]
+            )
+        elif model_config["norm"] == "in":
+            n_1 = nn.GroupNorm(in_planes, in_planes)
+            n_2 = nn.GroupNorm(planes, planes)
+        elif model_config["norm"] == "ln":
+            n_1 = nn.GroupNorm(1, in_planes)
+            n_2 = nn.GroupNorm(1, planes)
+        elif model_config["norm"] == "gn":
+            n_1 = nn.GroupNorm(4, in_planes)
+            n_2 = nn.GroupNorm(4, planes)
+        elif model_config["norm"] == "none":
+            n_1 = nn.Identity()
+            n_2 = nn.Identity()
+        else:
+            raise ValueError("Not valid norm")
+        self.n_1 = n_1
+        self.conv1 = nn.Conv2d(
+            in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
+        )
+        self.n_2 = n_2
+        self.conv2 = nn.Conv2d(
+            planes, planes, kernel_size=3, stride=1, padding=1, bias=False
+        )
+        if model_config["scale"]:
+            self.scaler = _Scaler(model_config["rate"])
+        else:
+            self.scaler = nn.Identity()
+
+        if stride != 1 or in_planes != self.expansion * planes:
+            self.shortcut = nn.Conv2d(
+                in_planes,
+                self.expansion * planes,
+                kernel_size=1,
+                stride=stride,
+                bias=False,
+            )
+
+    def forward(self, x):
+        """Forward pass of the Block.
+
+        Parameters
+        ----------
+        x : Dict
+            Dict that contains Input Tensor that will pass through the network.
+            label of that input to calculate loss.
+            label_split if masking is required.
+
+        Returns
+        -------
+        Dict
+            The resulting Tensor after it has passed through the network and the loss.
+        """
+        out = F.relu(self.n_1(self.scaler(x)))
+        shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x
+        out = self.conv1(out)
+        out = self.conv2(F.relu(self.n_2(self.scaler(out))))
+        out += shortcut
+        return out
+
+
+# pylint: disable=too-many-instance-attributes
+class ResNet(nn.Module):
+    """Implementation of a Residual Neural Network (ResNet) model with sBN."""
+
+    def __init__(
+        self,
+        model_config,
+        block,
+        num_blocks,
+    ):
+        self.model_config = model_config
+        super().__init__()
+        self.in_planes = model_config["hidden_size"][0]
+        self.conv1 = nn.Conv2d(
+            model_config["data_shape"][0],
+            model_config["hidden_size"][0],
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias=False,
+        )
+
+        self.layer1 = self._make_layer(
+            block,
+            model_config["hidden_size"][0],
+            num_blocks[0],
+            stride=1,
+        )
+        self.layer2 = self._make_layer(
+            block,
+            model_config["hidden_size"][1],
+            num_blocks[1],
+            stride=2,
+        )
+        self.layer3 = self._make_layer(
+            block,
+            model_config["hidden_size"][2],
+            num_blocks[2],
+            stride=2,
+        )
+        self.layer4 = self._make_layer(
+            block,
+            model_config["hidden_size"][3],
+            num_blocks[3],
+            stride=2,
+        )
+
+        # self.layers = [layer1, layer2, layer3, layer4]
+
+        if model_config["norm"] == "bn":
+            n_4 = nn.BatchNorm2d(
+                model_config["hidden_size"][3] * block.expansion,
+                momentum=None,
+                track_running_stats=model_config["track"],
+            )
+        elif model_config["norm"] == "in":
+            n_4 = nn.GroupNorm(
+                model_config["hidden_size"][3] * block.expansion,
+                model_config["hidden_size"][3] * block.expansion,
+            )
+        elif model_config["norm"] == "ln":
+            n_4 = nn.GroupNorm(1, model_config["hidden_size"][3] * block.expansion)
+        elif model_config["norm"] == "gn":
+            n_4 = nn.GroupNorm(4, model_config["hidden_size"][3] * block.expansion)
+        elif model_config["norm"] == "none":
+            n_4 = nn.Identity()
+        else:
+            raise ValueError("Not valid norm")
+        self.n_4 = n_4
+        if model_config["scale"]:
+            self.scaler = _Scaler(model_config["rate"])
+        else:
+            self.scaler = nn.Identity()
+        self.linear = nn.Linear(
+            model_config["hidden_size"][3] * block.expansion,
+            model_config["classes_size"],
+        )
+
+    def _make_layer(self, block, planes, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for strd in strides:
+            layers.append(block(self.in_planes, planes, strd, self.model_config.copy()))
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers)
+
+    def forward(self, input_dict):
+        """Forward pass of the ResNet.
+
+        Parameters
+        ----------
+        input_dict : Dict
+            Dict that contains Input Tensor that will pass through the network.
+            label of that input to calculate loss.
+            label_split if masking is required.
+
+        Returns
+        -------
+        Dict
+            The resulting Tensor after it has passed through the network and the loss.
+        """
+        output = {}
+        x = input_dict["img"]
+        out = self.conv1(x)
+        # for layer in self.layers:
+        #     out = layer(out)
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = self.layer4(out)
+        out = F.relu(self.n_4(self.scaler(out)))
+        out = F.adaptive_avg_pool2d(out, 1)
+        out = out.view(out.size(0), -1)
+        out = self.linear(out)
+        if "label_split" in input_dict and self.model_config["mask"]:
+            label_mask = torch.zeros(
+                self.model_config["classes_size"], device=out.device
+            )
+            label_mask[input_dict["label_split"]] = 1
+            out = out.masked_fill(label_mask == 0, 0)
+        output["score"] = out
+        output["loss"] = F.cross_entropy(output["score"], input_dict["label"])
+        return output
+
+
+def resnet18(
+    model_rate,
+    model_config,
+    device="cpu",
+):
+    """Create the ResNet18 model."""
+    model_config["hidden_size"] = [
+        int(np.ceil(model_rate * x)) for x in model_config["hidden_layers"]
+    ]
+    scaler_rate = model_rate / model_config["global_model_rate"]
+    model_config["rate"] = scaler_rate
+    model = ResNet(model_config, block=Block, num_blocks=[1, 1, 1, 2])
+    model.apply(_init_param)
+    return model.to(device)
+
+
+class MLP(nn.Module):
+    """Multi Layer Perceptron."""
+
+    def __init__(self):
+        super().__init__()
+        self.layer_input = nn.Linear(784, 512)
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout()
+        self.layer_hidden1 = nn.Linear(512, 256)
+        self.layer_hidden2 = nn.Linear(256, 256)
+        self.layer_hidden3 = nn.Linear(256, 128)
+        self.layer_out = nn.Linear(128, 10)
+        self.softmax = nn.Softmax(dim=1)
+        self.weight_keys = [
+            ["layer_input.weight", "layer_input.bias"],
+            ["layer_hidden1.weight", "layer_hidden1.bias"],
+            ["layer_hidden2.weight", "layer_hidden2.bias"],
+            ["layer_hidden3.weight", "layer_hidden3.bias"],
+            ["layer_out.weight", "layer_out.bias"],
+        ]
+
+    def forward(self, input_dict):
+        """Forward pass of the Conv.
+
+        Parameters
+        ----------
+        input_dict : Dict
+            Conatins input Tensor that will pass through the network.
+            label of that input to calculate loss.
+            label_split if masking is required.
+
+        Returns
+        -------
+        Dict
+            The resulting Tensor after it has passed through the network and the loss.
+        """
+        output = {}
+        x = input_dict["img"]
+        x = x.view(-1, x.shape[1] * x.shape[-2] * x.shape[-1])
+        x = self.layer_input(x)
+        x = self.relu(x)
+
+        x = self.layer_hidden1(x)
+        x = self.relu(x)
+
+        x = self.layer_hidden2(x)
+        x = self.relu(x)
+
+        x = self.layer_hidden3(x)
+        x = self.relu(x)
+
+        x = self.layer_out(x)
+        out = self.softmax(x)
+        output["score"] = out
+        output["loss"] = F.cross_entropy(out, input_dict["label"], reduction="mean")
+        return output
+
+
+class CNNCifar(nn.Module):
+    """Convolutional Neural Network architecture for cifar dataset."""
+
+    def __init__(self):
+        super().__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 100)
+        self.fc3 = nn.Linear(100, 10)
+
+        self.weight_keys = [
+            ["fc1.weight", "fc1.bias"],
+            ["fc2.weight", "fc2.bias"],
+            ["fc3.weight", "fc3.bias"],
+            ["conv2.weight", "conv2.bias"],
+            ["conv1.weight", "conv1.bias"],
+        ]
+
+    def forward(self, input_dict):
+        """Forward pass of the Conv.
+
+        Parameters
+        ----------
+        input_dict : Dict
+            Conatins input Tensor that will pass through the network.
+            label of that input to calculate loss.
+            label_split if masking is required.
+
+        Returns
+        -------
+        Dict
+            The resulting Tensor after it has passed through the network and the loss.
+        """
+        output = {}
+        x = input_dict["img"]
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        out = F.log_softmax(x, dim=1)
+        output["score"] = out
+        output["loss"] = F.cross_entropy(out, input_dict["label"], reduction="mean")
+        return output
+
+
+def create_model(model_config, model_rate=None, track=False, device="cpu"):
+    """Create the model based on the configuration given in hydra."""
+    model = None
+    model_config = model_config.copy()
+    model_config["track"] = track
+
+    if model_config["model"] == "MLP":
+        model = MLP()
+        model.to(device)
+    elif model_config["model"] == "CNNCifar":
+        model = CNNCifar()
+        model.to(device)
+    elif model_config["model"] == "conv":
+        model = conv(model_rate=model_rate, model_config=model_config, device=device)
+    elif model_config["model"] == "resnet18":
+        model = resnet18(
+            model_rate=model_rate, model_config=model_config, device=device
+        )
+    return model
+
+
+def _init_param(m_param):
+    if isinstance(m_param, (nn.BatchNorm2d, nn.InstanceNorm2d)):
+        m_param.weight.data.fill_(1)
+        m_param.bias.data.zero_()
+    elif isinstance(m_param, nn.Linear):
+        m_param.bias.data.zero_()
+    return m_param
+
+
+class _Scaler(nn.Module):
+    def __init__(self, rate):
+        super().__init__()
+        self.rate = rate
+
+    def forward(self, inp):
+        """Forward of Scalar nn.Module."""
+        output = inp / self.rate if self.training else inp
+        return output
+
+
+def get_parameters(net) -> List[np.ndarray]:
+    """Return the parameters of model as numpy.NDArrays."""
+    return [val.cpu().numpy() for _, val in net.state_dict().items()]
+
+
+def set_parameters(net, parameters: List[np.ndarray]):
+    """Set the model parameters with given 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 train(model, train_loader, label_split, settings):
+    """Train a model with given settings.
+
+    Parameters
+    ----------
+    model : nn.Module
+        The neural network to train.
+    train_loader : DataLoader
+        The DataLoader containing the data to train the network on.
+    label_split : torch.tensor
+        Tensor containing the labels of the data.
+    settings: Dict
+        Dictionary conatining the information about eopchs, optimizer,
+        lr, momentum, weight_decay, device to train on.
+    """
+    # criterion = torch.nn.CrossEntropyLoss()
+    optimizer = make_optimizer(
+        settings["optimizer"],
+        model.parameters(),
+        learning_rate=settings["lr"],
+        momentum=settings["momentum"],
+        weight_decay=settings["weight_decay"],
+    )
+
+    model.train()
+    for _ in range(settings["epochs"]):
+        for images, labels in train_loader:
+            input_dict = {}
+            input_dict["img"] = images.to(settings["device"])
+            input_dict["label"] = labels.to(settings["device"])
+            input_dict["label_split"] = label_split.type(torch.int).to(
+                settings["device"]
+            )
+            optimizer.zero_grad()
+            output = model(input_dict)
+            output["loss"].backward()
+            if ("clip" not in settings) or (
+                "clip" in settings and settings["clip"] is True
+            ):
+                torch.nn.utils.clip_grad_norm_(model.parameters(), 1)
+            optimizer.step()
+
+
+def test(model, test_loader, label_split=None, device="cpu"):
+    """Evaluate the network on the test set.
+
+    Parameters
+    ----------
+    model : nn.Module
+        The neural network to test.
+    test_loader : DataLoader
+        The DataLoader containing the data to test the network on.
+    device : torch.device
+        The device on which the model should be tested, either 'cpu' or 'cuda'.
+
+    Returns
+    -------
+    Tuple[float, float]
+        The loss and the accuracy of the input model on the given data.
+    """
+    model.eval()
+    size = len(test_loader.dataset)
+    num_batches = len(test_loader)
+    test_loss, correct = 0, 0
+
+    with torch.no_grad():
+        model.train(False)
+        for images, labels in test_loader:
+            input_dict = {}
+            input_dict["img"] = images.to(device)
+            input_dict["label"] = labels.to(device)
+            if label_split is not None:
+                input_dict["label_split"] = label_split.type(torch.int).to(device)
+            output = model(input_dict)
+            test_loss += output["loss"].item()
+            correct += (
+                (output["score"].argmax(1) == input_dict["label"])
+                .type(torch.float)
+                .sum()
+                .item()
+            )
+
+    test_loss /= num_batches
+    correct /= size
+    return test_loss, correct
+
+
+def param_model_rate_mapping(
+    model_name, parameters, clients_model_rate, global_model_rate=1
+):
+    """Map the model rate to subset of global parameters(as list of indices).
+
+    Parameters
+    ----------
+    model_name : str
+        The name of the neural network of global model.
+    parameters : Dict
+        state_dict of the global model.
+    client_model_rate : List[float]
+        List of model rates of active clients.
+    global_model_rate: float
+        Model rate of the global model.
+
+    Returns
+    -------
+    Dict
+        model rate to parameters indices relative to global model mapping.
+    """
+    unique_client_model_rate = list(set(clients_model_rate))
+    print(unique_client_model_rate)
+
+    if "conv" in model_name:
+        idx = _mr_to_param_idx_conv(
+            parameters, unique_client_model_rate, global_model_rate
+        )
+    elif "resnet" in model_name:
+        idx = _mr_to_param_idx_resnet18(
+            parameters, unique_client_model_rate, global_model_rate
+        )
+    else:
+        raise ValueError("Not valid model name")
+
+    # add model rate as key to the params calculated
+    param_idx_model_rate_mapping = OrderedDict()
+    for i, _ in enumerate(unique_client_model_rate):
+        param_idx_model_rate_mapping[unique_client_model_rate[i]] = idx[i]
+
+    return param_idx_model_rate_mapping
+
+
+def _mr_to_param_idx_conv(parameters, unique_client_model_rate, global_model_rate):
+    idx_i = [None for _ in range(len(unique_client_model_rate))]
+    idx = [OrderedDict() for _ in range(len(unique_client_model_rate))]
+    output_weight_name = [k for k in parameters.keys() if "weight" in k][-1]
+    output_bias_name = [k for k in parameters.keys() if "bias" in k][-1]
+    for k, val in parameters.items():
+        parameter_type = k.split(".")[-1]
+        for index, _ in enumerate(unique_client_model_rate):
+            if "weight" in parameter_type or "bias" in parameter_type:
+                scaler_rate = unique_client_model_rate[index] / global_model_rate
+                _get_key_k_idx_conv(
+                    idx,
+                    idx_i,
+                    {
+                        "index": index,
+                        "parameter_type": parameter_type,
+                        "k": k,
+                        "val": val,
+                    },
+                    output_names={
+                        "output_weight_name": output_weight_name,
+                        "output_bias_name": output_bias_name,
+                    },
+                    scaler_rate=scaler_rate,
+                )
+            else:
+                pass
+    return idx
+
+
+def _get_key_k_idx_conv(
+    idx,
+    idx_i,
+    param_info,
+    output_names,
+    scaler_rate,
+):
+    if param_info["parameter_type"] == "weight":
+        if param_info["val"].dim() > 1:
+            input_size = param_info["val"].size(1)
+            output_size = param_info["val"].size(0)
+            if idx_i[param_info["index"]] is None:
+                idx_i[param_info["index"]] = torch.arange(
+                    input_size, device=param_info["val"].device
+                )
+            input_idx_i_m = idx_i[param_info["index"]]
+            if param_info["k"] == output_names["output_weight_name"]:
+                output_idx_i_m = torch.arange(
+                    output_size, device=param_info["val"].device
+                )
+            else:
+                local_output_size = int(np.ceil(output_size * (scaler_rate)))
+                output_idx_i_m = torch.arange(
+                    output_size, device=param_info["val"].device
+                )[:local_output_size]
+            idx[param_info["index"]][param_info["k"]] = output_idx_i_m, input_idx_i_m
+            idx_i[param_info["index"]] = output_idx_i_m
+        else:
+            input_idx_i_m = idx_i[param_info["index"]]
+            idx[param_info["index"]][param_info["k"]] = input_idx_i_m
+    else:
+        if param_info["k"] == output_names["output_bias_name"]:
+            input_idx_i_m = idx_i[param_info["index"]]
+            idx[param_info["index"]][param_info["k"]] = input_idx_i_m
+        else:
+            input_idx_i_m = idx_i[param_info["index"]]
+            idx[param_info["index"]][param_info["k"]] = input_idx_i_m
+
+
+def _mr_to_param_idx_resnet18(parameters, unique_client_model_rate, global_model_rate):
+    idx_i = [None for _ in range(len(unique_client_model_rate))]
+    idx = [OrderedDict() for _ in range(len(unique_client_model_rate))]
+    for k, val in parameters.items():
+        parameter_type = k.split(".")[-1]
+        for index, _ in enumerate(unique_client_model_rate):
+            if "weight" in parameter_type or "bias" in parameter_type:
+                scaler_rate = unique_client_model_rate[index] / global_model_rate
+                _get_key_k_idx_resnet18(
+                    idx,
+                    idx_i,
+                    {
+                        "index": index,
+                        "parameter_type": parameter_type,
+                        "k": k,
+                        "val": val,
+                    },
+                    scaler_rate=scaler_rate,
+                )
+            else:
+                pass
+    return idx
+
+
+def _get_key_k_idx_resnet18(
+    idx,
+    idx_i,
+    param_info,
+    scaler_rate,
+):
+    if param_info["parameter_type"] == "weight":
+        if param_info["val"].dim() > 1:
+            input_size = param_info["val"].size(1)
+            output_size = param_info["val"].size(0)
+            if "conv1" in param_info["k"] or "conv2" in param_info["k"]:
+                if idx_i[param_info["index"]] is None:
+                    idx_i[param_info["index"]] = torch.arange(
+                        input_size, device=param_info["val"].device
+                    )
+                input_idx_i_m = idx_i[param_info["index"]]
+                local_output_size = int(np.ceil(output_size * (scaler_rate)))
+                output_idx_i_m = torch.arange(
+                    output_size, device=param_info["val"].device
+                )[:local_output_size]
+                idx_i[param_info["index"]] = output_idx_i_m
+            elif "shortcut" in param_info["k"]:
+                input_idx_i_m = idx[param_info["index"]][
+                    param_info["k"].replace("shortcut", "conv1")
+                ][1]
+                output_idx_i_m = idx_i[param_info["index"]]
+            elif "linear" in param_info["k"]:
+                input_idx_i_m = idx_i[param_info["index"]]
+                output_idx_i_m = torch.arange(
+                    output_size, device=param_info["val"].device
+                )
+            else:
+                raise ValueError("Not valid k")
+            idx[param_info["index"]][param_info["k"]] = (output_idx_i_m, input_idx_i_m)
+        else:
+            input_idx_i_m = idx_i[param_info["index"]]
+            idx[param_info["index"]][param_info["k"]] = input_idx_i_m
+    else:
+        input_size = param_info["val"].size(0)
+        if "linear" in param_info["k"]:
+            input_idx_i_m = torch.arange(input_size, device=param_info["val"].device)
+            idx[param_info["index"]][param_info["k"]] = input_idx_i_m
+        else:
+            input_idx_i_m = idx_i[param_info["index"]]
+            idx[param_info["index"]][param_info["k"]] = input_idx_i_m
+
+
+def param_idx_to_local_params(global_parameters, client_param_idx):
+    """Get the local parameters from the list of param indices.
+
+    Parameters
+    ----------
+    global_parameters : Dict
+        The state_dict of global model.
+    client_param_idx : List
+        Local parameters indices with respect to global model.
+
+    Returns
+    -------
+    Dict
+        state dict of local model.
+    """
+    local_parameters = OrderedDict()
+    for k, val in global_parameters.items():
+        parameter_type = k.split(".")[-1]
+        if "weight" in parameter_type or "bias" in parameter_type:
+            if "weight" in parameter_type:
+                if val.dim() > 1:
+                    local_parameters[k] = copy.deepcopy(
+                        val[torch.meshgrid(client_param_idx[k])]
+                    )
+                else:
+                    local_parameters[k] = copy.deepcopy(val[client_param_idx[k]])
+            else:
+                local_parameters[k] = copy.deepcopy(val[client_param_idx[k]])
+        else:
+            local_parameters[k] = copy.deepcopy(val)
+    return local_parameters
+
+
+def get_state_dict_from_param(model, parameters):
+    """Get the state dict from model & parameters as np.NDarrays.
+
+    Parameters
+    ----------
+    model : nn.Module
+        The neural network.
+    parameters : np.NDarray
+        Parameters of the model as np.NDarrays.
+
+    Returns
+    -------
+    Dict
+        state dict of model.
+    """
+    # Load the parameters into the model
+    for param_tensor, param_ndarray in zip(
+        model.state_dict(), parameters_to_ndarrays(parameters)
+    ):
+        model.state_dict()[param_tensor].copy_(torch.from_numpy(param_ndarray))
+    # Step 3: Obtain the state_dict of the model
+    state_dict = model.state_dict()
+    return state_dict
diff --git a/baselines/heterofl/heterofl/server.py b/baselines/heterofl/heterofl/server.py
new file mode 100644
index 000000000000..a1badd87cc11
--- /dev/null
+++ b/baselines/heterofl/heterofl/server.py
@@ -0,0 +1,102 @@
+"""Flower Server."""
+
+import time
+from collections import OrderedDict
+from typing import Callable, Dict, Optional, Tuple
+
+import torch
+from flwr.common.typing import NDArrays, Scalar
+from torch import nn
+
+from heterofl.models import test
+from heterofl.utils import save_model
+
+
+def gen_evaluate_fn(
+    data_loaders,
+    device: torch.device,
+    model: nn.Module,
+    keys,
+    enable_train_on_train_data: bool,
+) -> Callable[
+    [int, NDArrays, Dict[str, Scalar]], Optional[Tuple[float, Dict[str, Scalar]]]
+]:
+    """Generate the function for centralized evaluation.
+
+    Parameters
+    ----------
+    data_loaders :
+        A dictionary containing dataloaders for testing and
+        label split of each client.
+    device : torch.device
+        The device to test the model on.
+    model :
+        Model for testing.
+    keys :
+        keys of the model that it is trained on.
+
+    Returns
+    -------
+    Callable[ [int, NDArrays, Dict[str, Scalar]],
+            Optional[Tuple[float, Dict[str, Scalar]]] ]
+        The centralized evaluation function.
+    """
+    intermediate_keys = keys
+
+    def evaluate(
+        server_round: int, parameters_ndarrays: NDArrays, config: Dict[str, Scalar]
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        # pylint: disable=unused-argument
+        """Use the entire test set for evaluation."""
+        # if server_round % 5 != 0 and server_round < 395:
+        #     return 1, {}
+
+        net = model
+        params_dict = zip(intermediate_keys, parameters_ndarrays)
+        state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
+        net.load_state_dict(state_dict, strict=False)
+        net.to(device)
+
+        if server_round % 100 == 0:
+            save_model(net, f"model_after_round_{server_round}.pth")
+
+        if enable_train_on_train_data is True:
+            print("start of testing")
+            start_time = time.time()
+            with torch.no_grad():
+                net.train(True)
+                for images, labels in data_loaders["entire_trainloader"]:
+                    input_dict = {}
+                    input_dict["img"] = images.to(device)
+                    input_dict["label"] = labels.to(device)
+                    net(input_dict)
+            print(f"end of stat, time taken = {time.time() - start_time}")
+
+        local_metrics = {}
+        local_metrics["loss"] = 0
+        local_metrics["accuracy"] = 0
+        for i, clnt_tstldr in enumerate(data_loaders["valloaders"]):
+            client_test_res = test(
+                net,
+                clnt_tstldr,
+                data_loaders["label_split"][i].type(torch.int),
+                device=device,
+            )
+            local_metrics["loss"] += client_test_res[0]
+            local_metrics["accuracy"] += client_test_res[1]
+
+        global_metrics = {}
+        global_metrics["loss"], global_metrics["accuracy"] = test(
+            net, data_loaders["testloader"], device=device
+        )
+
+        # return statistics
+        print(f"global accuracy = {global_metrics['accuracy']}")
+        print(f"local_accuracy = {local_metrics['accuracy']}")
+        return global_metrics["loss"], {
+            "global_accuracy": global_metrics["accuracy"],
+            "local_loss": local_metrics["loss"],
+            "local_accuracy": local_metrics["accuracy"],
+        }
+
+    return evaluate
diff --git a/baselines/heterofl/heterofl/strategy.py b/baselines/heterofl/heterofl/strategy.py
new file mode 100644
index 000000000000..500d7880c189
--- /dev/null
+++ b/baselines/heterofl/heterofl/strategy.py
@@ -0,0 +1,468 @@
+"""Flower strategy for HeteroFL."""
+
+import copy
+from collections import OrderedDict
+from typing import Dict, List, Optional, Tuple, Union
+
+import flwr as fl
+import torch
+from flwr.common import (
+    EvaluateIns,
+    EvaluateRes,
+    FitIns,
+    FitRes,
+    Parameters,
+    Scalar,
+    ndarrays_to_parameters,
+    parameters_to_ndarrays,
+)
+from flwr.server.client_manager import ClientManager
+from flwr.server.client_proxy import ClientProxy
+from torch import nn
+
+from heterofl.client_manager_heterofl import ClientManagerHeteroFL
+from heterofl.models import (
+    get_parameters,
+    get_state_dict_from_param,
+    param_idx_to_local_params,
+    param_model_rate_mapping,
+)
+from heterofl.utils import make_optimizer, make_scheduler
+
+
+# pylint: disable=too-many-instance-attributes
+class HeteroFL(fl.server.strategy.Strategy):
+    """HeteroFL strategy.
+
+    Distribute subsets of a global model to clients according to their
+
+    computational complexity and aggregate received models from clients.
+    """
+
+    # pylint: disable=too-many-arguments
+    def __init__(
+        self,
+        model_name: str,
+        net: nn.Module,
+        optim_scheduler_settings: Dict,
+        global_model_rate: float = 1.0,
+        evaluate_fn=None,
+        fraction_fit: float = 1.0,
+        fraction_evaluate: float = 1.0,
+        min_fit_clients: int = 2,
+        min_evaluate_clients: int = 2,
+        min_available_clients: int = 2,
+    ) -> None:
+        super().__init__()
+        self.fraction_fit = fraction_fit
+        self.fraction_evaluate = fraction_evaluate
+        self.min_fit_clients = min_fit_clients
+        self.min_evaluate_clients = min_evaluate_clients
+        self.min_available_clients = min_available_clients
+        self.evaluate_fn = evaluate_fn
+        # # created client_to_model_mapping
+        # self.client_to_model_rate_mapping: Dict[str, ClientProxy] = {}
+
+        self.model_name = model_name
+        self.net = net
+        self.global_model_rate = global_model_rate
+        # info required for configure and aggregate
+        # to be filled in initialize
+        self.local_param_model_rate: OrderedDict = OrderedDict()
+        # to be filled in initialize
+        self.active_cl_labels: List[torch.tensor] = []
+        # to be filled in configure
+        self.active_cl_mr: OrderedDict = OrderedDict()
+        # required for scheduling the lr
+        self.optimizer = make_optimizer(
+            optim_scheduler_settings["optimizer"],
+            self.net.parameters(),
+            learning_rate=optim_scheduler_settings["lr"],
+            momentum=optim_scheduler_settings["momentum"],
+            weight_decay=optim_scheduler_settings["weight_decay"],
+        )
+        self.scheduler = make_scheduler(
+            optim_scheduler_settings["scheduler"],
+            self.optimizer,
+            milestones=optim_scheduler_settings["milestones"],
+        )
+
+    def __repr__(self) -> str:
+        """Return a string representation of the HeteroFL object."""
+        return "HeteroFL"
+
+    def initialize_parameters(
+        self, client_manager: ClientManager
+    ) -> Optional[Parameters]:
+        """Initialize global model parameters."""
+        # self.make_client_to_model_rate_mapping(client_manager)
+        # net = conv(model_rate = 1)
+        if not isinstance(client_manager, ClientManagerHeteroFL):
+            raise ValueError(
+                "Not valid client manager, use ClientManagerHeterFL instead"
+            )
+        clnt_mngr_heterofl: ClientManagerHeteroFL = client_manager
+
+        ndarrays = get_parameters(self.net)
+        self.local_param_model_rate = param_model_rate_mapping(
+            self.model_name,
+            self.net.state_dict(),
+            clnt_mngr_heterofl.get_all_clients_to_model_mapping(),
+            self.global_model_rate,
+        )
+
+        if clnt_mngr_heterofl.client_label_split is not None:
+            self.active_cl_labels = clnt_mngr_heterofl.client_label_split.copy()
+
+        return fl.common.ndarrays_to_parameters(ndarrays)
+
+    def configure_fit(
+        self,
+        server_round: int,
+        parameters: Parameters,
+        client_manager: ClientManager,
+    ) -> List[Tuple[ClientProxy, FitIns]]:
+        """Configure the next round of training."""
+        print(f"in configure fit , server round no. = {server_round}")
+        if not isinstance(client_manager, ClientManagerHeteroFL):
+            raise ValueError(
+                "Not valid client manager, use ClientManagerHeterFL instead"
+            )
+        clnt_mngr_heterofl: ClientManagerHeteroFL = client_manager
+        # Sample clients
+        # no need to change this
+        clientts_selection_config = {}
+        (
+            clientts_selection_config["sample_size"],
+            clientts_selection_config["min_num_clients"],
+        ) = self.num_fit_clients(clnt_mngr_heterofl.num_available())
+
+        # for sampling we pass the criterion to select the required clients
+        clients = clnt_mngr_heterofl.sample(
+            num_clients=clientts_selection_config["sample_size"],
+            min_num_clients=clientts_selection_config["min_num_clients"],
+        )
+
+        # update client model rate mapping
+        clnt_mngr_heterofl.update(server_round)
+
+        global_parameters = get_state_dict_from_param(self.net, parameters)
+
+        self.active_cl_mr = OrderedDict()
+
+        # Create custom configs
+        fit_configurations = []
+        learning_rate = self.optimizer.param_groups[0]["lr"]
+        print(f"lr = {learning_rate}")
+        for client in clients:
+            model_rate = clnt_mngr_heterofl.get_client_to_model_mapping(client.cid)
+            client_param_idx = self.local_param_model_rate[model_rate]
+            local_param = param_idx_to_local_params(
+                global_parameters=global_parameters, client_param_idx=client_param_idx
+            )
+            self.active_cl_mr[client.cid] = model_rate
+            # local param are in the form of state_dict,
+            #  so converting them only to values of tensors
+            local_param_fitres = [val.cpu() for val in local_param.values()]
+            fit_configurations.append(
+                (
+                    client,
+                    FitIns(
+                        ndarrays_to_parameters(local_param_fitres),
+                        {"lr": learning_rate},
+                    ),
+                )
+            )
+
+        self.scheduler.step()
+        return fit_configurations
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        """Aggregate fit results using weighted average.
+
+        Adopted from authors implementation.
+        """
+        print("in aggregate fit")
+        gl_model = self.net.state_dict()
+
+        param_idx = []
+        for res in results:
+            param_idx.append(
+                copy.deepcopy(
+                    self.local_param_model_rate[self.active_cl_mr[res[0].cid]]
+                )
+            )
+
+        local_param_as_parameters = [fit_res.parameters for _, fit_res in results]
+        local_parameters_as_ndarrays = [
+            parameters_to_ndarrays(local_param_as_parameters[i])
+            for i in range(len(local_param_as_parameters))
+        ]
+        local_parameters: List[OrderedDict] = [
+            OrderedDict() for _ in range(len(local_param_as_parameters))
+        ]
+        for i in range(len(results)):
+            j = 0
+            for k, _ in gl_model.items():
+                local_parameters[i][k] = local_parameters_as_ndarrays[i][j]
+                j += 1
+
+        if "conv" in self.model_name:
+            self._aggregate_conv(param_idx, local_parameters, results)
+
+        elif "resnet" in self.model_name:
+            self._aggregate_resnet18(param_idx, local_parameters, results)
+        else:
+            raise ValueError("Not valid model name")
+
+        return ndarrays_to_parameters([v for k, v in gl_model.items()]), {}
+
+    def _aggregate_conv(self, param_idx, local_parameters, results):
+        gl_model = self.net.state_dict()
+        count = OrderedDict()
+        output_bias_name = [k for k in gl_model.keys() if "bias" in k][-1]
+        output_weight_name = [k for k in gl_model.keys() if "weight" in k][-1]
+        for k, val in gl_model.items():
+            parameter_type = k.split(".")[-1]
+            count[k] = val.new_zeros(val.size(), dtype=torch.float32)
+            tmp_v = val.new_zeros(val.size(), dtype=torch.float32)
+            for clnt, _ in enumerate(local_parameters):
+                if "weight" in parameter_type or "bias" in parameter_type:
+                    self._agg_layer_conv(
+                        {
+                            "cid": int(results[clnt][0].cid),
+                            "param_idx": param_idx,
+                            "local_parameters": local_parameters,
+                        },
+                        {
+                            "tmp_v": tmp_v,
+                            "count": count,
+                        },
+                        {
+                            "clnt": clnt,
+                            "parameter_type": parameter_type,
+                            "k": k,
+                            "val": val,
+                        },
+                        {
+                            "output_weight_name": output_weight_name,
+                            "output_bias_name": output_bias_name,
+                        },
+                    )
+                else:
+                    tmp_v += local_parameters[clnt][k]
+                    count[k] += 1
+            tmp_v[count[k] > 0] = tmp_v[count[k] > 0].div_(count[k][count[k] > 0])
+            val[count[k] > 0] = tmp_v[count[k] > 0].to(val.dtype)
+
+    def _agg_layer_conv(
+        self,
+        clnt_params,
+        tmp_v_count,
+        param_info,
+        output_names,
+    ):
+        # pi = param_info
+        param_idx = clnt_params["param_idx"]
+        clnt = param_info["clnt"]
+        k = param_info["k"]
+        tmp_v = tmp_v_count["tmp_v"]
+        count = tmp_v_count["count"]
+
+        if param_info["parameter_type"] == "weight":
+            if param_info["val"].dim() > 1:
+                if k == output_names["output_weight_name"]:
+                    label_split = self.active_cl_labels[clnt_params["cid"]]
+                    label_split = label_split.type(torch.int)
+                    param_idx[clnt][k] = list(param_idx[clnt][k])
+                    param_idx[clnt][k][0] = param_idx[clnt][k][0][label_split]
+                    tmp_v[torch.meshgrid(param_idx[clnt][k])] += clnt_params[
+                        "local_parameters"
+                    ][clnt][k][label_split]
+                    count[k][torch.meshgrid(param_idx[clnt][k])] += 1
+                else:
+                    tmp_v[torch.meshgrid(param_idx[clnt][k])] += clnt_params[
+                        "local_parameters"
+                    ][clnt][k]
+                    count[k][torch.meshgrid(param_idx[clnt][k])] += 1
+            else:
+                tmp_v[param_idx[clnt][k]] += clnt_params["local_parameters"][clnt][k]
+                count[k][param_idx[clnt][k]] += 1
+        else:
+            if k == output_names["output_bias_name"]:
+                label_split = self.active_cl_labels[clnt_params["cid"]]
+                label_split = label_split.type(torch.int)
+                param_idx[clnt][k] = param_idx[clnt][k][label_split]
+                tmp_v[param_idx[clnt][k]] += clnt_params["local_parameters"][clnt][k][
+                    label_split
+                ]
+                count[k][param_idx[clnt][k]] += 1
+            else:
+                tmp_v[param_idx[clnt][k]] += clnt_params["local_parameters"][clnt][k]
+                count[k][param_idx[clnt][k]] += 1
+
+    def _aggregate_resnet18(self, param_idx, local_parameters, results):
+        gl_model = self.net.state_dict()
+        count = OrderedDict()
+        for k, val in gl_model.items():
+            parameter_type = k.split(".")[-1]
+            count[k] = val.new_zeros(val.size(), dtype=torch.float32)
+            tmp_v = val.new_zeros(val.size(), dtype=torch.float32)
+            for clnt, _ in enumerate(local_parameters):
+                if "weight" in parameter_type or "bias" in parameter_type:
+                    self._agg_layer_resnet18(
+                        {
+                            "cid": int(results[clnt][0].cid),
+                            "param_idx": param_idx,
+                            "local_parameters": local_parameters,
+                        },
+                        tmp_v,
+                        count,
+                        {
+                            "clnt": clnt,
+                            "parameter_type": parameter_type,
+                            "k": k,
+                            "val": val,
+                        },
+                    )
+                else:
+                    tmp_v += local_parameters[clnt][k]
+                    count[k] += 1
+            tmp_v[count[k] > 0] = tmp_v[count[k] > 0].div_(count[k][count[k] > 0])
+            val[count[k] > 0] = tmp_v[count[k] > 0].to(val.dtype)
+
+    def _agg_layer_resnet18(self, clnt_params, tmp_v, count, param_info):
+        param_idx = clnt_params["param_idx"]
+        k = param_info["k"]
+        clnt = param_info["clnt"]
+
+        if param_info["parameter_type"] == "weight":
+            if param_info["val"].dim() > 1:
+                if "linear" in k:
+                    label_split = self.active_cl_labels[clnt_params["cid"]]
+                    label_split = label_split.type(torch.int)
+                    param_idx[clnt][k] = list(param_idx[clnt][k])
+                    param_idx[clnt][k][0] = param_idx[clnt][k][0][label_split]
+                    tmp_v[torch.meshgrid(param_idx[clnt][k])] += clnt_params[
+                        "local_parameters"
+                    ][clnt][k][label_split]
+                    count[k][torch.meshgrid(param_idx[clnt][k])] += 1
+                else:
+                    tmp_v[torch.meshgrid(param_idx[clnt][k])] += clnt_params[
+                        "local_parameters"
+                    ][clnt][k]
+                    count[k][torch.meshgrid(param_idx[clnt][k])] += 1
+            else:
+                tmp_v[param_idx[clnt][k]] += clnt_params["local_parameters"][clnt][k]
+                count[k][param_idx[clnt][k]] += 1
+        else:
+            if "linear" in k:
+                label_split = self.active_cl_labels[clnt_params["cid"]]
+                label_split = label_split.type(torch.int)
+                param_idx[clnt][k] = param_idx[clnt][k][label_split]
+                tmp_v[param_idx[clnt][k]] += clnt_params["local_parameters"][clnt][k][
+                    label_split
+                ]
+                count[k][param_idx[clnt][k]] += 1
+            else:
+                tmp_v[param_idx[clnt][k]] += clnt_params["local_parameters"][clnt][k]
+                count[k][param_idx[clnt][k]] += 1
+
+    def configure_evaluate(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, EvaluateIns]]:
+        """Configure the next round of evaluation."""
+        # if self.fraction_evaluate == 0.0:
+        #     return []
+        # config = {}
+        # evaluate_ins = EvaluateIns(parameters, config)
+
+        # # Sample clients
+        # sample_size, min_num_clients = self.num_evaluation_clients(
+        #     client_manager.num_available()
+        # )
+        # clients = client_manager.sample(
+        #     num_clients=sample_size, min_num_clients=min_num_clients
+        # )
+
+        # global_parameters = get_state_dict_from_param(self.net, parameters)
+
+        # self.active_cl_mr = OrderedDict()
+
+        # # Create custom configs
+        # evaluate_configurations = []
+        # for idx, client in enumerate(clients):
+        #     model_rate = client_manager.get_client_to_model_mapping(client.cid)
+        #     client_param_idx = self.local_param_model_rate[model_rate]
+        #     local_param =
+        #     param_idx_to_local_params(global_parameters, client_param_idx)
+        #     self.active_cl_mr[client.cid] = model_rate
+        #     # local param are in the form of state_dict,
+        #     # so converting them only to values of tensors
+        #     local_param_fitres = [v.cpu() for v in local_param.values()]
+        #     evaluate_configurations.append(
+        #         (client, EvaluateIns(ndarrays_to_parameters(local_param_fitres), {}))
+        #     )
+        # return evaluate_configurations
+
+        return []
+
+        # return self.configure_fit(server_round , parameters , client_manager)
+
+    def aggregate_evaluate(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, EvaluateRes]],
+        failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
+    ) -> Tuple[Optional[float], Dict[str, Scalar]]:
+        """Aggregate evaluation losses using weighted average."""
+        # if not results:
+        #     return None, {}
+
+        # loss_aggregated = weighted_loss_avg(
+        #     [
+        #         (evaluate_res.num_examples, evaluate_res.loss)
+        #         for _, evaluate_res in results
+        #     ]
+        # )
+
+        # accuracy_aggregated = 0
+        # for cp, y in results:
+        #     print(f"{cp.cid}-->{y.metrics['accuracy']}", end=" ")
+        #     accuracy_aggregated += y.metrics["accuracy"]
+        # accuracy_aggregated /= len(results)
+
+        # metrics_aggregated = {"accuracy": accuracy_aggregated}
+        # print(f"\npaneer lababdar {metrics_aggregated}")
+        # return loss_aggregated, metrics_aggregated
+
+        return None, {}
+
+    def evaluate(
+        self, server_round: int, parameters: Parameters
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """Evaluate model parameters using an evaluation function."""
+        if self.evaluate_fn is None:
+            # No evaluation function provided
+            return None
+        parameters_ndarrays = parameters_to_ndarrays(parameters)
+        eval_res = self.evaluate_fn(server_round, parameters_ndarrays, {})
+        if eval_res is None:
+            return None
+        loss, metrics = eval_res
+        return loss, metrics
+
+    def num_fit_clients(self, num_available_clients: int) -> Tuple[int, int]:
+        """Return sample size and required number of clients."""
+        num_clients = int(num_available_clients * self.fraction_fit)
+        return max(num_clients, self.min_fit_clients), self.min_available_clients
+
+    def num_evaluation_clients(self, num_available_clients: int) -> Tuple[int, int]:
+        """Use a fraction of available clients for evaluation."""
+        num_clients = int(num_available_clients * self.fraction_evaluate)
+        return max(num_clients, self.min_evaluate_clients), self.min_available_clients
diff --git a/baselines/heterofl/heterofl/utils.py b/baselines/heterofl/heterofl/utils.py
new file mode 100644
index 000000000000..2ffe4a021cd9
--- /dev/null
+++ b/baselines/heterofl/heterofl/utils.py
@@ -0,0 +1,219 @@
+"""Contains utility functions."""
+
+import errno
+import os
+from pathlib import Path
+
+import numpy as np
+import torch
+from hydra.core.hydra_config import HydraConfig
+
+
+def preprocess_input(cfg_model, cfg_data):
+    """Preprocess the input to get input shape, other derivables.
+
+    Parameters
+    ----------
+    cfg_model : DictConfig
+        Retrieve model-related information from the base.yaml configuration in Hydra.
+    cfg_data : DictConfig
+        Retrieve data-related information required to construct the model.
+
+    Returns
+    -------
+    Dict
+        Dictionary contained derived information from config.
+    """
+    model_config = {}
+    # if cfg_model.model_name == "conv":
+    #     model_config["model_name"] =
+    # elif for others...
+    model_config["model"] = cfg_model.model_name
+    if cfg_data.dataset_name == "MNIST":
+        model_config["data_shape"] = [1, 28, 28]
+        model_config["classes_size"] = 10
+    elif cfg_data.dataset_name == "CIFAR10":
+        model_config["data_shape"] = [3, 32, 32]
+        model_config["classes_size"] = 10
+
+    if "hidden_layers" in cfg_model:
+        model_config["hidden_layers"] = cfg_model.hidden_layers
+    if "norm" in cfg_model:
+        model_config["norm"] = cfg_model.norm
+    if "scale" in cfg_model:
+        model_config["scale"] = cfg_model.scale
+    if "mask" in cfg_model:
+        model_config["mask"] = cfg_model.mask
+
+    return model_config
+
+
+def make_optimizer(optimizer_name, parameters, learning_rate, weight_decay, momentum):
+    """Make the optimizer with given config.
+
+    Parameters
+    ----------
+    optimizer_name : str
+        Name of the optimizer.
+    parameters : Dict
+        Parameters of the model.
+    learning_rate: float
+        Learning rate of the optimizer.
+    weight_decay: float
+        weight_decay of the optimizer.
+
+    Returns
+    -------
+    torch.optim.Optimizer
+        Optimizer.
+    """
+    optimizer = None
+    if optimizer_name == "SGD":
+        optimizer = torch.optim.SGD(
+            parameters, lr=learning_rate, momentum=momentum, weight_decay=weight_decay
+        )
+    return optimizer
+
+
+def make_scheduler(scheduler_name, optimizer, milestones):
+    """Make the scheduler with given config.
+
+    Parameters
+    ----------
+    scheduler_name : str
+        Name of the scheduler.
+    optimizer : torch.optim.Optimizer
+        Parameters of the model.
+    milestones: List[int]
+        List of epoch indices. Must be increasing.
+
+    Returns
+    -------
+    torch.optim.lr_scheduler.Scheduler
+        scheduler.
+    """
+    scheduler = None
+    if scheduler_name == "MultiStepLR":
+        scheduler = torch.optim.lr_scheduler.MultiStepLR(
+            optimizer, milestones=milestones
+        )
+    return scheduler
+
+
+def get_global_model_rate(model_mode):
+    """Give the global model rate from string(cfg.control.model_mode) .
+
+    Parameters
+    ----------
+    model_mode : str
+        Contains the division of computational complexties among clients.
+
+    Returns
+    -------
+    str
+        global model computational complexity.
+    """
+    model_mode = "" + model_mode
+    model_mode = model_mode.split("-")[0][0]
+    return model_mode
+
+
+class ModelRateManager:
+    """Control the model rate of clients in case of simulation."""
+
+    def __init__(self, model_split_mode, model_split_rate, model_mode):
+        self.model_split_mode = model_split_mode
+        self.model_split_rate = model_split_rate
+        self.model_mode = model_mode
+        self.model_mode = self.model_mode.split("-")
+
+    def create_model_rate_mapping(self, num_users):
+        """Change the client to model rate mapping accordingly."""
+        client_model_rate = []
+
+        if self.model_split_mode == "fix":
+            mode_rate, proportion = [], []
+            for comp_level_prop in self.model_mode:
+                mode_rate.append(self.model_split_rate[comp_level_prop[0]])
+                proportion.append(int(comp_level_prop[1:]))
+            num_users_proportion = num_users // sum(proportion)
+            for i, comp_level in enumerate(mode_rate):
+                client_model_rate += np.repeat(
+                    comp_level, num_users_proportion * proportion[i]
+                ).tolist()
+            client_model_rate = client_model_rate + [
+                client_model_rate[-1] for _ in range(num_users - len(client_model_rate))
+            ]
+            # return client_model_rate
+
+        elif self.model_split_mode == "dynamic":
+            mode_rate, proportion = [], []
+
+            for comp_level_prop in self.model_mode:
+                mode_rate.append(self.model_split_rate[comp_level_prop[0]])
+                proportion.append(int(comp_level_prop[1:]))
+
+            proportion = (np.array(proportion) / sum(proportion)).tolist()
+
+            rate_idx = torch.multinomial(
+                torch.tensor(proportion), num_samples=num_users, replacement=True
+            ).tolist()
+            client_model_rate = np.array(mode_rate)[rate_idx]
+
+            # return client_model_rate
+
+        else:
+            raise ValueError("Not valid model split mode")
+
+        return client_model_rate
+
+
+def save_model(model, path):
+    """To save the model in the given path."""
+    # print('in save model')
+    current_path = HydraConfig.get().runtime.output_dir
+    model_save_path = Path(current_path) / path
+    torch.save(model.state_dict(), model_save_path)
+
+
+# """ The following functions(check_exists, makedir_exit_ok, save, load)
+# are adopted from authors (of heterofl) implementation."""
+
+
+def check_exists(path):
+    """Check if the given path exists."""
+    return os.path.exists(path)
+
+
+def makedir_exist_ok(path):
+    """Create a directory."""
+    try:
+        os.makedirs(path)
+    except OSError as os_err:
+        if os_err.errno == errno.EEXIST:
+            pass
+        else:
+            raise
+
+
+def save(inp, path, protocol=2, mode="torch"):
+    """Save the inp in a given path."""
+    dirname = os.path.dirname(path)
+    makedir_exist_ok(dirname)
+    if mode == "torch":
+        torch.save(inp, path, pickle_protocol=protocol)
+    elif mode == "numpy":
+        np.save(path, inp, allow_pickle=True)
+    else:
+        raise ValueError("Not valid save mode")
+
+
+# pylint: disable=no-else-return
+def load(path, mode="torch"):
+    """Load the file from given path."""
+    if mode == "torch":
+        return torch.load(path, map_location=lambda storage, loc: storage)
+    elif mode == "numpy":
+        return np.load(path, allow_pickle=True)
+    else:
+        raise ValueError("Not valid save mode")
diff --git a/baselines/heterofl/pyproject.toml b/baselines/heterofl/pyproject.toml
new file mode 100644
index 000000000000..4a5233785f78
--- /dev/null
+++ b/baselines/heterofl/pyproject.toml
@@ -0,0 +1,145 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.masonry.api"
+
+[tool.poetry]
+name = "heterofl" # <----- Ensure it matches the name of your baseline directory containing all the source code
+version = "1.0.0"
+description = "HeteroFL : Computation And Communication Efficient Federated Learning For Heterogeneous Clients"
+license = "Apache-2.0"
+authors = ["M S Chaitanya Kumar <chaitanya.mangipudi2002@gmail.com>", "The Flower Authors <hello@flower.ai>"]
+readme = "README.md"
+homepage = "https://flower.ai"
+repository = "https://github.com/adap/flower"
+documentation = "https://flower.ai"
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: MacOS :: MacOS X",
+    "Operating System :: POSIX :: Linux",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: Implementation :: CPython",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    "Topic :: Scientific/Engineering :: Mathematics",
+    "Topic :: Software Development",
+    "Topic :: Software Development :: Libraries",
+    "Topic :: Software Development :: Libraries :: Python Modules",
+    "Typing :: Typed",
+]
+
+[tool.poetry.dependencies]
+python = ">=3.10.0, <3.11.0"
+flwr = { extras = ["simulation"], version = "1.5.0" }
+hydra-core = "1.3.2" # don't change this
+torch = { url = "https://download.pytorch.org/whl/cu118/torch-2.1.0%2Bcu118-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu118/torchvision-0.16.0%2Bcu118-cp310-cp310-linux_x86_64.whl" }
+anytree = "^2.12.1"
+types-six = "^1.16.21.9"
+tqdm = "4.66.1"
+
+[tool.poetry.dev-dependencies]
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
+mypy = "==1.4.1"
+pylint = "==2.8.2"
+flake8 = "==3.9.2"
+pytest = "==6.2.4"
+pytest-watch = "==4.2.0"
+ruff = "==0.0.272"
+types-requests = "==2.27.7"
+virtualenv = "20.21.0"
+
+[tool.isort]
+line_length = 88
+indent = "    "
+multi_line_output = 3
+include_trailing_comma = true
+force_grid_wrap = 0
+use_parentheses = true
+
+[tool.black]
+line-length = 88
+target-version = ["py38", "py39", "py310", "py311"]
+
+[tool.pytest.ini_options]
+minversion = "6.2"
+addopts = "-qq"
+testpaths = [
+    "flwr_baselines",
+]
+
+[tool.mypy]
+ignore_missing_imports = true
+strict = false
+plugins = "numpy.typing.mypy_plugin"
+
+[tool.pylint."MESSAGES CONTROL"]
+disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
+good-names = "i,j,k,_,x,y,X,Y"
+signature-mutators = "hydra.main.main"
+
+
+[tool.pylint.typecheck]
+generated-members = "numpy.*, torch.*, tensorflow.*"
+
+
+[[tool.mypy.overrides]]
+module = [
+    "importlib.metadata.*",
+    "importlib_metadata.*",
+]
+follow_imports = "skip"
+follow_imports_for_stubs = true
+disallow_untyped_calls = false
+
+[[tool.mypy.overrides]]
+module = "torch.*"
+follow_imports = "skip"
+follow_imports_for_stubs = true
+
+[tool.docformatter]
+wrap-summaries = 88
+wrap-descriptions = 88
+
+[tool.ruff]
+target-version = "py38"
+line-length = 88
+select = ["D", "E", "F", "W", "B", "ISC", "C4"]
+fixable = ["D", "E", "F", "W", "B", "ISC", "C4"]
+ignore = ["B024", "B027"]
+exclude = [
+    ".bzr",
+    ".direnv",
+    ".eggs",
+    ".git",
+    ".hg",
+    ".mypy_cache",
+    ".nox",
+    ".pants.d",
+    ".pytype",
+    ".ruff_cache",
+    ".svn",
+    ".tox",
+    ".venv",
+    "__pypackages__",
+    "_build",
+    "buck-out",
+    "build",
+    "dist",
+    "node_modules",
+    "venv",
+    "proto",
+]
+
+[tool.ruff.pydocstyle]
+convention = "numpy"
diff --git a/baselines/hfedxgboost/README.md b/baselines/hfedxgboost/README.md
index 29702496370b..2f31e2c4c584 100644
--- a/baselines/hfedxgboost/README.md
+++ b/baselines/hfedxgboost/README.md
@@ -11,7 +11,7 @@ dataset: [a9a, cod-rna, ijcnn1, space_ga, cpusmall, YearPredictionMSD]
 
 **Paper:** [arxiv.org/abs/2304.07537](https://arxiv.org/abs/2304.07537)
 
-**Authors:** Chenyang Ma, Xinchi Qiu, Daniel J. Beutel, Nicholas D. Laneearly_stop_patience_rounds: 100
+**Authors:** Chenyang Ma, Xinchi Qiu, Daniel J. Beutel, Nicholas D. Lane
 
 **Abstract:** The privacy-sensitive nature of decentralized datasets and the robustness of eXtreme Gradient Boosting (XGBoost) on tabular data raise the need to train XGBoost in the context of federated learning (FL). Existing works on federated XGBoost in the horizontal setting rely on the sharing of gradients, which induce per-node level communication frequency and serious privacy concerns. To alleviate these problems, we develop an innovative framework for horizontal federated XGBoost which does not depend on the sharing of gradients and simultaneously boosts privacy and communication efficiency by making the learning rates of the aggregated tree ensembles are learnable. We conduct extensive evaluations on various classification and regression datasets, showing our approach achieve performance comparable to the state-of-the-art method and effectively improves communication efficiency by lowering both communication rounds and communication overhead by factors ranging from 25x to 700x.
 
diff --git a/baselines/hfedxgboost/hfedxgboost/__init__.py b/baselines/hfedxgboost/hfedxgboost/__init__.py
index 543147a05591..ce8299b55c1a 100644
--- a/baselines/hfedxgboost/hfedxgboost/__init__.py
+++ b/baselines/hfedxgboost/hfedxgboost/__init__.py
@@ -1 +1 @@
-"""hfedxgboost baseline package."""
+"""Hfedxgboost baseline package."""
diff --git a/baselines/hfedxgboost/hfedxgboost/client.py b/baselines/hfedxgboost/hfedxgboost/client.py
index 22435e20415b..bc5bfa5807a4 100644
--- a/baselines/hfedxgboost/hfedxgboost/client.py
+++ b/baselines/hfedxgboost/hfedxgboost/client.py
@@ -3,6 +3,7 @@
 Please overwrite `flwr.client.NumPyClient` or `flwr.client.Client` and create a function
 to instantiate your client.
 """
+
 from typing import Any, Tuple
 
 import flwr as fl
@@ -213,7 +214,7 @@ def fit(self, ins: FitIns) -> FitRes:
         aggregated_trees = ins.parameters[1]  # type: ignore # noqa: E501 # pylint: disable=line-too-long
 
         if isinstance(aggregated_trees, list):
-            print("Client " + self.cid + ": recieved", len(aggregated_trees), "trees")
+            print("Client " + self.cid + ": received", len(aggregated_trees), "trees")
         else:
             print("Client " + self.cid + ": only had its own tree")
         trainloader: Any = single_tree_preds_from_each_client(
diff --git a/baselines/hfedxgboost/hfedxgboost/conf/base.yaml b/baselines/hfedxgboost/hfedxgboost/conf/base.yaml
index 310b123a9054..284aab88e316 100644
--- a/baselines/hfedxgboost/hfedxgboost/conf/base.yaml
+++ b/baselines/hfedxgboost/hfedxgboost/conf/base.yaml
@@ -37,7 +37,7 @@ client_resources:
     num_gpus: 0.0
 
 strategy:
-  _target_: flwr.server.strategy.FedXgbNnAvg 
+  _target_: hfedxgboost.strategy.FedXgbNnAvg
   _recursive_: true #everything to be instantiated
   fraction_fit: 1.0 
   fraction_evaluate: 0.0 # no clients will be sampled for federated evaluation (we will still perform global evaluation)
diff --git a/baselines/hfedxgboost/hfedxgboost/dataset.py b/baselines/hfedxgboost/hfedxgboost/dataset.py
index a03ce2cd59fa..9233df1a3211 100644
--- a/baselines/hfedxgboost/hfedxgboost/dataset.py
+++ b/baselines/hfedxgboost/hfedxgboost/dataset.py
@@ -8,6 +8,7 @@
 `dataset_preparation.py` module. You can use all those functions from functions/methods
 defined here of course.
 """
+
 from typing import List, Optional, Tuple, Union
 
 import torch
diff --git a/baselines/hfedxgboost/hfedxgboost/dataset_preparation.py b/baselines/hfedxgboost/hfedxgboost/dataset_preparation.py
index 3fd3cbfb68fd..31c366e9933a 100644
--- a/baselines/hfedxgboost/hfedxgboost/dataset_preparation.py
+++ b/baselines/hfedxgboost/hfedxgboost/dataset_preparation.py
@@ -6,6 +6,7 @@
 uncomment the lines below and tell us in the README.md (see the "Running the Experiment"
 block) that this file should be executed first.
 """
+
 import bz2
 import os
 import shutil
diff --git a/baselines/hfedxgboost/hfedxgboost/main.py b/baselines/hfedxgboost/hfedxgboost/main.py
index 061e635f024c..4267c0bdd885 100644
--- a/baselines/hfedxgboost/hfedxgboost/main.py
+++ b/baselines/hfedxgboost/hfedxgboost/main.py
@@ -3,6 +3,7 @@
 It includes processioning the dataset, instantiate strategy, specify how the global
 model is going to be evaluated, etc. At the end, this script saves the results.
 """
+
 import functools
 from typing import Dict, Union
 
diff --git a/baselines/hfedxgboost/hfedxgboost/models.py b/baselines/hfedxgboost/hfedxgboost/models.py
index fbfc2d966f69..c4aec1f6acb8 100644
--- a/baselines/hfedxgboost/hfedxgboost/models.py
+++ b/baselines/hfedxgboost/hfedxgboost/models.py
@@ -5,6 +5,7 @@
 config. In this way, swapping your model for  another one can be done without changing
 the python code at all
 """
+
 from collections import OrderedDict
 from typing import Union
 
diff --git a/baselines/hfedxgboost/hfedxgboost/strategy.py b/baselines/hfedxgboost/hfedxgboost/strategy.py
index 17436c401c30..38247746b695 100644
--- a/baselines/hfedxgboost/hfedxgboost/strategy.py
+++ b/baselines/hfedxgboost/hfedxgboost/strategy.py
@@ -3,3 +3,77 @@
 Needed only when the strategy is not yet implemented in Flower or because you want to
 extend or modify the functionality of an existing strategy.
 """
+
+from logging import WARNING
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+from flwr.common import FitRes, Scalar, ndarrays_to_parameters, parameters_to_ndarrays
+from flwr.common.logger import log
+from flwr.server.client_proxy import ClientProxy
+from flwr.server.strategy import FedAvg
+from flwr.server.strategy.aggregate import aggregate
+
+
+class FedXgbNnAvg(FedAvg):
+    """Configurable FedXgbNnAvg strategy implementation."""
+
+    def __init__(self, *args: Any, **kwargs: Any) -> None:
+        """Federated XGBoost [Ma et al., 2023] strategy.
+
+        Implementation based on https://arxiv.org/abs/2304.07537.
+        """
+        super().__init__(*args, **kwargs)
+
+    def __repr__(self) -> str:
+        """Compute a string representation of the strategy."""
+        rep = f"FedXgbNnAvg(accept_failures={self.accept_failures})"
+        return rep
+
+    def evaluate(
+        self, server_round: int, parameters: Any
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """Evaluate model parameters using an evaluation function."""
+        if self.evaluate_fn is None:
+            # No evaluation function provided
+            return None
+        eval_res = self.evaluate_fn(server_round, parameters, {})
+        if eval_res is None:
+            return None
+        loss, metrics = eval_res
+        return loss, metrics
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Any], Dict[str, Scalar]]:
+        """Aggregate fit results using weighted average."""
+        if not results:
+            return None, {}
+        # Do not aggregate if there are failures and failures are not accepted
+        if not self.accept_failures and failures:
+            return None, {}
+
+        # Convert results
+        weights_results = [
+            (
+                parameters_to_ndarrays(fit_res.parameters[0].parameters),  # type: ignore # noqa: E501 # pylint: disable=line-too-long
+                fit_res.num_examples,
+            )
+            for _, fit_res in results
+        ]
+        parameters_aggregated = ndarrays_to_parameters(aggregate(weights_results))
+
+        # Aggregate XGBoost trees from all clients
+        trees_aggregated = [fit_res.parameters[1] for _, fit_res in results]  # type: ignore # noqa: E501 # pylint: disable=line-too-long
+
+        # Aggregate custom metrics if aggregation fn was provided
+        metrics_aggregated = {}
+        if self.fit_metrics_aggregation_fn:
+            fit_metrics = [(res.num_examples, res.metrics) for _, res in results]
+            metrics_aggregated = self.fit_metrics_aggregation_fn(fit_metrics)
+        elif server_round == 1:  # Only log this warning once
+            log(WARNING, "No fit_metrics_aggregation_fn provided")
+
+        return [parameters_aggregated, trees_aggregated], metrics_aggregated
diff --git a/baselines/hfedxgboost/hfedxgboost/utils.py b/baselines/hfedxgboost/hfedxgboost/utils.py
index 67450f7b8af7..1772a37eb39b 100644
--- a/baselines/hfedxgboost/hfedxgboost/utils.py
+++ b/baselines/hfedxgboost/hfedxgboost/utils.py
@@ -4,6 +4,7 @@
 example, you may define here things like: loading a model from a checkpoint, saving
 results, plotting.
 """
+
 import csv
 import math
 import os
@@ -315,9 +316,9 @@ def single_tree_preds_from_each_client(
 
     for i, _ in enumerate(temp_trees):
         for j in range(n_estimators_client):
-            preds_from_all_trees_from_all_clients[
-                :, i * n_estimators_client + j
-            ] = single_tree_prediction(temp_trees[i], j, x_train)
+            preds_from_all_trees_from_all_clients[:, i * n_estimators_client + j] = (
+                single_tree_prediction(temp_trees[i], j, x_train)
+            )
 
     preds_from_all_trees_from_all_clients = torch.from_numpy(
         np.expand_dims(preds_from_all_trees_from_all_clients, axis=1)
diff --git a/baselines/hfedxgboost/pyproject.toml b/baselines/hfedxgboost/pyproject.toml
index 1fbbb85ac36b..9e4c5e95a527 100644
--- a/baselines/hfedxgboost/pyproject.toml
+++ b/baselines/hfedxgboost/pyproject.toml
@@ -7,11 +7,11 @@ name = "hfedxgboost" # <----- Ensure it matches the name of your baseline direct
 version = "1.0.0"
 description = "HFedXgboost: Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>", "Aml Hassan Esmil  <aml.hassan.esmil@gmail.com>"]
+authors = ["The Flower Authors <hello@flower.ai>", "Aml Hassan Esmil  <aml.hassan.esmil@gmail.com>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -49,9 +49,9 @@ torchvision = "0.14.1"
 wandb = "0.15.12"
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
-docformatter = "==1.5.1"
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
@@ -88,10 +88,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/moon/README.md b/baselines/moon/README.md
index 05ab4ef68469..ef5f568aaa31 100644
--- a/baselines/moon/README.md
+++ b/baselines/moon/README.md
@@ -105,7 +105,7 @@ python -m moon.main --config-name cifar100_fedprox
 
 ## Expected Results
 
-You can find the output logs of a single run in this [link](https://drive.google.com/drive/folders/1YZEU2NcHWEHVyuJMlc1QvBSAvNMjH-aR?usp=share_link). After running the above commands, you can see the accuracy list at the end of the ouput, which is the test accuracy of the global model. For example, in one running, for CIFAR-10 with MOON, the accuracy after running 100 rounds is 0.7071. 
+You can find the output logs of a single run in this [link](https://drive.google.com/drive/folders/1YZEU2NcHWEHVyuJMlc1QvBSAvNMjH-aR?usp=share_link). After running the above commands, you can see the accuracy list at the end of the output, which is the test accuracy of the global model. For example, in one running, for CIFAR-10 with MOON, the accuracy after running 100 rounds is 0.7071. 
 
 For CIFAR-10 with FedProx, the accuracy after running 100 rounds is 0.6852. For CIFAR100 with MOON, the accuracy after running 100 rounds is 0.6636. For CIFAR100 with FedProx, the accuracy after running 100 rounds is 0.6494. The results are summarized below:
 
diff --git a/baselines/moon/moon/main.py b/baselines/moon/moon/main.py
index 902ccfa8395c..4b33644ca71d 100644
--- a/baselines/moon/moon/main.py
+++ b/baselines/moon/moon/main.py
@@ -3,6 +3,7 @@
 It includes processioning the dataset, instantiate strategy, specify how the global
 model is going to be evaluated, etc. At the end, this script saves the results.
 """
+
 import os
 import random
 import shutil
diff --git a/baselines/moon/moon/models.py b/baselines/moon/moon/models.py
index a323a8e74727..2dea0ae8ea3a 100644
--- a/baselines/moon/moon/models.py
+++ b/baselines/moon/moon/models.py
@@ -6,7 +6,6 @@
 the python code at all
 """
 
-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
diff --git a/baselines/moon/moon/utils.py b/baselines/moon/moon/utils.py
index 4b99a480f77b..377e3575439b 100644
--- a/baselines/moon/moon/utils.py
+++ b/baselines/moon/moon/utils.py
@@ -4,6 +4,7 @@
 example, you may define here things like: loading a model from a checkpoint, saving
 results, plotting.
 """
+
 from pathlib import Path
 from typing import Optional
 
diff --git a/baselines/moon/pyproject.toml b/baselines/moon/pyproject.toml
index e9f826abb2ea..6dba034e5c82 100644
--- a/baselines/moon/pyproject.toml
+++ b/baselines/moon/pyproject.toml
@@ -7,11 +7,11 @@ name = "moon" # <----- Ensure it matches the name of your baseline directory con
 version = "1.0.0"
 description = "Model-Contrastive Federated Learning"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>", "Qinbin Li <liqinbin1998@gmail.com>"]
+authors = ["The Flower Authors <hello@flower.ai>", "Qinbin Li <liqinbin1998@gmail.com>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -42,13 +42,13 @@ flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
 scikit-learn = "1.3.0"
 matplotlib = "3.8.0"
-torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.12.0%2Bcu116-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.13.0%2Bcu116-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu116/torch-1.12.0%2Bcu116-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu116/torchvision-0.13.0%2Bcu116-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
-docformatter = "==1.5.1"
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
@@ -56,6 +56,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
@@ -89,10 +90,10 @@ max-attributes = 15
 max-locals = 36
 max-branches = 20
 max-statements = 55
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/niid_bench/README.md b/baselines/niid_bench/README.md
index 6553a3cfdb1e..8c6ba11c5755 100644
--- a/baselines/niid_bench/README.md
+++ b/baselines/niid_bench/README.md
@@ -21,7 +21,7 @@ algorithms: [FedAvg, SCAFFOLD, FedProx, FedNova]
 
 **Datasets:** MNIST, CIFAR10, and Fashion-mnist from PyTorch's Torchvision
 
-**Hardware Setup:** These experiments were run on a linux server with 56 CPU threads with 250 GB Ram. There are 105 configurations to run per seed and at any time 7 configurations have been run parallely. The experiments required close to 12 hrs to finish for one seed. Nevertheless, to run a subset of configurations, such as only one FL protocol across all datasets and splits, a machine with 4-8 threads and 16 GB memory can run in reasonable time.
+**Hardware Setup:** These experiments were run on a linux server with 56 CPU threads with 250 GB Ram. There are 105 configurations to run per seed and at any time 7 configurations have been run parallelly. The experiments required close to 12 hrs to finish for one seed. Nevertheless, to run a subset of configurations, such as only one FL protocol across all datasets and splits, a machine with 4-8 threads and 16 GB memory can run in reasonable time.
 
 **Contributors:** Aashish Kolluri, PhD Candidate, National University of Singapore
 
@@ -59,7 +59,7 @@ For FedProx algorithm the proximal parameter is tuned from values {0.001, 0.01,
 ## Environment Setup
 
 ```bash
-# Setup the base poetry enviroment from the niid_bench directory
+# Setup the base poetry environment from the niid_bench directory
 # Set python version
 pyenv local 3.10.6
 # Tell poetry to use python 3.10
diff --git a/baselines/niid_bench/niid_bench/main.py b/baselines/niid_bench/niid_bench/main.py
index c845925c66ed..ca629a289ed4 100644
--- a/baselines/niid_bench/niid_bench/main.py
+++ b/baselines/niid_bench/niid_bench/main.py
@@ -3,6 +3,7 @@
 It includes processioning the dataset, instantiate strategy, specify how the global
 model is going to be evaluated, etc. At the end, this script saves the results.
 """
+
 import os
 import pickle
 
diff --git a/baselines/niid_bench/niid_bench/server_scaffold.py b/baselines/niid_bench/niid_bench/server_scaffold.py
index b55bc538d081..22ef2e6bc1da 100644
--- a/baselines/niid_bench/niid_bench/server_scaffold.py
+++ b/baselines/niid_bench/niid_bench/server_scaffold.py
@@ -120,9 +120,9 @@ def fit_round(
         )
 
         # Aggregate training results
-        aggregated_result: Tuple[
-            Optional[Parameters], Dict[str, Scalar]
-        ] = self.strategy.aggregate_fit(server_round, results, failures)
+        aggregated_result: Tuple[Optional[Parameters], Dict[str, Scalar]] = (
+            self.strategy.aggregate_fit(server_round, results, failures)
+        )
 
         aggregated_result_arrays_combined = []
         if aggregated_result[0] is not None:
diff --git a/baselines/niid_bench/pyproject.toml b/baselines/niid_bench/pyproject.toml
index adb001e031ff..a5849d955452 100644
--- a/baselines/niid_bench/pyproject.toml
+++ b/baselines/niid_bench/pyproject.toml
@@ -9,9 +9,9 @@ description = "Federated Learning on Non-IID Data Silos: An Experimental Study"
 license = "Apache-2.0"
 authors = ["Aashish Kolluri <aashishkolluri6@gmail.com>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -40,14 +40,14 @@ classifiers = [
 python = ">=3.10.0, <3.11.0"  # don't change this
 flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2" # don't change this
-torch = { url = "https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu113/torch-1.12.1%2Bcu113-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu113/torchvision-0.13.1%2Bcu113-cp310-cp310-linux_x86_64.whl" }
 tqdm = "4.66.1"
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
-docformatter = "==1.5.1"
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
@@ -55,6 +55,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
@@ -83,10 +84,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/tamuna/pyproject.toml b/baselines/tamuna/pyproject.toml
index fedcde9eb1c2..22a6e499aa1e 100644
--- a/baselines/tamuna/pyproject.toml
+++ b/baselines/tamuna/pyproject.toml
@@ -9,9 +9,9 @@ description = "TAMUNA: Doubly Accelerated Federated Learning with Local Training
 license = "Apache-2.0"
 authors = ["Ivan Agarský <agarskyivan@gmail.com>", "Grigory Malinovsky <grigorii.malinovskii@kaust.edu.sa>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 classifiers = [
     "Development Status :: 3 - Alpha",
     "Intended Audience :: Developers",
@@ -41,14 +41,14 @@ python = ">=3.10.0, <3.11.0"
 flwr = { extras = ["simulation"], version = "1.5.0" }
 hydra-core = "1.3.2"
 matplotlib = "3.7.1"
-torch = { url = "https://download.pytorch.org/whl/cu118/torch-2.0.1%2Bcu118-cp310-cp310-linux_x86_64.whl"}
-torchvision = { url = "https://download.pytorch.org/whl/cu118/torchvision-0.15.2%2Bcu118-cp310-cp310-linux_x86_64.whl"}
-torchaudio = { url = "https://download.pytorch.org/whl/cu118/torchaudio-2.0.2%2Bcu118-cp310-cp310-linux_x86_64.whl"}
+torch = { url = "https://download.pytorch.org/whl/cu118/torch-2.0.1%2Bcu118-cp310-cp310-linux_x86_64.whl" }
+torchvision = { url = "https://download.pytorch.org/whl/cu118/torchvision-0.15.2%2Bcu118-cp310-cp310-linux_x86_64.whl" }
+torchaudio = { url = "https://download.pytorch.org/whl/cu118/torchaudio-2.0.2%2Bcu118-cp310-cp310-linux_x86_64.whl" }
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = "==23.1.0"
-docformatter = "==1.5.1"
+isort = "==5.13.2"
+black = "==24.2.0"
+docformatter = "==1.7.5"
 mypy = "==1.4.1"
 pylint = "==2.8.2"
 flake8 = "==3.9.2"
@@ -56,6 +56,7 @@ pytest = "==6.2.4"
 pytest-watch = "==4.2.0"
 ruff = "==0.0.272"
 types-requests = "==2.27.7"
+virtualenv = "==20.21.0"
 
 [tool.isort]
 line_length = 88
@@ -84,10 +85,10 @@ plugins = "numpy.typing.mypy_plugin"
 [tool.pylint."MESSAGES CONTROL"]
 disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
 good-names = "i,j,k,_,x,y,X,Y"
-signature-mutators="hydra.main.main"
+signature-mutators = "hydra.main.main"
 
 [tool.pylint.typecheck]
-generated-members="numpy.*, torch.*, tensorflow.*"
+generated-members = "numpy.*, torch.*, tensorflow.*"
 
 [[tool.mypy.overrides]]
 module = [
diff --git a/baselines/tamuna/tamuna/client.py b/baselines/tamuna/tamuna/client.py
index 8b85cf0c4026..a42faaa4610f 100644
--- a/baselines/tamuna/tamuna/client.py
+++ b/baselines/tamuna/tamuna/client.py
@@ -1,4 +1,5 @@
 """Defines the MNIST Flower Client and a function to instantiate it."""
+
 # pylint: disable=too-many-instance-attributes,too-many-arguments
 import copy
 import os
diff --git a/baselines/tamuna/tamuna/dataset.py b/baselines/tamuna/tamuna/dataset.py
index 7208a788e069..920a7b5a5b13 100644
--- a/baselines/tamuna/tamuna/dataset.py
+++ b/baselines/tamuna/tamuna/dataset.py
@@ -1,4 +1,5 @@
 """Load the MNIST dataset into train and test loaders."""
+
 from typing import List, Optional, Tuple
 
 from torch.utils.data import DataLoader
diff --git a/baselines/tamuna/tamuna/main.py b/baselines/tamuna/tamuna/main.py
index f9c4eff4aa72..c47798ec94bb 100644
--- a/baselines/tamuna/tamuna/main.py
+++ b/baselines/tamuna/tamuna/main.py
@@ -1,4 +1,5 @@
 """Run CNN federated learning for MNIST dataset."""
+
 import os
 from typing import Dict, List
 
diff --git a/baselines/tamuna/tamuna/models.py b/baselines/tamuna/tamuna/models.py
index 58d506d86df2..cc151b56daca 100644
--- a/baselines/tamuna/tamuna/models.py
+++ b/baselines/tamuna/tamuna/models.py
@@ -1,4 +1,5 @@
 """CNN model architecture, training, and testing functions for MNIST."""
+
 # pylint: disable=too-many-arguments
 
 import copy
diff --git a/datasets/README.md b/datasets/README.md
index 876b6f453fa5..cf5caac3e1cd 100644
--- a/datasets/README.md
+++ b/datasets/README.md
@@ -4,9 +4,9 @@
 [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/adap/flower/blob/main/CONTRIBUTING.md)
 ![Build](https://github.com/adap/flower/actions/workflows/framework.yml/badge.svg)
 ![Downloads](https://pepy.tech/badge/flwr-datasets)
-[![Slack](https://img.shields.io/badge/Chat-Slack-red)](https://flower.dev/join-slack)
+[![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. 
+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.
 Flower Datasets library supports:
 * **downloading datasets** - choose the dataset from Hugging Face's `datasets`,
 * **partitioning datasets** - customize the partitioning scheme,
@@ -14,10 +14,10 @@ Flower Datasets library supports:
 
 Thanks to using Hugging Face's `datasets` used under the hood, Flower Datasets integrates with the following popular formats/frameworks:
 * Hugging Face,
-* PyTorch, 
-* TensorFlow, 
-* Numpy, 
-* Pandas, 
+* PyTorch,
+* TensorFlow,
+* Numpy,
+* Pandas,
 * Jax,
 * Arrow.
 
@@ -25,7 +25,7 @@ Create **custom partitioning schemes** or choose from the **implemented partitio
 * Partitioner (the abstract base class) `Partitioner`
 * IID partitioning `IidPartitioner(num_partitions)`
 * Natural ID partitioner `NaturalIdPartitioner`
-* Size partitioner (the abstract base class for the partitioners dictating the division based the number of samples) `SizePartitioner` 
+* 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`
@@ -59,7 +59,7 @@ If you plan to change the type of the dataset to run the code with your ML frame
 
 # 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(node_id, split)` and `load_full(split)`.
+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)`.
 
 Here's a basic quickstart example of how to partition the MNIST dataset:
 
@@ -71,7 +71,7 @@ mnist_fds = FederatedDataset("mnist", partitioners={"train": 100}
 
 mnist_partition_0 = mnist_fds.load_partition(0, "train")
 
-centralized_data = mnist_fds.load_full("test")
+centralized_data = mnist_fds.load_split("test")
 ```
 
 For more details, please refer to the specific how-to guides or tutorial. They showcase customization and more advanced features.
@@ -83,7 +83,7 @@ Here are a few of the things that we will work on in future releases:
 * ✅ Support for more datasets (especially the ones that have user id present).
 * ✅ Creation of custom `Partitioner`s.
 * ✅ More out-of-the-box `Partitioner`s.
-* ✅ Passing `Partitioner`s via `FederatedDataset`'s `partitioners` argument. 
+* ✅ 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.
 * Creation of the synthetic data,
diff --git a/datasets/dev/build-flwr-datasets-docs.sh b/datasets/dev/build-flwr-datasets-docs.sh
index dc3cd979d5c8..aefa47f147f8 100755
--- a/datasets/dev/build-flwr-datasets-docs.sh
+++ b/datasets/dev/build-flwr-datasets-docs.sh
@@ -28,3 +28,6 @@ fi
 # Note if a package cannot be reach via the recursive traversal, even if it has __all__, it won't be documented.
 echo "Generating the docs based on only the functionality given in the __all__."
 sphinx-build -M html source build
+
+# Remove the autogenerated source files after the build
+rm source/ref-api/*.rst
diff --git a/datasets/doc/source/_templates/autosummary/base.rst b/datasets/doc/source/_templates/autosummary/base.rst
new file mode 100644
index 000000000000..5536fa10863f
--- /dev/null
+++ b/datasets/doc/source/_templates/autosummary/base.rst
@@ -0,0 +1,5 @@
+{{ name | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. auto{{ objtype }}:: {{ objname }}
diff --git a/datasets/doc/source/_templates/base.html b/datasets/doc/source/_templates/base.html
index 1d2e417ccc8a..4bc72e03ae24 100644
--- a/datasets/doc/source/_templates/base.html
+++ b/datasets/doc/source/_templates/base.html
@@ -98,6 +98,6 @@
     {%- endblock -%}
 
     {%- endblock scripts -%}
-    <script defer data-domain="flower.dev" data-api="/static/pla/api/event" src="/static/pla/js/script.js"></script>
+    <script defer data-domain="flower.ai" data-api="/static/pla/api/event" src="/static/pla/js/script.js"></script>
   </body>
 </html>
diff --git a/datasets/doc/source/conf.py b/datasets/doc/source/conf.py
index 32baa6dd1471..e5c61b5559cb 100644
--- a/datasets/doc/source/conf.py
+++ b/datasets/doc/source/conf.py
@@ -14,6 +14,7 @@
 # ==============================================================================
 
 
+import datetime
 import os
 import sys
 from sphinx.application import ConfigError
@@ -33,7 +34,7 @@
 # -- Project information -----------------------------------------------------
 
 project = "Flower Datasets"
-copyright = "2023 Flower Labs GmbH"
+copyright = f"{datetime.date.today().year} Flower Labs GmbH"
 author = "The Flower Authors"
 
 # The full version, including alpha/beta/rc tags
@@ -74,25 +75,27 @@
 # The full name is still at the top of the page
 add_module_names = False
 
+
 def find_test_modules(package_path):
     """Go through the python files and exclude every *_test.py file."""
     full_path_modules = []
     for root, dirs, files in os.walk(package_path):
         for file in files:
-            if file.endswith('_test.py'):
+            if file.endswith("_test.py"):
                 # Construct the module path relative to the package directory
                 full_path = os.path.join(root, file)
                 relative_path = os.path.relpath(full_path, package_path)
                 # Convert file path to dotted module path
-                module_path = os.path.splitext(relative_path)[0].replace(os.sep, '.')
+                module_path = os.path.splitext(relative_path)[0].replace(os.sep, ".")
                 full_path_modules.append(module_path)
     modules = []
     for full_path_module in full_path_modules:
-        parts = full_path_module.split('.')
+        parts = full_path_module.split(".")
         for i in range(len(parts)):
-            modules.append('.'.join(parts[i:]))
+            modules.append(".".join(parts[i:]))
     return modules
 
+
 # Stop from documenting the *_test.py files.
 # That's the only way to do that in autosummary (make the modules as mock_imports).
 autodoc_mock_imports = find_test_modules(os.path.abspath("../../"))
@@ -120,7 +123,7 @@ def find_test_modules(package_path):
 html_title = f"Flower Datasets {release}"
 html_logo = "_static/flower-datasets-logo.png"
 html_favicon = "_static/favicon.ico"
-html_baseurl = "https://flower.dev/docs/datasets/"
+html_baseurl = "https://flower.ai/docs/datasets/"
 
 html_theme_options = {
     #
diff --git a/datasets/doc/source/how-to-use-with-numpy.rst b/datasets/doc/source/how-to-use-with-numpy.rst
index db80b712a13e..a131ef940a01 100644
--- a/datasets/doc/source/how-to-use-with-numpy.rst
+++ b/datasets/doc/source/how-to-use-with-numpy.rst
@@ -9,7 +9,7 @@ Create a ``FederatedDataset``::
 
   fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
   partition = fds.load_partition(0, "train")
-  centralized_dataset = fds.load_full("test")
+  centralized_dataset = fds.load_split("test")
 
 Inspect the names of the features::
 
diff --git a/datasets/doc/source/how-to-use-with-pytorch.rst b/datasets/doc/source/how-to-use-with-pytorch.rst
index 85e7833b0869..613f00a9a059 100644
--- a/datasets/doc/source/how-to-use-with-pytorch.rst
+++ b/datasets/doc/source/how-to-use-with-pytorch.rst
@@ -8,7 +8,7 @@ Standard setup - download the dataset, choose the partitioning::
 
   fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
   partition = fds.load_partition(0, "train")
-  centralized_dataset = fds.load_full("test")
+  centralized_dataset = fds.load_split("test")
 
 Determine the names of the features (you can alternatively do that directly on the Hugging Face website). The name can
 vary e.g. "img" or "image", "label" or "labels"::
diff --git a/datasets/doc/source/how-to-use-with-tensorflow.rst b/datasets/doc/source/how-to-use-with-tensorflow.rst
index 3dc8474b726a..dc215819a9e8 100644
--- a/datasets/doc/source/how-to-use-with-tensorflow.rst
+++ b/datasets/doc/source/how-to-use-with-tensorflow.rst
@@ -11,7 +11,7 @@ Create a ``FederatedDataset``::
 
   fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
   partition = fds.load_partition(0, "train")
-  centralized_dataset = fds.load_full("test")
+  centralized_dataset = fds.load_split("test")
 
 Inspect the names of the features::
 
diff --git a/datasets/doc/source/index.rst b/datasets/doc/source/index.rst
index ae7e7259f504..fd226b308bd5 100644
--- a/datasets/doc/source/index.rst
+++ b/datasets/doc/source/index.rst
@@ -2,7 +2,7 @@ Flower Datasets
 ===============
 
 Flower Datasets (``flwr-datasets``) is a library to quickly and easily create datasets for federated
-learning/analytics/evaluation. It is created by the ``Flower Labs`` team that also created `Flower <https://flower.dev>`_ - a Friendly Federated Learning Framework.
+learning/analytics/evaluation. It is created by the ``Flower Labs`` team that also created `Flower <https://flower.ai>`_ - a Friendly Federated Learning Framework.
 
 Flower Datasets Framework
 -------------------------
@@ -98,7 +98,7 @@ Join the Flower Community
 
 The Flower Community is growing quickly - we're a friendly group of researchers, engineers, students, professionals, academics, and other enthusiasts.
 
-.. button-link:: https://flower.dev/join-slack
+.. button-link:: https://flower.ai/join-slack
     :color: primary
     :shadow:
 
diff --git a/datasets/doc/source/tutorial-quickstart.rst b/datasets/doc/source/tutorial-quickstart.rst
index b93a08f234f2..e820e116fc61 100644
--- a/datasets/doc/source/tutorial-quickstart.rst
+++ b/datasets/doc/source/tutorial-quickstart.rst
@@ -38,9 +38,9 @@ To iid partition your dataset, choose the split you want to partition and the nu
 
   fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
   partition = fds.load_partition(0, "train")
-  centralized_dataset = fds.load_full("test")
+  centralized_dataset = fds.load_split("test")
 
-Now you're ready to go. You have ten partitions created from the train split of the MNIST dataset and the test split
+Now you're ready to go. You have ten partitions created from the train split of the CIFAR10 dataset and the test split
 for the centralized evaluation. We will convert the type of the dataset from Hugging Face's `Dataset` type to the one
 supported by your framework.
 
diff --git a/datasets/e2e/pytorch/pyproject.toml b/datasets/e2e/pytorch/pyproject.toml
new file mode 100644
index 000000000000..009ad2d74235
--- /dev/null
+++ b/datasets/e2e/pytorch/pyproject.toml
@@ -0,0 +1,16 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "fds-e2e-pytorch"
+version = "0.1.0"
+description = "Flower Datasets with PyTorch"
+authors = ["The Flower Authors <hello@flower.ai>"]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+flwr-datasets = { path = "./../../", extras = ["vision"] }
+torch = "^1.12.0"
+torchvision = "^0.14.1"
+parameterized = "==0.9.0"
diff --git a/datasets/e2e/pytorch/pytorch_test.py b/datasets/e2e/pytorch/pytorch_test.py
new file mode 100644
index 000000000000..5bac8f770f23
--- /dev/null
+++ b/datasets/e2e/pytorch/pytorch_test.py
@@ -0,0 +1,131 @@
+import unittest
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from datasets.utils.logging import disable_progress_bar
+from parameterized import parameterized_class, parameterized
+from torch import Tensor
+from torch.utils.data import DataLoader
+from torchvision.transforms import Compose, ToTensor, Normalize
+
+from flwr_datasets import FederatedDataset
+
+
+class SimpleCNN(nn.Module):
+    def __init__(self):
+        super(SimpleCNN, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+
+# Using parameterized testing, two different sets of parameters are specified:
+# 1. CIFAR10 dataset with the simple ToTensor transform.
+# 2. CIFAR10 dataset with a composed transform that first converts an image to a tensor
+#    and then normalizes it.
+@parameterized_class(
+    [
+        {"dataset_name": "cifar10", "test_split": "test", "transforms": ToTensor()},
+        {"dataset_name": "cifar10", "test_split": "test", "transforms": Compose(
+            [ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
+        )},
+    ]
+)
+class FdsToPyTorch(unittest.TestCase):
+    """Test the conversion from FDS to PyTorch Dataset and Dataloader."""
+
+    dataset_name = ""
+    test_split = ""
+    transforms = None
+    trainloader = None
+    expected_img_shape_after_transform = [3, 32, 32]
+
+    @classmethod
+    def setUpClass(cls):
+        """Disable progress bar to keep the log clean.
+        """
+        disable_progress_bar()
+
+    def _create_trainloader(self, batch_size: int) -> DataLoader:
+        """Create a trainloader from the federated dataset."""
+        partition_id = 0
+        fds = FederatedDataset(dataset=self.dataset_name, partitioners={"train": 100})
+        partition = fds.load_partition(partition_id, "train")
+        partition_train_test = partition.train_test_split(test_size=0.2)
+        partition_train_test = partition_train_test.map(
+            lambda img: {"img": self.transforms(img)}, input_columns="img"
+        )
+        trainloader = DataLoader(
+            partition_train_test["train"].with_format("torch"), batch_size=batch_size,
+            shuffle=True
+        )
+        return trainloader
+
+    def test_create_partition_dataloader_with_transforms_shape(self) -> None:
+        """Test if the DataLoader returns batches with the expected shape."""
+        batch_size = 16
+        trainloader = self._create_trainloader(batch_size)
+        batch = next(iter(trainloader))
+        images = batch["img"]
+        self.assertEqual(tuple(images.shape),
+                         (batch_size, *self.expected_img_shape_after_transform))
+
+    def test_create_partition_dataloader_with_transforms_batch_type(self) -> None:
+        """Test if the DataLoader returns batches of type dictionary."""
+        batch_size = 16
+        trainloader = self._create_trainloader(batch_size)
+        batch = next(iter(trainloader))
+        self.assertIsInstance(batch, dict)
+
+    def test_create_partition_dataloader_with_transforms_data_type(self) -> None:
+        """Test to verify if the data in the DataLoader batches are of type Tensor."""
+        batch_size = 16
+        trainloader = self._create_trainloader(batch_size)
+        batch = next(iter(trainloader))
+        images = batch["img"]
+        self.assertIsInstance(images, Tensor)
+
+    @parameterized.expand([
+        ("not_nan", torch.isnan),
+        ("not_inf", torch.isinf),
+    ])
+    def test_train_model_loss_value(self, name, condition_func):
+        """Test if the model trains and if the loss is a correct number."""
+        trainloader = self._create_trainloader(16)
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+        # Create the model, criterion, and optimizer
+        net = SimpleCNN().to(device)
+        criterion = nn.CrossEntropyLoss()
+        optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
+
+        # Training loop for one epoch
+        net.train()
+        loss = None
+        for i, data in enumerate(trainloader, 0):
+            inputs, labels = data['img'].to(device), data['label'].to(device)
+            optimizer.zero_grad()
+            outputs = net(inputs)
+            loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+
+        self.assertFalse(condition_func(loss).item())
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/datasets/e2e/scikit-learn/pyproject.toml b/datasets/e2e/scikit-learn/pyproject.toml
index 7e22644566cf..48356e4a945f 100644
--- a/datasets/e2e/scikit-learn/pyproject.toml
+++ b/datasets/e2e/scikit-learn/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "fds-e2e-sklearn"
 version = "0.1.0"
 description = "Flower Datasets with scikit-learn"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/datasets/e2e/tensorflow/pyproject.toml b/datasets/e2e/tensorflow/pyproject.toml
index 9c5c72c46400..dbb6720219b2 100644
--- a/datasets/e2e/tensorflow/pyproject.toml
+++ b/datasets/e2e/tensorflow/pyproject.toml
@@ -6,10 +6,11 @@ build-backend = "poetry.core.masonry.api"
 name = "fds-e2-tensorflow"
 version = "0.1.0"
 description = "Flower Datasets with TensorFlow"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
-python = "^3.8"
+python = ">=3.8,<3.11"
 flwr-datasets = { path = "./../../", extras = ["vision"] }
 tensorflow-cpu = "^2.9.1, !=2.11.1"
+tensorflow-io-gcs-filesystem = "<0.35.0"
 parameterized = "==0.9.0"
diff --git a/datasets/e2e/tensorflow/tensorflow_test.py b/datasets/e2e/tensorflow/tensorflow_test.py
index e041bcb8f8cc..5e21b5d6386f 100644
--- a/datasets/e2e/tensorflow/tensorflow_test.py
+++ b/datasets/e2e/tensorflow/tensorflow_test.py
@@ -38,8 +38,7 @@ class FdsToTensorFlow(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls):
-        """Disable progress bar to keep the log clean.
-        """
+        """Disable progress bar to keep the log clean."""
         disable_progress_bar()
 
     def _create_tensorflow_dataset(self, batch_size: int) -> tf.data.Dataset:
diff --git a/datasets/flwr_datasets/__init__.py b/datasets/flwr_datasets/__init__.py
index 48d993037708..0b9a6685427b 100644
--- a/datasets/flwr_datasets/__init__.py
+++ b/datasets/flwr_datasets/__init__.py
@@ -16,6 +16,7 @@
 
 
 from flwr_datasets import partitioner, resplitter
+from flwr_datasets import utils as utils
 from flwr_datasets.common.version import package_version as _package_version
 from flwr_datasets.federated_dataset import FederatedDataset
 
@@ -23,6 +24,7 @@
     "FederatedDataset",
     "partitioner",
     "resplitter",
+    "utils",
 ]
 
 __version__ = _package_version
diff --git a/datasets/flwr_datasets/common/typing.py b/datasets/flwr_datasets/common/typing.py
new file mode 100644
index 000000000000..ffaefaeec313
--- /dev/null
+++ b/datasets/flwr_datasets/common/typing.py
@@ -0,0 +1,26 @@
+# 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 Datasets type definitions."""
+
+
+from typing import Any, List
+
+import numpy as np
+import numpy.typing as npt
+
+NDArray = npt.NDArray[Any]
+NDArrayInt = npt.NDArray[np.int_]
+NDArrayFloat = npt.NDArray[np.float_]
+NDArrays = List[NDArray]
diff --git a/datasets/flwr_datasets/federated_dataset.py b/datasets/flwr_datasets/federated_dataset.py
index c40f8cc34857..55a7e597f6b4 100644
--- a/datasets/flwr_datasets/federated_dataset.py
+++ b/datasets/flwr_datasets/federated_dataset.py
@@ -28,6 +28,8 @@
 )
 
 
+# flake8: noqa: E501
+# pylint: disable=line-too-long
 class FederatedDataset:
     """Representation of a dataset for federated learning/evaluation/analytics.
 
@@ -64,14 +66,10 @@ class FederatedDataset:
     Use MNIST dataset for Federated Learning with 100 clients (edge devices):
 
     >>> mnist_fds = FederatedDataset(dataset="mnist", partitioners={"train": 100})
-
-    Load partition for client with ID 10.
-
+    >>> # Load partition for client with ID 10.
     >>> partition = mnist_fds.load_partition(10, "train")
-
-    Use test split for centralized evaluation.
-
-    >>> centralized = mnist_fds.load_full("test")
+    >>> # Use test split for centralized evaluation.
+    >>> centralized = mnist_fds.load_split("test")
     """
 
     # pylint: disable=too-many-instance-attributes
@@ -97,20 +95,24 @@ def __init__(
         self._shuffle = shuffle
         self._seed = seed
         #  _dataset is prepared lazily on the first call to `load_partition`
-        #  or `load_full`. See _prepare_datasets for more details
+        #  or `load_split`. See _prepare_datasets for more details
         self._dataset: Optional[DatasetDict] = None
-        # Indicate if the dataset is prepared for `load_partition` or `load_full`
+        # Indicate if the dataset is prepared for `load_partition` or `load_split`
         self._dataset_prepared: bool = False
 
-    def load_partition(self, node_id: int, split: Optional[str] = None) -> Dataset:
+    def load_partition(
+        self,
+        partition_id: int,
+        split: Optional[str] = None,
+    ) -> Dataset:
         """Load the partition specified by the idx in the selected split.
 
         The dataset is downloaded only when the first call to `load_partition` or
-        `load_full` is made.
+        `load_split` is made.
 
         Parameters
         ----------
-        node_id : int
+        partition_id : int
             Partition index for the selected split, idx in {0, ..., num_partitions - 1}.
         split : Optional[str]
             Name of the (partitioned) split (e.g. "train", "test"). You can skip this
@@ -136,13 +138,13 @@ def load_partition(self, node_id: int, split: Optional[str] = None) -> Dataset:
         self._check_if_split_possible_to_federate(split)
         partitioner: Partitioner = self._partitioners[split]
         self._assign_dataset_to_partitioner(split)
-        return partitioner.load_partition(node_id)
+        return partitioner.load_partition(partition_id)
 
-    def load_full(self, split: str) -> Dataset:
+    def load_split(self, split: str) -> Dataset:
         """Load the full split of the dataset.
 
         The dataset is downloaded only when the first call to `load_partition` or
-        `load_full` is made.
+        `load_split` is made.
 
         Parameters
         ----------
@@ -161,6 +163,25 @@ def load_full(self, split: str) -> Dataset:
         self._check_if_split_present(split)
         return self._dataset[split]
 
+    @property
+    def partitioners(self) -> Dict[str, Partitioner]:
+        """Dictionary mapping each split to its associated partitioner.
+
+        The returned partitioners have the splits of the dataset assigned to them.
+        """
+        # This function triggers the dataset download (lazy download) and checks
+        # the partitioner specification correctness (which can also happen lazily only
+        # after the dataset download).
+        if not self._dataset_prepared:
+            self._prepare_dataset()
+        if self._dataset is None:
+            raise ValueError("Dataset is not loaded yet.")
+        partitioners_keys = list(self._partitioners.keys())
+        for split in partitioners_keys:
+            self._check_if_split_present(split)
+            self._assign_dataset_to_partitioner(split)
+        return self._partitioners
+
     def _check_if_split_present(self, split: str) -> None:
         """Check if the split (for partitioning or full return) is in the dataset."""
         if self._dataset is None:
diff --git a/datasets/flwr_datasets/federated_dataset_test.py b/datasets/flwr_datasets/federated_dataset_test.py
index e02b6ed5add8..7ca2b44570ca 100644
--- a/datasets/flwr_datasets/federated_dataset_test.py
+++ b/datasets/flwr_datasets/federated_dataset_test.py
@@ -67,12 +67,12 @@ def test_load_partition_size(self, _: str, train_num_partitions: int) -> None:
             len(dataset_partition0), len(dataset["train"]) // train_num_partitions
         )
 
-    def test_load_full(self) -> None:
-        """Test if the load_full works with the correct split name."""
+    def test_load_split(self) -> None:
+        """Test if the load_split works with the correct split name."""
         dataset_fds = FederatedDataset(
             dataset=self.dataset_name, partitioners={"train": 100}
         )
-        dataset_fds_test = dataset_fds.load_full(self.test_split)
+        dataset_fds_test = dataset_fds.load_split(self.test_split)
         dataset_test = datasets.load_dataset(self.dataset_name)[self.test_split]
         self.assertEqual(len(dataset_fds_test), len(dataset_test))
 
@@ -116,7 +116,7 @@ def test_resplit_dataset_into_one(self) -> None:
             partitioners={"train": 100},
             resplitter={"full": ("train", self.test_split)},
         )
-        full = fds.load_full("full")
+        full = fds.load_split("full")
         self.assertEqual(dataset_length, len(full))
 
     # pylint: disable=protected-access
@@ -151,7 +151,7 @@ def resplit(dataset: DatasetDict) -> DatasetDict:
         fds = FederatedDataset(
             dataset=self.dataset_name, partitioners={"train": 100}, resplitter=resplit
         )
-        full = fds.load_full("full")
+        full = fds.load_split("full")
         dataset = datasets.load_dataset(self.dataset_name)
         dataset_length = sum([len(ds) for ds in dataset.values()])
         self.assertEqual(len(full), dataset_length)
@@ -185,7 +185,7 @@ def test_shuffling_applied(self, mock_func: Mock) -> None:
         fds = FederatedDataset(
             dataset="does-not-matter", partitioners={"train": 10}, shuffle=True, seed=42
         )
-        train = fds.load_full("train")
+        train = fds.load_split("train")
         # This should be shuffled
         result = train["features"]
 
@@ -203,7 +203,7 @@ def test_shuffling_not_applied(self, mock_func: Mock) -> None:
             partitioners={"train": 10},
             shuffle=False,
         )
-        train = fds.load_full("train")
+        train = fds.load_split("train")
         # This should not be shuffled
         result = train["features"]
 
@@ -236,7 +236,7 @@ def resplit(dataset: DatasetDict) -> DatasetDict:
             resplitter=resplit,
             shuffle=True,
         )
-        train = fds.load_full("train")
+        train = fds.load_split("train")
         # This should not be shuffled
         result = train["features"]
 
diff --git a/datasets/flwr_datasets/partitioner/__init__.py b/datasets/flwr_datasets/partitioner/__init__.py
index 5e7c86718f67..0d1edbfcb04a 100644
--- a/datasets/flwr_datasets/partitioner/__init__.py
+++ b/datasets/flwr_datasets/partitioner/__init__.py
@@ -15,11 +15,14 @@
 """Flower Datasets Partitioner package."""
 
 
+from .dirichlet_partitioner import DirichletPartitioner
 from .exponential_partitioner import ExponentialPartitioner
 from .iid_partitioner import IidPartitioner
+from .inner_dirichlet_partitioner import InnerDirichletPartitioner
 from .linear_partitioner import LinearPartitioner
 from .natural_id_partitioner import NaturalIdPartitioner
 from .partitioner import Partitioner
+from .shard_partitioner import ShardPartitioner
 from .size_partitioner import SizePartitioner
 from .square_partitioner import SquarePartitioner
 
@@ -27,8 +30,11 @@
     "IidPartitioner",
     "Partitioner",
     "NaturalIdPartitioner",
+    "DirichletPartitioner",
     "SizePartitioner",
     "LinearPartitioner",
+    "InnerDirichletPartitioner",
     "SquarePartitioner",
+    "ShardPartitioner",
     "ExponentialPartitioner",
 ]
diff --git a/datasets/flwr_datasets/partitioner/dirichlet_partitioner.py b/datasets/flwr_datasets/partitioner/dirichlet_partitioner.py
new file mode 100644
index 000000000000..f3feb2174bde
--- /dev/null
+++ b/datasets/flwr_datasets/partitioner/dirichlet_partitioner.py
@@ -0,0 +1,338 @@
+# 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.
+# ==============================================================================
+"""Dirichlet partitioner class that works with Hugging Face Datasets."""
+
+
+import warnings
+from typing import Dict, List, Optional, Union
+
+import numpy as np
+
+import datasets
+from flwr_datasets.common.typing import NDArrayFloat
+from flwr_datasets.partitioner.partitioner import Partitioner
+
+
+# pylint: disable=R0902, R0912, R0914
+class DirichletPartitioner(Partitioner):
+    """Partitioner based on Dirichlet distribution.
+
+    Implementation based on Bayesian Nonparametric Federated Learning of Neural Networks
+    https://arxiv.org/abs/1905.12022.
+
+    The algorithm sequentially divides the data with each label. The fractions of the
+    data with each label is drawn from Dirichlet distribution and adjusted in case of
+    balancing. The data is assigned. In case the `min_partition_size` is not satisfied
+    the algorithm is run again (the fractions will change since it is a random process
+    even though the alpha stays the same).
+
+    The notion of balancing is explicitly introduced here (not mentioned in paper but
+    implemented in the code). It is a mechanism that excludes the partition from
+    assigning new samples to it if the current number of samples on that partition
+    exceeds the average number that the partition would get in case of even data
+    distribution. It is controlled by`self_balancing` parameter.
+
+    Parameters
+    ----------
+    num_partitions : int
+        The total number of partitions that the data will be divided into.
+    partition_by : str
+        Column name of the labels (targets) based on which Dirichlet sampling works.
+    alpha : Union[int, float, List[float], NDArrayFloat]
+        Concentration parameter to the Dirichlet distribution
+    min_partition_size : int
+        The minimum number of samples that each partitions will have (the sampling
+        process is repeated if any partition is too small).
+    self_balancing : bool
+        Whether assign further samples to a partition after the number of samples
+        exceeded the average number of samples per partition. (True in the original
+        paper's code although not mentioned in paper itself).
+    shuffle: bool
+        Whether to randomize the order of samples. Shuffling applied after the
+        samples assignment to partitions.
+    seed: int
+        Seed used for dataset shuffling. It has no effect if `shuffle` is False.
+
+    Examples
+    --------
+    >>> 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,
+    >>>                                    self_balancing=True)
+    >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": partitioner})
+    >>> partition = fds.load_partition(0)
+    >>> print(partition[0])  # Print the first example
+    {'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x127B92170>,
+    'label': 4}
+    >>> partition_sizes = partition_sizes = [
+    >>>     len(fds.load_partition(partition_id)) for partition_id in range(10)
+    >>> ]
+    >>> print(sorted(partition_sizes))
+    [2134, 2615, 3646, 6011, 6170, 6386, 6715, 7653, 8435, 10235]
+    """
+
+    def __init__(  # pylint: disable=R0913
+        self,
+        num_partitions: int,
+        partition_by: str,
+        alpha: Union[int, float, List[float], NDArrayFloat],
+        min_partition_size: int = 10,
+        self_balancing: bool = False,
+        shuffle: bool = True,
+        seed: Optional[int] = 42,
+    ) -> None:
+        super().__init__()
+        # Attributes based on the constructor
+        self._num_partitions = num_partitions
+        self._check_num_partitions_greater_than_zero()
+        self._alpha: NDArrayFloat = self._initialize_alpha(alpha)
+        self._partition_by = partition_by
+        self._min_partition_size: int = min_partition_size
+        self._self_balancing = self_balancing
+        self._shuffle = shuffle
+        self._seed = seed
+        self._rng = np.random.default_rng(seed=self._seed)  # NumPy random generator
+
+        # Utility attributes
+        # The attributes below are determined during the first call to load_partition
+        self._avg_num_of_samples_per_partition: Optional[float] = None
+        self._unique_classes: Optional[Union[List[int], List[str]]] = None
+        self._partition_id_to_indices: Dict[int, List[int]] = {}
+        self._partition_id_to_indices_determined = False
+
+    def load_partition(self, partition_id: int) -> datasets.Dataset:
+        """Load a partition based on the partition index.
+
+        Parameters
+        ----------
+        partition_id : int
+            the index that corresponds to the requested partition
+
+        Returns
+        -------
+        dataset_partition : Dataset
+            single partition of a dataset
+        """
+        # The partitioning is done lazily - only when the first partition is
+        # requested. Only the first call creates the indices assignments for all the
+        # partition indices.
+        self._check_num_partitions_correctness_if_needed()
+        self._determine_partition_id_to_indices_if_needed()
+        return self.dataset.select(self._partition_id_to_indices[partition_id])
+
+    @property
+    def num_partitions(self) -> int:
+        """Total number of partitions."""
+        self._check_num_partitions_correctness_if_needed()
+        self._determine_partition_id_to_indices_if_needed()
+        return self._num_partitions
+
+    def _initialize_alpha(
+        self, alpha: Union[int, float, List[float], NDArrayFloat]
+    ) -> NDArrayFloat:
+        """Convert alpha to the used format in the code a NDArrayFloat.
+
+        The alpha can be provided in constructor can be in different format for user
+        convenience. The format into which it's transformed here is used throughout the
+        code for computation.
+
+        Parameters
+        ----------
+            alpha : Union[int, float, List[float], NDArrayFloat]
+                Concentration parameter to the Dirichlet distribution
+
+        Returns
+        -------
+        alpha : NDArrayFloat
+            Concentration parameter in a format ready to used in computation.
+        """
+        if isinstance(alpha, int):
+            alpha = np.array([float(alpha)], dtype=float).repeat(self._num_partitions)
+        elif isinstance(alpha, float):
+            alpha = np.array([alpha], dtype=float).repeat(self._num_partitions)
+        elif isinstance(alpha, List):
+            if len(alpha) != self._num_partitions:
+                raise ValueError(
+                    "If passing alpha as a List, it needs to be of length of equal to "
+                    "num_partitions."
+                )
+            alpha = np.asarray(alpha)
+        elif isinstance(alpha, np.ndarray):
+            # pylint: disable=R1720
+            if alpha.ndim == 1 and alpha.shape[0] != self._num_partitions:
+                raise ValueError(
+                    "If passing alpha as an NDArray, its length needs to be of length "
+                    "equal to num_partitions."
+                )
+            elif alpha.ndim == 2:
+                alpha = alpha.flatten()
+                if alpha.shape[0] != self._num_partitions:
+                    raise ValueError(
+                        "If passing alpha as an NDArray, its size needs to be of length"
+                        " equal to num_partitions."
+                    )
+        else:
+            raise ValueError("The given alpha format is not supported.")
+        if not (alpha > 0).all():
+            raise ValueError(
+                f"Alpha values should be strictly greater than zero. "
+                f"Instead it'd be converted to {alpha}"
+            )
+        return alpha
+
+    def _determine_partition_id_to_indices_if_needed(
+        self,
+    ) -> None:
+        """Create an assignment of indices to the partition indices."""
+        if self._partition_id_to_indices_determined:
+            return
+
+        # Generate information needed for Dirichlet partitioning
+        self._unique_classes = self.dataset.unique(self._partition_by)
+        assert self._unique_classes is not None
+        # This is needed only if self._self_balancing is True (the default option)
+        self._avg_num_of_samples_per_partition = (
+            self.dataset.num_rows / self._num_partitions
+        )
+
+        # Change targets list data type to numpy
+        targets = np.array(self.dataset[self._partition_by])
+
+        # Repeat the sampling procedure based on the Dirichlet distribution until the
+        # min_partition_size is reached.
+        sampling_try = 0
+        while True:
+            # Prepare data structure to store indices assigned to partition ids
+            partition_id_to_indices: Dict[int, List[int]] = {}
+            for nid in range(self._num_partitions):
+                partition_id_to_indices[nid] = []
+
+            # Iterated over all unique labels (they are not necessarily of type int)
+            for k in self._unique_classes:
+                # Access all the indices associated with class k
+                indices_representing_class_k = np.nonzero(targets == k)[0]
+                # Determine division (the fractions) of the data representing class k
+                # among the partitions
+                class_k_division_proportions = self._rng.dirichlet(self._alpha)
+                nid_to_proportion_of_k_samples = {}
+                for nid in range(self._num_partitions):
+                    nid_to_proportion_of_k_samples[nid] = class_k_division_proportions[
+                        nid
+                    ]
+                # Balancing (not mentioned in the paper but implemented)
+                # Do not assign additional samples to the partition if it already has
+                # more than the average numbers of samples per partition. Note that it
+                # might especially affect classes that are later in the order. This is
+                # the reason for more sparse division that the alpha might suggest.
+                if self._self_balancing:
+                    assert self._avg_num_of_samples_per_partition is not None
+                    for nid in nid_to_proportion_of_k_samples.copy():
+                        if (
+                            len(partition_id_to_indices[nid])
+                            > self._avg_num_of_samples_per_partition
+                        ):
+                            nid_to_proportion_of_k_samples[nid] = 0
+
+                    # Normalize the proportions such that they sum up to 1
+                    sum_proportions = sum(nid_to_proportion_of_k_samples.values())
+                    for nid, prop in nid_to_proportion_of_k_samples.copy().items():
+                        nid_to_proportion_of_k_samples[nid] = prop / sum_proportions
+
+                # Determine the split indices
+                cumsum_division_fractions = np.cumsum(
+                    list(nid_to_proportion_of_k_samples.values())
+                )
+                cumsum_division_numbers = cumsum_division_fractions * len(
+                    indices_representing_class_k
+                )
+                # [:-1] is because the np.split requires the division indices but the
+                # last element represents the sum = total number of samples
+                indices_on_which_split = cumsum_division_numbers.astype(int)[:-1]
+
+                split_indices = np.split(
+                    indices_representing_class_k, indices_on_which_split
+                )
+
+                # Append new indices (coming from class k) to the existing indices
+                for nid, indices in partition_id_to_indices.items():
+                    indices.extend(split_indices[nid].tolist())
+
+            # Determine if the indices assignment meets the min_partition_size
+            # If it does not mean the requirement repeat the Dirichlet sampling process
+            # Otherwise break the while loop
+            min_sample_size_on_client = min(
+                len(indices) for indices in partition_id_to_indices.values()
+            )
+            if min_sample_size_on_client >= self._min_partition_size:
+                break
+            sample_sizes = [
+                len(indices) for indices in partition_id_to_indices.values()
+            ]
+            alpha_not_met = [
+                self._alpha[i]
+                for i, ss in enumerate(sample_sizes)
+                if ss == min(sample_sizes)
+            ]
+            mssg_list_alphas = (
+                (
+                    "Generating partitions by sampling from a list of very wide range "
+                    "of alpha values can be hard to achieve. Try reducing the range "
+                    f"between maximum ({max(self._alpha)}) and minimum alpha "
+                    f"({min(self._alpha)}) values or increasing all the values."
+                )
+                if len(self._alpha.flatten().tolist()) > 0
+                else ""
+            )
+            warnings.warn(
+                f"The specified min_partition_size ({self._min_partition_size}) was "
+                f"not satisfied for alpha ({alpha_not_met}) after "
+                f"{sampling_try} attempts at sampling from the Dirichlet "
+                f"distribution. The probability sampling from the Dirichlet "
+                f"distribution will be repeated. Note: This is not a desired "
+                f"behavior. It is recommended to adjust the alpha or "
+                f"min_partition_size instead. {mssg_list_alphas}",
+                stacklevel=1,
+            )
+            if sampling_try == 10:
+                raise ValueError(
+                    "The max number of attempts (10) was reached. "
+                    "Please update the values of alpha and try again."
+                )
+            sampling_try += 1
+
+        # Shuffle the indices not to have the datasets with targets in sequences like
+        # [00000, 11111, ...]) if the shuffle is True
+        if self._shuffle:
+            for indices in partition_id_to_indices.values():
+                # In place shuffling
+                self._rng.shuffle(indices)
+        self._partition_id_to_indices = partition_id_to_indices
+        self._partition_id_to_indices_determined = True
+
+    def _check_num_partitions_correctness_if_needed(self) -> None:
+        """Test num_partitions when the dataset is given (in load_partition)."""
+        if not self._partition_id_to_indices_determined:
+            if self._num_partitions > self.dataset.num_rows:
+                raise ValueError(
+                    "The number of partitions needs to be smaller than the number of "
+                    "samples in the dataset."
+                )
+
+    def _check_num_partitions_greater_than_zero(self) -> None:
+        """Test num_partition left sides correctness."""
+        if not self._num_partitions > 0:
+            raise ValueError("The number of partitions needs to be greater than zero.")
diff --git a/datasets/flwr_datasets/partitioner/dirichlet_partitioner_test.py b/datasets/flwr_datasets/partitioner/dirichlet_partitioner_test.py
new file mode 100644
index 000000000000..b2407b5d5822
--- /dev/null
+++ b/datasets/flwr_datasets/partitioner/dirichlet_partitioner_test.py
@@ -0,0 +1,172 @@
+# 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 DirichletPartitioner."""
+
+
+# pylint: disable=W0212
+import unittest
+from typing import Tuple, Union
+
+import numpy as np
+from numpy.typing import NDArray
+from parameterized import parameterized
+
+from datasets import Dataset
+from flwr_datasets.partitioner.dirichlet_partitioner import DirichletPartitioner
+
+
+def _dummy_setup(
+    num_partitions: int,
+    alpha: Union[float, NDArray[np.float_]],
+    num_rows: int,
+    partition_by: str,
+    self_balancing: bool = True,
+) -> Tuple[Dataset, DirichletPartitioner]:
+    """Create a dummy dataset and partitioner for testing."""
+    data = {
+        partition_by: [i % 3 for i in range(num_rows)],
+        "features": list(range(num_rows)),
+    }
+    dataset = Dataset.from_dict(data)
+    partitioner = DirichletPartitioner(
+        num_partitions=num_partitions,
+        alpha=alpha,
+        partition_by=partition_by,
+        self_balancing=self_balancing,
+    )
+    partitioner.dataset = dataset
+    return dataset, partitioner
+
+
+class TestDirichletPartitionerSuccess(unittest.TestCase):
+    """Test DirichletPartitioner used with no exceptions."""
+
+    @parameterized.expand(  # type: ignore
+        [
+            # num_partitions, alpha, num_rows, partition_by
+            (3, 0.5, 100, "labels"),
+            (5, 1.0, 150, "labels"),
+        ]
+    )
+    def test_valid_initialization(
+        self, num_partitions: int, alpha: float, num_rows: int, partition_by: str
+    ) -> None:
+        """Test if alpha is correct scaled based on the given num_partitions."""
+        _, partitioner = _dummy_setup(num_partitions, alpha, num_rows, partition_by)
+        self.assertEqual(
+            (
+                partitioner._num_partitions,
+                len(partitioner._alpha),
+                partitioner._partition_by,
+            ),
+            (num_partitions, num_partitions, partition_by),
+        )
+
+    def test_min_partition_size_requirement(self) -> None:
+        """Test if partitions are created with min partition size required."""
+        _, partitioner = _dummy_setup(3, 0.5, 100, "labels")
+        partition_list = [
+            partitioner.load_partition(partition_id) for partition_id in [0, 1, 2]
+        ]
+        self.assertTrue(
+            all(len(p) > partitioner._min_partition_size for p in partition_list)
+        )
+
+    def test_alpha_in_ndarray_initialization(self) -> None:
+        """Test alpha does not change when in NDArrayFloat format."""
+        _, partitioner = _dummy_setup(3, np.array([1.0, 1.0, 1.0]), 100, "labels")
+        self.assertTrue(np.all(partitioner._alpha == np.array([1.0, 1.0, 1.0])))
+
+    def test__determine_partition_id_to_indices(self) -> None:
+        """Test the determine_nod_id_to_indices matches the flag after the call."""
+        num_partitions, alpha, num_rows, partition_by = 3, 0.5, 100, "labels"
+        _, partitioner = _dummy_setup(num_partitions, alpha, num_rows, partition_by)
+        partitioner._determine_partition_id_to_indices_if_needed()
+        self.assertTrue(
+            partitioner._partition_id_to_indices_determined
+            and len(partitioner._partition_id_to_indices) == num_partitions
+        )
+
+
+class TestDirichletPartitionerFailure(unittest.TestCase):
+    """Test DirichletPartitioner failures (exceptions) by incorrect usage."""
+
+    @parameterized.expand([(-2,), (-1,), (3,), (4,), (100,)])  # type: ignore
+    def test_load_invalid_partition_index(self, partition_id):
+        """Test if raises when the load_partition is above the num_partitions."""
+        _, partitioner = _dummy_setup(3, 0.5, 100, "labels")
+        with self.assertRaises(KeyError):
+            partitioner.load_partition(partition_id)
+
+    @parameterized.expand(  # type: ignore
+        [
+            # alpha, num_partitions
+            (-0.5, 1),
+            (-0.5, 2),
+            (-0.5, 3),
+            (-0.5, 10),
+            ([0.5, 0.5, -0.5], 3),
+            ([-0.5, 0.5, -0.5], 3),
+            ([-0.5, 0.5, 0.5], 3),
+            ([-0.5, -0.5, -0.5], 3),
+            ([0.5, 0.5, -0.5, -0.5, 0.5], 5),
+            (np.array([0.5, 0.5, -0.5]), 3),
+            (np.array([-0.5, 0.5, -0.5]), 3),
+            (np.array([-0.5, 0.5, 0.5]), 3),
+            (np.array([-0.5, -0.5, -0.5]), 3),
+            (np.array([0.5, 0.5, -0.5, -0.5, 0.5]), 5),
+        ]
+    )
+    def test_negative_values_in_alpha(self, alpha, num_partitions):
+        """Test if giving the negative value of alpha raises error."""
+        num_rows, partition_by = 100, "labels"
+        with self.assertRaises(ValueError):
+            _, _ = _dummy_setup(num_partitions, alpha, num_rows, partition_by)
+
+    @parameterized.expand(  # type: ignore
+        [
+            # alpha, num_partitions
+            # alpha greater than the num_partitions
+            ([0.5, 0.5], 1),
+            ([0.5, 0.5, 0.5], 2),
+            (np.array([0.5, 0.5]), 1),
+            (np.array([0.5, 0.5, 0.5]), 2),
+            (np.array([0.5, 0.5, 0.5, 0.5]), 3),
+        ]
+    )
+    def test_incorrect_alpha_shape(self, alpha, num_partitions):
+        """Test alpha list len not matching the num_partitions."""
+        with self.assertRaises(ValueError):
+            DirichletPartitioner(
+                num_partitions=num_partitions, alpha=alpha, partition_by="labels"
+            )
+
+    @parameterized.expand(  # type: ignore
+        [(0,), (-1,), (11,), (100,)]
+    )  # num_partitions,
+    def test_invalid_num_partitions(self, num_partitions):
+        """Test if 0 is invalid num_partitions."""
+        with self.assertRaises(ValueError):
+            _, partitioner = _dummy_setup(
+                num_partitions=num_partitions,
+                alpha=1.0,
+                num_rows=10,
+                partition_by="labels",
+            )
+            partitioner.load_partition(0)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/datasets/flwr_datasets/partitioner/exponential_partitioner.py b/datasets/flwr_datasets/partitioner/exponential_partitioner.py
index 10b11eb3e126..d35944f29f6f 100644
--- a/datasets/flwr_datasets/partitioner/exponential_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/exponential_partitioner.py
@@ -21,7 +21,7 @@
 
 
 class ExponentialPartitioner(SizePartitioner):
-    """Partitioner creates partitions of size that are correlated with exp(node_id).
+    """Partitioner creates partitions of size that are correlated with exp(id).
 
     The amount of data each client gets is correlated with the exponent of partition ID.
     For instance, if the IDs range from 1 to M, client with ID 1 gets e units of
@@ -29,7 +29,7 @@ class ExponentialPartitioner(SizePartitioner):
     The floor operation is applied on each of these numbers, it means floor(2.71...)
     = 2; e^2 ~ 7.39 floor(7.39) = 7. The number is rounded down = the fraction is
     always cut. The remainders of theses unassigned (fraction) samples is added to the
-    biggest partition (the one with the biggest node_id).
+    biggest partition (the one with the biggest partition_id).
 
     Parameters
     ----------
@@ -38,6 +38,6 @@ class ExponentialPartitioner(SizePartitioner):
     """
 
     def __init__(self, num_partitions: int) -> None:
-        super().__init__(num_partitions=num_partitions, node_id_to_size_fn=np.exp)
+        super().__init__(num_partitions=num_partitions, partition_id_to_size_fn=np.exp)
         if num_partitions <= 0:
             raise ValueError("The number of partitions must be greater than zero.")
diff --git a/datasets/flwr_datasets/partitioner/iid_partitioner.py b/datasets/flwr_datasets/partitioner/iid_partitioner.py
index c72b34f081f2..ceddd386c7d3 100644
--- a/datasets/flwr_datasets/partitioner/iid_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/iid_partitioner.py
@@ -34,12 +34,12 @@ def __init__(self, num_partitions: int) -> None:
             raise ValueError("The number of partitions must be greater than zero.")
         self._num_partitions = num_partitions
 
-    def load_partition(self, node_id: int) -> datasets.Dataset:
+    def load_partition(self, partition_id: int) -> datasets.Dataset:
         """Load a single IID partition based on the partition index.
 
         Parameters
         ----------
-        node_id : int
+        partition_id : int
             the index that corresponds to the requested partition
 
         Returns
@@ -48,5 +48,10 @@ def load_partition(self, node_id: int) -> datasets.Dataset:
             single dataset partition
         """
         return self.dataset.shard(
-            num_shards=self._num_partitions, index=node_id, contiguous=True
+            num_shards=self._num_partitions, index=partition_id, contiguous=True
         )
+
+    @property
+    def num_partitions(self) -> int:
+        """Total number of partitions."""
+        return self._num_partitions
diff --git a/datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner.py b/datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner.py
new file mode 100644
index 000000000000..e3e46813dfc8
--- /dev/null
+++ b/datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner.py
@@ -0,0 +1,322 @@
+# 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.
+# ==============================================================================
+"""InnerDirichlet partitioner."""
+import warnings
+from typing import Dict, List, Optional, Union
+
+import numpy as np
+
+import datasets
+from flwr_datasets.common.typing import NDArrayFloat, NDArrayInt
+from flwr_datasets.partitioner.partitioner import Partitioner
+
+
+class InnerDirichletPartitioner(Partitioner):  # pylint: disable=R0902
+    """Partitioner based on Dirichlet distribution.
+
+    Each partition is created based on the Dirichlet distribution, where the
+    probability corresponds to the fractions of samples of specific classes.
+    This process is iterative (sample by sample assignment), where first, the
+    partition ID to which the class will be assigned is chosen (at random, uniformly),
+    and then the class is decided based on the Dirichlet probabilities (note that when
+    a class gets exhausted - no more samples exists to sample from - the probability of
+    sampling this class is set as zero and the remaining probabilities renormalized).
+
+    Implementation based on: Federated Learning Based on Dynamic Regularization
+    (https://arxiv.org/abs/2111.04263).
+
+    Parameters
+    ----------
+    partition_sizes : Union[List[int], NDArrayInt]
+        The sizes of all partitions.
+    partition_by : str
+        Column name of the labels (targets) based on which Dirichlet sampling works.
+    alpha : Union[int, float, List[float], NDArrayFloat]
+        Concentration parameter to the Dirichlet distribution (a single value for
+        symmetric Dirichlet distribution, or a list/NDArray of length equal to the
+        number of unique classes)
+    shuffle: bool
+        Whether to randomize the order of samples. Shuffling applied after the
+        samples assignment to partitions.
+    seed: int
+        Seed used for dataset shuffling. It has no effect if `shuffle` is False.
+
+    Examples
+    --------
+    >>> from flwr_datasets import FederatedDataset
+    >>> from flwr_datasets.partitioner import InnerDirichletPartitioner
+    >>>
+    >>> partitioner = InnerDirichletPartitioner(
+    >>>     partition_sizes=[6_000] * 10, partition_by="label", alpha=0.5
+    >>> )
+    >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": partitioner})
+    >>> partition = fds.load_partition(0)
+    >>> print(partition[0])  # Print the first example
+    """
+
+    def __init__(  # pylint: disable=R0913
+        self,
+        partition_sizes: Union[List[int], NDArrayInt],
+        partition_by: str,
+        alpha: Union[int, float, List[float], NDArrayFloat],
+        shuffle: bool = True,
+        seed: Optional[int] = 42,
+    ) -> None:
+        super().__init__()
+        # Attributes based on the constructor
+        self._partition_sizes = _instantiate_partition_sizes(partition_sizes)
+        self._initial_alpha = alpha
+        self._alpha: Optional[NDArrayFloat] = None
+        self._partition_by = partition_by
+        self._shuffle = shuffle
+        self._seed = seed
+
+        # Utility attributes
+        self._initialized_alpha = False
+        self._rng = np.random.default_rng(seed=self._seed)  # NumPy random generator
+        # The attributes below are determined during the first call to load_partition
+        self._unique_classes: Optional[Union[List[int], List[str]]] = None
+        self._num_unique_classes: Optional[int] = None
+        self._num_partitions = len(self._partition_sizes)
+
+        self._partition_id_to_indices: Dict[int, List[int]] = {}
+        self._partition_id_to_indices_determined = False
+
+    def load_partition(self, partition_id: int) -> datasets.Dataset:
+        """Load a partition based on the partition index.
+
+        Parameters
+        ----------
+        partition_id : int
+            the index that corresponds to the requested partition
+
+        Returns
+        -------
+        dataset_partition : Dataset
+            single partition of a dataset
+        """
+        # The partitioning is done lazily - only when the first partition is
+        # requested. Only the first call creates the indices assignments for all the
+        # partition indices.
+        self._check_num_partitions_correctness_if_needed()
+        self._check_partition_sizes_correctness_if_needed()
+        self._check_the_sum_of_partition_sizes()
+        self._determine_num_unique_classes_if_needed()
+        self._alpha = self._initialize_alpha_if_needed(self._initial_alpha)
+        self._determine_partition_id_to_indices_if_needed()
+        return self.dataset.select(self._partition_id_to_indices[partition_id])
+
+    @property
+    def num_partitions(self) -> int:
+        """Total number of partitions."""
+        self._check_num_partitions_correctness_if_needed()
+        self._check_partition_sizes_correctness_if_needed()
+        self._check_the_sum_of_partition_sizes()
+        self._determine_num_unique_classes_if_needed()
+        self._alpha = self._initialize_alpha_if_needed(self._initial_alpha)
+        self._determine_partition_id_to_indices_if_needed()
+        return self._num_partitions
+
+    def _initialize_alpha_if_needed(
+        self, alpha: Union[int, float, List[float], NDArrayFloat]
+    ) -> NDArrayFloat:
+        """Convert alpha to the used format in the code a NDArrayFloat.
+
+        The alpha can be provided in constructor can be in different format for user
+        convenience. The format into which it's transformed here is used throughout the
+        code for computation.
+
+        Parameters
+        ----------
+        alpha : Union[int, float, List[float], NDArrayFloat]
+            Concentration parameter to the Dirichlet distribution
+
+        Returns
+        -------
+        alpha : NDArrayFloat
+            Concentration parameter in a format ready to used in computation.
+        """
+        if self._initialized_alpha:
+            assert self._alpha is not None
+            return self._alpha
+        if isinstance(alpha, int):
+            assert self._num_unique_classes is not None
+            alpha = np.array([float(alpha)], dtype=float).repeat(
+                self._num_unique_classes
+            )
+        elif isinstance(alpha, float):
+            assert self._num_unique_classes is not None
+            alpha = np.array([alpha], dtype=float).repeat(self._num_unique_classes)
+        elif isinstance(alpha, List):
+            if len(alpha) != self._num_unique_classes:
+                raise ValueError(
+                    "When passing alpha as a List, its length needs needs to be "
+                    "of length equal to the number of unique classes."
+                )
+            alpha = np.asarray(alpha)
+        elif isinstance(alpha, np.ndarray):
+            # pylint: disable=R1720
+            if alpha.ndim == 1 and alpha.shape[0] != self._num_unique_classes:
+                raise ValueError(
+                    "When passing alpha as an NDArray, its length needs needs to be "
+                    "of length equal to the number of unique classes."
+                )
+            elif alpha.ndim == 2:
+                alpha = alpha.flatten()
+                if alpha.shape[0] != self._num_unique_classes:
+                    raise ValueError(
+                        "When passing alpha as an NDArray, its length needs needs to be"
+                        " of length equal to the number of unique classes."
+                    )
+        else:
+            raise ValueError("The given alpha format is not supported.")
+        if not (alpha > 0).all():
+            raise ValueError(
+                f"Alpha values should be strictly greater than zero. "
+                f"Instead it'd be converted to {alpha}"
+            )
+        return alpha
+
+    def _determine_partition_id_to_indices_if_needed(
+        self,
+    ) -> None:  # pylint: disable=R0914
+        """Create an assignment of indices to the partition indices."""
+        if self._partition_id_to_indices_determined:
+            return
+
+        # Create class priors for the whole partitioning process
+        assert self._alpha is not None
+        class_priors = self._rng.dirichlet(alpha=self._alpha, size=self._num_partitions)
+        targets = np.asarray(self.dataset[self._partition_by])
+        # List representing indices of each class
+        assert self._num_unique_classes is not None
+        idx_list = [np.where(targets == i)[0] for i in range(self._num_unique_classes)]
+        class_sizes = [len(idx_list[i]) for i in range(self._num_unique_classes)]
+
+        client_indices = [
+            np.zeros(self._partition_sizes[cid]).astype(np.int64)
+            for cid in range(self._num_partitions)
+        ]
+
+        # Node id to number of sample left for allocation for that partition id
+        partition_id_to_left_to_allocate = dict(
+            zip(range(self._num_partitions), self._partition_sizes)
+        )
+
+        not_full_partition_ids = list(range(self._num_partitions))
+        while np.sum(list(partition_id_to_left_to_allocate.values())) != 0:
+            # Choose a partition
+            current_partition_id = self._rng.choice(not_full_partition_ids)
+            # If current partition is full resample a client
+            if partition_id_to_left_to_allocate[current_partition_id] == 0:
+                # When the partition is full, exclude it from the sampling list
+                not_full_partition_ids.pop(
+                    not_full_partition_ids.index(current_partition_id)
+                )
+                continue
+            partition_id_to_left_to_allocate[current_partition_id] -= 1
+            # Access the label distribution of the chosen client
+            current_probabilities = class_priors[current_partition_id]
+            while True:
+                # curr_class = np.argmax(np.random.uniform() <= curr_prior)
+                curr_class = self._rng.choice(
+                    list(range(self._num_unique_classes)), p=current_probabilities
+                )
+                # Redraw class label if there are no samples left to be allocated from
+                # that class
+                if class_sizes[curr_class] == 0:
+                    # Class got exhausted, set probabilities to 0
+                    class_priors[:, curr_class] = 0
+                    # Renormalize such that the probability sums to 1
+                    row_sums = class_priors.sum(axis=1, keepdims=True)
+                    class_priors = class_priors / row_sums
+                    # Adjust the current_probabilities (it won't sum up to 1 otherwise)
+                    current_probabilities = class_priors[current_partition_id]
+                    continue
+                class_sizes[curr_class] -= 1
+                # Store sample index at the empty array cell
+                index = partition_id_to_left_to_allocate[current_partition_id]
+                client_indices[current_partition_id][index] = idx_list[curr_class][
+                    class_sizes[curr_class]
+                ]
+                break
+
+        partition_id_to_indices = {
+            cid: client_indices[cid].tolist() for cid in range(self._num_partitions)
+        }
+        # Shuffle the indices if the shuffle is True.
+        # Note that the samples from this partitioning do not necessarily require
+        # shuffling, the order should exhibit consecutive samples.
+        if self._shuffle:
+            for indices in partition_id_to_indices.values():
+                # In place shuffling
+                self._rng.shuffle(indices)
+        self._partition_id_to_indices = partition_id_to_indices
+        self._partition_id_to_indices_determined = True
+
+    def _check_num_partitions_correctness_if_needed(self) -> None:
+        """Test num_partitions when the dataset is given (in load_partition)."""
+        if not self._partition_id_to_indices_determined:
+            if self._num_partitions > self.dataset.num_rows:
+                raise ValueError(
+                    "The number of partitions needs to be smaller or equal to "
+                    " the number of samples in the dataset."
+                )
+
+    def _check_partition_sizes_correctness_if_needed(self) -> None:
+        """Test partition_sizes when the dataset is given (in load_partition)."""
+        if not self._partition_id_to_indices_determined:
+            if sum(self._partition_sizes) > self.dataset.num_rows:
+                raise ValueError(
+                    "The sum of the `partition_sizes` needs to be smaller or equal to "
+                    "the number of samples in the dataset."
+                )
+
+    def _check_num_partitions_greater_than_zero(self) -> None:
+        """Test num_partition left sides correctness."""
+        if not self._num_partitions > 0:
+            raise ValueError("The number of partitions needs to be greater than zero.")
+
+    def _determine_num_unique_classes_if_needed(self) -> None:
+        self._unique_classes = self.dataset.unique(self._partition_by)
+        assert self._unique_classes is not None
+        self._num_unique_classes = len(self._unique_classes)
+
+    def _check_the_sum_of_partition_sizes(self) -> None:
+        if np.sum(self._partition_sizes) != len(self.dataset):
+            warnings.warn(
+                "The sum of the partition_sizes does not sum to the whole "
+                "dataset size. Make sure that is the desired behavior.",
+                stacklevel=1,
+            )
+
+
+def _instantiate_partition_sizes(
+    partition_sizes: Union[List[int], NDArrayInt]
+) -> NDArrayInt:
+    """Transform list to the ndarray of ints if needed."""
+    if isinstance(partition_sizes, List):
+        partition_sizes = np.asarray(partition_sizes)
+    elif isinstance(partition_sizes, np.ndarray):
+        pass
+    else:
+        raise ValueError(
+            f"The type of partition_sizes is incorrect. Given: "
+            f"{type(partition_sizes)}"
+        )
+
+    if not all(partition_sizes >= 0):
+        raise ValueError("The samples numbers must be greater or equal to zero.")
+    return partition_sizes
diff --git a/datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner_test.py b/datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner_test.py
new file mode 100644
index 000000000000..86dc8a5df532
--- /dev/null
+++ b/datasets/flwr_datasets/partitioner/inner_dirichlet_partitioner_test.py
@@ -0,0 +1,106 @@
+# 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.
+# ==============================================================================
+"""Test DirichletPartitioner."""
+# pylint: disable=W0212
+import unittest
+from typing import List, Tuple, Union
+
+from datasets import Dataset
+from flwr_datasets.common.typing import NDArrayFloat, NDArrayInt
+from flwr_datasets.partitioner.inner_dirichlet_partitioner import (
+    InnerDirichletPartitioner,
+)
+
+
+def _dummy_setup(
+    num_rows: int,
+    partition_by: str,
+    partition_sizes: Union[List[int], NDArrayInt],
+    alpha: Union[float, List[float], NDArrayFloat],
+) -> Tuple[Dataset, InnerDirichletPartitioner]:
+    """Create a dummy dataset and partitioner for testing."""
+    data = {
+        partition_by: [i % 3 for i in range(num_rows)],
+        "features": list(range(num_rows)),
+    }
+    dataset = Dataset.from_dict(data)
+    partitioner = InnerDirichletPartitioner(
+        partition_sizes=partition_sizes,
+        alpha=alpha,
+        partition_by=partition_by,
+    )
+    partitioner.dataset = dataset
+    return dataset, partitioner
+
+
+class TestInnerDirichletPartitionerSuccess(unittest.TestCase):
+    """Test InnerDirichletPartitioner used with no exceptions."""
+
+    def test_correct_num_of_partitions(self) -> None:
+        """Test correct number of partitions."""
+        num_rows = 113
+        partition_by = "labels"
+        alpha = 1.0
+        partition_sizes = [20, 20, 30, 43]
+
+        _, partitioner = _dummy_setup(num_rows, partition_by, partition_sizes, alpha)
+        _ = partitioner.load_partition(0)
+        self.assertEqual(
+            len(partitioner._partition_id_to_indices.keys()), len(partition_sizes)
+        )
+
+    def test_correct_partition_sizes(self) -> None:
+        """Test correct partition sizes."""
+        num_rows = 113
+        partition_by = "labels"
+        alpha = 1.0
+        partition_sizes = [20, 20, 30, 43]
+
+        _, partitioner = _dummy_setup(num_rows, partition_by, partition_sizes, alpha)
+        _ = partitioner.load_partition(0)
+        sizes_created = [
+            len(indices) for indices in partitioner._partition_id_to_indices.values()
+        ]
+        self.assertEqual(sorted(sizes_created), partition_sizes)
+
+
+class TestInnerDirichletPartitionerFailure(unittest.TestCase):
+    """Test InnerDirichletPartitioner failures (exceptions) by incorrect usage."""
+
+    def test_incorrect_shape_of_alpha(self) -> None:
+        """Test the alpha shape not equal to the number of unique classes."""
+        num_rows = 113
+        partition_by = "labels"
+        alpha = [1.0, 1.0]
+        partition_sizes = [20, 20, 30, 43]
+
+        _, partitioner = _dummy_setup(num_rows, partition_by, partition_sizes, alpha)
+        with self.assertRaises(ValueError):
+            _ = partitioner.load_partition(0)
+
+    def test_too_big_sum_of_partition_sizes(self) -> None:
+        """Test sum of partition_sizes greater than the size of the dataset."""
+        num_rows = 113
+        partition_by = "labels"
+        alpha = 1.0
+        partition_sizes = [60, 60, 30, 43]
+
+        _, partitioner = _dummy_setup(num_rows, partition_by, partition_sizes, alpha)
+        with self.assertRaises(ValueError):
+            _ = partitioner.load_partition(0)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/datasets/flwr_datasets/partitioner/linear_partitioner.py b/datasets/flwr_datasets/partitioner/linear_partitioner.py
index f77b0b87146d..84d419ab5592 100644
--- a/datasets/flwr_datasets/partitioner/linear_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/linear_partitioner.py
@@ -19,7 +19,7 @@
 
 
 class LinearPartitioner(SizePartitioner):
-    """Partitioner creates partitions of size that are linearly correlated with node_id.
+    """Partitioner creates partitions of size that are linearly correlated with id.
 
     The amount of data each client gets is linearly correlated with the partition ID.
     For instance, if the IDs range from 1 to M, client with ID 1 gets 1 unit of data,
@@ -32,6 +32,8 @@ class LinearPartitioner(SizePartitioner):
     """
 
     def __init__(self, num_partitions: int) -> None:
-        super().__init__(num_partitions=num_partitions, node_id_to_size_fn=lambda x: x)
+        super().__init__(
+            num_partitions=num_partitions, partition_id_to_size_fn=lambda x: x
+        )
         if num_partitions <= 0:
             raise ValueError("The number of partitions must be greater than zero.")
diff --git a/datasets/flwr_datasets/partitioner/natural_id_partitioner.py b/datasets/flwr_datasets/partitioner/natural_id_partitioner.py
index b8f28696f3b7..8bad0668595b 100644
--- a/datasets/flwr_datasets/partitioner/natural_id_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/natural_id_partitioner.py
@@ -29,28 +29,28 @@ def __init__(
         partition_by: str,
     ):
         super().__init__()
-        self._node_id_to_natural_id: Dict[int, str] = {}
+        self._partition_id_to_natural_id: Dict[int, str] = {}
         self._partition_by = partition_by
 
-    def _create_int_node_id_to_natural_id(self) -> None:
+    def _create_int_partition_id_to_natural_id(self) -> None:
         """Create a mapping from int indices to unique client ids from dataset.
 
         Natural ids come from the column specified in `partition_by`.
         """
         unique_natural_ids = self.dataset.unique(self._partition_by)
-        self._node_id_to_natural_id = dict(
+        self._partition_id_to_natural_id = dict(
             zip(range(len(unique_natural_ids)), unique_natural_ids)
         )
 
-    def load_partition(self, node_id: int) -> datasets.Dataset:
-        """Load a single partition corresponding to a single `node_id`.
+    def load_partition(self, partition_id: int) -> datasets.Dataset:
+        """Load a single partition corresponding to a single `partition_id`.
 
         The choice of the partition is based on unique integers assigned to each
         natural id present in the dataset in the `partition_by` column.
 
         Parameters
         ----------
-        node_id : int
+        partition_id : int
             the index that corresponds to the requested partition
 
         Returns
@@ -58,24 +58,32 @@ def load_partition(self, node_id: int) -> datasets.Dataset:
         dataset_partition : Dataset
             single dataset partition
         """
-        if len(self._node_id_to_natural_id) == 0:
-            self._create_int_node_id_to_natural_id()
+        if len(self._partition_id_to_natural_id) == 0:
+            self._create_int_partition_id_to_natural_id()
 
         return self.dataset.filter(
-            lambda row: row[self._partition_by] == self._node_id_to_natural_id[node_id]
+            lambda row: row[self._partition_by]
+            == self._partition_id_to_natural_id[partition_id]
         )
 
     @property
-    def node_id_to_natural_id(self) -> Dict[int, str]:
+    def num_partitions(self) -> int:
+        """Total number of partitions."""
+        if len(self._partition_id_to_natural_id) == 0:
+            self._create_int_partition_id_to_natural_id()
+        return len(self._partition_id_to_natural_id)
+
+    @property
+    def partition_id_to_natural_id(self) -> Dict[int, str]:
         """Node id to corresponding natural id present.
 
         Natural ids are the unique values in `partition_by` column in dataset.
         """
-        return self._node_id_to_natural_id
+        return self._partition_id_to_natural_id
 
     # pylint: disable=R0201
-    @node_id_to_natural_id.setter
-    def node_id_to_natural_id(self, value: Dict[int, str]) -> None:
+    @partition_id_to_natural_id.setter
+    def partition_id_to_natural_id(self, value: Dict[int, str]) -> None:
         raise AttributeError(
-            "Setting the node_id_to_natural_id dictionary is not allowed."
+            "Setting the partition_id_to_natural_id dictionary is not allowed."
         )
diff --git a/datasets/flwr_datasets/partitioner/natural_id_partitioner_test.py b/datasets/flwr_datasets/partitioner/natural_id_partitioner_test.py
index fb296294aec3..f447634ad9ed 100644
--- a/datasets/flwr_datasets/partitioner/natural_id_partitioner_test.py
+++ b/datasets/flwr_datasets/partitioner/natural_id_partitioner_test.py
@@ -65,9 +65,11 @@ def test_load_partition_num_partitions(
         Only the correct data is tested in this method.
         """
         _, partitioner = _dummy_setup(num_rows, num_unique_natural_id)
-        # Simulate usage to start lazy node_id_to_natural_id creation
+        # Simulate usage to start lazy partition_id_to_natural_id creation
         _ = partitioner.load_partition(0)
-        self.assertEqual(len(partitioner.node_id_to_natural_id), num_unique_natural_id)
+        self.assertEqual(
+            len(partitioner.partition_id_to_natural_id), num_unique_natural_id
+        )
 
     @parameterized.expand(  # type: ignore
         # num_rows, num_unique_natural_ids
@@ -105,14 +107,16 @@ def test_correct_number_of_partitions(
     ) -> None:
         """Test if the # of available partitions is equal to # of unique clients."""
         _, partitioner = _dummy_setup(num_rows, num_unique_natural_ids)
-        _ = partitioner.load_partition(node_id=0)
-        self.assertEqual(len(partitioner.node_id_to_natural_id), num_unique_natural_ids)
+        _ = partitioner.load_partition(partition_id=0)
+        self.assertEqual(
+            len(partitioner.partition_id_to_natural_id), num_unique_natural_ids
+        )
 
-    def test_cannot_set_node_id_to_natural_id(self) -> None:
-        """Test the lack of ability to set node_id_to_natural_id."""
+    def test_cannot_set_partition_id_to_natural_id(self) -> None:
+        """Test the lack of ability to set partition_id_to_natural_id."""
         _, partitioner = _dummy_setup(num_rows=10, n_unique_natural_ids=2)
         with self.assertRaises(AttributeError):
-            partitioner.node_id_to_natural_id = {0: "0"}
+            partitioner.partition_id_to_natural_id = {0: "0"}
 
 
 if __name__ == "__main__":
diff --git a/datasets/flwr_datasets/partitioner/partitioner.py b/datasets/flwr_datasets/partitioner/partitioner.py
index 92405152efc6..10ade52640e8 100644
--- a/datasets/flwr_datasets/partitioner/partitioner.py
+++ b/datasets/flwr_datasets/partitioner/partitioner.py
@@ -53,12 +53,12 @@ def dataset(self, value: Dataset) -> None:
         self._dataset = value
 
     @abstractmethod
-    def load_partition(self, node_id: int) -> Dataset:
+    def load_partition(self, partition_id: int) -> Dataset:
         """Load a single partition based on the partition index.
 
         Parameters
         ----------
-        node_id : int
+        partition_id : int
             the index that corresponds to the requested partition
 
         Returns
@@ -79,3 +79,8 @@ def is_dataset_assigned(self) -> bool:
             True if a dataset is assigned, otherwise False.
         """
         return self._dataset is not None
+
+    @property
+    @abstractmethod
+    def num_partitions(self) -> int:
+        """Total number of partitions."""
diff --git a/datasets/flwr_datasets/partitioner/shard_partitioner.py b/datasets/flwr_datasets/partitioner/shard_partitioner.py
new file mode 100644
index 000000000000..31eac2309fff
--- /dev/null
+++ b/datasets/flwr_datasets/partitioner/shard_partitioner.py
@@ -0,0 +1,378 @@
+# 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.
+# ==============================================================================
+"""Shard partitioner class."""
+
+
+# pylint: disable=R0912, R0914
+import math
+from typing import Dict, List, Optional
+
+import numpy as np
+
+import datasets
+from flwr_datasets.partitioner.partitioner import Partitioner
+
+
+class ShardPartitioner(Partitioner):  # pylint: disable=R0902
+    """Partitioner based on shard of (typically) unique classes.
+
+    The algorithm works as follows: the dataset is sorted by label e.g. [samples with
+    label 1, samples with labels 2 ...], then the shards are created, with each
+    shard of size = `shard_size` if provided or automatically calculated:
+    shards_size = len(dataset) / `num_partitions` * `num_shards_per_partition`.
+
+    A shard is just a block (chunk) of a `dataset` that contains `shard_size`
+    consecutive samples. There might be shards that contain samples associated with more
+    than a single unique label. The first case is (remember the preprocessing step sorts
+    the dataset by label) when a shard is constructed from samples at the boundaries of
+    the sorted dataset and therefore belonging to different classes e.g. the "leftover"
+    of samples of class 1 and the majority of class 2. The another scenario when a shard
+    has samples with more than one unique label is when the shard size is bigger than
+    the  number of samples of a certain class.
+
+    Each partition is created from `num_shards_per_partition` that are chosen randomly.
+
+    There are a few ways of partitioning data that result in certain properties
+    (depending on the parameters specification):
+    1) same number of shards per partitions + the same shard size (specify:
+    a) `num_shards_per_partitions`, `shard_size`; or b) `num_shards_per_partition`)
+    In case of b the `shard_size` is calculated as floor(len(dataset) /
+    (`num_shards_per_partitions` * `num_partitions`))
+    2) possibly different number of shards per partition (use nearly all data) + the
+    same shard size (specify: `shard_size` + `keep_incomplete_shard=False`)
+    3) possibly different number of shards per partition (use all data) + possibly
+    different shard size (specify: `shard_size` + `keep_incomplete_shard=True`)
+
+
+    Algorithm based on the description in Communication-Efficient Learning of Deep
+    Networks from Decentralized Data https://arxiv.org/abs/1602.05629. This
+    implementation expands on the initial idea by enabling more hyperparameters
+    specification therefore providing more control on how partitions are created.
+    It enables the division obtained in original paper.
+
+    Parameters
+    ----------
+    num_partitions : int
+        The total number of partitions that the data will be divided into.
+    partition_by : str
+        Column name of the labels (targets) based on which Dirichlet sampling works.
+    num_shards_per_partition : Optional[int]
+        Number of shards to assign to a single partitioner. It's an alternative to
+        `num_partitions`.
+    shard_size : Optional[int]
+        Size of a single shards (a partition has one or more shards). If the size is not
+        given it will be automatically computed.
+    keep_incomplete_shard : bool
+        Whether to drop the last shard which might be incomplete (smaller than the
+        others). If it is dropped each shard is equal size. (It does not mean that each
+        client gets equal number of shards, which only happens if
+        `num_partitions` % `num_shards` = 0). This parameter has no effect if
+        `num_shards_per_partitions` and `shard_size` are specified.
+    shuffle: bool
+        Whether to randomize the order of samples. Shuffling applied after the
+        samples assignment to partitions.
+    seed: int
+        Seed used for dataset shuffling. It has no effect if `shuffle` is False.
+
+    Examples
+    --------
+    1) If you need same number of shards per partitions + the same shard size (and you
+    know both of these values)
+
+    >>> from flwr_datasets import FederatedDataset
+    >>> from flwr_datasets.partitioner import ShardPartitioner
+    >>>
+    >>> partitioner = ShardPartitioner(num_partitions=10, partition_by="label",
+    >>>                                num_shards_per_partition=2, shard_size=1_000)
+    >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": partitioner})
+    >>> partition = fds.load_partition(0)
+    >>> print(partition[0])  # Print the first example
+    {'image': <PIL.PngImagePlugin.PngImageFile image mode=L size=28x28 at 0x15F616C50>,
+    'label': 3}
+    >>> partition_sizes = [
+    >>>     len(fds.load_partition(partition_id)) for partition_id in range(10)
+    >>> ]
+    >>> print(partition_sizes)
+    [2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000]
+
+    2) If you want to use nearly all the data and do not need to have the number of
+    shard per each partition to be the same
+
+    >>> from flwr_datasets import FederatedDataset
+    >>> from flwr_datasets.partitioner import ShardPartitioner
+    >>>
+    >>> partitioner = ShardPartitioner(num_partitions=9, partition_by="label",
+    >>>                                shard_size=1_000)
+    >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": partitioner})
+    >>> partition_sizes = [
+    >>>     len(fds.load_partition(partition_id)) for partition_id in range(9)
+    >>> ]
+    >>> print(partition_sizes)
+    [7000, 7000, 7000, 7000, 7000, 7000, 6000, 6000, 6000]
+
+    3) If you want to use all the data
+
+    >>> from flwr_datasets import FederatedDataset
+    >>> from flwr_datasets.partitioner import ShardPartitioner
+    >>>
+    >>> partitioner = ShardPartitioner(num_partitions=10, partition_by="label",
+    >>>                                shard_size=990, keep_incomplete_shard=True)
+    >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": partitioner})
+    >>> partition_sizes = [
+    >>>     len(fds.load_partition(partition_id)) for partition_id in range(10)
+    >>> ]
+    >>> print(sorted(partition_sizes))
+    [5550, 5940, 5940, 5940, 5940, 5940, 5940, 5940, 5940, 6930]
+    """
+
+    def __init__(  # pylint: disable=R0913
+        self,
+        num_partitions: int,
+        partition_by: str,
+        num_shards_per_partition: Optional[int] = None,
+        shard_size: Optional[int] = None,
+        keep_incomplete_shard: bool = False,
+        shuffle: bool = True,
+        seed: Optional[int] = 42,
+    ) -> None:
+        super().__init__()
+        # Attributes based on the constructor
+        _check_if_natual_number(num_partitions, "num_partitions")
+        self._num_partitions = num_partitions
+        self._partition_by = partition_by
+        _check_if_natual_number(
+            num_shards_per_partition, "num_shards_per_partition", True
+        )
+        self._num_shards_per_partition = num_shards_per_partition
+        self._num_shards_used: Optional[int] = None
+        _check_if_natual_number(shard_size, "shard_size", True)
+        self._shard_size = shard_size
+        self._keep_incomplete_shard = keep_incomplete_shard
+        self._shuffle = shuffle
+        self._seed = seed
+
+        # Utility attributes
+        self._rng = np.random.default_rng(seed=self._seed)  # NumPy random generator
+        self._partition_id_to_indices: Dict[int, List[int]] = {}
+        self._partition_id_to_indices_determined = False
+
+    def load_partition(self, partition_id: int) -> datasets.Dataset:
+        """Load a partition based on the partition index.
+
+        Parameters
+        ----------
+        partition_id : int
+            the index that corresponds to the requested partition
+
+        Returns
+        -------
+        dataset_partition : Dataset
+            single partition of a dataset
+        """
+        # The partitioning is done lazily - only when the first partition is
+        # requested. Only the first call creates the indices assignments for all the
+        # partition indices.
+        self._check_num_partitions_correctness_if_needed()
+        self._check_possibility_of_partitions_creation()
+        self._sort_dataset_if_needed()
+        self._determine_partition_id_to_indices_if_needed()
+        return self.dataset.select(self._partition_id_to_indices[partition_id])
+
+    @property
+    def num_partitions(self) -> int:
+        """Total number of partitions."""
+        self._check_num_partitions_correctness_if_needed()
+        self._check_possibility_of_partitions_creation()
+        self._sort_dataset_if_needed()
+        self._determine_partition_id_to_indices_if_needed()
+        return self._num_partitions
+
+    def _determine_partition_id_to_indices_if_needed(
+        self,
+    ) -> None:
+        """Assign sample indices to each partition id.
+
+        This method works on sorted datasets. A "shard" is a part of the dataset of
+        consecutive samples (if self._keep_incomplete_shard is False, each shard is same
+        size).
+        """
+        # No need to do anything if that partition_id_to_indices are already determined
+        if self._partition_id_to_indices_determined:
+            return
+
+        # One of the specification allows to skip the `num_shards_per_partition` param
+        if self._num_shards_per_partition is not None:
+            self._num_shards_used = int(
+                self._num_partitions * self._num_shards_per_partition
+            )
+            num_shards_per_partition_array = (
+                np.ones(self._num_partitions) * self._num_shards_per_partition
+            )
+            if self._shard_size is None:
+                self._compute_shard_size_if_missing()
+                assert self._shard_size is not None
+                if self._keep_incomplete_shard:
+                    num_usable_shards_in_dataset = int(
+                        math.ceil(len(self.dataset) / self._shard_size)
+                    )
+                else:
+                    num_usable_shards_in_dataset = int(
+                        math.floor(len(self.dataset) / self._shard_size)
+                    )
+            else:
+                num_usable_shards_in_dataset = int(
+                    math.floor(len(self.dataset) / self._shard_size)
+                )
+        elif self._num_shards_per_partition is None:
+            if self._shard_size is None:
+                raise ValueError(
+                    "The shard_size needs to be specified if the "
+                    "num_shards_per_partition is None"
+                )
+            if self._keep_incomplete_shard is False:
+                self._num_shards_used = int(
+                    math.floor(len(self.dataset) / self._shard_size)
+                )
+                num_usable_shards_in_dataset = self._num_shards_used
+            elif self._keep_incomplete_shard is True:
+                self._num_shards_used = int(
+                    math.ceil(len(self.dataset) / self._shard_size)
+                )
+                num_usable_shards_in_dataset = self._num_shards_used
+                if num_usable_shards_in_dataset < self._num_partitions:
+                    raise ValueError(
+                        "Based on the given arguments the creation of the partitions "
+                        "is impossible. The implied number of partitions that can be "
+                        "used is lower than the number of requested partitions "
+                        "resulting in empty partitions. Please decrease the size of "
+                        "shards: `shard_size`."
+                    )
+            else:
+                raise ValueError(
+                    "The keep_incomplete_shards need to be specified "
+                    "when _num_shards_per_partition is None."
+                )
+            num_shards_per_partition = int(self._num_shards_used / self._num_partitions)
+            # Assign the shards per partitions (so far, the same as in ideal case)
+            num_shards_per_partition_array = (
+                np.ones(self._num_partitions) * num_shards_per_partition
+            )
+            num_shards_assigned = self._num_partitions * num_shards_per_partition
+            num_shards_to_assign = self._num_shards_used - num_shards_assigned
+            # Assign the "missing" shards
+            for i in range(num_shards_to_assign):
+                num_shards_per_partition_array[i] += 1
+
+        else:
+            raise ValueError(
+                "The specification of nm_shards_per_partition and "
+                "keep_incomplete_shards is not correct."
+            )
+
+        if num_usable_shards_in_dataset < self._num_partitions:
+            raise ValueError(
+                "The specified configuration results in empty partitions because the "
+                "number of usable shards is smaller that the number partitions. "
+                "Try decreasing the shard size or the number of partitions. "
+            )
+
+        indices_on_which_to_split_shards = np.cumsum(
+            num_shards_per_partition_array, dtype=int
+        )
+
+        shard_indices_array = self._rng.permutation(num_usable_shards_in_dataset)[
+            : self._num_shards_used
+        ]
+        # Randomly assign shards to partition_id
+        nid_to_shard_indices = np.split(
+            shard_indices_array, indices_on_which_to_split_shards
+        )[:-1]
+        partition_id_to_indices: Dict[int, List[int]] = {
+            cid: [] for cid in range(self._num_partitions)
+        }
+        # Compute partition_id to sample indices based on the shard indices
+        for partition_id in range(self._num_partitions):
+            for shard_idx in nid_to_shard_indices[partition_id]:
+                start_id = int(shard_idx * self._shard_size)
+                end_id = min(int((shard_idx + 1) * self._shard_size), len(self.dataset))
+                partition_id_to_indices[partition_id].extend(
+                    list(range(start_id, end_id))
+                )
+        if self._shuffle:
+            for indices in partition_id_to_indices.values():
+                # In place shuffling
+                self._rng.shuffle(indices)
+        self._partition_id_to_indices = partition_id_to_indices
+        self._partition_id_to_indices_determined = True
+
+    def _check_num_partitions_correctness_if_needed(self) -> None:
+        """Test num_partitions when the dataset is given (in load_partition)."""
+        if not self._partition_id_to_indices_determined:
+            if self._num_partitions > self.dataset.num_rows:
+                raise ValueError(
+                    "The number of partitions needs to be smaller than the number of "
+                    "samples in the dataset."
+                )
+
+    def _sort_dataset_if_needed(self) -> None:
+        """Sort dataset prior to determining the partitions.
+
+        Operation only needed to be performed one time. It's required for the creation
+        of shards with the same labels.
+        """
+        if self._partition_id_to_indices_determined:
+            return
+        self._dataset = self.dataset.sort(self._partition_by)
+
+    def _compute_shard_size_if_missing(self) -> None:
+        """Compute the parameters needed to perform sharding.
+
+        This method should be called after the dataset is assigned.
+        """
+        if self._shard_size is None:
+            # If shard size is not specified it needs to be computed
+            num_rows = self.dataset.num_rows
+            self._shard_size = int(num_rows / self._num_shards_used)
+
+    def _check_possibility_of_partitions_creation(self) -> None:
+        if self._shard_size is not None and self._num_shards_per_partition is not None:
+            implied_min_dataset_size = (
+                self._shard_size * self._num_shards_per_partition * self._num_partitions
+            )
+            if implied_min_dataset_size > len(self.dataset):
+                raise ValueError(
+                    f"Based on the given arguments the creation of the "
+                    "partitions is impossible. The implied minimum dataset"
+                    f"size is {implied_min_dataset_size} but the dataset"
+                    f"size is {len(self.dataset)}"
+                )
+
+
+def _check_if_natual_number(
+    number: Optional[int], parameter_name: str, none_acceptable: bool = False
+) -> None:
+    if none_acceptable and number is None:
+        return
+    if not isinstance(number, int):
+        raise TypeError(
+            f"The expected type of {parameter_name} is int but given: {number} of type "
+            f"{type(number)}. Please specify the correct type."
+        )
+    if not number >= 1:
+        raise ValueError(
+            f"The expected value of {parameter_name} is >= 1 (greater or equal to 1) "
+            f"but given: {number} which does not meet this condition. Please "
+            f"provide a correct number."
+        )
diff --git a/datasets/flwr_datasets/partitioner/shard_partitioner_test.py b/datasets/flwr_datasets/partitioner/shard_partitioner_test.py
new file mode 100644
index 000000000000..d6fa8b529595
--- /dev/null
+++ b/datasets/flwr_datasets/partitioner/shard_partitioner_test.py
@@ -0,0 +1,392 @@
+# 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.
+# ==============================================================================
+"""Test ShardPartitioner."""
+
+
+# pylint: disable=W0212, R0913
+import unittest
+from typing import Optional, Tuple
+
+from datasets import Dataset
+from flwr_datasets.partitioner.shard_partitioner import ShardPartitioner
+
+
+def _dummy_setup(
+    num_rows: int,
+    partition_by: str,
+    num_partitions: int,
+    num_shards_per_partition: Optional[int],
+    shard_size: Optional[int],
+    keep_incomplete_shard: bool = False,
+) -> Tuple[Dataset, ShardPartitioner]:
+    """Create a dummy dataset for testing."""
+    data = {
+        partition_by: [i % 3 for i in range(num_rows)],
+        "features": list(range(num_rows)),
+    }
+    dataset = Dataset.from_dict(data)
+    partitioner = ShardPartitioner(
+        num_partitions=num_partitions,
+        num_shards_per_partition=num_shards_per_partition,
+        partition_by=partition_by,
+        shard_size=shard_size,
+        keep_incomplete_shard=keep_incomplete_shard,
+    )
+    partitioner.dataset = dataset
+    return dataset, partitioner
+
+
+class TestShardPartitionerSpec1(unittest.TestCase):
+    """Test first possible initialization of ShardPartitioner.
+
+    Specify num_shards_per_partition and shard_size arguments.
+    """
+
+    def test_correct_num_partitions(self) -> None:
+        """Test the correct number of partitions is created."""
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_partition = 3
+        shard_size = 10
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_partition,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        _ = partitioner.load_partition(0)
+        num_partitions_created = len(partitioner._partition_id_to_indices.keys())
+        self.assertEqual(num_partitions_created, num_partitions)
+
+    def test_correct_partition_sizes(self) -> None:
+        """Test if the partitions sizes are as theoretically calculated."""
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_partition = 3
+        shard_size = 10
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_partition,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        sizes = [len(partitioner.load_partition(i)) for i in range(num_partitions)]
+        sizes = sorted(sizes)
+        self.assertEqual(sizes, [30, 30, 30])
+
+    def test_unique_samples(self) -> None:
+        """Test if each partition has unique samples.
+
+        (No duplicates along partitions).
+        """
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_partition = 3
+        shard_size = 10
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_partition,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        partitions = [
+            partitioner.load_partition(i)["features"] for i in range(num_partitions)
+        ]
+        combined_list = [item for sublist in partitions for item in sublist]
+        combined_set = set(combined_list)
+        self.assertEqual(len(combined_list), len(combined_set))
+
+
+class TestShardPartitionerSpec2(unittest.TestCase):
+    """Test second possible initialization of ShardPartitioner.
+
+    Specify shard_size and keep_incomplete_shard=False. This setting creates partitions
+    that might have various sizes (each shard is same size).
+    """
+
+    def test_correct_num_partitions(self) -> None:
+        """Test the correct number of partitions is created."""
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_partition = None
+        shard_size = 10
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_partition,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        _ = partitioner.load_partition(0)
+        num_partitions_created = len(partitioner._partition_id_to_indices.keys())
+        self.assertEqual(num_partitions_created, num_partitions)
+
+    def test_correct_partition_sizes(self) -> None:
+        """Test if the partitions sizes are as theoretically calculated."""
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_partition = None
+        shard_size = 10
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_partition,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        sizes = [len(partitioner.load_partition(i)) for i in range(num_partitions)]
+        sizes = sorted(sizes)
+        self.assertEqual(sizes, [30, 40, 40])
+
+    def test_unique_samples(self) -> None:
+        """Test if each partition has unique samples.
+
+        (No duplicates along partitions).
+        """
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_partition = None
+        shard_size = 10
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_partition,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        partitions = [
+            partitioner.load_partition(i)["features"] for i in range(num_partitions)
+        ]
+        combined_list = [item for sublist in partitions for item in sublist]
+        combined_set = set(combined_list)
+        self.assertEqual(len(combined_list), len(combined_set))
+
+
+class TestShardPartitionerSpec3(unittest.TestCase):
+    """Test third possible initialization of ShardPartitioner.
+
+    Specify shard_size and keep_incomplete_shard=True. This setting creates partitions
+    that might have various sizes (each shard is same size).
+    """
+
+    def test_correct_num_partitions(self) -> None:
+        """Test the correct number of partitions is created."""
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_partition = None
+        shard_size = 10
+        keep_incomplete_shard = True
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_partition,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        _ = partitioner.load_partition(0)
+        num_partitions_created = len(partitioner._partition_id_to_indices.keys())
+        self.assertEqual(num_partitions_created, num_partitions)
+
+    def test_correct_partition_sizes(self) -> None:
+        """Test if the partitions sizes are as theoretically calculated."""
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_partition = None
+        shard_size = 10
+        keep_incomplete_shard = True
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_partition,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        sizes = [len(partitioner.load_partition(i)) for i in range(num_partitions)]
+        sizes = sorted(sizes)
+        self.assertEqual(sizes, [33, 40, 40])
+
+    def test_unique_samples(self) -> None:
+        """Test if each partition has unique samples.
+
+        (No duplicates along partitions).
+        """
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_partition = None
+        shard_size = 10
+        keep_incomplete_shard = True
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_partition,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        partitions = [
+            partitioner.load_partition(i)["features"] for i in range(num_partitions)
+        ]
+        combined_list = [item for sublist in partitions for item in sublist]
+        combined_set = set(combined_list)
+        self.assertEqual(len(combined_list), len(combined_set))
+
+
+class TestShardPartitionerSpec4(unittest.TestCase):
+    """Test fourth possible initialization of ShardPartitioner.
+
+    Specify num_shards_per_partition but not shard_size arguments.
+    """
+
+    def test_correct_num_partitions(self) -> None:
+        """Test the correct number of partitions is created."""
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_partition = 3
+        shard_size = None
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_partition,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        _ = partitioner.load_partition(0)
+        num_partitions_created = len(partitioner._partition_id_to_indices.keys())
+        self.assertEqual(num_partitions_created, num_partitions)
+
+    def test_correct_partition_sizes(self) -> None:
+        """Test if the partitions sizes are as theoretically calculated."""
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_partition = 3
+        shard_size = None
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_partition,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        sizes = [len(partitioner.load_partition(i)) for i in range(num_partitions)]
+        sizes = sorted(sizes)
+        self.assertEqual(sizes, [36, 36, 36])
+
+    def test_unique_samples(self) -> None:
+        """Test if each partition has unique samples.
+
+        (No duplicates along partitions).
+        """
+        partition_by = "label"
+        num_rows = 113
+        num_partitions = 3
+        num_shards_per_partition = 3
+        shard_size = None
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_partition,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        partitions = [
+            partitioner.load_partition(i)["features"] for i in range(num_partitions)
+        ]
+        combined_list = [item for sublist in partitions for item in sublist]
+        combined_set = set(combined_list)
+        self.assertEqual(len(combined_list), len(combined_set))
+
+
+class TestShardPartitionerIncorrectSpec(unittest.TestCase):
+    """Test the incorrect specification cases.
+
+    The lack of correctness can be caused by the num_partitions, shard_size and
+    num_shards_per_partition can create.
+    """
+
+    def test_incorrect_specification(self) -> None:
+        """Test if the given specification makes the partitioning possible."""
+        partition_by = "label"
+        num_rows = 10
+        num_partitions = 3
+        num_shards_per_partition = 2
+        shard_size = 10
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_partition,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        with self.assertRaises(ValueError):
+            _ = partitioner.load_partition(0)
+
+    def test_too_big_shard_size(self) -> None:
+        """Test if it is impossible to create an empty partition."""
+        partition_by = "label"
+        num_rows = 20
+        num_partitions = 3
+        num_shards_per_partition = None
+        shard_size = 10
+        keep_incomplete_shard = False
+        _, partitioner = _dummy_setup(
+            num_rows,
+            partition_by,
+            num_partitions,
+            num_shards_per_partition,
+            shard_size,
+            keep_incomplete_shard,
+        )
+        with self.assertRaises(ValueError):
+            _ = partitioner.load_partition(2).num_rows
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/datasets/flwr_datasets/partitioner/size_partitioner.py b/datasets/flwr_datasets/partitioner/size_partitioner.py
index 35ca750949ee..35937d8b9cc7 100644
--- a/datasets/flwr_datasets/partitioner/size_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/size_partitioner.py
@@ -24,54 +24,54 @@
 
 
 class SizePartitioner(Partitioner):
-    """Base class for the deterministic size partitioning based on the `node_id`.
+    """Base class for the deterministic size partitioning based on the `partition_id`.
 
-    The client with `node_id` has the following relationship regarding the number of
-    samples.
+    The client with `partition_id` has the following relationship regarding the number
+    of samples.
 
-    `node_id_to_size_fn(node_id)` ~ number of samples for `node_id`
+    `partition_id_to_size_fn(partition_id)` ~ number of samples for `partition_id`
 
-    If the function doesn't transform the `node_id` it's a linear correlation between
-    the number of sample for the node and the value of `node_id`. For instance, if the
-    node ids range from 1 to M, node with id 1 gets 1 unit of data, client 2 gets 2
-    units, and so on, up to node M which gets M units.
+    If the function doesn't transform the `partition_id` it's a linear correlation
+    between the number of sample for the partition and the value of `partition_id`. For
+    instance, if the partition ids range from 1 to M, partition with id 1 gets 1 unit of
+    data, client 2 gets 2 units, and so on, up to partition M which gets M units.
 
-    Note that size corresponding to the `node_id` is deterministic, yet in case of
-    different dataset shuffling the assignment of samples to `node_id` will vary.
+    Note that size corresponding to the `partition_id` is deterministic, yet in case of
+    different dataset shuffling the assignment of samples to `partition_id` will vary.
 
     Parameters
     ----------
     num_partitions : int
         The total number of partitions that the data will be divided into.
-    node_id_to_size_fn : Callable
-        Function that defines the relationship between node id and the number of
+    partition_id_to_size_fn : Callable
+        Function that defines the relationship between partition id and the number of
         samples.
     """
 
     def __init__(
         self,
         num_partitions: int,
-        node_id_to_size_fn: Callable,  # type: ignore[type-arg]
+        partition_id_to_size_fn: Callable,  # type: ignore[type-arg]
     ) -> None:
         super().__init__()
         if num_partitions <= 0:
             raise ValueError("The number of partitions must be greater than zero.")
         self._num_partitions = num_partitions
-        self._node_id_to_size_fn = node_id_to_size_fn
+        self._partition_id_to_size_fn = partition_id_to_size_fn
 
-        self._node_id_to_size: Dict[int, int] = {}
-        self._node_id_to_indices: Dict[int, List[int]] = {}
+        self._partition_id_to_size: Dict[int, int] = {}
+        self._partition_id_to_indices: Dict[int, List[int]] = {}
         # A flag to perform only a single compute to determine the indices
-        self._node_id_to_indices_determined = False
+        self._partition_id_to_indices_determined = False
 
-    def load_partition(self, node_id: int) -> datasets.Dataset:
+    def load_partition(self, partition_id: int) -> datasets.Dataset:
         """Load a single partition based on the partition index.
 
-        The number of samples is dependent on the partition node_id.
+        The number of samples is dependent on the partition partition_id.
 
         Parameters
         ----------
-        node_id : int
+        partition_id : int
             the index that corresponds to the requested partition
 
         Returns
@@ -81,22 +81,28 @@ def load_partition(self, node_id: int) -> datasets.Dataset:
         """
         # The partitioning is done lazily - only when the first partition is requested.
         # A single run creates the indices assignments for all the partition indices.
-        self._determine_node_id_to_indices_if_needed()
-        return self.dataset.select(self._node_id_to_indices[node_id])
+        self._determine_partition_id_to_indices_if_needed()
+        return self.dataset.select(self._partition_id_to_indices[partition_id])
 
     @property
-    def node_id_to_size(self) -> Dict[int, int]:
+    def num_partitions(self) -> int:
+        """Total number of partitions."""
+        self._determine_partition_id_to_indices_if_needed()
+        return self._num_partitions
+
+    @property
+    def partition_id_to_size(self) -> Dict[int, int]:
         """Node id to the number of samples."""
-        return self._node_id_to_size
+        return self._partition_id_to_size
 
     @property
-    def node_id_to_indices(self) -> Dict[int, List[int]]:
+    def partition_id_to_indices(self) -> Dict[int, List[int]]:
         """Node id to the list of indices."""
-        return self._node_id_to_indices
+        return self._partition_id_to_indices
 
-    def _determine_node_id_to_size(self) -> None:
+    def _determine_partition_id_to_size(self) -> None:
         """Determine data quantity associated with partition indices."""
-        data_division_in_units = self._node_id_to_size_fn(
+        data_division_in_units = self._partition_id_to_size_fn(
             np.linspace(start=1, stop=self._num_partitions, num=self._num_partitions)
         )
         total_units: Union[int, float] = data_division_in_units.sum()
@@ -112,25 +118,25 @@ def _determine_node_id_to_size(self) -> None:
         # If there is any sample(s) left unassigned, assign it to the largest partition.
         partition_sizes_as_num_of_samples[-1] += left_unassigned_samples
         for idx, partition_size in enumerate(partition_sizes_as_num_of_samples):
-            self._node_id_to_size[idx] = partition_size
+            self._partition_id_to_size[idx] = partition_size
 
-        self._check_if_node_id_to_size_possible()
+        self._check_if_partition_id_to_size_possible()
 
-    def _determine_node_id_to_indices_if_needed(self) -> None:
+    def _determine_partition_id_to_indices_if_needed(self) -> None:
         """Create an assignment of indices to the partition indices.."""
-        if self._node_id_to_indices_determined is True:
+        if self._partition_id_to_indices_determined is True:
             return
-        self._determine_node_id_to_size()
+        self._determine_partition_id_to_size()
         total_samples_assigned = 0
-        for idx, quantity in self._node_id_to_size.items():
-            self._node_id_to_indices[idx] = list(
+        for idx, quantity in self._partition_id_to_size.items():
+            self._partition_id_to_indices[idx] = list(
                 range(total_samples_assigned, total_samples_assigned + quantity)
             )
             total_samples_assigned += quantity
-        self._node_id_to_indices_determined = True
+        self._partition_id_to_indices_determined = True
 
-    def _check_if_node_id_to_size_possible(self) -> None:
-        all_positive = all(value >= 1 for value in self.node_id_to_size.values())
+    def _check_if_partition_id_to_size_possible(self) -> None:
+        all_positive = all(value >= 1 for value in self.partition_id_to_size.values())
         if not all_positive:
             raise ValueError(
                 f"The given specification of the parameter num_partitions"
diff --git a/datasets/flwr_datasets/partitioner/size_partitioner_test.py b/datasets/flwr_datasets/partitioner/size_partitioner_test.py
index 390f6a613fce..086ca3731e58 100644
--- a/datasets/flwr_datasets/partitioner/size_partitioner_test.py
+++ b/datasets/flwr_datasets/partitioner/size_partitioner_test.py
@@ -49,13 +49,13 @@ def test_linear_distribution(self, num_partitions: int, num_rows: int) -> None:
         partitioner.dataset = dataset
         # Run a single partition loading to trigger the division
         _ = partitioner.load_partition(0)
-        total_samples = sum(partitioner.node_id_to_size.values())
+        total_samples = sum(partitioner.partition_id_to_size.values())
         self.assertEqual(total_samples, num_rows)
 
         # Testing if each partition is getting more than the previous one
         last_count = 0
         for i in range(num_partitions):
-            current_count = partitioner.node_id_to_size[i]
+            current_count = partitioner.partition_id_to_size[i]
             self.assertGreaterEqual(current_count, last_count)
             last_count = current_count
 
@@ -77,7 +77,7 @@ def test_undivided_samples(self, num_partitions: int, num_rows: int) -> None:
         actual_samples_in_last_partition = len(
             partitioner.load_partition(last_partition_id)
         )
-        expected_samples_in_last_partition = partitioner.node_id_to_size[
+        expected_samples_in_last_partition = partitioner.partition_id_to_size[
             last_partition_id
         ]
         self.assertEqual(
diff --git a/datasets/flwr_datasets/partitioner/square_partitioner.py b/datasets/flwr_datasets/partitioner/square_partitioner.py
index 109b8397870b..4c894e47eedf 100644
--- a/datasets/flwr_datasets/partitioner/square_partitioner.py
+++ b/datasets/flwr_datasets/partitioner/square_partitioner.py
@@ -21,7 +21,7 @@
 
 
 class SquarePartitioner(SizePartitioner):
-    """Partitioner creates partitions of size that are correlated with squared node_id.
+    """Partitioner creates partitions of size that are correlated with squared id.
 
     The amount of data each client gets is correlated with the squared partition ID.
     For instance, if the IDs range from 1 to M, client with ID 1 gets 1 unit of data,
@@ -34,6 +34,8 @@ class SquarePartitioner(SizePartitioner):
     """
 
     def __init__(self, num_partitions: int) -> None:
-        super().__init__(num_partitions=num_partitions, node_id_to_size_fn=np.square)
+        super().__init__(
+            num_partitions=num_partitions, partition_id_to_size_fn=np.square
+        )
         if num_partitions <= 0:
             raise ValueError("The number of partitions must be greater than zero.")
diff --git a/datasets/flwr_datasets/utils.py b/datasets/flwr_datasets/utils.py
index e3d0fdfffa63..a6e4fa8d0f0b 100644
--- a/datasets/flwr_datasets/utils.py
+++ b/datasets/flwr_datasets/utils.py
@@ -16,8 +16,9 @@
 
 
 import warnings
-from typing import Dict, Optional, Tuple, Union, cast
+from typing import Dict, List, Optional, Tuple, Union, cast
 
+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
@@ -85,3 +86,225 @@ def _check_if_dataset_tested(dataset: str) -> None:
             f"The currently tested dataset are {tested_datasets}. Given: {dataset}.",
             stacklevel=1,
         )
+
+
+def divide_dataset(
+    dataset: Dataset, division: Union[List[float], Tuple[float, ...], Dict[str, float]]
+) -> Union[List[Dataset], DatasetDict]:
+    """Divide the dataset according to the `division`.
+
+    The division support varying number of splits, which you can name. The splits are
+    created from the beginning of the dataset.
+
+    Parameters
+    ----------
+    dataset : Dataset
+        Dataset to be divided.
+    division: Union[List[float], Tuple[float, ...], Dict[str, float]]
+        Configuration specifying how the dataset is divided. Each fraction has to be
+        >0 and <=1. They have to sum up to at most 1 (smaller sum is possible).
+
+    Returns
+    -------
+    divided_dataset : Union[List[Dataset], DatasetDict]
+        If `division` is `List` or `Tuple` then `List[Dataset]` is returned else if
+        `division` is `Dict` then `DatasetDict` is returned.
+
+    Examples
+    --------
+    Use `divide_dataset` with division specified as a list.
+
+    >>> from flwr_datasets import FederatedDataset
+    >>> from flwr_datasets.utils import divide_dataset
+    >>>
+    >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": 100})
+    >>> partition = fds.load_partition(0)
+    >>> division = [0.8, 0.2]
+    >>> train, test = divide_dataset(dataset=partition, division=division)
+
+    Use `divide_dataset` with division specified as a dict.
+
+    >>> from flwr_datasets import FederatedDataset
+    >>> from flwr_datasets.utils import divide_dataset
+    >>>
+    >>> fds = FederatedDataset(dataset="mnist", partitioners={"train": 100})
+    >>> partition = fds.load_partition(0)
+    >>> division = {"train": 0.8, "test": 0.2}
+    >>> train_test = divide_dataset(dataset=partition, division=division)
+    >>> train, test = train_test["train"], train_test["test"]
+    """
+    dataset_length = len(dataset)
+    ranges = _create_division_indices_ranges(dataset_length, division)
+    if isinstance(division, (list, tuple)):
+        split_partition: List[Dataset] = []
+        for single_range in ranges:
+            split_partition.append(dataset.select(single_range))
+        return split_partition
+    if isinstance(division, dict):
+        split_partition_dict: Dict[str, Dataset] = {}
+        for split_name, single_range in zip(division.keys(), ranges):
+            split_partition_dict[split_name] = dataset.select(single_range)
+        return DatasetDict(split_partition_dict)
+    raise TypeError(
+        f"The type of the `division` should be dict, "
+        f"tuple or list but is {type(division)} instead."
+    )
+
+
+def _create_division_indices_ranges(
+    dataset_length: int,
+    division: Union[List[float], Tuple[float, ...], Dict[str, float]],
+) -> List[range]:
+    ranges = []
+    if isinstance(division, (list, tuple)):
+        start_idx = 0
+        end_idx = 0
+        for fraction in division:
+            end_idx += int(dataset_length * fraction)
+            ranges.append(range(start_idx, end_idx))
+            start_idx += end_idx
+    elif isinstance(division, dict):
+        ranges = []
+        start_idx = 0
+        end_idx = 0
+        for fraction in division.values():
+            end_idx += int(dataset_length * fraction)
+            ranges.append(range(start_idx, end_idx))
+            start_idx += end_idx
+    else:
+        TypeError(
+            f"The type of the `division` should be dict, "
+            f"tuple or list but is {type(division)} instead. "
+        )
+    return ranges
+
+
+def _check_division_config_types_correctness(
+    division: Union[List[float], Tuple[float, ...], Dict[str, float]]
+) -> None:
+    if isinstance(division, (list, tuple)):
+        if not all(isinstance(x, float) for x in division):
+            raise TypeError(
+                "List or tuple values of `division` must contain only floats, "
+                "other types are not allowed."
+            )
+    elif isinstance(division, dict):
+        if not all(isinstance(x, float) for x in division.values()):
+            raise TypeError(
+                "Dict values of `division` must be only floats, "
+                "other types are not allowed."
+            )
+    else:
+        raise TypeError("`division` must be a list, tuple, or dict.")
+
+
+def _check_division_config_values_correctness(
+    division: Union[List[float], Tuple[float, ...], Dict[str, float]]
+) -> None:
+    if isinstance(division, (list, tuple)):
+        if not all(0 < x <= 1 for x in division):
+            raise ValueError(
+                "All fractions for the division must be greater than 0 and smaller or "
+                "equal to 1."
+            )
+        fraction_sum_from_list_tuple = sum(division)
+        if fraction_sum_from_list_tuple > 1:
+            raise ValueError("Sum of fractions for division must not exceed 1.")
+        if fraction_sum_from_list_tuple < 1:
+            warnings.warn(
+                f"Sum of fractions for division is {sum(division)}, which is below 1. "
+                f"Make sure that's the desired behavior. Some data will not be used "
+                f"in the current specification.",
+                stacklevel=1,
+            )
+    elif isinstance(division, dict):
+        values = list(division.values())
+        if not all(0 < x <= 1 for x in values):
+            raise ValueError(
+                "All fractions must be greater than 0 and smaller or equal to 1."
+            )
+        if sum(values) > 1:
+            raise ValueError("Sum of fractions must not exceed 1.")
+        if sum(values) < 1:
+            warnings.warn(
+                f"Sum of fractions in `division` is {values}, which is below 1. "
+                f"Make sure that's the desired behavior. Some data will not be used "
+                f"in the current specification.",
+                stacklevel=1,
+            )
+    else:
+        raise TypeError("`division` must be a list, tuple, or dict.")
+
+
+def _check_division_config_correctness(
+    division: Union[List[float], Tuple[float, ...], Dict[str, float]]
+) -> None:
+    _check_division_config_types_correctness(division)
+    _check_division_config_values_correctness(division)
+
+
+def concatenate_divisions(
+    partitioner: Partitioner,
+    partition_division: Union[List[float], Tuple[float, ...], Dict[str, float]],
+    division_id: Union[int, str],
+) -> Dataset:
+    """Create a dataset by concatenation of all partitions in the same division.
+
+    The divisions are created based on the `partition_division` and accessed based
+    on the `division_id`. It can be used to create e.g. centralized dataset from
+    federated on-edge test sets.
+
+    Parameters
+    ----------
+    partitioner : Partitioner
+        Partitioner object with assigned dataset.
+    partition_division : Union[List[float], Tuple[float, ...], Dict[str, float]]
+        Fractions specifying the division of the partitions of a `partitioner`. You can
+        think of this as on-edge division of the data into multiple divisions
+        (e.g. into train and validation). E.g. [0.8, 0.2] or
+        {"partition_train": 0.8, "partition_test": 0.2}.
+    division_id : Union[int, str]
+        The way to access the division (from a List or DatasetDict). If your
+        `partition_division` is specified as a list, then `division_id` represents an
+        index to an element in that list. If `partition_division` is passed as a
+        `Dict`, then `division_id` is a key of such dictionary.
+
+    Returns
+    -------
+    concatenated_divisions : Dataset
+        A dataset created as concatenation of the divisions from all partitions.
+    """
+    divisions = []
+    zero_len_divisions = 0
+    for partition_id in range(partitioner.num_partitions):
+        partition = partitioner.load_partition(partition_id)
+        if isinstance(partition_division, (list, tuple)):
+            if not isinstance(division_id, int):
+                raise TypeError(
+                    "The `division_id` needs to be an int in case of "
+                    "`partition_division` specification as List."
+                )
+            partition = divide_dataset(partition, partition_division)
+            division = partition[division_id]
+        elif isinstance(partition_division, Dict):
+            partition = divide_dataset(partition, partition_division)
+            division = partition[division_id]
+        else:
+            raise TypeError(
+                "The type of partition needs to be List of DatasetDict in this "
+                "context."
+            )
+        if len(division) == 0:
+            zero_len_divisions += 1
+        divisions.append(division)
+
+    if zero_len_divisions == partitioner.num_partitions:
+        raise ValueError(
+            "The concatenated dataset is of length 0. Please change the "
+            "`partition_division` parameter to change this behavior."
+        )
+    if zero_len_divisions != 0:
+        warnings.warn(
+            f"{zero_len_divisions} division(s) have length zero.", stacklevel=1
+        )
+    return concatenate_datasets(divisions)
diff --git a/datasets/flwr_datasets/utils_test.py b/datasets/flwr_datasets/utils_test.py
new file mode 100644
index 000000000000..3bf5afddf978
--- /dev/null
+++ b/datasets/flwr_datasets/utils_test.py
@@ -0,0 +1,107 @@
+# 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 tests."""
+import unittest
+from typing import Dict, List, Tuple, Union
+
+from parameterized import parameterized_class
+
+from datasets import Dataset, DatasetDict
+from flwr_datasets.partitioner import IidPartitioner
+from flwr_datasets.utils import concatenate_divisions, divide_dataset
+
+
+@parameterized_class(
+    (
+        "partition_division",
+        "sizes",
+        "division_id",
+        "expected_concatenation_size",
+    ),
+    [
+        ((0.8, 0.2), [32, 8], 1, 8),
+        ([0.8, 0.2], [32, 8], 1, 8),
+        ({"train": 0.8, "test": 0.2}, [32, 8], "test", 8),
+        # Not full dataset
+        ([0.2, 0.1], [8, 4], 1, 4),
+        ((0.2, 0.1), [8, 4], 0, 8),
+        ({"train": 0.2, "test": 0.1}, [8, 4], "test", 4),
+    ],
+)
+class UtilsTests(unittest.TestCase):
+    """Utils for tests."""
+
+    partition_division: Union[List[float], Tuple[float, ...], Dict[str, float]]
+    sizes: Tuple[int]
+    division_id: Union[int, str]
+    expected_concatenation_size: int
+
+    def setUp(self) -> None:
+        """Set up a dataset."""
+        self.dataset = Dataset.from_dict({"data": range(40)})
+
+    def test_correct_sizes(self) -> None:
+        """Test correct size of the division."""
+        divided_dataset = divide_dataset(self.dataset, self.partition_division)
+        if isinstance(divided_dataset, (list, tuple)):
+            lengths = [len(split) for split in divided_dataset]
+        else:
+            lengths = [len(split) for split in divided_dataset.values()]
+
+        self.assertEqual(lengths, self.sizes)
+
+    def test_correct_return_types(self) -> None:
+        """Test correct types of the divided dataset based on the config."""
+        divided_dataset = divide_dataset(self.dataset, self.partition_division)
+        if isinstance(self.partition_division, (list, tuple)):
+            self.assertIsInstance(divided_dataset, list)
+        else:
+            self.assertIsInstance(divided_dataset, DatasetDict)
+
+    def test_concatenate_divisions(
+        self,
+    ) -> None:
+        """Test if the length of the divisions match the concatenated dataset."""
+        num_partitions = 4
+        partitioner = IidPartitioner(num_partitions=num_partitions)
+        partitioner.dataset = self.dataset
+        centralized_from_federated_test = concatenate_divisions(
+            partitioner,
+            self.partition_division,
+            self.division_id,
+        )
+
+        self.assertEqual(
+            len(centralized_from_federated_test), self.expected_concatenation_size
+        )
+
+
+class TestIncorrectUtilsUsage(unittest.TestCase):
+    """Test incorrect utils usage."""
+
+    def test_all_divisions_to_concat_size_zero(self) -> None:
+        """Test raises when all divisions for concatenations are zero."""
+        num_partitions = 4
+        partitioner = IidPartitioner(num_partitions=num_partitions)
+        partitioner.dataset = Dataset.from_dict({"data": range(40)})
+        division_id = 1
+        partition_division = [0.8, 0.0]
+
+        with self.assertRaises(ValueError):
+            _ = concatenate_divisions(partitioner, partition_division, division_id)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/datasets/pyproject.toml b/datasets/pyproject.toml
index 7145345f7d94..5800faf3f272 100644
--- a/datasets/pyproject.toml
+++ b/datasets/pyproject.toml
@@ -4,14 +4,14 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "flwr-datasets"
-version = "0.0.2"
+version = "0.1.0"
 description = "Flower Datasets"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev/docs/datasets"
+documentation = "https://flower.ai/docs/datasets"
 keywords = [
     "flower",
     "fl",
@@ -22,7 +22,7 @@ keywords = [
     "dataset",
 ]
 classifiers = [
-    "Development Status :: 3 - Alpha",
+    "Development Status :: 4 - Beta",
     "Intended Audience :: Developers",
     "Intended Audience :: Science/Research",
     "License :: OSI Approved :: Apache Software License",
@@ -56,19 +56,19 @@ python = "^3.8"
 numpy = "^1.21.0"
 datasets = "^2.14.3"
 pillow = { version = ">=6.2.1", optional = true }
-soundfile = {version = ">=0.12.1", optional = true}
+soundfile = { version = ">=0.12.1", optional = true }
 librosa = { version = ">=0.10.0.post2", optional = true }
 
 [tool.poetry.dev-dependencies]
-isort = "==5.11.5"
-black = { version = "==23.3.0", extras = ["jupyter"] }
-docformatter = "==1.7.1"
+isort = "==5.13.2"
+black = { version = "==24.2.0", extras = ["jupyter"] }
+docformatter = "==1.7.5"
 mypy = "==1.4.0"
 pylint = "==2.13.9"
 flake8 = "==3.9.2"
 parameterized = "==0.9.0"
 pytest = "==7.1.2"
-pytest-watch = "==4.2.0"
+pytest-watcher = "==0.4.1"
 ruff = "==0.0.277"
 types-requests = "==2.28.11.17"
 
diff --git a/dev/deploy-docs.sh b/dev/add-swift-api-footer.sh
old mode 100755
new mode 100644
similarity index 51%
rename from dev/deploy-docs.sh
rename to dev/add-swift-api-footer.sh
index 69fbd567baf3..b18caaf7d801
--- a/dev/deploy-docs.sh
+++ b/dev/add-swift-api-footer.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 
-# 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.
@@ -18,25 +18,17 @@
 set -e
 cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
 
-ROOT=`pwd`
+footer_template_file="dev/swift-docs-resources/footer.html"
+footer_template=$(<"$footer_template_file")
+placeholder='<body data-color-scheme="auto">'
+html_file="Swiftdoc/html/documentation/flwr/index.html"
 
-# Build and deploy Flower Framework docs
-cd doc
-./build-versioned-docs.sh
-cd build/html
-aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./ s3://flower.dev/docs/framework
+sed -i '' -e "s#$(printf '%s' "$placeholder" | sed 's/[&/\]/\\&/g')#$placeholder\n$footer_template#" "$html_file"
 
-# Build and deploy Flower Baselines docs
-cd $ROOT
-cd baselines/doc
-make docs
-cd build/html
-aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./ s3://flower.dev/docs/baselines
+flower_logo='dev/swift-docs-resources/logo_secondary-w-border.png'
+footer_template_css="dev/swift-docs-resources/footer.css"
+destination_images="Swiftdoc/html/images/"
+destination_css="Swiftdoc/html/css/"
 
-# Build and deploy Flower Examples docs
-cd $ROOT
-./dev/update-examples.sh
-cd examples/doc
-make docs
-cd build/html
-aws s3 sync --delete --exclude ".*" --exclude "v/*" --cache-control "no-cache" ./ s3://flower.dev/docs/examples
+cp "$flower_logo" "$destination_images"
+cp "$footer_template_css" "$destination_css"
diff --git a/dev/bootstrap.sh b/dev/bootstrap.sh
index 1700c3774767..b4bb82d73b51 100755
--- a/dev/bootstrap.sh
+++ b/dev/bootstrap.sh
@@ -11,7 +11,7 @@ cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
 # 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 poetry==1.5.1
+python -m pip install -U poetry==1.7.1
 
 # Use `poetry` to install project dependencies
 python -m poetry install --all-extras
diff --git a/dev/build-swift-api-ref.sh b/dev/build-swift-api-ref.sh
index 4a7943f6828f..d5ed0a872c16 100755
--- a/dev/build-swift-api-ref.sh
+++ b/dev/build-swift-api-ref.sh
@@ -28,7 +28,7 @@ find ~/Library/Developer/Xcode/DerivedData -name "flwr.doccarchive" -exec rm -Rf
 # In case no XCode, please refer to: https://github.com/nodejs/node-gyp/issues/569.
 # Generate API reference for the Swift SDK by running `xcodebuild docbuild` in src directory.
 cd src/swift/flwr && \
-arch -x86_64 xcodebuild docbuild -scheme flwr -destination 'platform=iOS Simulator,name=iPhone 14 Pro Max,OS=16.2'
+arch -x86_64 xcodebuild docbuild -scheme flwr -destination 'platform=iOS Simulator,name=iPhone 15 Pro Max,OS=17.0.1'
 
 # Find the generated `doccarchive` file in XCode's derived data folder and copy it to the SwiftDoc directory.
 cd ../../../
diff --git a/dev/deploy-swift-docs.sh b/dev/deploy-swift-docs.sh
index 5abe618f83d3..eea3b287aa8b 100755
--- a/dev/deploy-swift-docs.sh
+++ b/dev/deploy-swift-docs.sh
@@ -18,10 +18,11 @@
 set -e
 cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
 
-ROOT=`pwd`
+ROOT=$(pwd)
 
 # Build and deploy Flower iOS docs
-cd $ROOT
+cd "$ROOT"
 ./dev/build-swift-api-ref.sh
+./dev/add-swift-api-footer.sh
 cd SwiftDoc/html
-aws s3 sync --delete --exclude ".*" --cache-control "no-cache" ./ s3://flower.dev/docs/ios
+aws s3 sync --delete --exclude ".*" --cache-control "no-cache" ./ s3://flower.ai/docs/ios
diff --git a/dev/format.sh b/dev/format.sh
index a5bc32915545..6b9cdaf5f44c 100755
--- a/dev/format.sh
+++ b/dev/format.sh
@@ -18,7 +18,7 @@ python -m docformatter -i -r examples
 
 # 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"
+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 doc/source/*.ipynb --extra-keys "$KEYS"
 python -m nbstripout examples/*/*.ipynb --extra-keys "$KEYS"
 
diff --git a/dev/publish-nightly.sh b/dev/publish-nightly.sh
index 2d2e82325fb9..f3e8d170f6c5 100755
--- a/dev/publish-nightly.sh
+++ b/dev/publish-nightly.sh
@@ -20,7 +20,7 @@ cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
 
 # This script will build and publish a nightly release of Flower under the condition
 # that at least one commit was made in the last 24 hours.
-# It will rename the the package name in the pyproject.toml to from "flwr" to "flwr-nightly".
+# It will rename the package name in the pyproject.toml to from "flwr" to "flwr-nightly".
 # 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/
diff --git a/dev/swift-docs-resources/footer.css b/dev/swift-docs-resources/footer.css
new file mode 100644
index 000000000000..5e04c6742f50
--- /dev/null
+++ b/dev/swift-docs-resources/footer.css
@@ -0,0 +1,737 @@
+/*
+! tailwindcss v3.4.1 | MIT License | https://tailwindcss.com
+*/
+
+/*
+1. Prevent padding and border from affecting element width. (https://github.com/mozdevs/cssremedy/issues/4)
+2. Allow adding a border to an element by just adding a border-width. (https://github.com/tailwindcss/tailwindcss/pull/116)
+*/
+
+*,
+::before,
+::after {
+  box-sizing: border-box;
+  /* 1 */
+  border-width: 0;
+  /* 2 */
+  border-style: solid;
+  /* 2 */
+  border-color: #ccc;
+  /* 2 */
+}
+
+::before,
+::after {
+  --tw-content: '';
+}
+
+/*
+1. Use a consistent sensible line-height in all browsers.
+2. Prevent adjustments of font size after orientation changes in iOS.
+3. Use a more readable tab size.
+4. Use the user's configured `sans` font-family by default.
+5. Use the user's configured `sans` font-feature-settings by default.
+6. Use the user's configured `sans` font-variation-settings by default.
+7. Disable tap highlights on iOS
+*/
+
+html,
+:host {
+  line-height: 1.5;
+  /* 1 */
+  -webkit-text-size-adjust: 100%;
+  /* 2 */
+  -moz-tab-size: 4;
+  /* 3 */
+  -o-tab-size: 4;
+     tab-size: 4;
+  /* 3 */
+  font-family: ui-sans-serif, system-ui, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";
+  /* 4 */
+  font-feature-settings: normal;
+  /* 5 */
+  font-variation-settings: normal;
+  /* 6 */
+  -webkit-tap-highlight-color: transparent;
+  /* 7 */
+}
+
+/*
+1. Remove the margin in all browsers.
+2. Inherit line-height from `html` so users can set them as a class directly on the `html` element.
+*/
+
+body {
+  margin: 0;
+  /* 1 */
+  line-height: inherit;
+  /* 2 */
+}
+
+/*
+1. Add the correct height in Firefox.
+2. Correct the inheritance of border color in Firefox. (https://bugzilla.mozilla.org/show_bug.cgi?id=190655)
+3. Ensure horizontal rules are visible by default.
+*/
+
+hr {
+  height: 0;
+  /* 1 */
+  color: inherit;
+  /* 2 */
+  border-top-width: 1px;
+  /* 3 */
+}
+
+/*
+Add the correct text decoration in Chrome, Edge, and Safari.
+*/
+
+abbr:where([title]) {
+  -webkit-text-decoration: underline dotted;
+          text-decoration: underline dotted;
+}
+
+/*
+Remove the default font size and weight for headings.
+*/
+
+h1,
+h2,
+h3,
+h4,
+h5,
+h6 {
+  font-size: inherit;
+  font-weight: inherit;
+}
+
+/*
+Reset links to optimize for opt-in styling instead of opt-out.
+*/
+
+a {
+  color: inherit;
+  text-decoration: inherit;
+}
+
+/*
+Add the correct font weight in Edge and Safari.
+*/
+
+b,
+strong {
+  font-weight: bolder;
+}
+
+/*
+1. Use the user's configured `mono` font-family by default.
+2. Use the user's configured `mono` font-feature-settings by default.
+3. Use the user's configured `mono` font-variation-settings by default.
+4. Correct the odd `em` font sizing in all browsers.
+*/
+
+code,
+kbd,
+samp,
+pre {
+  font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
+  /* 1 */
+  font-feature-settings: normal;
+  /* 2 */
+  font-variation-settings: normal;
+  /* 3 */
+  font-size: 1em;
+  /* 4 */
+}
+
+/*
+Add the correct font size in all browsers.
+*/
+
+small {
+  font-size: 80%;
+}
+
+/*
+Prevent `sub` and `sup` elements from affecting the line height in all browsers.
+*/
+
+sub,
+sup {
+  font-size: 75%;
+  line-height: 0;
+  position: relative;
+  vertical-align: baseline;
+}
+
+sub {
+  bottom: -0.25em;
+}
+
+sup {
+  top: -0.5em;
+}
+
+/*
+1. Remove text indentation from table contents in Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=999088, https://bugs.webkit.org/show_bug.cgi?id=201297)
+2. Correct table border color inheritance in all Chrome and Safari. (https://bugs.chromium.org/p/chromium/issues/detail?id=935729, https://bugs.webkit.org/show_bug.cgi?id=195016)
+3. Remove gaps between table borders by default.
+*/
+
+table {
+  text-indent: 0;
+  /* 1 */
+  border-color: inherit;
+  /* 2 */
+  border-collapse: collapse;
+  /* 3 */
+}
+
+/*
+1. Change the font styles in all browsers.
+2. Remove the margin in Firefox and Safari.
+3. Remove default padding in all browsers.
+*/
+
+button,
+input,
+optgroup,
+select,
+textarea {
+  font-family: inherit;
+  /* 1 */
+  font-feature-settings: inherit;
+  /* 1 */
+  font-variation-settings: inherit;
+  /* 1 */
+  font-size: 100%;
+  /* 1 */
+  font-weight: inherit;
+  /* 1 */
+  line-height: inherit;
+  /* 1 */
+  color: inherit;
+  /* 1 */
+  margin: 0;
+  /* 2 */
+  padding: 0;
+  /* 3 */
+}
+
+/*
+Remove the inheritance of text transform in Edge and Firefox.
+*/
+
+button,
+select {
+  text-transform: none;
+}
+
+/*
+1. Correct the inability to style clickable types in iOS and Safari.
+2. Remove default button styles.
+*/
+
+button,
+[type='button'],
+[type='reset'],
+[type='submit'] {
+  -webkit-appearance: button;
+  /* 1 */
+  background-color: transparent;
+  /* 2 */
+  background-image: none;
+  /* 2 */
+}
+
+/*
+Use the modern Firefox focus style for all focusable elements.
+*/
+
+:-moz-focusring {
+  outline: auto;
+}
+
+/*
+Remove the additional `:invalid` styles in Firefox. (https://github.com/mozilla/gecko-dev/blob/2f9eacd9d3d995c937b4251a5557d95d494c9be1/layout/style/res/forms.css#L728-L737)
+*/
+
+:-moz-ui-invalid {
+  box-shadow: none;
+}
+
+/*
+Add the correct vertical alignment in Chrome and Firefox.
+*/
+
+progress {
+  vertical-align: baseline;
+}
+
+/*
+Correct the cursor style of increment and decrement buttons in Safari.
+*/
+
+::-webkit-inner-spin-button,
+::-webkit-outer-spin-button {
+  height: auto;
+}
+
+/*
+1. Correct the odd appearance in Chrome and Safari.
+2. Correct the outline style in Safari.
+*/
+
+[type='search'] {
+  -webkit-appearance: textfield;
+  /* 1 */
+  outline-offset: -2px;
+  /* 2 */
+}
+
+/*
+Remove the inner padding in Chrome and Safari on macOS.
+*/
+
+::-webkit-search-decoration {
+  -webkit-appearance: none;
+}
+
+/*
+1. Correct the inability to style clickable types in iOS and Safari.
+2. Change font properties to `inherit` in Safari.
+*/
+
+::-webkit-file-upload-button {
+  -webkit-appearance: button;
+  /* 1 */
+  font: inherit;
+  /* 2 */
+}
+
+/*
+Add the correct display in Chrome and Safari.
+*/
+
+summary {
+  display: list-item;
+}
+
+/*
+Removes the default spacing and border for appropriate elements.
+*/
+
+blockquote,
+dl,
+dd,
+h1,
+h2,
+h3,
+h4,
+h5,
+h6,
+hr,
+figure,
+p,
+pre {
+  margin: 0;
+}
+
+fieldset {
+  margin: 0;
+  padding: 0;
+}
+
+legend {
+  padding: 0;
+}
+
+ol,
+ul,
+menu {
+  list-style: none;
+  margin: 0;
+  padding: 0;
+}
+
+/*
+Reset default styling for dialogs.
+*/
+
+dialog {
+  padding: 0;
+}
+
+/*
+Prevent resizing textareas horizontally by default.
+*/
+
+textarea {
+  resize: vertical;
+}
+
+/*
+1. Reset the default placeholder opacity in Firefox. (https://github.com/tailwindlabs/tailwindcss/issues/3300)
+2. Set the default placeholder color to the user's configured gray 400 color.
+*/
+
+input::-moz-placeholder, textarea::-moz-placeholder {
+  opacity: 1;
+  /* 1 */
+  color: #9ca3af;
+  /* 2 */
+}
+
+input::placeholder,
+textarea::placeholder {
+  opacity: 1;
+  /* 1 */
+  color: #9ca3af;
+  /* 2 */
+}
+
+/*
+Set the default cursor for buttons.
+*/
+
+button,
+[role="button"] {
+  cursor: pointer;
+}
+
+/*
+Make sure disabled buttons don't get the pointer cursor.
+*/
+
+:disabled {
+  cursor: default;
+}
+
+/*
+1. Make replaced elements `display: block` by default. (https://github.com/mozdevs/cssremedy/issues/14)
+2. Add `vertical-align: middle` to align replaced elements more sensibly by default. (https://github.com/jensimmons/cssremedy/issues/14#issuecomment-634934210)
+   This can trigger a poorly considered lint error in some tools but is included by design.
+*/
+
+img,
+svg,
+video,
+canvas,
+audio,
+iframe,
+embed,
+object {
+  display: block;
+  /* 1 */
+  vertical-align: middle;
+  /* 2 */
+}
+
+/*
+Constrain images and videos to the parent width and preserve their intrinsic aspect ratio. (https://github.com/mozdevs/cssremedy/issues/14)
+*/
+
+img,
+video {
+  max-width: 100%;
+  height: auto;
+}
+
+/* Make elements with the HTML hidden attribute stay hidden by default */
+
+[hidden] {
+  display: none;
+}
+
+*, ::before, ::after {
+  --tw-border-spacing-x: 0;
+  --tw-border-spacing-y: 0;
+  --tw-translate-x: 0;
+  --tw-translate-y: 0;
+  --tw-rotate: 0;
+  --tw-skew-x: 0;
+  --tw-skew-y: 0;
+  --tw-scale-x: 1;
+  --tw-scale-y: 1;
+  --tw-pan-x:  ;
+  --tw-pan-y:  ;
+  --tw-pinch-zoom:  ;
+  --tw-scroll-snap-strictness: proximity;
+  --tw-gradient-from-position:  ;
+  --tw-gradient-via-position:  ;
+  --tw-gradient-to-position:  ;
+  --tw-ordinal:  ;
+  --tw-slashed-zero:  ;
+  --tw-numeric-figure:  ;
+  --tw-numeric-spacing:  ;
+  --tw-numeric-fraction:  ;
+  --tw-ring-inset:  ;
+  --tw-ring-offset-width: 0px;
+  --tw-ring-offset-color: #fff;
+  --tw-ring-color: rgb(59 130 246 / 0.5);
+  --tw-ring-offset-shadow: 0 0 #0000;
+  --tw-ring-shadow: 0 0 #0000;
+  --tw-shadow: 0 0 #0000;
+  --tw-shadow-colored: 0 0 #0000;
+  --tw-blur:  ;
+  --tw-brightness:  ;
+  --tw-contrast:  ;
+  --tw-grayscale:  ;
+  --tw-hue-rotate:  ;
+  --tw-invert:  ;
+  --tw-saturate:  ;
+  --tw-sepia:  ;
+  --tw-drop-shadow:  ;
+  --tw-backdrop-blur:  ;
+  --tw-backdrop-brightness:  ;
+  --tw-backdrop-contrast:  ;
+  --tw-backdrop-grayscale:  ;
+  --tw-backdrop-hue-rotate:  ;
+  --tw-backdrop-invert:  ;
+  --tw-backdrop-opacity:  ;
+  --tw-backdrop-saturate:  ;
+  --tw-backdrop-sepia:  ;
+}
+
+::backdrop {
+  --tw-border-spacing-x: 0;
+  --tw-border-spacing-y: 0;
+  --tw-translate-x: 0;
+  --tw-translate-y: 0;
+  --tw-rotate: 0;
+  --tw-skew-x: 0;
+  --tw-skew-y: 0;
+  --tw-scale-x: 1;
+  --tw-scale-y: 1;
+  --tw-pan-x:  ;
+  --tw-pan-y:  ;
+  --tw-pinch-zoom:  ;
+  --tw-scroll-snap-strictness: proximity;
+  --tw-gradient-from-position:  ;
+  --tw-gradient-via-position:  ;
+  --tw-gradient-to-position:  ;
+  --tw-ordinal:  ;
+  --tw-slashed-zero:  ;
+  --tw-numeric-figure:  ;
+  --tw-numeric-spacing:  ;
+  --tw-numeric-fraction:  ;
+  --tw-ring-inset:  ;
+  --tw-ring-offset-width: 0px;
+  --tw-ring-offset-color: #fff;
+  --tw-ring-color: rgb(59 130 246 / 0.5);
+  --tw-ring-offset-shadow: 0 0 #0000;
+  --tw-ring-shadow: 0 0 #0000;
+  --tw-shadow: 0 0 #0000;
+  --tw-shadow-colored: 0 0 #0000;
+  --tw-blur:  ;
+  --tw-brightness:  ;
+  --tw-contrast:  ;
+  --tw-grayscale:  ;
+  --tw-hue-rotate:  ;
+  --tw-invert:  ;
+  --tw-saturate:  ;
+  --tw-sepia:  ;
+  --tw-drop-shadow:  ;
+  --tw-backdrop-blur:  ;
+  --tw-backdrop-brightness:  ;
+  --tw-backdrop-contrast:  ;
+  --tw-backdrop-grayscale:  ;
+  --tw-backdrop-hue-rotate:  ;
+  --tw-backdrop-invert:  ;
+  --tw-backdrop-opacity:  ;
+  --tw-backdrop-saturate:  ;
+  --tw-backdrop-sepia:  ;
+}
+
+.sr-only {
+  position: absolute;
+  width: 1px;
+  height: 1px;
+  padding: 0;
+  margin: -1px;
+  overflow: hidden;
+  clip: rect(0, 0, 0, 0);
+  white-space: nowrap;
+  border-width: 0;
+}
+
+.mx-auto {
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.mt-10 {
+  margin-top: 2.5rem;
+}
+
+.mt-16 {
+  margin-top: 4rem;
+}
+
+.mt-6 {
+  margin-top: 1.5rem;
+}
+
+.flex {
+  display: flex;
+}
+
+.grid {
+  display: grid;
+}
+
+.h-6 {
+  height: 1.5rem;
+}
+
+.w-6 {
+  width: 1.5rem;
+}
+
+.max-w-7xl {
+  max-width: 80rem;
+}
+
+.grid-cols-2 {
+  grid-template-columns: repeat(2, minmax(0, 1fr));
+}
+
+.gap-8 {
+  gap: 2rem;
+}
+
+.space-x-6 > :not([hidden]) ~ :not([hidden]) {
+  --tw-space-x-reverse: 0;
+  margin-right: calc(1.5rem * var(--tw-space-x-reverse));
+  margin-left: calc(1.5rem * calc(1 - var(--tw-space-x-reverse)));
+}
+
+.space-y-4 > :not([hidden]) ~ :not([hidden]) {
+  --tw-space-y-reverse: 0;
+  margin-top: calc(1rem * calc(1 - var(--tw-space-y-reverse)));
+  margin-bottom: calc(1rem * var(--tw-space-y-reverse));
+}
+
+.space-y-8 > :not([hidden]) ~ :not([hidden]) {
+  --tw-space-y-reverse: 0;
+  margin-top: calc(2rem * calc(1 - var(--tw-space-y-reverse)));
+  margin-bottom: calc(2rem * var(--tw-space-y-reverse));
+}
+
+.border-t {
+  border-top-width: 1px;
+}
+
+.bg-white {
+  --tw-bg-opacity: 1;
+  background-color: rgb(255 255 255 / var(--tw-bg-opacity));
+}
+
+.px-6 {
+  padding-left: 1.5rem;
+  padding-right: 1.5rem;
+}
+
+.pb-8 {
+  padding-bottom: 2rem;
+}
+
+.pt-8 {
+  padding-top: 2rem;
+}
+
+.text-base {
+  font-size: 1rem;
+  line-height: 1.5rem;
+}
+
+.text-sm {
+  font-size: 0.875rem;
+  line-height: 1.25rem;
+}
+
+.font-semibold {
+  font-weight: 600;
+}
+
+.leading-5 {
+  line-height: 1.25rem;
+}
+
+.leading-6 {
+  line-height: 1.5rem;
+}
+
+.opacity-90 {
+  opacity: 0.9;
+}
+
+@media (min-width: 640px) {
+  .sm\:mt-20 {
+    margin-top: 5rem;
+  }
+
+  .sm\:pt-10 {
+    padding-top: 2.5rem;
+  }
+}
+
+@media (min-width: 768px) {
+  .md\:mt-0 {
+    margin-top: 0px;
+  }
+
+  .md\:grid {
+    display: grid;
+  }
+
+  .md\:grid-cols-2 {
+    grid-template-columns: repeat(2, minmax(0, 1fr));
+  }
+
+  .md\:grid-cols-3 {
+    grid-template-columns: repeat(3, minmax(0, 1fr));
+  }
+
+  .md\:gap-8 {
+    gap: 2rem;
+  }
+}
+
+@media (min-width: 1024px) {
+  .lg\:mt-24 {
+    margin-top: 6rem;
+  }
+
+  .lg\:px-8 {
+    padding-left: 2rem;
+    padding-right: 2rem;
+  }
+
+  .lg\:pt-10 {
+    padding-top: 2.5rem;
+  }
+}
+
+@media (min-width: 1280px) {
+  .xl\:col-span-2 {
+    grid-column: span 2 / span 2;
+  }
+
+  .xl\:mt-0 {
+    margin-top: 0px;
+  }
+
+  .xl\:grid {
+    display: grid;
+  }
+
+  .xl\:grid-cols-3 {
+    grid-template-columns: repeat(3, minmax(0, 1fr));
+  }
+
+  .xl\:gap-8 {
+    gap: 2rem;
+  }
+}
\ No newline at end of file
diff --git a/dev/swift-docs-resources/footer.html b/dev/swift-docs-resources/footer.html
new file mode 100644
index 000000000000..6a3a5492c83b
--- /dev/null
+++ b/dev/swift-docs-resources/footer.html
@@ -0,0 +1 @@
+<template id="custom-footer"><link rel="stylesheet" href="/docs/ios/css/footer.css"><footer class="bg-white border-t" aria-labelledby="footer-heading"><h2 id="footer-heading" class="sr-only">Footer</h2><div class="mx-auto max-w-7xl px-6 pb-8 pt-8 sm:pt-10 lg:px-8 lg:pt-10"><div class="xl:grid xl:grid-cols-3 xl:gap-8"><div class="space-y-8"><img alt="Flower Logo" loading="lazy" width="50" height="50" decoding="async" data-nimg="1" class="opacity-90" style="color:transparent" srcset="/docs/ios/images/logo_secondary-w-border.png 1x, /docs/ios/images/logo_secondary-w-border.png 2x" src="/docs/ios/images/logo_secondary-w-border.png"><p class="text-sm leading-6 text-secondary-600">Flower: The Friendly <br> Federated Learning Framework.</p><div class="flex space-x-6"><a href="/join-slack/" class="text-secondary-400 hover:text-secondary-500"><span class="sr-only">Slack</span><svg fill="currentColor" viewBox="0 0 24 24" class="h-6 w-6" aria-hidden="true"><path fill-rule="evenodd" d="M3.362 10.11c0 .926-.756 1.681-1.681 1.681S0 11.036 0 10.111C0 9.186.756 8.43 1.68 8.43h1.682v1.68zm.846 0c0-.924.756-1.68 1.681-1.68s1.681.756 1.681 1.68v4.21c0 .924-.756 1.68-1.68 1.68a1.685 1.685 0 0 1-1.682-1.68v-4.21zM5.89 3.362c-.926 0-1.682-.756-1.682-1.681S4.964 0 5.89 0s1.68.756 1.68 1.68v1.682H5.89zm0 .846c.924 0 1.68.756 1.68 1.681S6.814 7.57 5.89 7.57H1.68C.757 7.57 0 6.814 0 5.89c0-.926.756-1.682 1.68-1.682h4.21zm6.749 1.682c0-.926.755-1.682 1.68-1.682.925 0 1.681.756 1.681 1.681s-.756 1.681-1.68 1.681h-1.681V5.89zm-.848 0c0 .924-.755 1.68-1.68 1.68A1.685 1.685 0 0 1 8.43 5.89V1.68C8.43.757 9.186 0 10.11 0c.926 0 1.681.756 1.681 1.68v4.21zm-1.681 6.748c.926 0 1.682.756 1.682 1.681S11.036 16 10.11 16s-1.681-.756-1.681-1.68v-1.682h1.68zm0-.847c-.924 0-1.68-.755-1.68-1.68 0-.925.756-1.681 1.68-1.681h4.21c.924 0 1.68.756 1.68 1.68 0 .926-.756 1.681-1.68 1.681h-4.21z" clip-rule="evenodd"></path></svg></a><a href="https://de.linkedin.com/company/flwrlabs" class="text-secondary-400 hover:text-secondary-500"><span class="sr-only">LinkedIn</span><svg fill="currentColor" viewBox="0 0 24 24" class="h-6 w-6" aria-hidden="true"><path fill-rule="evenodd" d="M19 0h-14c-2.761 0-5 2.239-5 5v14c0 2.761 2.239 5 5 5h14c2.762 0 5-2.239 5-5v-14c0-2.761-2.238-5-5-5zm-11 19h-3v-11h3v11zm-1.5-12.268c-.966 0-1.75-.79-1.75-1.764s.784-1.764 1.75-1.764 1.75.79 1.75 1.764-.783 1.764-1.75 1.764zm13.5 12.268h-3v-5.604c0-3.368-4-3.113-4 0v5.604h-3v-11h3v1.765c1.396-2.586 7-2.777 7 2.476v6.759z" clip-rule="evenodd"></path></svg></a><a href="https://twitter.com/flwrlabs" class="text-secondary-400 hover:text-secondary-500"><span class="sr-only">Twitter</span><svg fill="currentColor" viewBox="0 0 24 24" class="h-6 w-6" aria-hidden="true"><path d="M8.29 20.251c7.547 0 11.675-6.253 11.675-11.675 0-.178 0-.355-.012-.53A8.348 8.348 0 0022 5.92a8.19 8.19 0 01-2.357.646 4.118 4.118 0 001.804-2.27 8.224 8.224 0 01-2.605.996 4.107 4.107 0 00-6.993 3.743 11.65 11.65 0 01-8.457-4.287 4.106 4.106 0 001.27 5.477A4.072 4.072 0 012.8 9.713v.052a4.105 4.105 0 003.292 4.022 4.095 4.095 0 01-1.853.07 4.108 4.108 0 003.834 2.85A8.233 8.233 0 012 18.407a11.616 11.616 0 006.29 1.84"></path></svg></a><a href="https://github.com/adap/flower" class="text-secondary-400 hover:text-secondary-500"><span class="sr-only">GitHub</span><svg fill="currentColor" viewBox="0 0 24 24" class="h-6 w-6" aria-hidden="true"><path fill-rule="evenodd" d="M12 2C6.477 2 2 6.484 2 12.017c0 4.425 2.865 8.18 6.839 9.504.5.092.682-.217.682-.483 0-.237-.008-.868-.013-1.703-2.782.605-3.369-1.343-3.369-1.343-.454-1.158-1.11-1.466-1.11-1.466-.908-.62.069-.608.069-.608 1.003.07 1.531 1.032 1.531 1.032.892 1.53 2.341 1.088 2.91.832.092-.647.35-1.088.636-1.338-2.22-.253-4.555-1.113-4.555-4.951 0-1.093.39-1.988 1.029-2.688-.103-.253-.446-1.272.098-2.65 0 0 .84-.27 2.75 1.026A9.564 9.564 0 0112 6.844c.85.004 1.705.115 2.504.337 1.909-1.296 2.747-1.027 2.747-1.027.546 1.379.202 2.398.1 2.651.64.7 1.028 1.595 1.028 2.688 0 3.848-2.339 4.695-4.566 4.943.359.309.678.92.678 1.855 0 1.338-.012 2.419-.012 2.747 0 .268.18.58.688.482A10.019 10.019 0 0022 12.017C22 6.484 17.522 2 12 2z" clip-rule="evenodd"></path></svg></a><a href="https://www.youtube.com/@flowerlabs" class="text-secondary-400 hover:text-secondary-500"><span class="sr-only">YouTube</span><svg fill="currentColor" viewBox="0 0 24 24" class="h-6 w-6" aria-hidden="true"><path fill-rule="evenodd" d="M19.812 5.418c.861.23 1.538.907 1.768 1.768C21.998 8.746 22 12 22 12s0 3.255-.418 4.814a2.504 2.504 0 0 1-1.768 1.768c-1.56.419-7.814.419-7.814.419s-6.255 0-7.814-.419a2.505 2.505 0 0 1-1.768-1.768C2 15.255 2 12 2 12s0-3.255.417-4.814a2.507 2.507 0 0 1 1.768-1.768C5.744 5 11.998 5 11.998 5s6.255 0 7.814.418ZM15.194 12 10 15V9l5.194 3Z" clip-rule="evenodd"></path></svg></a></div></div><div class="mt-16 grid grid-cols-2 gap-8 xl:col-span-2 xl:mt-0"><div class="md:grid md:grid-cols-2 md:gap-8"><div><h3 class="text-sm font-semibold leading-6 text-secondary-700">Documentation</h3><ul role="list" class="mt-6 space-y-4"><li><a href="/docs/framework/" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Flower Framework</a></li><li><a href="/docs/baselines/" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Flower Baselines</a></li><li><a href="/docs/android/" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Flower Android SDK</a></li><li><a href="/docs/ios/documentation/flwr/" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Flower iOS SDK</a></li><li><a href="https://github.com/adap/flower/tree/main/examples/quickstart-cpp" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Flower C++ SDK</a></li><li><a href="/docs/examples/" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Example Projects</a></li></ul></div><div class="mt-10 md:mt-0"><h3 class="text-sm font-semibold leading-6 text-secondary-700">Events</h3><ul role="list" class="mt-6 space-y-4"><li><a href="/conf/flower-monthly/" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Flower Monthly</a></li><li><a href="/conf/flower-summit-2023/" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Flower Summit 2023</a></li><li><a href="/conf/flower-summit-2022/" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Flower Summit 2022</a></li><li><a href="/conf/flower-summit-2021/" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Flower Summit 2021</a></li></ul></div></div><div class="md:grid md:grid-cols-3 md:gap-8"><div><h3 class="text-sm font-semibold leading-6 text-secondary-700">Other</h3><ul role="list" class="mt-6 space-y-4"><li><a href="/blog/" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Blog</a></li><li><a href="https://discuss.flower.ai" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Discuss</a></li><li><a href="/careers" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Careers</a></li><li><a href="/visuals" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Flower Visuals</a></li><li><a href="https://calendar.google.com/calendar/u/0?cid=Y18zOWI5ODZiZDhjMmI5MTEzZDE0NmRmZTdjMmQyM2M0MjI5N2Y2ZmVlN2FjYTk5ZGY5MWUzZGJmZTcwMzc4N2I2QGdyb3VwLmNhbGVuZGFyLmdvb2dsZS5jb20" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Event Calendar</a></li></ul></div><div class="mt-10 md:mt-0"><h3 class="text-sm font-semibold leading-6 text-secondary-700">Socials</h3><ul role="list" class="mt-6 space-y-4"><li><a href="/join-slack/" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Slack</a></li><li><a href="https://de.linkedin.com/company/flwrlabs" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">LinkedIn</a></li><li><a href="https://twitter.com/flwrlabs" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Twitter / X</a></li><li><a href="https://github.com/adap/flower" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">GitHub</a></li><li><a href="https://www.youtube.com/@flowerlabs" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">YouTube</a></li><li><a href="https://space.bilibili.com/3546373735582167" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">bilibili</a></li></ul></div><div class="mt-10 md:mt-0"><h3 class="text-sm font-semibold leading-6 text-secondary-700">Legal</h3><ul role="list" class="mt-6 space-y-4"><li><a href="/imprint/" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Imprint</a></li><li><a href="/privacy/" class="text-sm leading-6 text-secondary-600 hover:text-secondary-900">Privacy</a></li></ul></div></div></div><div class="mt-16 pt-8 sm:mt-20 lg:mt-24"><p class="text-base leading-5 text-secondary-500">© 2024 Flower Labs GmbH. All rights reserved.</p></div></div></footer></template>
\ No newline at end of file
diff --git a/dev/swift-docs-resources/logo_secondary-w-border.png b/dev/swift-docs-resources/logo_secondary-w-border.png
new file mode 100644
index 000000000000..b8bf329beeba
Binary files /dev/null and b/dev/swift-docs-resources/logo_secondary-w-border.png differ
diff --git a/dev/test-copyright.sh b/dev/test-copyright.sh
new file mode 100755
index 000000000000..5ba8fedfe1ef
--- /dev/null
+++ b/dev/test-copyright.sh
@@ -0,0 +1,15 @@
+#!/bin/bash -e
+
+cd "$(cd "$(dirname "${BASH_SOURCE[0]}")" >/dev/null 2>&1 && pwd)"/../
+
+EXIT_CODE=0
+
+while IFS= read -r -d '' file; do
+    COPYRIGHT=$(grep -h -r "copyright = " "$file")
+    if [ "$COPYRIGHT" != "copyright = f\"{datetime.date.today().year} Flower Labs GmbH\"" ]; then
+        echo "::error file=$file::Wrong copyright line. Expected: copyright = f\"{datetime.date.today().year} Flower Labs GmbH\""
+        EXIT_CODE=1
+    fi
+done < <(find . -path "*/doc/source/conf.py" -print0)
+
+exit $EXIT_CODE
diff --git a/dev/test.sh b/dev/test.sh
index d3130bf3f6c2..1ed1b96edea1 100755
--- a/dev/test.sh
+++ b/dev/test.sh
@@ -15,7 +15,7 @@ python -m isort --check-only --skip src/py/flwr/proto src/py/flwr
 echo "- isort: done"
 
 echo "- black: start"
-python -m black --exclude "src\/py\/flwr\/proto" --check src/py/flwr
+python -m black --exclude "src\/py\/flwr\/proto" --check src/py/flwr examples
 echo "- black: done"
 
 echo "- init_py_check: start"
diff --git a/doc/Makefile b/doc/Makefile
index 085fe301e162..0e11bb3b4b3c 100644
--- a/doc/Makefile
+++ b/doc/Makefile
@@ -7,6 +7,7 @@ SPHINXOPTS    ?=
 SPHINXBUILD   ?= sphinx-build
 SOURCEDIR     = source
 BUILDDIR      = build
+LINKCHECKDIR  = build/linkcheck
 
 # Put it first so that "make" without argument is like "make help".
 help:
@@ -29,6 +30,11 @@ serve:
 	make docs
 	python -m http.server --directory build/html
 
+checklinks:
+	$(SPHINXBUILD) -b linkcheck "$(SOURCEDIR)" "$(LINKCHECKDIR)"
+	@echo
+	@echo "Check finished. Report is in $(LINKCHECKDIR)."
+
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
diff --git a/doc/build-versioned-docs.sh b/doc/build-versioned-docs.sh
index 0d7185b5e470..b61472d216e9 100755
--- a/doc/build-versioned-docs.sh
+++ b/doc/build-versioned-docs.sh
@@ -76,6 +76,9 @@ END
 
     # Restore branch as it was to avoid conflicts
     git restore source/_templates
+    git restore source/_templates/autosummary || rm -rf source/_templates/autosummary
+    rm source/ref-api/*.rst
+
     if [ "$current_version" = "v1.5.0" ]; then
       git restore source/conf.py
     fi
@@ -100,6 +103,7 @@ for current_language in ${languages}; do
   export current_language
   sphinx-build -b html source/ build/html/${current_version}/${current_language} -A lang=True -D language=${current_language}
 done
+rm source/ref-api/*.rst
 
 # Copy main version to the root of the built docs
 cp -r build/html/main/en/* build/html/
diff --git a/doc/locales/fr/LC_MESSAGES/framework-docs.po b/doc/locales/fr/LC_MESSAGES/framework-docs.po
index eb1ae9a9d28b..e7c7783c48ff 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: 2023-08-30 18:55+0200\n"
+"POT-Creation-Date: 2024-02-13 11:23+0100\n"
 "PO-Revision-Date: 2023-09-05 17:54+0000\n"
 "Last-Translator: Charles Beauville <charles@adap.com>\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 2.12.1\n"
+"Generated-By: Babel 2.13.1\n"
 
 #: ../../source/contributor-explanation-architecture.rst:2
 msgid "Flower Architecture"
@@ -25,10 +25,10 @@ msgstr "Moteur client Edge"
 
 #: ../../source/contributor-explanation-architecture.rst:7
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with Edge "
+"`Flower <https://flower.ai>`_ core framework architecture with Edge "
 "Client Engine"
 msgstr ""
-"`Flower <https://flower.dev>`_ architecture de base avec Edge Client "
+"`Flower <https://flower.ai>`_ architecture de base avec Edge Client "
 "Engine"
 
 #: ../../source/contributor-explanation-architecture.rst:13
@@ -37,10 +37,10 @@ msgstr "Moteur de client virtuel"
 
 #: ../../source/contributor-explanation-architecture.rst:15
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with Virtual "
+"`Flower <https://flower.ai>`_ core framework architecture with Virtual "
 "Client Engine"
 msgstr ""
-"`Flower <https://flower.dev>`_ architecture de base avec moteur de client"
+"`Flower <https://flower.ai>`_ architecture de base avec moteur de client"
 " virtuel"
 
 #: ../../source/contributor-explanation-architecture.rst:21
@@ -49,17 +49,359 @@ msgstr "Moteur client virtuel et moteur client Edge dans la même charge de trav
 
 #: ../../source/contributor-explanation-architecture.rst:23
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with both "
+"`Flower <https://flower.ai>`_ core framework architecture with both "
 "Virtual Client Engine and Edge Client Engine"
 msgstr ""
-"`Flower <https://flower.dev>`_ architecture de base avec un moteur de "
+"`Flower <https://flower.ai>`_ architecture de base avec un moteur de "
 "client virtuel et un moteur de client périphérique"
 
-#: ../../source/contributor-how-create-new-messages.rst:2
+#: ../../source/contributor-how-to-build-docker-images.rst:2
+msgid "How to build Docker Flower images locally"
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:4
+msgid ""
+"Flower provides pre-made docker images on `Docker Hub "
+"<https://hub.docker.com/r/flwr/server/tags>`_ 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
+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
+#, fuzzy
+msgid "Clone the flower repository."
+msgstr "**Fourche le dépôt de Flower**"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:17
+msgid "Verify the Docker daemon is running."
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:19
+msgid ""
+"Please follow the first section on `Run Flower using Docker "
+"<https://flower.ai/docs/framework/how-to-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
+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
+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 "
+"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
+#, 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
+#, 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
+#, fuzzy
+msgid "Description"
+msgstr "Dépréciations"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:46
+#: ../../source/contributor-how-to-build-docker-images.rst:88
+#, fuzzy
+msgid "Required"
+msgstr "Changements nécessaires"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:47
+#: ../../source/contributor-how-to-build-docker-images.rst:89
+#, fuzzy
+msgid "Example"
+msgstr "Exemples de PyTorch"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:48
+#, fuzzy
+msgid "``PYTHON_VERSION``"
+msgstr "Version Python"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:49
+msgid "Version of ``python`` to be installed."
+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
+#, fuzzy
+msgid "Yes"
+msgstr "Types"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:51
+#, fuzzy
+msgid "``3.11``"
+msgstr "1.0.0rc1"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:52
+msgid "``PIP_VERSION``"
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:53
+msgid "Version of ``pip`` to be installed."
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:55
+#, fuzzy
+msgid "``23.0.1``"
+msgstr "1.0.0rc1"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:56
+msgid "``SETUPTOOLS_VERSION``"
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:57
+msgid "Version of ``setuptools`` to be installed."
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:59
+#, 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:61
+msgid "Version of the official Ubuntu Docker image."
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:62
+msgid "Defaults to ``22.04``."
+msgstr ""
+
+#: ../../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: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:80
+#, fuzzy
+msgid "Building the server image"
+msgstr "Démarrer le serveur"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:90
+msgid "``BASE_REPOSITORY``"
+msgstr ""
+
+#: ../../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: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``"
+msgstr ""
+
+#: ../../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:101
+#, fuzzy
+msgid "``1.7.0``"
+msgstr "``1.0.0b0``"
+
+#: ../../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:"
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:114
+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 ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:117
+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 ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:131
+msgid "After creating the image, we can test whether the image is working:"
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:2
+#, fuzzy
+msgid "Contribute translations"
+msgstr "Explications"
+
+#: ../../source/contributor-how-to-contribute-translations.rst:4
+msgid ""
+"Since `Flower 1.5 <https://flower.ai/docs/framework/ref-"
+"changelog.html#v1-5-0-2023-08-31>`_ 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 ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:13
+msgid ""
+"Our translation project is publicly available over on `Weblate "
+"<https://hosted.weblate.org/projects/flower-docs/framework/>`_, this "
+"where most of the work will happen."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:18
+#, fuzzy
+msgid "Contribute to existing languages"
+msgstr "Explications"
+
+#: ../../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 "
+"<https://hosted.weblate.org/accounts/register/>`_. More information about"
+" profile settings can be found `here "
+"<https://docs.weblate.org/en/latest/user/profile.html>`_."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:29
+msgid ""
+"Once you are signed in to Weblate, you can navigate to the `Flower "
+"Framework project <https://hosted.weblate.org/projects/flower-"
+"docs/framework/>`_. Here, you should see the different existing languages"
+" that can be found on the website."
+msgstr ""
+
+#: ../../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 ""
+
+#: ../../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."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:43
+msgid "This is what the interface looks like:"
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:47
+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 ""
+
+#: ../../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."
+msgstr ""
+
+#: ../../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."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:63
+msgid ""
+"For more information about translating using Weblate, you can check out "
+"this `in-depth guide "
+"<https://docs.weblate.org/en/latest/user/translating.html>`_."
+msgstr ""
+
+#: ../../source/contributor-how-to-contribute-translations.rst:67
+msgid "Add new languages"
+msgstr ""
+
+#: ../../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 <https://flower.ai/join-slack>`_, or by opening an "
+"issue on our `GitHub repo <https://github.com/adap/flower>`_."
+msgstr ""
+
+#: ../../source/contributor-how-to-create-new-messages.rst:2
 msgid "Creating New Messages"
 msgstr "Création de nouveaux messages"
 
-#: ../../source/contributor-how-create-new-messages.rst:4
+#: ../../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."
@@ -67,7 +409,7 @@ msgstr ""
 "Voici un guide simple pour créer un nouveau type de message entre le "
 "serveur et les clients dans Flower."
 
-#: ../../source/contributor-how-create-new-messages.rst:6
+#: ../../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`..."
@@ -75,15 +417,15 @@ msgstr ""
 "Supposons que nous ayons les fonctions suivantes dans :code:`server.py` "
 "et :code:`numpy_client.py`..."
 
-#: ../../source/contributor-how-create-new-messages.rst:8
+#: ../../source/contributor-how-to-create-new-messages.rst:8
 msgid "Server's side:"
 msgstr "Côté serveur :"
 
-#: ../../source/contributor-how-create-new-messages.rst:17
+#: ../../source/contributor-how-to-create-new-messages.rst:17
 msgid "Client's side:"
 msgstr "Côté client :"
 
-#: ../../source/contributor-how-create-new-messages.rst:26
+#: ../../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!"
@@ -91,11 +433,11 @@ 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-create-new-messages.rst:30
+#: ../../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-create-new-messages.rst:32
+#: ../../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 "
@@ -110,15 +452,15 @@ msgstr ""
 "officielle <https://developers.google.com/protocol-"
 "buffers/docs/proto3>`_."
 
-#: ../../source/contributor-how-create-new-messages.rst:35
+#: ../../source/contributor-how-to-create-new-messages.rst:35
 msgid "Within the :code:`ServerMessage` block:"
 msgstr "Dans le bloc :code:`ServerMessage` :"
 
-#: ../../source/contributor-how-create-new-messages.rst:52
+#: ../../source/contributor-how-to-create-new-messages.rst:52
 msgid "Within the ClientMessage block:"
 msgstr "Dans le bloc ClientMessage :"
 
-#: ../../source/contributor-how-create-new-messages.rst:70
+#: ../../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`."
@@ -126,19 +468,19 @@ msgstr ""
 "Veille à ajouter également un champ du type de message nouvellement créé "
 "dans :code:`oneof msg`."
 
-#: ../../source/contributor-how-create-new-messages.rst:72
+#: ../../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-create-new-messages.rst:78
-msgid "If it compiles succesfully, you should see the following message:"
+#: ../../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-create-new-messages.rst:87
+#: ../../source/contributor-how-to-create-new-messages.rst:87
 msgid "Serialization and Deserialization Functions"
 msgstr "Fonctions de sérialisation et de désérialisation"
 
-#: ../../source/contributor-how-create-new-messages.rst:89
+#: ../../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 "
@@ -149,15 +491,15 @@ msgstr ""
 "messages RPC définis. Tu dois ajouter ces fonctions dans "
 ":code:`serde.py`."
 
-#: ../../source/contributor-how-create-new-messages.rst:91
+#: ../../source/contributor-how-to-create-new-messages.rst:91
 msgid "The four functions:"
 msgstr "Les quatre fonctions :"
 
-#: ../../source/contributor-how-create-new-messages.rst:112
+#: ../../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-create-new-messages.rst:114
+#: ../../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:"
@@ -166,11 +508,11 @@ msgstr ""
 "exemple, :code:`grpc_client_proxy.py`) en utilisant les fonctions serde "
 "que tu viens de créer :"
 
-#: ../../source/contributor-how-create-new-messages.rst:128
+#: ../../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-create-new-messages.rst:130
+#: ../../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. "
@@ -180,15 +522,15 @@ msgstr ""
 "vérifier le champ de ton message et appeler la fonction "
 ":code:`example_response`. N'oublie pas d'utiliser les fonctions serde !"
 
-#: ../../source/contributor-how-create-new-messages.rst:132
+#: ../../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-create-new-messages.rst:139
+#: ../../source/contributor-how-to-create-new-messages.rst:139
 msgid "And add a new function:"
 msgstr "Et ajoute une nouvelle fonction :"
 
-#: ../../source/contributor-how-create-new-messages.rst:149
+#: ../../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"
@@ -262,13 +604,15 @@ msgid "Getting started"
 msgstr "Pour commencer"
 
 #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:17
+#, fuzzy
 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 <vscode:extension"
-"/ms-vscode-remote.remote-containers>`_."
+"thing is you don't have to do it. Usually it should be enough to install "
+"`Docker <https://docs.docker.com/engine/install/>`_ on your system and "
+"ensure its available on your command line. Additionally, install the "
+"`VSCode Containers Extension <vscode:extension/ms-vscode-remote.remote-"
+"containers>`_."
 msgstr ""
 "La configuration et le paramétrage du :code:`Dockerfile` ainsi que la "
 "configuration du devcontainer peuvent être un peu plus complexes. "
@@ -385,16 +729,18 @@ msgid "Install ``flwr`` from a local wheel file via ``pyproject.toml``:"
 msgstr "Installez ``flwr`` à partir d'un fichier local via ``pyproject.toml`` :"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:22
+#, fuzzy
 msgid ""
-"``flwr = { path = \"../../dist/flwr-1.0.0-py3-none-any.whl\" }`` (without"
+"``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\" }`` (without"
 " extras)"
 msgstr ""
 "``flwr = { path = \"../../dist/flwr-1.0.0-py3-none-any.whl\" }`` (sans "
 "extras)"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:23
+#, fuzzy
 msgid ""
-"``flwr = { path = \"../../dist/flwr-1.0.0-py3-none-any.whl\", extras = "
+"``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\", extras = "
 "[\"simulation\"] }`` (with extras)"
 msgstr ""
 "``flwr = { path = \"../../dist/flwr-1.0.0-py3-none-any.whl\", extras = "
@@ -514,6 +860,27 @@ msgstr ""
 "https://colab.research.google.com/github/adap/flower/blob/branch-"
 "name/doc/source/tutorial-get-started-with-flower-pytorch.ipynb"
 
+#: ../../source/contributor-how-to-install-development-versions.rst:59
+msgid "Install a `whl` on Google Colab:"
+msgstr ""
+
+#: ../../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``"
+msgstr ""
+
+#: ../../source/contributor-how-to-install-development-versions.rst:62
+msgid "Upload the whl (e.g., ``flwr-1.8.0-py3-none-any.whl``)"
+msgstr ""
+
+#: ../../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``"
+msgstr ""
+
 #: ../../source/contributor-how-to-release-flower.rst:2
 msgid "Release Flower"
 msgstr "Publier Flower"
@@ -527,45 +894,10 @@ msgstr ""
 "changer à l'avenir."
 
 #: ../../source/contributor-how-to-release-flower.rst:7
-msgid "Before the release"
-msgstr "Avant la sortie"
-
-#: ../../source/contributor-how-to-release-flower.rst:9
-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 ""
-"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 :"
-
-#: ../../source/contributor-how-to-release-flower.rst:11
-msgid ""
-"`GitHub: Compare v1.2.0...main "
-"<https://github.com/adap/flower/compare/v1.2.0...main>`_"
-msgstr ""
-"`GitHub : Compare v1.2.0...main "
-"<https://github.com/adap/flower/compare/v1.2.0...main>`_"
-
-#: ../../source/contributor-how-to-release-flower.rst:13
-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``."
-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``."
-
-#: ../../source/contributor-how-to-release-flower.rst:17
 msgid "During the release"
 msgstr "Lors de la sortie"
 
-#: ../../source/contributor-how-to-release-flower.rst:19
+#: ../../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 "
@@ -575,121 +907,115 @@ msgstr ""
 "Pour publier une nouvelle version de Flower, les choses suivantes doivent"
 " se produire (dans cet ordre) :"
 
-#: ../../source/contributor-how-to-release-flower.rst:21
+#: ../../source/contributor-how-to-release-flower.rst:11
 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."
+"Run ``python3 src/py/flwr_tool/update_changelog.py <YOUR_GH_TOKEN>`` 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 ""
-"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."
 
-#: ../../source/contributor-how-to-release-flower.rst:22
+#: ../../source/contributor-how-to-release-flower.rst:12
 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``"
+"Once the changelog has been updated with all the changes, run ``./dev"
+"/prepare-release-changelog.sh v<NEW_VERSION>``, where ``<NEW_VERSION>`` "
+"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 ""
-"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``"
 
-#: ../../source/contributor-how-to-release-flower.rst:23
+#: ../../source/contributor-how-to-release-flower.rst:13
 msgid ""
-"Build the release with ``./dev/build.sh``, then publish it with "
-"``./dev/publish.sh``"
+"Once the pull request is merged, tag the release commit with the version "
+"number as soon as the PR is merged: ``git tag v<NEW_VERSION>`` (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 ""
-"Construisez la version avec ``./dev/build.sh``, puis publiez-la avec "
-"``./dev/publish.sh``"
 
-#: ../../source/contributor-how-to-release-flower.rst:24
-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`)."
+#: ../../source/contributor-how-to-release-flower.rst:14
+msgid "Check the draft release on GitHub, and if everything is good, publish it."
 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`)."
 
-#: ../../source/contributor-how-to-release-flower.rst:27
+#: ../../source/contributor-how-to-release-flower.rst:17
 msgid "After the release"
 msgstr "Après la publication"
 
-#: ../../source/contributor-how-to-release-flower.rst:29
+#: ../../source/contributor-how-to-release-flower.rst:19
 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:31
+#: ../../source/contributor-how-to-release-flower.rst:21
 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:32
+#: ../../source/contributor-how-to-release-flower.rst:22
 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:33
+#: ../../source/contributor-how-to-release-flower.rst:23
 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:35
+#: ../../source/contributor-how-to-release-flower.rst:25
 msgid ""
-"Merge the pull request on the same day (i.e., before a new nighly release"
+"Merge the pull request on the same day (i.e., before a new nightly release"
 " gets published to PyPI)."
 msgstr ""
 "Fusionne la pull request le jour même (c'est-à-dire avant qu'une nouvelle"
-" version nighly ne soit publiée sur PyPI)."
+" version nightly ne soit publiée sur PyPI)."
 
-#: ../../source/contributor-how-to-release-flower.rst:38
+#: ../../source/contributor-how-to-release-flower.rst:28
 msgid "Publishing a pre-release"
 msgstr "Publier une pré-version"
 
-#: ../../source/contributor-how-to-release-flower.rst:41
+#: ../../source/contributor-how-to-release-flower.rst:31
 msgid "Pre-release naming"
 msgstr "Nom de la pré-version"
 
-#: ../../source/contributor-how-to-release-flower.rst:43
+#: ../../source/contributor-how-to-release-flower.rst:33
 msgid ""
-"PyPI supports pre-releases (alpha, beta, release candiate). Pre-releases "
+"PyPI supports pre-releases (alpha, beta, release candidate). Pre-releases "
 "MUST use one of the following naming patterns:"
 msgstr ""
-"PyPI prend en charge les préversions (alpha, bêta, version candiate). Les"
+"PyPI prend en charge les préversions (alpha, bêta, version candidate). Les"
 " préversions DOIVENT utiliser l'un des modèles de dénomination suivants :"
 
-#: ../../source/contributor-how-to-release-flower.rst:45
+#: ../../source/contributor-how-to-release-flower.rst:35
 msgid "Alpha: ``MAJOR.MINOR.PATCHaN``"
 msgstr "Alpha : ``MAJOR.MINOR.PATCHaN``"
 
-#: ../../source/contributor-how-to-release-flower.rst:46
+#: ../../source/contributor-how-to-release-flower.rst:36
 msgid "Beta: ``MAJOR.MINOR.PATCHbN``"
 msgstr "Bêta : ``MAJOR.MINOR.PATCHbN``"
 
-#: ../../source/contributor-how-to-release-flower.rst:47
-msgid "Release candiate (RC): ``MAJOR.MINOR.PATCHrcN``"
+#: ../../source/contributor-how-to-release-flower.rst:37
+msgid "Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``"
 msgstr "Candidat à la publication (RC) : ``MAJOR.MINOR.PATCHrcN``"
 
-#: ../../source/contributor-how-to-release-flower.rst:49
+#: ../../source/contributor-how-to-release-flower.rst:39
 msgid "Examples include:"
 msgstr "Voici quelques exemples :"
 
-#: ../../source/contributor-how-to-release-flower.rst:51
+#: ../../source/contributor-how-to-release-flower.rst:41
 msgid "``1.0.0a0``"
 msgstr "``1.0.0a0``"
 
-#: ../../source/contributor-how-to-release-flower.rst:52
+#: ../../source/contributor-how-to-release-flower.rst:42
 msgid "``1.0.0b0``"
 msgstr "``1.0.0b0``"
 
-#: ../../source/contributor-how-to-release-flower.rst:53
+#: ../../source/contributor-how-to-release-flower.rst:43
 msgid "``1.0.0rc0``"
 msgstr "``1.0.0rc0``"
 
-#: ../../source/contributor-how-to-release-flower.rst:54
+#: ../../source/contributor-how-to-release-flower.rst:44
 msgid "``1.0.0rc1``"
 msgstr "1.0.0rc1"
 
-#: ../../source/contributor-how-to-release-flower.rst:56
+#: ../../source/contributor-how-to-release-flower.rst:46
 msgid ""
 "This is in line with PEP-440 and the recommendations from the Python "
 "Packaging Authority (PyPA):"
@@ -697,11 +1023,11 @@ 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:59
+#: ../../source/contributor-how-to-release-flower.rst:49
 msgid "`PEP-440 <https://peps.python.org/pep-0440/>`_"
 msgstr "`PEP-440 <https://peps.python.org/pep-0440/>`_"
 
-#: ../../source/contributor-how-to-release-flower.rst:60
+#: ../../source/contributor-how-to-release-flower.rst:50
 msgid ""
 "`PyPA Choosing a versioning scheme "
 "<https://packaging.python.org/en/latest/guides/distributing-packages-"
@@ -711,7 +1037,7 @@ msgstr ""
 "<https://packaging.python.org/en/latest/guides/distributing-packages-"
 "using-setuptools/#choosing-a-versioning-scheme>`_"
 
-#: ../../source/contributor-how-to-release-flower.rst:62
+#: ../../source/contributor-how-to-release-flower.rst:52
 msgid ""
 "Note that the approach defined by PyPA is not compatible with SemVer "
 "2.0.0 spec, for details consult the `Semantic Versioning Specification "
@@ -723,17 +1049,17 @@ msgstr ""
 "Versioning Specification <https://semver.org/spec/v2.0.0.html#spec-"
 "item-11>`_ (en particulier le point 11 sur la préséance)."
 
-#: ../../source/contributor-how-to-release-flower.rst:65
+#: ../../source/contributor-how-to-release-flower.rst:55
 msgid "Pre-release classification"
 msgstr "Classification avant publication"
 
-#: ../../source/contributor-how-to-release-flower.rst:67
+#: ../../source/contributor-how-to-release-flower.rst:57
 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:69
+#: ../../source/contributor-how-to-release-flower.rst:59
 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 "
@@ -744,11 +1070,11 @@ msgstr ""
 "version stable) - si aucun problème n'apparaît, cette version deviendra "
 "la prochaine version stable"
 
-#: ../../source/contributor-how-to-release-flower.rst:70
+#: ../../source/contributor-how-to-release-flower.rst:60
 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:71
+#: ../../source/contributor-how-to-release-flower.rst:61
 msgid "Alpha: not feature complete, allowed to have known issues"
 msgstr ""
 "Alpha : les fonctionnalités ne sont pas complètes, les problèmes connus "
@@ -894,11 +1220,12 @@ 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:119
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:142
+#, fuzzy
 msgid ""
 "Note that, in order to build the documentation locally (with ``poetry run"
 " make html``, like described below), `Pandoc "
-"<https://pandoc.org/installing.html>_` needs to be installed on the "
+"<https://pandoc.org/installing.html>`_ needs to be installed on the "
 "system."
 msgstr ""
 "Noter que, pour compiler la documentation localement (avec ``poetry run "
@@ -993,12 +1320,12 @@ msgstr "Demande pour une nouvelle Flower Baseline"
 #: ../../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 <https://flower.dev/docs"
+"out our `contributing guide for baselines <https://flower.ai/docs"
 "/contributing-baselines.html>`_."
 msgstr ""
 "Si tu n'es pas familier avec les Flower Baselines, tu devrais "
 "probablement consulter notre `guide de contribution pour les baselines "
-"<https://flower.dev/docs/contributing-baselines.html>`_."
+"<https://flower.ai/docs/baselines/how-to-contribute-baselines.html>`_."
 
 #: ../../source/contributor-ref-good-first-contributions.rst:27
 msgid ""
@@ -1114,28 +1441,28 @@ msgstr ""
 "GitHub."
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:6
+#, fuzzy
 msgid ""
 "If you're familiar with how contributing on GitHub works, you can "
 "directly checkout our `getting started guide for contributors "
-"<https://flower.dev/docs/getting-started-for-contributors.html>`_ and "
-"examples of `good first contributions <https://flower.dev/docs/good-"
-"first-contributions.html>`_."
+"<https://flower.ai/docs/contributor-tutorial-get-started-"
+"as-a-contributor.html>`_."
 msgstr ""
 "Si tu es familier avec le fonctionnement des contributions sur GitHub, tu"
 " peux directement consulter notre `guide de démarrage pour les "
-"contributeurs <https://flower.dev/docs/getting-started-for-"
+"contributeurs <https://flower.ai/docs/getting-started-for-"
 "contributors.html>`_ et des exemples de `bonnes premières contributions "
-"<https://flower.dev/docs/good-first-contributions.html>`_."
+"<https://flower.ai/docs/good-first-contributions.html>`_."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:12
+#: ../../source/contributor-tutorial-contribute-on-github.rst:11
 msgid "Setting up the repository"
 msgstr "Mise en place du référentiel"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:23
+#: ../../source/contributor-tutorial-contribute-on-github.rst:22
 msgid "**Create a GitHub account and setup Git**"
 msgstr "**Créer un compte GitHub et configurer Git**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:15
+#: ../../source/contributor-tutorial-contribute-on-github.rst:14
 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 "
@@ -1149,7 +1476,7 @@ msgstr ""
 "locale, tu peux suivre ce `guide <https://docs.github.com/en/get-"
 "started/quickstart/set-up-git>`_ pour le mettre en place."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:18
+#: ../../source/contributor-tutorial-contribute-on-github.rst:17
 msgid ""
 "GitHub, itself, is a code hosting platform for version control and "
 "collaboration. It allows for everyone to collaborate and work from "
@@ -1159,7 +1486,7 @@ msgstr ""
 "contrôle des versions et la collaboration. Il permet à chacun de "
 "collaborer et de travailler de n'importe où sur des dépôts à distance."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:20
+#: ../../source/contributor-tutorial-contribute-on-github.rst:19
 msgid ""
 "If you haven't already, you will need to create an account on `GitHub "
 "<https://github.com/signup>`_."
@@ -1167,7 +1494,7 @@ msgstr ""
 "Si ce n'est pas déjà fait, tu devras créer un compte sur `GitHub "
 "<https://github.com/signup>`_."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:22
+#: ../../source/contributor-tutorial-contribute-on-github.rst:21
 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 "
@@ -1179,11 +1506,11 @@ msgstr ""
 " des modifications localement et tu en gardes une trace à l'aide de Git, "
 "puis tu télécharges ton nouvel historique à nouveau sur GitHub."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:34
+#: ../../source/contributor-tutorial-contribute-on-github.rst:33
 msgid "**Forking the Flower repository**"
 msgstr "**Fourche le dépôt de Flower**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:26
+#: ../../source/contributor-tutorial-contribute-on-github.rst:25
 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 "
@@ -1195,7 +1522,7 @@ msgstr ""
 "étant connecté à ton compte GitHub) et cliquer sur le bouton ``Fork`` "
 "situé en haut à droite de la page."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:31
+#: ../../source/contributor-tutorial-contribute-on-github.rst:30
 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 "
@@ -1208,11 +1535,11 @@ msgstr ""
 " devrais voir dans le coin supérieur gauche que tu es en train de "
 "regarder ta propre version de Flower."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:49
+#: ../../source/contributor-tutorial-contribute-on-github.rst:48
 msgid "**Cloning your forked repository**"
 msgstr "**Clonage de ton dépôt forké**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:37
+#: ../../source/contributor-tutorial-contribute-on-github.rst:36
 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 "
@@ -1224,7 +1551,7 @@ msgstr ""
 "forké, tu dois d'abord cliquer sur le bouton ``Code`` à droite, ce qui te"
 " permettra de copier le lien HTTPS du dépôt."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:43
+#: ../../source/contributor-tutorial-contribute-on-github.rst:42
 msgid ""
 "Once you copied the \\<URL\\>, you can open a terminal on your machine, "
 "navigate to the place you want to download the repository to and type:"
@@ -1233,23 +1560,24 @@ msgstr ""
 "machine, naviguer jusqu'à l'endroit où tu veux télécharger le référentiel"
 " et taper :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:49
+#: ../../source/contributor-tutorial-contribute-on-github.rst:48
+#, fuzzy
 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 ""
 "Cela créera un dossier `flower/` (ou le nom de ta fourche si tu l'as "
 "renommée) dans le répertoire de travail actuel."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:68
+#: ../../source/contributor-tutorial-contribute-on-github.rst:67
 msgid "**Add origin**"
 msgstr "**Ajouter l'origine**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:52
+#: ../../source/contributor-tutorial-contribute-on-github.rst:51
 msgid "You can then go into the repository folder:"
 msgstr "Tu peux ensuite aller dans le dossier du référentiel :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:58
+#: ../../source/contributor-tutorial-contribute-on-github.rst:57
 msgid ""
 "And here we will need to add an origin to our repository. The origin is "
 "the \\<URL\\> of the remote fork repository. To obtain it, we can do as "
@@ -1261,7 +1589,7 @@ msgstr ""
 "indiqué précédemment en allant sur notre dépôt fork sur notre compte "
 "GitHub et en copiant le lien."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:63
+#: ../../source/contributor-tutorial-contribute-on-github.rst:62
 msgid ""
 "Once the \\<URL\\> is copied, we can type the following command in our "
 "terminal:"
@@ -1269,11 +1597,11 @@ msgstr ""
 "Une fois que le \\<URL\\> est copié, nous pouvons taper la commande "
 "suivante dans notre terminal :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:92
+#: ../../source/contributor-tutorial-contribute-on-github.rst:91
 msgid "**Add upstream**"
 msgstr "**Ajouter en amont**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:71
+#: ../../source/contributor-tutorial-contribute-on-github.rst:70
 msgid ""
 "Now we will add an upstream address to our repository. Still in the same "
 "directroy, we must run the following command:"
@@ -1282,13 +1610,13 @@ msgstr ""
 "Toujours dans le même directroy, nous devons exécuter la commande "
 "suivante :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:78
+#: ../../source/contributor-tutorial-contribute-on-github.rst:77
 msgid "The following diagram visually explains what we did in the previous steps:"
 msgstr ""
 "Le schéma suivant explique visuellement ce que nous avons fait dans les "
 "étapes précédentes :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:82
+#: ../../source/contributor-tutorial-contribute-on-github.rst:81
 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 "
@@ -1302,7 +1630,7 @@ msgstr ""
 "simplement l'adresse distante GitHub du dépôt forké que nous avons créé, "
 "c'est-à-dire la copie (fork) dans notre propre compte."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:86
+#: ../../source/contributor-tutorial-contribute-on-github.rst:85
 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:"
@@ -1311,11 +1639,11 @@ msgstr ""
 "dernières modifications du dépôt Flower, nous pouvons exécuter la "
 "commande suivante :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:95
+#: ../../source/contributor-tutorial-contribute-on-github.rst:94
 msgid "Setting up the coding environment"
 msgstr "Mise en place de l'environnement de codage"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:97
+#: ../../source/contributor-tutorial-contribute-on-github.rst:96
 msgid ""
 "This can be achieved by following this `getting started guide for "
 "contributors`_ (note that you won't need to clone the repository). Once "
@@ -1327,11 +1655,11 @@ msgstr ""
 "fois que tu es capable d'écrire du code et de le tester, tu peux enfin "
 "commencer à faire des changements !"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:102
+#: ../../source/contributor-tutorial-contribute-on-github.rst:101
 msgid "Making changes"
 msgstr "Apporter des changements"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:104
+#: ../../source/contributor-tutorial-contribute-on-github.rst:103
 msgid ""
 "Before making any changes make sure you are up-to-date with your "
 "repository:"
@@ -1339,15 +1667,15 @@ msgstr ""
 "Avant de faire des changements, assure-toi que tu es à jour avec ton "
 "référentiel :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:110
+#: ../../source/contributor-tutorial-contribute-on-github.rst:109
 msgid "And with Flower's repository:"
 msgstr "Et avec le référentiel de Flower :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:124
+#: ../../source/contributor-tutorial-contribute-on-github.rst:123
 msgid "**Create a new branch**"
 msgstr "**Créer une nouvelle branche**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:117
+#: ../../source/contributor-tutorial-contribute-on-github.rst:116
 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 "
@@ -1357,7 +1685,7 @@ msgstr ""
 "une bonne pratique de créer une nouvelle branche pour chaque "
 "fonctionnalité/projet qui doit être mis en œuvre."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:120
+#: ../../source/contributor-tutorial-contribute-on-github.rst:119
 msgid ""
 "To do so, just run the following command inside the repository's "
 "directory:"
@@ -1365,21 +1693,21 @@ msgstr ""
 "Pour ce faire, il suffit d'exécuter la commande suivante dans le "
 "répertoire du référentiel :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:127
+#: ../../source/contributor-tutorial-contribute-on-github.rst:126
 msgid "**Make changes**"
 msgstr "**Apporter des modifications**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:127
+#: ../../source/contributor-tutorial-contribute-on-github.rst:126
 msgid "Write great code and create wonderful changes using your favorite editor!"
 msgstr ""
 "Écris du bon code et crée de merveilleuses modifications à l'aide de ton "
 "éditeur préféré !"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:140
+#: ../../source/contributor-tutorial-contribute-on-github.rst:139
 msgid "**Test and format your code**"
 msgstr "**Teste et mets en forme ton code**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:130
+#: ../../source/contributor-tutorial-contribute-on-github.rst:129
 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 "
@@ -1389,15 +1717,15 @@ msgstr ""
 "pourra pas être fusionné dans le dépôt Flower, et ce, afin que la base de"
 " code reste cohérente et facile à comprendre."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:133
+#: ../../source/contributor-tutorial-contribute-on-github.rst:132
 msgid "To do so, we have written a few scripts that you can execute:"
 msgstr "Pour ce faire, nous avons écrit quelques scripts que tu peux exécuter :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:152
+#: ../../source/contributor-tutorial-contribute-on-github.rst:151
 msgid "**Stage changes**"
 msgstr "**Changements de scène**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:143
+#: ../../source/contributor-tutorial-contribute-on-github.rst:142
 msgid ""
 "Before creating a commit that will update your history, you must specify "
 "to Git which files it needs to take into account."
@@ -1405,11 +1733,11 @@ msgstr ""
 "Avant de créer un commit qui mettra à jour ton historique, tu dois "
 "spécifier à Git les fichiers qu'il doit prendre en compte."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:145
+#: ../../source/contributor-tutorial-contribute-on-github.rst:144
 msgid "This can be done with:"
 msgstr "Cela peut se faire avec :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:151
+#: ../../source/contributor-tutorial-contribute-on-github.rst:150
 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 "
@@ -1419,11 +1747,11 @@ msgstr ""
 "version (last commit) et pour voir quels fichiers sont mis à disposition "
 "pour le commit, tu peux utiliser la commande :code:`git status`."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:162
+#: ../../source/contributor-tutorial-contribute-on-github.rst:161
 msgid "**Commit changes**"
 msgstr "**Commit changes**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:155
+#: ../../source/contributor-tutorial-contribute-on-github.rst:154
 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:"
@@ -1432,7 +1760,7 @@ msgstr ""
 "l'aide de :code:`git add`, tu peux enfin créer ta livraison à l'aide de "
 "cette commande :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:161
+#: ../../source/contributor-tutorial-contribute-on-github.rst:160
 msgid ""
 "The \\<commit_message\\> is there to explain to others what the commit "
 "does. It should be written in an imperative style and be concise. An "
@@ -1442,11 +1770,11 @@ msgstr ""
 "commit. Il doit être écrit dans un style impératif et être concis. Un "
 "exemple serait :code:`git commit -m \"Ajouter des images au README\"`."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:173
+#: ../../source/contributor-tutorial-contribute-on-github.rst:172
 msgid "**Push the changes to the fork**"
 msgstr "**Pousser les changements vers la fourche**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:165
+#: ../../source/contributor-tutorial-contribute-on-github.rst:164
 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 "
@@ -1457,7 +1785,7 @@ msgstr ""
 "moyen de le savoir à moins que nous ne poussions nos modifications vers "
 "l'adresse distante de notre origine :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:172
+#: ../../source/contributor-tutorial-contribute-on-github.rst:171
 msgid ""
 "Once this is done, you will see on the GitHub that your forked repo was "
 "updated with the changes you have made."
@@ -1465,15 +1793,15 @@ msgstr ""
 "Une fois que c'est fait, tu verras sur GitHub que ton repo forké a été "
 "mis à jour avec les modifications que tu as apportées."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:176
+#: ../../source/contributor-tutorial-contribute-on-github.rst:175
 msgid "Creating and merging a pull request (PR)"
 msgstr "Créer et fusionner une pull request (PR)"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:203
+#: ../../source/contributor-tutorial-contribute-on-github.rst:206
 msgid "**Create the PR**"
 msgstr "**Créer le PR**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:179
+#: ../../source/contributor-tutorial-contribute-on-github.rst:178
 msgid ""
 "Once you have pushed changes, on the GitHub webpage of your repository "
 "you should see the following message:"
@@ -1481,25 +1809,27 @@ msgstr ""
 "Une fois que tu as poussé les modifications, sur la page web GitHub de "
 "ton dépôt, tu devrais voir le message suivant :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:183
-msgid "Otherwise you can always find this option in the `Branches` page."
+#: ../../source/contributor-tutorial-contribute-on-github.rst:182
+#, fuzzy
+msgid "Otherwise you can always find this option in the ``Branches`` page."
 msgstr "Sinon, tu peux toujours trouver cette option dans la page `Branches`."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:185
+#: ../../source/contributor-tutorial-contribute-on-github.rst:184
+#, fuzzy
 msgid ""
-"Once you click the `Compare & pull request` button, you should see "
+"Once you click the ``Compare & pull request`` button, you should see "
 "something similar to this:"
 msgstr ""
 "Une fois que tu as cliqué sur le bouton `Compare & pull request`, tu "
 "devrais voir quelque chose de similaire à ceci :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:189
+#: ../../source/contributor-tutorial-contribute-on-github.rst:188
 msgid "At the top you have an explanation of which branch will be merged where:"
 msgstr ""
 "En haut, tu as une explication de quelle branche sera fusionnée à quel "
 "endroit :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:193
+#: ../../source/contributor-tutorial-contribute-on-github.rst:192
 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 "
@@ -1509,7 +1839,7 @@ msgstr ""
 "branche ``doc-fixes`` de mon dépôt forké à la branche ``main`` du dépôt "
 "Flower."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:195
+#: ../../source/contributor-tutorial-contribute-on-github.rst:194
 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 "
@@ -1521,7 +1851,16 @@ 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:198
+#: ../../source/contributor-tutorial-contribute-on-github.rst:197
+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: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"
@@ -1531,7 +1870,7 @@ 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:201
+#: ../../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:"
@@ -1540,11 +1879,11 @@ msgstr ""
 " personne, tu as la possibilité de créer un brouillon de demande de "
 "traction :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:206
+#: ../../source/contributor-tutorial-contribute-on-github.rst:209
 msgid "**Making new changes**"
 msgstr "**Faire de nouveaux changements**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:206
+#: ../../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"
@@ -1554,11 +1893,11 @@ 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:228
+#: ../../source/contributor-tutorial-contribute-on-github.rst:231
 msgid "**Review the PR**"
 msgstr "**Review the PR**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:209
+#: ../../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:"
@@ -1567,7 +1906,7 @@ msgstr ""
 " étant prêt, une révision des propriétaires de code sera automatiquement "
 "demandée :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:213
+#: ../../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."
@@ -1575,11 +1914,11 @@ 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:215
+#: ../../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:219
+#: ../../source/contributor-tutorial-contribute-on-github.rst:222
 msgid ""
 "To resolve them, just push the necessary changes to the branch associated"
 " with the PR:"
@@ -1587,11 +1926,11 @@ msgstr ""
 "Pour les résoudre, il suffit de pousser les changements nécessaires vers "
 "la branche associée au PR :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:223
+#: ../../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:227
+#: ../../source/contributor-tutorial-contribute-on-github.rst:230
 msgid ""
 "Once all the conversations have been resolved, you can re-request a "
 "review."
@@ -1599,11 +1938,11 @@ msgstr ""
 "Une fois que toutes les conversations ont été résolues, tu peux "
 "redemander un examen."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:248
+#: ../../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:231
+#: ../../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."
@@ -1612,7 +1951,7 @@ msgstr ""
 " de modifications à demander, ils peuvent approuver le PR et le "
 "fusionner."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:235
+#: ../../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:"
@@ -1621,19 +1960,19 @@ msgstr ""
 "(un bouton devrait apparaître pour le faire) et aussi la supprimer "
 "localement en faisant :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:242
+#: ../../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:251
+#: ../../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:254
+#: ../../source/contributor-tutorial-contribute-on-github.rst:257
 msgid "Problem"
 msgstr "Problème"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:256
+#: ../../source/contributor-tutorial-contribute-on-github.rst:259
 msgid ""
 "For our documentation, we’ve started to use the `Diàtaxis framework "
 "<https://diataxis.fr/>`_."
@@ -1641,7 +1980,7 @@ msgstr ""
 "Pour notre documentation, nous avons commencé à utiliser le cadre "
 "`Diàtaxis <https://diataxis.fr/>`_."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:258
+#: ../../source/contributor-tutorial-contribute-on-github.rst:261
 msgid ""
 "Our “How to” guides should have titles that continue the sencence “How to"
 " …”, for example, “How to upgrade to Flower 1.0”."
@@ -1650,7 +1989,7 @@ msgstr ""
 "la phrase \"Comment faire pour...\", par exemple, \"Comment passer à "
 "Flower 1.0\"."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:260
+#: ../../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."
@@ -1659,7 +1998,7 @@ msgstr ""
 "changer leur titre est (malheureusement) plus compliqué qu'on ne le "
 "pense."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:262
+#: ../../source/contributor-tutorial-contribute-on-github.rst:265
 msgid ""
 "This issue is about changing the title of a doc from present continious "
 "to present simple."
@@ -1667,7 +2006,7 @@ 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:264
+#: ../../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?"
@@ -1676,19 +2015,19 @@ msgstr ""
 "remplacé par \"Sauvegarder la progression\". Est-ce que cela passe notre "
 "contrôle ?"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:266
+#: ../../source/contributor-tutorial-contribute-on-github.rst:269
 msgid "Before: ”How to saving progress” ❌"
 msgstr "Avant : \"Comment sauvegarder les progrès\" ❌"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:268
+#: ../../source/contributor-tutorial-contribute-on-github.rst:271
 msgid "After: ”How to save progress” ✅"
 msgstr "Après : \"Comment sauvegarder la progression\" ✅"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:271
+#: ../../source/contributor-tutorial-contribute-on-github.rst:274
 msgid "Solution"
 msgstr "Solution"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:273
+#: ../../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:"
@@ -1697,32 +2036,34 @@ 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:275
-msgid "Find the source file in `doc/source`"
+#: ../../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:276
+#: ../../source/contributor-tutorial-contribute-on-github.rst:279
+#, fuzzy
 msgid ""
-"Make the change in the `.rst` file (beware, the dashes under the title "
+"Make the change in the ``.rst`` file (beware, the dashes under the title "
 "should be the same length as the title itself)"
 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:277
+#: ../../source/contributor-tutorial-contribute-on-github.rst:280
 msgid ""
-"Build the docs and check the result: `<https://flower.dev/docs/writing-"
+"Build the docs and check the result: `<https://flower.ai/docs/writing-"
 "documentation.html#edit-an-existing-page>`_"
 msgstr ""
 "Construis les documents et vérifie le résultat : "
-"`<https://flower.dev/docs/writing-documentation.html#edit-an-existing-"
+"`<https://flower.ai/docs/writing-documentation.html#edit-an-existing-"
 "page>`_"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:280
+#: ../../source/contributor-tutorial-contribute-on-github.rst:283
 msgid "Rename file"
 msgstr "Renommer le fichier"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:282
+#: ../../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 "
@@ -1735,49 +2076,54 @@ 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:285
+#: ../../source/contributor-tutorial-contribute-on-github.rst:288
 msgid "Here’s how to change the file name:"
 msgstr "Voici comment changer le nom du fichier :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:287
-msgid "Change the file name to `save-progress.rst`"
+#: ../../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:288
-msgid "Add a redirect rule to `doc/source/conf.py`"
+#: ../../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:290
+#: ../../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."
+"This will cause a redirect from ``saving-progress.html`` to ``save-"
+"progress.html``, old links will continue to work."
 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:293
+#: ../../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:295
+#: ../../source/contributor-tutorial-contribute-on-github.rst:298
+#, fuzzy
 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 "
+"update the ``index.rst`` file as well. This is where we define the whole "
 "arborescence of the navbar."
 msgstr ""
 "Pour que la barre de navigation latérale fonctionne correctement, il est "
 "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:298
-msgid "Find and modify the file name in `index.rst`"
+#: ../../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:301
+#: ../../source/contributor-tutorial-contribute-on-github.rst:304
 msgid "Open PR"
 msgstr "Open PR"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:303
+#: ../../source/contributor-tutorial-contribute-on-github.rst:306
 msgid ""
 "Commit the changes (commit messages are always imperative: “Do "
 "something”, in this case “Change …”)"
@@ -1785,29 +2131,29 @@ 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:304
+#: ../../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:305
+#: ../../source/contributor-tutorial-contribute-on-github.rst:308
 msgid "Open a PR (as shown above)"
 msgstr "Ouvre un RP (comme indiqué ci-dessus)"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:306
+#: ../../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:307
+#: ../../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:311
+#: ../../source/contributor-tutorial-contribute-on-github.rst:314
 msgid "How to write a good PR title"
 msgstr "Comment écrire un bon titre de PR"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:313
+#: ../../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 "
@@ -1817,7 +2163,7 @@ msgstr ""
 "comprendre l'intérêt et le scope des changements proposés. Voici un guide"
 " pour vous aider à écrire des bons titres de PR :"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:315
+#: ../../source/contributor-tutorial-contribute-on-github.rst:318
 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,\" "
@@ -1835,7 +2181,7 @@ msgstr ""
 "capitalisation et une ponctuation : Suivre les règles de grammaire pour "
 "la clarté."
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:321
+#: ../../source/contributor-tutorial-contribute-on-github.rst:324
 msgid ""
 "Let's start with a few examples for titles that should be avoided because"
 " they do not provide meaningful information:"
@@ -1843,27 +2189,27 @@ 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:323
+#: ../../source/contributor-tutorial-contribute-on-github.rst:326
 msgid "Implement Algorithm"
 msgstr "Implement Algorithm"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:324
+#: ../../source/contributor-tutorial-contribute-on-github.rst:327
 msgid "Database"
 msgstr "Database"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:325
+#: ../../source/contributor-tutorial-contribute-on-github.rst:328
 msgid "Add my_new_file.py to codebase"
 msgstr "Add my_new_file.py to codebase"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:326
+#: ../../source/contributor-tutorial-contribute-on-github.rst:329
 msgid "Improve code in module"
 msgstr "Improve code in module"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:327
+#: ../../source/contributor-tutorial-contribute-on-github.rst:330
 msgid "Change SomeModule"
 msgstr "Change SomeModule"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:329
+#: ../../source/contributor-tutorial-contribute-on-github.rst:332
 msgid ""
 "Here are a few positive examples which provide helpful information "
 "without repeating how they do it, as that is already visible in the "
@@ -1873,23 +2219,24 @@ msgstr ""
 "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:331
+#: ../../source/contributor-tutorial-contribute-on-github.rst:334
 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:332
+#: ../../source/contributor-tutorial-contribute-on-github.rst:335
 msgid "Remove unnecessary XGBoost dependency"
 msgstr "Remove unnecessary XGBoost dependency"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:333
+#: ../../source/contributor-tutorial-contribute-on-github.rst:336
 msgid "Remove redundant attributes in strategies subclassing FedAvg"
 msgstr "Remove redundant attributes in strategies subclassing FedAvg"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:334
-msgid "Add CI job to deploy the staging system when the `main` branch changes"
+#: ../../source/contributor-tutorial-contribute-on-github.rst:337
+#, 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:335
+#: ../../source/contributor-tutorial-contribute-on-github.rst:338
 msgid ""
 "Add new amazing library which will be used to improve the simulation "
 "engine"
@@ -1897,16 +2244,16 @@ msgstr ""
 "Add new amazing library which will be used to improve the simulation "
 "engine"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:339
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:548
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:946
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:747
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:713
-#: ../../source/tutorial-what-is-federated-learning.ipynb:367
+#: ../../source/contributor-tutorial-contribute-on-github.rst:342
+#: ../../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:341
+#: ../../source/contributor-tutorial-contribute-on-github.rst:344
 msgid ""
 "Once you have made your first PR, and want to contribute more, be sure to"
 " check out the following :"
@@ -1914,16 +2261,153 @@ 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:343
+#: ../../source/contributor-tutorial-contribute-on-github.rst:346
 msgid ""
-"`Good first contributions <https://flower.dev/docs/framework/contributor-"
+"`Good first contributions <https://flower.ai/docs/framework/contributor-"
 "ref-good-first-contributions.html>`_, where you should particularly look "
 "into the :code:`baselines` contributions."
 msgstr ""
-"`Bonnes premières contributions <https://flower.dev/docs/framework"
+"`Bonnes premières contributions <https://flower.ai/docs/framework"
 "/contributor-ref-good-first-contributions.html>`_, où vous devriez "
 "particulièrement regarder les contributions :code:`baselines`."
 
+#: ../../source/contributor-tutorial-contribute-on-github.rst:350
+#: ../../source/fed/0000-20200102-fed-template.md:60
+msgid "Appendix"
+msgstr "Annexe"
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:355
+#, fuzzy
+msgid "Changelog entry"
+msgstr "Changelog"
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:357
+msgid ""
+"When opening a new PR, inside its description, there should be a "
+"``Changelog entry`` header."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:359
+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:361
+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:364
+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 ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:367
+msgid ""
+"If the section contains some text other than tokens, it will use it to "
+"add a description to the change."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:369
+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:371
+msgid "<general> is for classifying a PR as a general improvement."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:373
+msgid "<skip> is to not add the PR to the changelog"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:375
+msgid "<baselines> is to add a general baselines change to the PR"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:377
+msgid "<examples> is to add a general examples change to the PR"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:379
+msgid "<sdk> is to add a general sdk change to the PR"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:381
+msgid "<simulations> is to add a general simulations change to the PR"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:383
+msgid "Note that only one token should be used."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:385
+msgid ""
+"Its content must have a specific format. We will break down what each "
+"possibility does:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:387
+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:391
+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:397
+msgid ""
+"If the ``### Changelog entry`` section contains ``<skip>``, nothing will "
+"change in the changelog."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:399
+msgid ""
+"If the ``### Changelog entry`` section contains ``<general>``, the "
+"following text will be added to the changelog::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:403
+msgid ""
+"If the ``### Changelog entry`` section contains ``<baselines>``, the "
+"following text will be added to the changelog::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:407
+msgid ""
+"If the ``### Changelog entry`` section contains ``<examples>``, the "
+"following text will be added to the changelog::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:411
+msgid ""
+"If the ``### Changelog entry`` section contains ``<sdk>``, the following "
+"text will be added to the changelog::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:415
+msgid ""
+"If the ``### Changelog entry`` section contains ``<simulations>``, the "
+"following text will be added to the changelog::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:419
+msgid ""
+"Note that only one token must be provided, otherwise, only the first "
+"action (in the order listed above), will be performed."
+msgstr ""
+
 #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:2
 msgid "Get started as a contributor"
 msgstr "Devenez un·e contributeur·ice"
@@ -1933,7 +2417,8 @@ msgid "Prerequisites"
 msgstr "Prérequis"
 
 #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:7
-msgid "`Python 3.7 <https://docs.python.org/3.7/>`_ or above"
+#, fuzzy
+msgid "`Python 3.8 <https://docs.python.org/3.8/>`_ or above"
 msgstr "`Python 3.7 <https://docs.python.org/3.7/>`_ ou plus"
 
 #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:8
@@ -1965,117 +2450,166 @@ msgstr ""
 msgid "Developer Machine Setup"
 msgstr "Setup de la machine"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:20
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:21
+msgid "Preliminarities"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:22
+msgid "Some system-wide dependencies are needed."
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:25
+msgid "For macOS"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:27
+msgid ""
+"Install `homebrew <https://brew.sh/>`_. Don't forget the post-"
+"installation actions to add `brew` to your PATH."
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:28
+msgid ""
+"Install `xz` (to install different Python versions) and `pandoc` to build"
+" the docs::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:34
+msgid "For Ubuntu"
+msgstr ""
+
+#: ../../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::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:44
+#, fuzzy
+msgid "Create Flower Dev Environment"
+msgstr "Créer/Supprimer l'environment virtuel"
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:46
+#, fuzzy
 msgid ""
-"First, clone the `Flower repository <https://github.com/adap/flower>`_ "
-"from GitHub::"
+"1. Clone the `Flower repository <https://github.com/adap/flower>`_ from "
+"GitHub::"
 msgstr ""
 "Pour commencer, cloner la `repo Flower <https://github.com/adap/flower>`_"
 " depuis GitHub::"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:26
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:52
 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:`<version>`)::"
+"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 ""
-"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:`<version>` spécifique)::"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:33
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:54
+#, fuzzy
+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)::"
+msgstr ""
+"Si vous n'avez pas :code:`pyenv` installé, vous pouvez utiliser le script"
+" suivant qui l'installera, le configurera et créera l'environnement "
+"virtuel (avec :code:`Python 3.8.17` par défaut)::"
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:58
+#, fuzzy
 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)::"
+"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)::"
 msgstr ""
 "Si vous n'avez pas :code:`pyenv` installé, vous pouvez utiliser le script"
 " suivant qui l'installera, le configurera et créera l'environnement "
 "virtuel (avec :code:`Python 3.8.17` par défaut)::"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:39
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:62
+#, fuzzy
 msgid ""
-"Third, install the Flower package in development mode (think :code:`pip "
+"3. Install the Flower package in development mode (think :code:`pip "
 "install -e`) along with all necessary dependencies::"
 msgstr ""
 "Troisièmement, installez le paquet Flower en mode de développement ( "
 ":code :`pip install -e`) avec toutes les dépendances nécessaires :"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:46
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:69
 msgid "Convenience Scripts"
 msgstr "Scripts pratiques"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:48
+#: ../../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 "
-"amonst the most important ones:"
+"amongst the most important ones:"
 msgstr ""
 "La repo de Flower contient un certain nombre de scripts de commodité pour"
 " rendre les tâches de développement récurrentes plus faciles et moins "
 "problématiques. Voir le sous-répertoire :code :`/dev` pour une liste "
 "complète. Les scripts suivants sont parmis les plus importants :"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:54
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:77
 msgid "Create/Delete Virtual Environment"
 msgstr "Créer/Supprimer l'environment virtuel"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:62
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:85
 msgid "Compile ProtoBuf Definitions"
 msgstr "Compiler les définitions ProtoBuf"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:69
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:92
 msgid "Auto-Format Code"
 msgstr "Formatter le code"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:76
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:99
 msgid "Run Linters and Tests"
 msgstr "Vérifier le format et tester le code"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:83
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:106
 msgid "Run Github Actions (CI) locally"
 msgstr "Exécuter les GitHub Actions (CI) localement"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:85
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:108
+#, fuzzy
 msgid ""
 "Developers could run the full set of Github Actions workflows under their"
-" local environment by using `Act <https://github.com/nektos/act>_`. "
+" local environment by using `Act <https://github.com/nektos/act>`_. "
 "Please refer to the installation instructions under the linked repository"
 " and run the next command under Flower main cloned repository folder::"
 msgstr ""
 "Il est possible d'exécuter l'ensemble des Github Actions sous leur "
-"environnement local en utilisant `Act <https ://github.com/nektos/act>_`. En "
-"suivant le lien vous trouverez les instructions d'installation. Une fois "
-"installé, exécuter la commande suivante dans le dossier principale de Flower "
-":"
+"environnement local en utilisant `Act <https ://github.com/nektos/act>_`."
+" En suivant le lien vous trouverez les instructions d'installation. Une "
+"fois installé, exécuter la commande suivante dans le dossier principale "
+"de Flower :"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:92
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:115
 msgid ""
 "The Flower default workflow would run by setting up the required Docker "
 "machines underneath."
 msgstr ""
-"Le workflow par défaut de Flower sera exécuté en configurant les machines "
-"Docker requises en arrière plan."
+"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:97
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:120
 #, fuzzy
 msgid "Build Release"
 msgstr "Inédit"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:99
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:122
 msgid ""
 "Flower uses Poetry to build releases. The necessary command is wrapped in"
 " a simple script::"
 msgstr ""
-"Flower utilise Poetry pour construire les nouvelles versions. La commande "
-"nécessaire est comprise dans un script simple ::"
+"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:104
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:127
 msgid ""
 "The resulting :code:`.whl` and :code:`.tar.gz` releases will be stored in"
 " the :code:`/dist` subdirectory."
@@ -2083,19 +2617,19 @@ 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:109
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:132
 #, fuzzy
 msgid "Build Documentation"
 msgstr "Amélioration de la documentation"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:111
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:134
 msgid ""
 "Flower's documentation uses `Sphinx <https://www.sphinx-doc.org/>`_. "
 "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:117
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:140
 msgid "This will generate HTML documentation in ``doc/build/html``."
 msgstr ""
 
@@ -2104,6 +2638,7 @@ msgid "Example: FedBN in PyTorch - From Centralized To Federated"
 msgstr "Exemple : FedBN dans PyTorch - De la centralisation à la fédération"
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:4
+#, fuzzy
 msgid ""
 "This tutorial will show you how to use Flower to build a federated "
 "version of an existing machine learning workload with `FedBN "
@@ -2111,8 +2646,8 @@ msgid ""
 "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 <https://flower.dev/docs/example-"
-"pytorch-from-centralized-to-federated.html>`_."
+"PyTorch - From Centralized To Federated <https://flower.ai/docs/examples"
+"/pytorch-from-centralized-to-federated.html>`_."
 msgstr ""
 "Ce tutoriel te montrera comment utiliser Flower pour construire une "
 "version fédérée d'une charge de travail d'apprentissage automatique "
@@ -2122,7 +2657,7 @@ msgstr ""
 "couches de normalisation par lots) sur l'ensemble de données CIFAR-10. "
 "Lors de l'application de FedBN, seules quelques modifications sont "
 "nécessaires par rapport à `Exemple : PyTorch - De la centralisation à la "
-"fédération <https://flower.dev/docs/example-pytorch-from-centralized-to-"
+"fédération <https://flower.ai/docs/example-pytorch-from-centralized-to-"
 "federated.html>`_."
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:9
@@ -2131,14 +2666,15 @@ msgid "Centralized Training"
 msgstr "Formation centralisée"
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:10
+#, fuzzy
 msgid ""
 "All files are revised based on `Example: PyTorch - From Centralized To "
-"Federated <https://flower.dev/docs/example-pytorch-from-centralized-to-"
+"Federated <https://flower.ai/docs/examples/pytorch-from-centralized-to-"
 "federated.html>`_. The only thing to do is modifying the file called "
 ":code:`cifar.py`, revised part is shown below:"
 msgstr ""
 "Tous les fichiers sont révisés sur la base de `Exemple : PyTorch - From "
-"Centralized To Federated <https://flower.dev/docs/example-pytorch-from-"
+"Centralized To Federated <https://flower.ai/docs/example-pytorch-from-"
 "centralized-to-federated.html>`_. La seule chose à faire est de modifier "
 "le fichier appelé :code:`cifar.py`, la partie révisée est montrée ci-"
 "dessous :"
@@ -2176,22 +2712,23 @@ msgid "Federated Training"
 msgstr "Formation fédérée"
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:53
+#, fuzzy
 msgid ""
 "If you have read `Example: PyTorch - From Centralized To Federated "
-"<https://flower.dev/docs/example-pytorch-from-centralized-to-"
+"<https://flower.ai/docs/examples/pytorch-from-centralized-to-"
 "federated.html>`_, 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 <https://flower.dev/docs/example-"
-"pytorch-from-centralized-to-federated.html>`_. first."
+"PyTorch - From Centralized To Federated <https://flower.ai/docs/examples"
+"/pytorch-from-centralized-to-federated.html>`_. first."
 msgstr ""
 "Si vous avez lu `Exemple : PyTorch - From Centralized To Federated "
-"<https://flower.dev/docs/example-pytorch-from-centralized-to-"
+"<https://flower.ai/docs/example-pytorch-from-centralized-to-"
 "federated.html>`_, les parties suivantes sont faciles à suivre, seules "
 "les fonctions :code:`get_parameters` et :code:`set_parameters` dans "
 ":code:`client.py` ont besoin d'être révisées. Si ce n'est pas le cas, "
 "veuillez lire `Exemple : PyTorch - From Centralized To Federated "
-"<https://flower.dev/docs/example-pytorch-from-centralized-to-"
+"<https://flower.ai/docs/example-pytorch-from-centralized-to-"
 "federated.html>`. d'abord."
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:56
@@ -2235,7 +2772,7 @@ msgstr ""
 #: ../../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:280
+#: ../../source/tutorial-quickstart-jax.rst:283
 msgid "Next Steps"
 msgstr "Prochaines étapes"
 
@@ -2264,7 +2801,7 @@ msgid "Example: JAX - Run JAX Federated"
 msgstr "Exemple : JAX - Exécuter JAX Federated"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:4
-#: ../../source/tutorial-quickstart-jax.rst:7
+#: ../../source/tutorial-quickstart-jax.rst:10
 #, fuzzy
 msgid ""
 "This tutorial will show you how to use Flower to build a federated "
@@ -2292,7 +2829,7 @@ msgstr ""
 "exécuter l'entraînement de manière fédérée."
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:10
-#: ../../source/tutorial-quickstart-jax.rst:13
+#: ../../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`:"
@@ -2302,12 +2839,12 @@ msgstr ""
 ":code:`flwr` :"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:18
-#: ../../source/tutorial-quickstart-jax.rst:21
+#: ../../source/tutorial-quickstart-jax.rst:24
 msgid "Linear Regression with JAX"
 msgstr "Régression linéaire avec JAX"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:20
-#: ../../source/tutorial-quickstart-jax.rst:23
+#: ../../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 "
@@ -2320,7 +2857,7 @@ msgstr ""
 "la documentation officielle `JAX <https://jax.readthedocs.io/>`_."
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:23
-#: ../../source/tutorial-quickstart-jax.rst:26
+#: ../../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 "
@@ -2342,7 +2879,7 @@ msgstr ""
 "fédéré, ce qui sera fait plus tard."
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:37
-#: ../../source/tutorial-quickstart-jax.rst:40
+#: ../../source/tutorial-quickstart-jax.rst:43
 msgid ""
 "The :code:`load_data()` function loads the mentioned training and test "
 "sets."
@@ -2351,7 +2888,7 @@ msgstr ""
 " test mentionnés."
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:47
-#: ../../source/tutorial-quickstart-jax.rst:50
+#: ../../source/tutorial-quickstart-jax.rst:53
 msgid ""
 "The model architecture (a very simple :code:`Linear Regression` model) is"
 " defined in :code:`load_model()`."
@@ -2360,7 +2897,7 @@ msgstr ""
 "simple) est définie dans :code:`load_model()`."
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:59
-#: ../../source/tutorial-quickstart-jax.rst:62
+#: ../../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 "
@@ -2376,7 +2913,7 @@ msgstr ""
 ":code:`train()`)."
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:77
-#: ../../source/tutorial-quickstart-jax.rst:80
+#: ../../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 "
@@ -2387,7 +2924,7 @@ msgstr ""
 " de régression linéaire."
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:88
-#: ../../source/tutorial-quickstart-jax.rst:91
+#: ../../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. "
@@ -2401,14 +2938,14 @@ msgstr ""
 "à :code:`train()`."
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:105
-#: ../../source/tutorial-quickstart-jax.rst:108
+#: ../../source/tutorial-quickstart-jax.rst:111
 msgid "You can now run your (centralized) JAX linear regression workload:"
 msgstr ""
 "Tu peux maintenant exécuter ta charge de travail (centralisée) de "
 "régression linéaire JAX :"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:111
-#: ../../source/tutorial-quickstart-jax.rst:114
+#: ../../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 "
@@ -2420,12 +2957,12 @@ msgstr ""
 "composé d'un serveur et de deux clients."
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:115
-#: ../../source/tutorial-quickstart-jax.rst:118
+#: ../../source/tutorial-quickstart-jax.rst:121
 msgid "JAX meets Flower"
 msgstr "JAX rencontre Flower"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:117
-#: ../../source/tutorial-quickstart-jax.rst:120
+#: ../../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 "
@@ -2449,7 +2986,7 @@ 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:126
+#: ../../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 "
@@ -2465,12 +3002,12 @@ 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:136
+#: ../../source/tutorial-quickstart-jax.rst:139
 msgid "We can already start the *server*:"
 msgstr "Nous pouvons déjà démarrer le *serveur* :"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:139
-#: ../../source/tutorial-quickstart-jax.rst:142
+#: ../../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"
@@ -2484,7 +3021,7 @@ msgstr ""
 " les paramètres de notre modèle JAX :"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:154
-#: ../../source/tutorial-quickstart-jax.rst:157
+#: ../../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`. "
@@ -2511,28 +3048,28 @@ msgstr ""
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:161
 #: ../../source/example-mxnet-walk-through.rst:242
-#: ../../source/tutorial-quickstart-jax.rst:164
+#: ../../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:163
+#: ../../source/tutorial-quickstart-jax.rst:166
 msgid ""
 "set the model parameters on the local model that are received from the "
 "server"
 msgstr "règle les paramètres du modèle local reçus du serveur"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:161
-#: ../../source/tutorial-quickstart-jax.rst:164
+#: ../../source/tutorial-quickstart-jax.rst:167
 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:165
+#: ../../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)"
@@ -2543,17 +3080,17 @@ 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:166
-#: ../../source/tutorial-quickstart-mxnet.rst:166
-#: ../../source/tutorial-quickstart-pytorch.rst:152
-#: ../../source/tutorial-quickstart-scikitlearn.rst:105
+#: ../../source/tutorial-quickstart-jax.rst:169
+#: ../../source/tutorial-quickstart-mxnet.rst:171
+#: ../../source/tutorial-quickstart-pytorch.rst:155
+#: ../../source/tutorial-quickstart-scikitlearn.rst:108
 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:167
+#: ../../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)"
@@ -2565,10 +3102,10 @@ 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:170
-#: ../../source/tutorial-quickstart-mxnet.rst:172
-#: ../../source/tutorial-quickstart-pytorch.rst:158
-#: ../../source/tutorial-quickstart-scikitlearn.rst:112
+#: ../../source/tutorial-quickstart-jax.rst:173
+#: ../../source/tutorial-quickstart-mxnet.rst:177
+#: ../../source/tutorial-quickstart-pytorch.rst:161
+#: ../../source/tutorial-quickstart-scikitlearn.rst:115
 msgid ":code:`fit`"
 msgstr ":code:`fit`"
 
@@ -2578,8 +3115,8 @@ msgstr ":code:`fit`"
 #: ../../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:169
-#: ../../source/tutorial-quickstart-jax.rst:173
+#: ../../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"
@@ -2590,12 +3127,12 @@ 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:170
+#: ../../source/tutorial-quickstart-jax.rst:173
 msgid "train the model on the local training set"
 msgstr "entraîne le modèle sur l'ensemble d'apprentissage local"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:168
-#: ../../source/tutorial-quickstart-jax.rst:171
+#: ../../source/tutorial-quickstart-jax.rst:174
 msgid "get the updated local model parameters and return them to the server"
 msgstr ""
 "récupère les paramètres du modèle local mis à jour et les renvoie au "
@@ -2604,27 +3141,27 @@ 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:175
-#: ../../source/tutorial-quickstart-mxnet.rst:175
-#: ../../source/tutorial-quickstart-pytorch.rst:161
-#: ../../source/tutorial-quickstart-scikitlearn.rst:115
+#: ../../source/tutorial-quickstart-jax.rst:178
+#: ../../source/tutorial-quickstart-mxnet.rst:180
+#: ../../source/tutorial-quickstart-pytorch.rst:164
+#: ../../source/tutorial-quickstart-scikitlearn.rst:118
 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:174
+#: ../../source/tutorial-quickstart-jax.rst:177
 msgid "evaluate the updated model on the local test set"
 msgstr "évaluer le modèle mis à jour sur l'ensemble de test local"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:172
-#: ../../source/tutorial-quickstart-jax.rst:175
+#: ../../source/tutorial-quickstart-jax.rst:178
 msgid "return the local loss to the server"
 msgstr "renvoie la perte locale au serveur"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:174
-#: ../../source/tutorial-quickstart-jax.rst:177
+#: ../../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"
@@ -2635,7 +3172,7 @@ msgstr ""
 "rendre compatibles avec `NumPyClient`."
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:176
-#: ../../source/tutorial-quickstart-jax.rst:179
+#: ../../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 "
@@ -2655,21 +3192,21 @@ msgstr ""
 "de données qui sont transmis."
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:245
-#: ../../source/tutorial-quickstart-jax.rst:248
+#: ../../source/tutorial-quickstart-jax.rst:251
 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:271
+#: ../../source/tutorial-quickstart-jax.rst:274
 msgid "And that's it. You can now open two additional terminal windows and run"
 msgstr ""
 "Tu peux maintenant ouvrir deux autres fenêtres de terminal et exécuter "
 "les commandes suivantes"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:274
-#: ../../source/tutorial-quickstart-jax.rst:277
+#: ../../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. "
@@ -2680,7 +3217,7 @@ msgstr ""
 "l'apprentissage fédéré sur deux clients. Félicitations !"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:279
-#: ../../source/tutorial-quickstart-jax.rst:282
+#: ../../source/tutorial-quickstart-jax.rst:285
 #, fuzzy
 msgid ""
 "The source code of this example was improved over time and can be found "
@@ -2695,7 +3232,7 @@ msgstr ""
 "chargent le même jeu de données."
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:282
-#: ../../source/tutorial-quickstart-jax.rst:285
+#: ../../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 "
@@ -2909,7 +3446,7 @@ 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 slighly easier "
+"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 "
@@ -2920,7 +3457,7 @@ 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 slighly easier "
+"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 "
@@ -3161,7 +3698,7 @@ 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 paramters. The updated "
+"*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."
@@ -3180,7 +3717,7 @@ 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 paramters on our PyTorch model:"
+"update the parameters on our PyTorch model:"
 msgstr ""
 "Enfin, nous allons définir notre logique *client* dans :code:`client.py` "
 "et nous appuyer sur la formation centralisée définie précédemment dans "
@@ -3192,7 +3729,7 @@ 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 slighly easier "
+"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. "
@@ -3203,7 +3740,7 @@ 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 slighly easier "
+"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. "
@@ -3239,15 +3776,15 @@ 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 adress we used in :code:`server.py`:"
+"with the function :code:`fl.client.start_client()` by pointing it at the "
+"same IP adress we used in :code:`server.py`:"
 msgstr ""
 "Il ne reste plus qu'à définir une fonction qui charge le modèle et les "
 "données, crée un :code:`CifarClient` et démarre ce client. Tu charges tes"
 " données et ton modèle en utilisant :code:`cifar.py`. Démarre "
-":code:`CifarClient` avec la fonction "
-":code:`fl.client.start_numpy_client()` en la faisant pointer sur la même "
-"adresse IP que celle que nous avons utilisée dans :code:`server.py` :"
+":code:`CifarClient` avec la fonction :code:`fl.client.start_client()` en "
+"la faisant pointer sur la même adresse IP que celle que nous avons "
+"utilisée dans :code:`server.py` :"
 
 #: ../../source/example-pytorch-from-centralized-to-federated.rst:307
 msgid ""
@@ -3292,9 +3829,9 @@ msgstr ""
 "neuronal convolutif sur MNIST en utilisant Flower et PyTorch."
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:6
-#: ../../source/tutorial-quickstart-mxnet.rst:11
-#: ../../source/tutorial-quickstart-pytorch.rst:14
-#: ../../source/tutorial-quickstart-scikitlearn.rst:11
+#: ../../source/tutorial-quickstart-mxnet.rst:16
+#: ../../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."
@@ -3303,7 +3840,7 @@ msgstr ""
 "même modèle."
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:8
-#: ../../source/tutorial-quickstart-pytorch.rst:16
+#: ../../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 "
@@ -3319,7 +3856,7 @@ msgstr ""
 "mises à jour de poids s'appelle un *round*."
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:12
-#: ../../source/tutorial-quickstart-pytorch.rst:20
+#: ../../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 :"
@@ -3385,11 +3922,12 @@ msgid "Now, let's see what is really happening inside."
 msgstr "Maintenant, voyons ce qui se passe réellement à l'intérieur."
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:69
-#: ../../source/tutorial-quickstart-ios.rst:126
-#: ../../source/tutorial-quickstart-mxnet.rst:221
-#: ../../source/tutorial-quickstart-pytorch.rst:200
-#: ../../source/tutorial-quickstart-scikitlearn.rst:154
-#: ../../source/tutorial-quickstart-tensorflow.rst:95
+#: ../../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"
 
@@ -3419,11 +3957,12 @@ msgstr ""
 "peut le voir ci-dessous."
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:89
-#: ../../source/tutorial-quickstart-ios.rst:31
-#: ../../source/tutorial-quickstart-mxnet.rst:31
-#: ../../source/tutorial-quickstart-pytorch.rst:34
-#: ../../source/tutorial-quickstart-scikitlearn.rst:37
-#: ../../source/tutorial-quickstart-tensorflow.rst:26
+#: ../../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"
 
@@ -3646,8 +4185,9 @@ msgstr ""
 "des divers cadres de ML avec lesquels Flower est compatible."
 
 #: ../../source/explanation-differential-privacy.rst:7
+#, fuzzy
 msgid ""
-"Please note that these components are still experimental, the correct "
+"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 "
@@ -3697,11 +4237,14 @@ msgstr ""
 "ajouté à la moyenne calculée au niveau du serveur."
 
 #: ../../source/explanation-differential-privacy.rst:20
+#, fuzzy
 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."
+"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"
@@ -3714,9 +4257,10 @@ msgid "Simplifying Assumptions"
 msgstr "Simplifier les hypothèses"
 
 #: ../../source/explanation-differential-privacy.rst:25
+#, fuzzy
 msgid ""
 "We make (and attempt to enforce) a number of assumptions that must be "
-"satisfied to ensure that the training process actually realises the "
+"satisfied to ensure that the training process actually realizes the "
 ":math:`(\\epsilon, \\delta)` guarantees the user has in mind when "
 "configuring the setup."
 msgstr ""
@@ -3757,10 +4301,11 @@ msgstr ""
 "termes, les clients ne peuvent pas abandonner ou échouer."
 
 #: ../../source/explanation-differential-privacy.rst:31
+#, fuzzy
 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 "
+"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 "
@@ -3851,9 +4396,10 @@ msgid "Server-side logic"
 msgstr "Logique côté serveur"
 
 #: ../../source/explanation-differential-privacy.rst:51
+#, fuzzy
 msgid ""
 "The first version of our solution was to define a decorator whose "
-"constructor accepted, among other things, a boolean valued variable "
+"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 "
@@ -3878,6 +4424,7 @@ msgstr ""
 "l'indique, détermine l'endroit où le noising doit être effectué."
 
 #: ../../source/explanation-differential-privacy.rst:54
+#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst:2
 msgid "DPFedAvgFixed"
 msgstr "DPFedAvgFixed"
 
@@ -3912,6 +4459,7 @@ msgstr ""
 "client (indiqué sous :code:`dpfedavg_noise_stddev`)."
 
 #: ../../source/explanation-differential-privacy.rst:59
+#, fuzzy
 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 "
@@ -3922,7 +4470,7 @@ msgid ""
 ":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 "
+"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 ""
@@ -3974,6 +4522,7 @@ msgstr ""
 "clients."
 
 #: ../../source/explanation-differential-privacy.rst:67
+#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst:2
 msgid "DPFedAvgAdaptive"
 msgstr "DPFedAvgAdaptive"
 
@@ -4075,25 +4624,28 @@ msgstr ""
 "pour un :math:`\\delta` particulier, le script suivant peut être utilisé."
 
 #: ../../source/explanation-differential-privacy.rst:98
+#, fuzzy
 msgid ""
-"McMahan, H. Brendan, et al. \"Learning differentially private recurrent "
-"language models.\" arXiv preprint arXiv:1710.06963 (2017)."
+"McMahan et al. \"Learning Differentially Private Recurrent Language "
+"Models.\" International Conference on Learning Representations (ICLR), "
+"2017."
 msgstr ""
 "McMahan, H. Brendan, et al. \"Learning differentially private recurrent "
 "language models\", arXiv preprint arXiv:1710.06963 (2017)."
 
 #: ../../source/explanation-differential-privacy.rst:100
+#, fuzzy
 msgid ""
-"Andrew, Galen, et al. \"Differentially private learning with adaptive "
-"clipping.\" Advances in Neural Information Processing Systems 34 (2021): "
-"17455-17466."
+"Andrew, Galen, et al. \"Differentially Private Learning with Adaptive "
+"Clipping.\" Advances in Neural Information Processing Systems (NeurIPS), "
+"2021."
 msgstr ""
 "Andrew, Galen, et al. \"Differentially private learning with adaptive "
 "clipping\" Advances in Neural Information Processing Systems 34 (2021) : "
 "17455-17466."
 
 #: ../../source/explanation-federated-evaluation.rst:2
-#: ../../source/tutorial-what-is-federated-learning.ipynb:292
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:292
 msgid "Federated evaluation"
 msgstr "Évaluation fédérée"
 
@@ -4391,10 +4943,6 @@ msgstr "[Alternative 1]"
 msgid "\\[Alternative 2\\]"
 msgstr "[Alternative 2]"
 
-#: ../../source/fed/0000-20200102-fed-template.md:60
-msgid "Appendix"
-msgstr "Annexe"
-
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:10
 msgid "Flower Enhancement Doc"
 msgstr "Doc sur l'amélioration des fleurs"
@@ -5047,22 +5595,24 @@ msgstr ""
 "configuration différentes à des clients différents."
 
 #: ../../source/how-to-configure-clients.rst:89
+#, fuzzy
 msgid ""
 "This can be achieved by customizing an existing strategy or by "
-"`implementing a custom strategy from scratch <https://flower.dev/docs"
-"/how-to-implement-strategies.html>`_. 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):"
+"`implementing a custom strategy from scratch "
+"<https://flower.ai/docs/framework/how-to-implement-strategies.html>`_. "
+"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 ""
 "Ceci peut être réalisé en personnalisant une stratégie existante ou en "
 "`mettant en œuvre une stratégie personnalisée à partir de zéro "
-"<https://flower.dev/docs/framework/how-to-implement-strategies.html>`_. Voici un "
-"exemple absurde qui personnalise :code:`FedAvg` en ajoutant une paire "
-"clé/valeur de configuration personnalisée ``\"hello\" : \"world\"`` au "
-"config dict d'un *seul client* (uniquement le premier client de la liste,"
-" les autres clients de cette série ne recevant pas cette valeur de "
+"<https://flower.ai/docs/framework/how-to-implement-strategies.html>`_. "
+"Voici un exemple absurde qui personnalise :code:`FedAvg` en ajoutant une "
+"paire clé/valeur de configuration personnalisée ``\"hello\" : \"world\"``"
+" au config dict d'un *seul client* (uniquement le premier client de la "
+"liste, les autres clients de cette série ne recevant pas cette valeur de "
 "configuration \"spéciale\") :"
 
 #: ../../source/how-to-configure-logging.rst:2
@@ -5111,9 +5661,9 @@ msgid ""
 msgstr ""
 "Avec ce qui précède, Flower enregistrera le log que vous voyez sur votre "
 "terminal dans :code:`log.txt`. Ce fichier sera créé dans le répertoire "
-"depuis lequel le code est exécuté. Si nous inspectons nous voyons que le log "
-"ci-dessous est également enregistré mais préfixé avec :code:`identifier` sur "
-"chaque ligne :"
+"depuis lequel le code est exécuté. Si nous inspectons nous voyons que le "
+"log ci-dessous est également enregistré mais préfixé avec "
+":code:`identifier` sur chaque ligne :"
 
 #: ../../source/how-to-configure-logging.rst:74
 msgid "Log your own messages"
@@ -5145,7 +5695,7 @@ msgid ""
 "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 whish to backup or analyze the logs "
+":code:`HTTPHandler` should you wish to backup or analyze the logs "
 "somewhere else."
 msgstr ""
 
@@ -5239,6 +5789,7 @@ msgstr ""
 "signés générés à l'aide des scripts qui font partie de ce guide."
 
 #: ../../source/how-to-enable-ssl-connections.rst:41
+#: ../../source/ref-api/flwr.server.Server.rst:2
 msgid "Server"
 msgstr "Serveur"
 
@@ -5264,7 +5815,7 @@ msgstr ""
 
 #: ../../source/how-to-enable-ssl-connections.rst:65
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:37
-#: ../../source/ref-api-flwr.rst:15
+#: ../../source/ref-api/flwr.client.Client.rst:2
 msgid "Client"
 msgstr "Client"
 
@@ -5288,7 +5839,8 @@ msgstr ""
 "certificats sous forme de chaînes d'octets."
 
 #: ../../source/how-to-enable-ssl-connections.rst:89
-#: ../../source/tutorial-what-is-federated-learning.ipynb:287
+#: ../../source/how-to-use-built-in-mods.rst:85
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:287
 msgid "Conclusion"
 msgstr "Conclusion"
 
@@ -5713,7 +6265,11 @@ msgstr "Version Python"
 msgid "Install stable release"
 msgstr "Installe la version stable"
 
-#: ../../source/how-to-install-flower.rst:14
+#: ../../source/how-to-install-flower.rst:15
+msgid "Using pip"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:17
 msgid ""
 "Stable releases are available on `PyPI "
 "<https://pypi.org/project/flwr/>`_::"
@@ -5721,7 +6277,7 @@ msgstr ""
 "Les versions stables sont disponibles sur `PyPI "
 "<https://pypi.org/project/flwr/>`_: :"
 
-#: ../../source/how-to-install-flower.rst:18
+#: ../../source/how-to-install-flower.rst:21
 msgid ""
 "For simulations that use the Virtual Client Engine, ``flwr`` should be "
 "installed with the ``simulation`` extra::"
@@ -5729,13 +6285,38 @@ msgstr ""
 "Pour les simulations qui utilisent le moteur de client virtuel, ``flwr`` "
 "doit être installé avec l'option ``simulation``: :"
 
-#: ../../source/how-to-install-flower.rst:24
+#: ../../source/how-to-install-flower.rst:27
+msgid "Using conda (or mamba)"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:29
+msgid "Flower can also be installed from the ``conda-forge`` channel."
+msgstr ""
+
+#: ../../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 ""
+
+#: ../../source/how-to-install-flower.rst:36
+msgid ""
+"Once the ``conda-forge`` channel has been enabled, ``flwr`` can be "
+"installed with ``conda``::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:40
+msgid "or with ``mamba``::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:46
 msgid "Verify installation"
 msgstr "Vérifie l'installation"
 
-#: ../../source/how-to-install-flower.rst:26
+#: ../../source/how-to-install-flower.rst:48
+#, fuzzy
 msgid ""
-"The following command can be used to verfiy if Flower was successfully "
+"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::"
 msgstr ""
@@ -5743,15 +6324,26 @@ msgstr ""
 "installé avec succès. Si tout a fonctionné, la version de Flower devrait "
 "être imprimée sur la ligne de commande: :"
 
-#: ../../source/how-to-install-flower.rst:33
+#: ../../source/how-to-install-flower.rst:55
 msgid "Advanced installation options"
 msgstr "Options d'installation avancées"
 
-#: ../../source/how-to-install-flower.rst:36
+#: ../../source/how-to-install-flower.rst:58
+#, fuzzy
+msgid "Install via Docker"
+msgstr "Installer Flower"
+
+#: ../../source/how-to-install-flower.rst:60
+msgid ""
+"`How to run Flower using Docker <https://flower.ai/docs/framework/how-"
+"to-run-flower-using-docker.html>`_"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:63
 msgid "Install pre-release"
 msgstr "Installer la version pre-release"
 
-#: ../../source/how-to-install-flower.rst:38
+#: ../../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 "
@@ -5761,7 +6353,7 @@ msgstr ""
 "disponibles en tant que versions préliminaires (alpha, bêta, release "
 "candidate) avant que la version stable n'arrive : :"
 
-#: ../../source/how-to-install-flower.rst:42
+#: ../../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::"
@@ -5770,11 +6362,11 @@ msgstr ""
 "versions de ``flwr`` doivent être installées avec l'option "
 "``simulation``: :"
 
-#: ../../source/how-to-install-flower.rst:47
+#: ../../source/how-to-install-flower.rst:74
 msgid "Install nightly release"
 msgstr "Installer la version nightly"
 
-#: ../../source/how-to-install-flower.rst:49
+#: ../../source/how-to-install-flower.rst:76
 msgid ""
 "The latest (potentially unstable) changes in Flower are available as "
 "nightly releases::"
@@ -5782,7 +6374,7 @@ msgstr ""
 "Les dernières modifications (potentiellement instables) de Flower sont "
 "disponibles sous forme de versions nocturnes: :"
 
-#: ../../source/how-to-install-flower.rst:53
+#: ../../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::"
@@ -6112,43 +6704,226 @@ msgstr ""
 "Ray Metrics : `<https://docs.ray.io/en/latest/ray-observability/ray-"
 "metrics.html>`_"
 
-#: ../../source/how-to-run-simulations.rst:2
-#, fuzzy
-msgid "Run simulations"
-msgstr "Simulation de moniteur"
+#: ../../source/how-to-run-flower-using-docker.rst:2
+msgid "Run Flower using Docker"
+msgstr ""
 
-#: ../../source/how-to-run-simulations.rst:8
+#: ../../source/how-to-run-flower-using-docker.rst:4
 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 "
-"<contributor-explanation-architecture.html#virtual-client-engine>`_ or "
-"VCE."
+"The simplest way to get started with Flower is by using the pre-made "
+"Docker images, which you can find on `Docker Hub "
+"<https://hub.docker.com/r/flwr/server/tags>`_."
 msgstr ""
 
-#: ../../source/how-to-run-simulations.rst:10
+#: ../../source/how-to-run-flower-using-docker.rst:7
+msgid "Before you start, make sure that the Docker daemon is running:"
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:14
 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 <ref-api-"
-"flwr.html#start-numpy-client>`_) in the sense that they can be configure "
-"by creating a class inheriting, for example, from "
-"`flwr.client.NumPyClient <ref-api-flwr.html#flwr.client.NumPyClient>`_ "
-"and therefore behave in an identical way. In addition to that, clients "
-"managed by the :code:`VirtualClientEngine` are:"
+"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 <https://docs.docker.com/get-"
+"docker/>`_."
 msgstr ""
 
-#: ../../source/how-to-run-simulations.rst:12
+#: ../../source/how-to-run-flower-using-docker.rst:20
 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 "
+"On Linux, Docker commands require ``sudo`` privilege. If you want to "
+"avoid using ``sudo``, you can follow the `Post-installation steps "
+"<https://docs.docker.com/engine/install/linux-postinstall/>`_ on the "
+"official Docker website."
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:25
+#, fuzzy
+msgid "Flower server"
+msgstr "Serveur de Flower"
+
+#: ../../source/how-to-run-flower-using-docker.rst:28
+#, fuzzy
+msgid "Quickstart"
+msgstr "Démarrage rapide de JAX"
+
+#: ../../source/how-to-run-flower-using-docker.rst:30
+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
+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 ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:44
+msgid ""
+"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 ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:48
+msgid ""
+"The ``-p <host>:<container>`` 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 ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:55
+msgid ""
+"The ``--insecure`` flag enables insecure communication (using HTTP, not "
+"HTTPS) and should only be used for testing purposes. We strongly "
+"recommend enabling `SSL <https://flower.ai/docs/framework/how-to-run-"
+"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
+msgid ""
+"You can use ``--help`` to view all available flags that the server "
+"supports:"
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:67
+msgid "Mounting a volume to store the state on the host system"
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:69
+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 ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:82
+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."
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:87
+#, fuzzy
+msgid "Enabling SSL for secure connections"
+msgstr "Collecte centralisée des données"
+
+#: ../../source/how-to-run-flower-using-docker.rst:89
+msgid ""
+"To enable SSL, you will need a CA certificate, a server certificate and a"
+" server private key."
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:92
+msgid ""
+"For testing purposes, you can generate your own self-signed certificates."
+" The `Enable SSL connections <https://flower.ai/docs/framework/how-to-"
+"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
+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"
+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 <https://hub.docker.com/r/flwr/server/tags>`_."
+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:116
+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:121
+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:"
+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
+msgid ""
+"To set a variable inside a Docker container, you can use the ``-e "
+"<name>=<value>`` 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 "
+"<contributor-explanation-architecture.html#virtual-client-engine>`_ 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 <ref-api-"
+"flwr.html#start-client>`_) in the sense that they can be configure by "
+"creating a class inheriting, for example, from `flwr.client.NumPyClient "
+"<ref-api-flwr.html#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."
@@ -6600,6 +7375,12 @@ msgstr ""
 "à ton code. Note que cela va itérer sur tous les points de contrôle "
 "sauvegardés et charger le plus récent :"
 
+#: ../../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``."
+msgstr ""
+
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:2
 msgid "Upgrade to Flower 1.0"
 msgstr "Passe à Flower 1.0"
@@ -6914,7 +7695,7 @@ msgstr ""
 msgid ""
 "Remove \"placeholder\" methods from subclasses of ``Client`` or "
 "``NumPyClient``. If you, for example, use server-side evaluation, then "
-"empy placeholder implementations of ``evaluate`` are no longer necessary."
+"empty placeholder implementations of ``evaluate`` are no longer necessary."
 msgstr ""
 "Supprime les méthodes \"placeholder\" des sous-classes de ``Client`` ou "
 "de ``NumPyClient``. Si tu utilises, par exemple, l'évaluation côté "
@@ -6940,16 +7721,136 @@ msgid ""
 "Most official `Flower code examples "
 "<https://github.com/adap/flower/tree/main/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 questionsm, `join the Flower Slack "
-"<https://flower.dev/join-slack/>`_ and use the channgel ``#questions``."
+"API. If there are further questions, `join the Flower Slack "
+"<https://flower.ai/join-slack/>`_ and use the channel ``#questions``."
 msgstr ""
 "La plupart des `exemples de code Flower officiels "
 "<https://github.com/adap/flower/tree/main/examples>`_ 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 <https://flower.dev/join-slack/>`_ et utilise le canal "
+"Flower <https://flower.ai/join-slack/>`_ et utilise le canal "
 "``#questions``."
 
+#: ../../source/how-to-use-built-in-mods.rst:2
+msgid "Use Built-in Mods"
+msgstr ""
+
+#: ../../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.**"
+msgstr ""
+
+#: ../../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/how-to-use-built-in-mods.rst:9
+msgid "What are Mods?"
+msgstr ""
+
+#: ../../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/how-to-use-built-in-mods.rst:18
+msgid "A typical mod function might look something like this:"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:31
+msgid "Using Mods"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:33
+msgid "To use mods in your ``ClientApp``, you can follow these steps:"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:36
+msgid "1. Import the required mods"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:38
+msgid "First, import the built-in mod you intend to use:"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:46
+msgid "2. Define your client function"
+msgstr ""
+
+#: ../../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/how-to-use-built-in-mods.rst:57
+msgid "3. Create the ``ClientApp`` with mods"
+msgstr ""
+
+#: ../../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/how-to-use-built-in-mods.rst:72
+#, fuzzy
+msgid "Order of execution"
+msgstr "Dépréciations"
+
+#: ../../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/how-to-use-built-in-mods.rst:76
+msgid "``example_mod_1`` (outermost mod)"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:77
+msgid "``example_mod_2`` (next mod)"
+msgstr ""
+
+#: ../../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/how-to-use-built-in-mods.rst:79
+msgid "``example_mod_2`` (on the way back)"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:80
+msgid "``example_mod_1`` (outermost mod on the way back)"
+msgstr ""
+
+#: ../../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/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."
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:89
+msgid "Enjoy building more robust and flexible ``ClientApp``s with mods!"
+msgstr ""
+
 #: ../../source/how-to-use-strategies.rst:2
 #, fuzzy
 msgid "Use strategies"
@@ -7030,7 +7931,7 @@ msgid ""
 "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 arbitraty configuration values  :code:`client.fit`"
+"return a dictionary of arbitrary configuration values  :code:`client.fit`"
 " and :code:`client.evaluate` functions during each round of federated "
 "learning."
 msgstr ""
@@ -7080,82 +7981,89 @@ msgstr ""
 "d'évaluation à :code:`evaluate_fn`."
 
 #: ../../source/how-to-use-strategies.rst:89
+#, fuzzy
 msgid ""
 "Writing a fully custom strategy is a bit more involved, but it provides "
-"the most flexibility. Read the `Implementing Strategies <implementing-"
-"strategies.html>`_ guide to learn more."
+"the most flexibility. Read the `Implementing Strategies <how-to-"
+"implement-strategies.html>`_ guide to learn more."
 msgstr ""
 "L'écriture d'une stratégie entièrement personnalisée est un peu plus "
 "complexe, mais c'est celle qui offre le plus de souplesse. Lis le guide "
-"`Implémentation des stratégies <how-to-implement-strategies.html>`_ pour en "
-"savoir plus."
+"`Implémentation des stratégies <how-to-implement-strategies.html>`_ pour "
+"en savoir plus."
 
-#: ../../source/index.rst:31
+#: ../../source/index.rst:34
 msgid "Tutorial"
 msgstr "Tutoriel"
 
-#: ../../source/index.rst:41
+#: ../../source/index.rst:44
 msgid "Quickstart tutorials"
 msgstr "Quickstart tutorials"
 
-#: ../../source/index.rst:74 ../../source/index.rst:78
+#: ../../source/index.rst:75 ../../source/index.rst:79
 msgid "How-to guides"
 msgstr "Guides"
 
-#: ../../source/index.rst:94
+#: ../../source/index.rst:97
 msgid "Legacy example guides"
 msgstr ""
 
-#: ../../source/index.rst:105 ../../source/index.rst:109
+#: ../../source/index.rst:108 ../../source/index.rst:112
 msgid "Explanations"
 msgstr "Explications"
 
-#: ../../source/index.rst:121
+#: None:-1
 msgid "API reference"
 msgstr "Référence pour l'API"
 
-#: ../../source/index.rst:128
+#: ../../source/index.rst:137
 msgid "Reference docs"
 msgstr "Référence pour la documentation"
 
-#: ../../source/index.rst:144
+#: ../../source/index.rst:153
 #, fuzzy
 msgid "Contributor tutorials"
 msgstr "Configuration du contributeur"
 
-#: ../../source/index.rst:151
+#: ../../source/index.rst:160
 #, fuzzy
 msgid "Contributor how-to guides"
 msgstr "Guide pour les contributeurs"
 
-#: ../../source/index.rst:162
+#: ../../source/index.rst:173
 #, fuzzy
 msgid "Contributor explanations"
 msgstr "Explications"
 
-#: ../../source/index.rst:168
+#: ../../source/index.rst:179
 #, fuzzy
 msgid "Contributor references"
 msgstr "Configuration du contributeur"
 
+#: ../../source/index.rst:-1
+msgid ""
+"Check out the documentation of the main Flower Framework enabling easy "
+"Python development for Federated Learning."
+msgstr ""
+
 #: ../../source/index.rst:2
 #, fuzzy
 msgid "Flower Framework Documentation"
 msgstr "Rédiger de la documentation"
 
-#: ../../source/index.rst:4
+#: ../../source/index.rst:7
 msgid ""
-"Welcome to Flower's documentation. `Flower <https://flower.dev>`_ is a "
+"Welcome to Flower's documentation. `Flower <https://flower.ai>`_ is a "
 "friendly federated learning framework."
 msgstr ""
-"Bienvenue sur la documentation de Flower. `Flower <https://flower.dev>`_ "
+"Bienvenue sur la documentation de Flower. `Flower <https://flower.ai>`_ "
 "est un framework de federated learning convivial et facile à utiliser."
 
-#: ../../source/index.rst:8
+#: ../../source/index.rst:11
 msgid "Join the Flower Community"
 msgstr "Rejoignez la communauté de Flower"
 
-#: ../../source/index.rst:10
+#: ../../source/index.rst:13
 msgid ""
 "The Flower Community is growing quickly - we're a friendly group of "
 "researchers, engineers, students, professionals, academics, and other "
@@ -7165,15 +8073,15 @@ msgstr ""
 " chercheurs, ingénieurs, étudiants, professionnels, académiques, et "
 "autres hobbyistes."
 
-#: ../../source/index.rst:12
+#: ../../source/index.rst:15
 msgid "Join us on Slack"
 msgstr "Join us on Slack"
 
-#: ../../source/index.rst:20
+#: ../../source/index.rst:23
 msgid "Flower Framework"
 msgstr "Flower Framework"
 
-#: ../../source/index.rst:22
+#: ../../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 "
@@ -7185,11 +8093,11 @@ msgstr ""
 " existantes dans un scenario fédéré. Un des buts de Flower est de rendre "
 "cela le plus evident possible. Lisez la suite pour en apprendre plus."
 
-#: ../../source/index.rst:27
+#: ../../source/index.rst:30
 msgid "Tutorials"
 msgstr "Tutoriels"
 
-#: ../../source/index.rst:29
+#: ../../source/index.rst:32
 msgid ""
 "A learning-oriented series of federated learning tutorials, the best "
 "place to start."
@@ -7197,7 +8105,7 @@ msgstr ""
 "Une serie de tutoriels de Federated Learning, l'endroit parfait pour "
 "débuter."
 
-#: ../../source/index.rst:59
+#: ../../source/index.rst:62
 #, fuzzy
 msgid ""
 "QUICKSTART TUTORIALS: :doc:`PyTorch <tutorial-quickstart-pytorch>` | "
@@ -7219,17 +8127,16 @@ msgstr ""
 "<quickstart-xgboost>` | :ref:`Android <quickstart-android>` | :ref:`iOS "
 "<quickstart-ios>`"
 
-#: ../../source/index.rst:63
-#, fuzzy
-msgid "PyTorch"
-msgstr "Exemples de PyTorch"
+#: ../../source/index.rst:64
+msgid "We also made video tutorials for PyTorch:"
+msgstr ""
 
-#: ../../source/index.rst:68
+#: ../../source/index.rst:69
 #, fuzzy
-msgid "TensorFlow"
+msgid "And TensorFlow:"
 msgstr "Exemples de TensorFlow"
 
-#: ../../source/index.rst:76
+#: ../../source/index.rst:77
 msgid ""
 "Problem-oriented how-to guides show step-by-step how to achieve a "
 "specific goal."
@@ -7237,7 +8144,7 @@ msgstr ""
 "Guides orientés sur la résolutions étapes par étapes de problèmes ou "
 "objectifs specifiques."
 
-#: ../../source/index.rst:107
+#: ../../source/index.rst:110
 msgid ""
 "Understanding-oriented concept guides explain and discuss key topics and "
 "underlying ideas behind Flower and collaborative AI."
@@ -7245,21 +8152,29 @@ 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:117
+#: ../../source/index.rst:120
 #, fuzzy
 msgid "References"
 msgstr "Référence"
 
-#: ../../source/index.rst:119
+#: ../../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:139
+#: ../../source/index.rst:131:<autosummary>:1
+msgid ":py:obj:`flwr <flwr>`\\"
+msgstr ""
+
+#: ../../source/index.rst:131:<autosummary>:1 flwr:1 of
+msgid "Flower main package."
+msgstr ""
+
+#: ../../source/index.rst:148
 #, fuzzy
 msgid "Contributor docs"
 msgstr "Configuration du contributeur"
 
-#: ../../source/index.rst:141
+#: ../../source/index.rst:150
 #, fuzzy
 msgid ""
 "The Flower community welcomes contributions. The following docs are "
@@ -7275,8 +8190,8 @@ msgid "Flower CLI reference"
 msgstr "Client de Flower"
 
 #: ../../source/ref-api-cli.rst:7
-msgid "flower-server"
-msgstr "flower-server"
+msgid "flower-superlink"
+msgstr "flower-superlink"
 
 #: ../../source/ref-api-cli.rst:17
 msgid "flower-driver-api"
@@ -7286,7496 +8201,13237 @@ msgstr "flower-driver-api"
 msgid "flower-fleet-api"
 msgstr "flower-fleet-api"
 
-#: ../../source/ref-api-flwr.rst:2
+#: ../../source/ref-api/flwr.rst:2
 #, fuzzy
-msgid "flwr (Python API reference)"
-msgstr "Référence pour l'API"
+msgid "flwr"
+msgstr "Fleur"
 
-#: ../../source/ref-api-flwr.rst:8
-msgid "client"
-msgstr "client"
+#: ../../source/ref-api/flwr.rst:25 ../../source/ref-api/flwr.server.rst:48
+msgid "Modules"
+msgstr ""
 
-#: ../../source/ref-api-flwr.rst:24
-msgid "start_client"
-msgstr "start_client"
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1
+msgid ":py:obj:`flwr.client <flwr.client>`\\"
+msgstr ""
 
-#: ../../source/ref-api-flwr.rst:32
-msgid "NumPyClient"
-msgstr "NumPyClient"
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1 flwr.client:1 of
+#, fuzzy
+msgid "Flower client."
+msgstr "Client de Flower"
 
-#: ../../source/ref-api-flwr.rst:41
-msgid "start_numpy_client"
-msgstr "start_numpy_client"
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1
+msgid ":py:obj:`flwr.common <flwr.common>`\\"
+msgstr ""
 
-#: ../../source/ref-api-flwr.rst:49
-msgid "start_simulation"
-msgstr "démarrer_simulation"
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1 flwr.common:1 of
+msgid "Common components shared between server and client."
+msgstr "Composants communs partagés entre le serveur et le client."
 
-#: ../../source/ref-api-flwr.rst:57
-msgid "server"
-msgstr "serveur"
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1
+msgid ":py:obj:`flwr.server <flwr.server>`\\"
+msgstr ""
 
-#: ../../source/ref-api-flwr.rst:65
-msgid "server.start_server"
-msgstr "serveur.start_server"
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1 flwr.server:1
+#: flwr.server.server.Server:1 of
+#, fuzzy
+msgid "Flower server."
+msgstr "Serveur de Flower"
 
-#: ../../source/ref-api-flwr.rst:73
-msgid "server.strategy"
-msgstr "stratégie.du.serveur"
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1
+msgid ":py:obj:`flwr.simulation <flwr.simulation>`\\"
+msgstr ""
 
-#: ../../source/ref-api-flwr.rst:81
-msgid "server.strategy.Strategy"
-msgstr "serveur.stratégie.Stratégie"
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1 flwr.simulation:1 of
+#, fuzzy
+msgid "Flower simulation."
+msgstr "Simulation de moniteur"
 
-#: ../../source/ref-api-flwr.rst:90
-msgid "server.strategy.FedAvg"
-msgstr "serveur.stratégie.FedAvg"
+#: ../../source/ref-api/flwr.client.rst:2
+msgid "client"
+msgstr "client"
 
-#: ../../source/ref-api-flwr.rst:101
-msgid "server.strategy.FedAvgM"
-msgstr "stratégie.serveur.FedAvgM"
+#: ../../source/ref-api/flwr.client.rst:13
+#: ../../source/ref-api/flwr.common.rst:13
+#: ../../source/ref-api/flwr.server.driver.rst:13
+#: ../../source/ref-api/flwr.server.rst:13
+#: ../../source/ref-api/flwr.simulation.rst:13
+#, fuzzy
+msgid "Functions"
+msgstr "Les quatre fonctions :"
 
-#: ../../source/ref-api-flwr.rst:112
-msgid "server.strategy.QFedAvg"
-msgstr "server.strategy.QFedAvg"
+#: ../../source/ref-api/flwr.client.rst:24:<autosummary>:1
+msgid ":py:obj:`run_client_app <flwr.client.run_client_app>`\\ \\(\\)"
+msgstr ""
 
-#: ../../source/ref-api-flwr.rst:123
-msgid "server.strategy.FaultTolerantFedAvg"
-msgstr "server.strategy.FaultTolerantFedAvg"
+#: ../../source/ref-api/flwr.client.rst:24:<autosummary>:1
+#: flwr.client.app.run_client_app:1 of
+#, fuzzy
+msgid "Run Flower client app."
+msgstr "Client de Flower"
 
-#: ../../source/ref-api-flwr.rst:134
-msgid "server.strategy.FedOpt"
-msgstr "serveur.stratégie.FedOpt"
+#: ../../source/ref-api/flwr.client.rst:24:<autosummary>:1
+msgid ""
+":py:obj:`start_client <flwr.client.start_client>`\\ \\(\\*\\, "
+"server\\_address\\[\\, client\\_fn\\, ...\\]\\)"
+msgstr ""
 
-#: ../../source/ref-api-flwr.rst:145
-msgid "server.strategy.FedProx"
-msgstr "serveur.stratégie.FedProx"
+#: ../../source/ref-api/flwr.client.rst:24:<autosummary>: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.rst:156
-msgid "server.strategy.FedAdagrad"
-msgstr "serveur.stratégie.FedAdagrad"
+#: ../../source/ref-api/flwr.client.rst:24:<autosummary>:1
+msgid ""
+":py:obj:`start_numpy_client <flwr.client.start_numpy_client>`\\ \\(\\*\\,"
+" server\\_address\\, client\\)"
+msgstr ""
 
-#: ../../source/ref-api-flwr.rst:167
-msgid "server.strategy.FedAdam"
-msgstr "serveur.stratégie.FedAdam"
+#: ../../source/ref-api/flwr.client.rst:24:<autosummary>: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.rst:178
-msgid "server.strategy.FedYogi"
-msgstr "serveur.stratégie.FedYogi"
+#: ../../source/ref-api/flwr.client.rst:26
+#: ../../source/ref-api/flwr.common.rst:31
+#: ../../source/ref-api/flwr.server.driver.rst:24
+#: ../../source/ref-api/flwr.server.rst:28
+#: ../../source/ref-api/flwr.server.strategy.rst:17
+msgid "Classes"
+msgstr ""
 
-#: ../../source/ref-api-flwr.rst:186
-msgid "common"
-msgstr "commun"
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
+msgid ":py:obj:`Client <flwr.client.Client>`\\ \\(\\)"
+msgstr ""
 
-#: flwr.common:1 of
-msgid "Common components shared between server and client."
-msgstr "Composants communs partagés entre le serveur et le client."
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
+#: flwr.client.client.Client:1 of
+msgid "Abstract base class for Flower clients."
+msgstr ""
 
-#: flwr.common.typing.ClientMessage:1 of
-msgid "ClientMessage is a container used to hold one result message."
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
+msgid ""
+":py:obj:`ClientApp <flwr.client.ClientApp>`\\ \\(client\\_fn\\[\\, "
+"mods\\]\\)"
 msgstr ""
-"ClientMessage est un conteneur utilisé pour contenir un message de "
-"résultat."
 
-#: flwr.common.typing.Code:1 of
-msgid "Client status codes."
-msgstr "Codes d'état du client."
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
+#: flwr.client.client_app.ClientApp:1 of
+#, fuzzy
+msgid "Flower ClientApp."
+msgstr "Flower ClientApp."
+
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
+msgid ":py:obj:`NumPyClient <flwr.client.NumPyClient>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
+#: flwr.client.numpy_client.NumPyClient:1 of
+msgid "Abstract base class for Flower clients using NumPy."
+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`"
+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.ClientMessage.rst:15
+#: ../../source/ref-api/flwr.common.DisconnectRes.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.Parameters.rst:15
+#: ../../source/ref-api/flwr.common.ReconnectIns.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.History.rst:15
+#: ../../source/ref-api/flwr.server.Server.rst:15
+#: ../../source/ref-api/flwr.server.ServerConfig.rst:15
+#: ../../source/ref-api/flwr.server.SimpleClientManager.rst:15
+#: ../../source/ref-api/flwr.server.driver.Driver.rst:15
+#: ../../source/ref-api/flwr.server.driver.GrpcDriver.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.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
+msgid "Methods"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`evaluate <flwr.client.Client.evaluate>`\\ \\(ins\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+#: flwr.client.client.Client.evaluate:1
+#: flwr.client.numpy_client.NumPyClient.evaluate:1 of
+#, fuzzy
+msgid "Evaluate the provided parameters using the locally held dataset."
+msgstr "évaluer le modèle mis à jour sur l'ensemble de test local"
 
-#: flwr.common.typing.DisconnectRes:1 of
-msgid "DisconnectRes message from client to server."
-msgstr "Message DisconnectRes envoyé par le client au serveur."
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`fit <flwr.client.Client.fit>`\\ \\(ins\\)"
+msgstr ""
 
-#: flwr.common.typing.EvaluateIns:1 of
-msgid "Evaluate instructions for a client."
-msgstr "Évaluer les instructions pour un client."
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: flwr.client.client.Client.fit:1 of
+msgid "Refine the provided parameters using the locally held dataset."
+msgstr ""
 
-#: flwr.common.typing.EvaluateRes:1 of
-msgid "Evaluate response from a client."
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`get_context <flwr.client.Client.get_context>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+#: flwr.client.client.Client.get_context:1
+#: flwr.client.numpy_client.NumPyClient.get_context:1 of
+#, fuzzy
+msgid "Get the run context from this client."
 msgstr "Évaluer la réponse d'un client."
 
-#: flwr.common.telemetry.EventType:1 of
-msgid "Types of telemetry events."
-msgstr "Types d'événements télémétriques."
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`get_parameters <flwr.client.Client.get_parameters>`\\ \\(ins\\)"
+msgstr ""
 
-#: flwr.common.typing.FitIns:1 of
-msgid "Fit instructions for a client."
-msgstr "Instructions d'ajustement pour un client."
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+#: flwr.client.client.Client.get_parameters:1
+#: flwr.client.numpy_client.NumPyClient.get_parameters:1 of
+#, fuzzy
+msgid "Return the current local model parameters."
+msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel"
 
-#: flwr.common.typing.FitRes:1 of
-msgid "Fit response from a client."
-msgstr "Réponse adaptée d'un client."
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`get_properties <flwr.client.Client.get_properties>`\\ \\(ins\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: flwr.client.client.Client.get_properties:1 of
+msgid "Return set of client's properties."
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`set_context <flwr.client.Client.set_context>`\\ \\(context\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>: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.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`to_client <flwr.client.Client.to_client>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: flwr.client.client.Client.to_client:1 of
+msgid "Return client (itself)."
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:46
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:46
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:25
+#: ../../source/ref-api/flwr.common.Code.rst:19
+#: ../../source/ref-api/flwr.common.DisconnectRes.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.Parameters.rst:25
+#: ../../source/ref-api/flwr.common.ReconnectIns.rst:25
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:25
+#: ../../source/ref-api/flwr.common.Status.rst:25
+#: ../../source/ref-api/flwr.server.ServerConfig.rst:25
+msgid "Attributes"
+msgstr ""
+
+#: flwr.client.client.Client.evaluate:1:<autosummary>:1 of
+msgid ":py:obj:`context <flwr.client.Client.context>`\\"
+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.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.app.start_driver flwr.server.driver.driver.Driver
+#: flwr.server.strategy.bulyan.Bulyan
+#: 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.simulation.app.start_simulation of
+#, fuzzy
+msgid "Parameters"
+msgstr "Paramètres du modèle."
 
-#: flwr.common.typing.GetParametersIns:1 of
-msgid "Parameters request for a client."
-msgstr "Demande de paramètres pour un client."
-
-#: flwr.common.typing.GetParametersRes:1 of
-msgid "Response when asked to return parameters."
-msgstr "Réponse lorsqu'on te demande de renvoyer des paramètres."
+#: 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 ""
+
+#: 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.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.app.start_driver
+#: 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
+#, fuzzy
+msgid "Returns"
+msgstr "Ressources"
 
-#: flwr.common.typing.GetPropertiesIns:1 of
-msgid "Properties request for a client."
-msgstr "Demande de propriétés pour un 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 ""
 
-#: flwr.common.typing.GetPropertiesRes:1 of
-msgid "Properties response from a client."
-msgstr "Réponse des propriétés d'un 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.get_parameters
+#: flwr.client.numpy_client.NumPyClient.get_properties
+#: 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.app.start_driver
+#: 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 ""
 
-#: flwr.common.typing.Parameters:1 of
-msgid "Model parameters."
-msgstr "Paramètres du modèle."
+#: 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 ""
 
-#: flwr.common.typing.ReconnectIns:1 of
-msgid "ReconnectIns message from server to client."
-msgstr "Message de reconnexion du serveur au client."
+#: 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 ""
 
-#: flwr.common.typing.ServerMessage:1 of
-msgid "ServerMessage is a container used to hold one instruction message."
+#: flwr.client.client.Client.get_parameters:3 of
+msgid ""
+"The get parameters instructions received from the server containing a "
+"dictionary of configuration values."
 msgstr ""
-"ServerMessage est un conteneur utilisé pour contenir un message "
-"d'instruction."
 
-#: flwr.common.typing.Status:1 of
-msgid "Client status."
-msgstr "Statut du client."
+#: flwr.client.client.Client.get_parameters:7 of
+#, fuzzy
+msgid "The current local model parameters."
+msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel"
 
-#: flwr.common.parameter.bytes_to_ndarray:1 of
-msgid "Deserialize NumPy ndarray from bytes."
-msgstr "Désérialise le tableau numérique NumPy à partir d'octets."
+#: flwr.client.client.Client.get_properties:3 of
+msgid ""
+"The get properties instructions received from the server containing a "
+"dictionary of configuration values."
+msgstr ""
 
-#: flwr.common.logger.configure:1 of
-msgid "Configure logging to file and/or remote log server."
+#: flwr.client.client.Client.get_properties:7 of
+msgid "The current client properties."
 msgstr ""
-"Configure la journalisation vers un fichier et/ou un serveur de "
-"journalisation distant."
 
-#: logging.Logger.log:1 of
-msgid "Log 'msg % args' with the integer severity 'level'."
-msgstr "Enregistre 'msg % args' avec le niveau de sévérité entier 'level'."
+#: ../../source/ref-api/flwr.client.ClientApp.rst:2
+#, fuzzy
+msgid "ClientApp"
+msgstr "client"
 
-#: logging.Logger.log:3 of
+#: flwr.client.client_app.ClientApp: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.app.ServerConfig:1
+#: flwr.server.driver.driver.Driver:1
+#: flwr.server.driver.grpc_driver.GrpcDriver:1 flwr.server.history.History:1
+#: flwr.server.server.Server:1 of
+msgid "Bases: :py:class:`object`"
+msgstr ""
+
+#: flwr.client.app.start_client:33 flwr.client.app.start_numpy_client:36
+#: flwr.client.client_app.ClientApp:4 flwr.server.app.start_server:41
+#: flwr.server.driver.app.start_driver:30 of
+#, fuzzy
+msgid "Examples"
+msgstr "Exemples de PyTorch"
+
+#: flwr.client.client_app.ClientApp:5 of
 msgid ""
-"To pass exception information, use the keyword argument exc_info with a "
-"true value, e.g."
+"Assuming a typical `Client` implementation named `FlowerClient`, you can "
+"wrap it in a `ClientApp` as follows:"
 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.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 ""
 
-#: flwr.common.parameter.ndarray_to_bytes:1 of
-msgid "Serialize NumPy ndarray to bytes."
-msgstr "Sérialise le tableau numérique NumPy en octets."
+#: 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 ""
 
-#: flwr.common.parameter.ndarrays_to_parameters:1 of
-msgid "Convert NumPy ndarrays to parameters object."
-msgstr "Convertit les ndarrays NumPy en objets de paramètres."
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:2
+msgid "NumPyClient"
+msgstr "NumPyClient"
 
-#: flwr.common.date.now:1 of
-msgid "Construct a datetime from time.time() with time zone set to UTC."
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+msgid ""
+":py:obj:`evaluate <flwr.client.NumPyClient.evaluate>`\\ \\(parameters\\, "
+"config\\)"
 msgstr ""
-"Construit une date à partir de time.time() avec le fuseau horaire réglé "
-"sur UTC."
-
-#: flwr.common.parameter.parameters_to_ndarrays:1 of
-msgid "Convert parameters object to NumPy ndarrays."
-msgstr "Convertit l'objet des paramètres en ndarrays NumPy."
 
-#: ../../source/ref-changelog.md:1
-msgid "Changelog"
-msgstr "Changelog"
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+msgid ":py:obj:`fit <flwr.client.NumPyClient.fit>`\\ \\(parameters\\, config\\)"
+msgstr ""
 
-#: ../../source/ref-changelog.md:3
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+#: flwr.client.numpy_client.NumPyClient.fit:1 of
 #, fuzzy
-msgid "Unreleased"
-msgstr "Inédit"
+msgid "Train the provided parameters using the locally held dataset."
+msgstr "entraîne le modèle sur l'ensemble d'apprentissage local"
 
-#: ../../source/ref-changelog.md:5 ../../source/ref-changelog.md:21
-#: ../../source/ref-changelog.md:105 ../../source/ref-changelog.md:169
-#: ../../source/ref-changelog.md:227 ../../source/ref-changelog.md:296
-#: ../../source/ref-changelog.md:425 ../../source/ref-changelog.md:467
-#: ../../source/ref-changelog.md:534 ../../source/ref-changelog.md:600
-#: ../../source/ref-changelog.md:645 ../../source/ref-changelog.md:684
-#: ../../source/ref-changelog.md:717 ../../source/ref-changelog.md:767
-msgid "What's new?"
-msgstr "Quoi de neuf ?"
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+msgid ":py:obj:`get_context <flwr.client.NumPyClient.get_context>`\\ \\(\\)"
+msgstr ""
 
-#: ../../source/ref-changelog.md:7 ../../source/ref-changelog.md:93
-#: ../../source/ref-changelog.md:157 ../../source/ref-changelog.md:215
-#: ../../source/ref-changelog.md:284 ../../source/ref-changelog.md:346
-#: ../../source/ref-changelog.md:365 ../../source/ref-changelog.md:521
-#: ../../source/ref-changelog.md:592 ../../source/ref-changelog.md:629
-#: ../../source/ref-changelog.md:672
-msgid "Incompatible changes"
-msgstr "Changements incompatibles"
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+msgid ""
+":py:obj:`get_parameters <flwr.client.NumPyClient.get_parameters>`\\ "
+"\\(config\\)"
+msgstr ""
 
-#: ../../source/ref-changelog.md:9
-#, fuzzy
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
 msgid ""
-"**Remove support for Python 3.7** "
-"([#2280](https://github.com/adap/flower/pull/2280), "
-"[#2299](https://github.com/adap/flower/pull/2299))"
+":py:obj:`get_properties <flwr.client.NumPyClient.get_properties>`\\ "
+"\\(config\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+#: flwr.client.numpy_client.NumPyClient.get_properties:1 of
+msgid "Return a client's set of properties."
 msgstr ""
-"**Nouvelles stratégies intégrées** "
-"([#828](https://github.com/adap/flower/pull/828) "
-"[#822](https://github.com/adap/flower/pull/822))"
 
-#: ../../source/ref-changelog.md:11
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
 msgid ""
-"Python 3.7 support was deprecated in Flower 1.5, and this release removes"
-" support. Flower now requires Python 3.8."
+":py:obj:`set_context <flwr.client.NumPyClient.set_context>`\\ "
+"\\(context\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+msgid ":py:obj:`to_client <flwr.client.NumPyClient.to_client>`\\ \\(\\)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:13
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+#: flwr.client.numpy_client.NumPyClient.to_client:1 of
 #, fuzzy
-msgid "v1.5.0 (2023-08-31)"
-msgstr "v1.4.0 (2023-04-21)"
+msgid "Convert to object to Client type and return it."
+msgstr "Convertit l'objet des paramètres en ndarrays NumPy."
 
-#: ../../source/ref-changelog.md:15 ../../source/ref-changelog.md:99
-#: ../../source/ref-changelog.md:163 ../../source/ref-changelog.md:221
-#: ../../source/ref-changelog.md:290 ../../source/ref-changelog.md:359
-msgid "Thanks to our contributors"
-msgstr "Merci à nos contributeurs"
+#: flwr.client.numpy_client.NumPyClient.evaluate:1:<autosummary>:1 of
+msgid ":py:obj:`context <flwr.client.NumPyClient.context>`\\"
+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
+#, fuzzy
+msgid "The current (global) model parameters."
+msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel"
 
-#: ../../source/ref-changelog.md:17 ../../source/ref-changelog.md:101
-#: ../../source/ref-changelog.md:165 ../../source/ref-changelog.md:223
+#: flwr.client.numpy_client.NumPyClient.evaluate:5 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):"
+"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 ""
-"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:19
+#: flwr.client.numpy_client.NumPyClient.evaluate:11 of
 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`"
+"* **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-changelog.md:23
-#, fuzzy
+#: flwr.client.numpy_client.NumPyClient.evaluate:11 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))"
+"**loss** (*float*) -- The evaluation loss of the model on the local "
+"dataset."
 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:25
+#: flwr.client.numpy_client.NumPyClient.evaluate:12 of
+msgid "**num_examples** (*int*) -- The number of examples used for evaluation."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:13
+#: flwr.client.numpy_client.NumPyClient.fit:13 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."
+"**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-changelog.md:27
+#: flwr.client.numpy_client.NumPyClient.evaluate:19 of
 msgid ""
-"Comprehensive documentation includes a new [how-to run "
-"simulations](https://flower.dev/docs/framework/how-to-run-"
-"simulations.html) guide, new [simulation-"
-"pytorch](https://flower.dev/docs/examples/simulation-pytorch.html) and "
-"[simulation-tensorflow](https://flower.dev/docs/examples/simulation-"
-"tensorflow.html) notebooks, and a new [YouTube tutorial "
-"series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB)."
+"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-changelog.md:29
+#: flwr.client.numpy_client.NumPyClient.fit:5 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))"
+"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-changelog.md:31
+#: flwr.client.numpy_client.NumPyClient.fit:11 of
 msgid ""
-"Much effort went into a completely restructured Flower docs experience. "
-"The documentation on [flower.dev/docs](flower.dev/docs) is now divided "
-"into Flower Framework, Flower Baselines, Flower Android SDK, Flower iOS "
-"SDK, and code example projects."
+"* **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-changelog.md:33
+#: flwr.client.numpy_client.NumPyClient.fit:11 of
 #, fuzzy
-msgid ""
-"**Introduce Flower Swift SDK** "
-"([#1858](https://github.com/adap/flower/pull/1858), "
-"[#1897](https://github.com/adap/flower/pull/1897))"
+msgid "**parameters** (*NDArrays*) -- The locally updated model parameters."
+msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel"
+
+#: flwr.client.numpy_client.NumPyClient.fit:12 of
+msgid "**num_examples** (*int*) -- The number of examples used for training."
 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:35
+#: flwr.client.numpy_client.NumPyClient.get_parameters:3 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."
+"Configuration parameters requested by the server. This can be used to "
+"tell the client which parameters are needed along with some Scalar "
+"attributes."
 msgstr ""
 
-#: ../../source/ref-changelog.md:37
+#: flwr.client.numpy_client.NumPyClient.get_parameters:8 of
 #, fuzzy
+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"
+
+#: flwr.client.numpy_client.NumPyClient.get_properties:3 of
 msgid ""
-"**Introduce Flower Android SDK** "
-"([#2131](https://github.com/adap/flower/pull/2131))"
+"Configuration parameters requested by the server. This can be used to "
+"tell the client which properties are needed along with some Scalar "
+"attributes."
 msgstr ""
-"**Introduire une nouvelle ligne de base pour les fleurs : FedAvg "
-"FEMNIST** ([#1655](https://github.com/adap/flower/pull/1655))"
 
-#: ../../source/ref-changelog.md:39
+#: flwr.client.numpy_client.NumPyClient.get_properties:8 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."
+"**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-changelog.md:41
+#: ../../source/ref-api/flwr.client.run_client_app.rst:2
+msgid "run\\_client\\_app"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.start_client.rst:2
 #, fuzzy
+msgid "start\\_client"
+msgstr "start_client"
+
+#: flwr.client.app.start_client:3 flwr.client.app.start_numpy_client:9 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))"
+"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 ""
-"**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:43
-msgid ""
-"A new testing infrastructure ensures that new changes stay compatible "
-"with existing framework integrations or strategies."
+#: flwr.client.app.start_client:7 of
+msgid "A callable that instantiates a Client. (default: None)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:45
-#, fuzzy
-msgid "**Deprecate Python 3.7**"
-msgstr "**Créer le PR**"
+#: flwr.client.app.start_client:9 of
+msgid ""
+"An implementation of the abstract base class `flwr.client.Client` "
+"(default: None)"
+msgstr ""
 
-#: ../../source/ref-changelog.md:47
+#: flwr.client.app.start_client:12 flwr.client.app.start_numpy_client:15 of
 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."
+"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-changelog.md:49
-#, fuzzy
+#: flwr.client.app.start_client:19 flwr.client.app.start_numpy_client:22
+#: flwr.server.driver.app.start_driver:21 of
 msgid ""
-"**Add new** `FedTrimmedAvg` **strategy** "
-"([#1769](https://github.com/adap/flower/pull/1769), "
-"[#1853](https://github.com/adap/flower/pull/1853))"
+"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 ""
-"**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:51
-#, fuzzy
+#: flwr.client.app.start_client:23 flwr.client.app.start_numpy_client:26 of
 msgid ""
-"The new `FedTrimmedAvg` strategy implements Trimmed Mean by [Dong Yin, "
-"2018](https://arxiv.org/abs/1803.01498)."
+"Starts an insecure gRPC connection when True. Enables HTTPS connection "
+"when False, using system certificates if `root_certificates` is None."
 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:53
-#, fuzzy
+#: flwr.client.app.start_client:26 flwr.client.app.start_numpy_client:29 of
 msgid ""
-"**Introduce start_driver** "
-"([#1697](https://github.com/adap/flower/pull/1697))"
+"Configure the transport layer. Allowed values: - 'grpc-bidi': gRPC, "
+"bidirectional streaming - 'grpc-rere': gRPC, request-response "
+"(experimental) - 'rest': HTTP (experimental)"
 msgstr ""
-"**Ajouter une nouvelle stratégie `FedProx`** "
-"([#1619](https://github.com/adap/flower/pull/1619))"
 
-#: ../../source/ref-changelog.md:55
-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`."
+#: flwr.client.app.start_client:34 flwr.client.app.start_numpy_client:37 of
+msgid "Starting a gRPC client with an insecure server connection:"
 msgstr ""
 
-#: ../../source/ref-changelog.md:57
-#, fuzzy
-msgid ""
-"**Add parameter aggregation to** `mt-pytorch` **code example** "
-"([#1785](https://github.com/adap/flower/pull/1785))"
+#: flwr.client.app.start_client:41 flwr.client.app.start_numpy_client:44 of
+msgid "Starting an SSL-enabled gRPC client using system certificates:"
 msgstr ""
-"**Nouvel exemple de code PyTorch avancé** "
-"([#1007](https://github.com/adap/flower/pull/1007))"
 
-#: ../../source/ref-changelog.md:59
-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."
+#: flwr.client.app.start_client:52 flwr.client.app.start_numpy_client:52 of
+msgid "Starting an SSL-enabled gRPC client using provided certificates:"
 msgstr ""
 
-#: ../../source/ref-changelog.md:61
+#: ../../source/ref-api/flwr.client.start_numpy_client.rst:2
 #, fuzzy
+msgid "start\\_numpy\\_client"
+msgstr "start_numpy_client"
+
+#: flwr.client.app.start_numpy_client:5 of
 msgid ""
-"**Migrate experimental REST API to Starlette** "
-"([2171](https://github.com/adap/flower/pull/2171))"
+"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 ""
-"**Nouvelle stratégie expérimentale TensorBoard** "
-"([#789](https://github.com/adap/flower/pull/789))"
 
-#: ../../source/ref-changelog.md:63
-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."
+#: flwr.client.app.start_numpy_client:13 of
+msgid "An implementation of the abstract base class `flwr.client.NumPyClient`."
 msgstr ""
 
-#: ../../source/ref-changelog.md:65
-#, fuzzy
-msgid ""
-"Please note: The REST request-response API is still experimental and will"
-" likely change significantly over time."
+#: ../../source/ref-api/flwr.common.rst:2
+msgid "common"
+msgstr "commun"
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+msgid ":py:obj:`bytes_to_ndarray <flwr.common.bytes_to_ndarray>`\\ \\(tensor\\)"
 msgstr ""
-"Remarque : l'API REST est encore expérimentale et est susceptible de "
-"changer de manière significative au fil du temps."
 
-#: ../../source/ref-changelog.md:67
-#, fuzzy
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.parameter.bytes_to_ndarray:1 of
+msgid "Deserialize NumPy ndarray from bytes."
+msgstr "Désérialise le tableau numérique NumPy à partir d'octets."
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
 msgid ""
-"**Introduce experimental gRPC request-response API** "
-"([#1867](https://github.com/adap/flower/pull/1867), "
-"[#1901](https://github.com/adap/flower/pull/1901))"
+":py:obj:`configure <flwr.common.configure>`\\ \\(identifier\\[\\, "
+"filename\\, host\\]\\)"
 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:69
-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."
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.logger.configure:1 of
+msgid "Configure logging to file and/or remote log server."
 msgstr ""
+"Configure la journalisation vers un fichier et/ou un serveur de "
+"journalisation distant."
 
-#: ../../source/ref-changelog.md:71
-#, fuzzy
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
 msgid ""
-"Please note: The gRPC request-response API is still experimental and will"
-" likely change significantly over time."
+":py:obj:`event <flwr.common.event>`\\ \\(event\\_type\\[\\, "
+"event\\_details\\]\\)"
 msgstr ""
-"Remarque : l'API REST est encore expérimentale et est susceptible de "
-"changer de manière significative au fil du temps."
 
-#: ../../source/ref-changelog.md:73
-#, fuzzy
-msgid ""
-"**Replace the experimental** `start_client(rest=True)` **with the new** "
-"`start_client(transport=\"rest\")` "
-"([#1880](https://github.com/adap/flower/pull/1880))"
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.telemetry.event:1 of
+msgid "Submit create_event to ThreadPoolExecutor to avoid blocking."
 msgstr ""
-"**Initialise** `start_simulation` **avec une liste d'ID de clients** "
-"([#860](https://github.com/adap/flower/pull/860))"
 
-#: ../../source/ref-changelog.md:75
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
 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:`log <flwr.common.log>`\\ \\(level\\, msg\\, \\*args\\, "
+"\\*\\*kwargs\\)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:77
-#, fuzzy
-msgid ""
-"**Add a new gRPC option** "
-"([#2197](https://github.com/adap/flower/pull/2197))"
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1 logging.Logger.log:1
+#: of
+msgid "Log 'msg % args' with the integer severity 'level'."
+msgstr "Enregistre 'msg % args' avec le niveau de sévérité entier 'level'."
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+msgid ":py:obj:`ndarray_to_bytes <flwr.common.ndarray_to_bytes>`\\ \\(ndarray\\)"
 msgstr ""
-"**Ajouter une nouvelle stratégie `FedProx`** "
-"([#1619](https://github.com/adap/flower/pull/1619))"
 
-#: ../../source/ref-changelog.md:79
-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."
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.parameter.ndarray_to_bytes:1 of
+msgid "Serialize NumPy ndarray to bytes."
+msgstr "Sérialise le tableau numérique NumPy en octets."
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+msgid ":py:obj:`now <flwr.common.now>`\\ \\(\\)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:81
-#, fuzzy
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.date.now:1 of
+msgid "Construct a datetime from time.time() with time zone set to UTC."
+msgstr ""
+"Construit une date à partir de time.time() avec le fuseau horaire réglé "
+"sur UTC."
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
 msgid ""
-"**Improve example notebooks** "
-"([#2005](https://github.com/adap/flower/pull/2005))"
+":py:obj:`ndarrays_to_parameters <flwr.common.ndarrays_to_parameters>`\\ "
+"\\(ndarrays\\)"
 msgstr ""
-"**Supprimer les stratégies expérimentales** "
-"([#1280](https://github.com/adap/flower/pull/1280))"
 
-#: ../../source/ref-changelog.md:83
-#, fuzzy
-msgid "There's a new 30min Federated Learning PyTorch tutorial!"
-msgstr "Bienvenue au tutoriel sur l'apprentissage fédéré de la fleur !"
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.parameter.ndarrays_to_parameters:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.ndarrays_to_parameters:1
+#: of
+msgid "Convert NumPy ndarrays to parameters object."
+msgstr "Convertit les ndarrays NumPy en objets de paramètres."
 
-#: ../../source/ref-changelog.md:85
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
 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:`parameters_to_ndarrays <flwr.common.parameters_to_ndarrays>`\\ "
+"\\(parameters\\)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:87
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.parameter.parameters_to_ndarrays:1 of
+msgid "Convert parameters object to NumPy ndarrays."
+msgstr "Convertit l'objet des paramètres en ndarrays NumPy."
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
 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`)."
+":py:obj:`ClientMessage <flwr.common.ClientMessage>`\\ "
+"\\(\\[get\\_properties\\_res\\, ...\\]\\)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:89
-#, 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))"
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.ClientMessage:1 of
+msgid "ClientMessage is a container used to hold one result message."
 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))"
+"ClientMessage est un conteneur utilisé pour contenir un message de "
+"résultat."
 
-#: ../../source/ref-changelog.md:91 ../../source/ref-changelog.md:155
-#: ../../source/ref-changelog.md:209 ../../source/ref-changelog.md:276
-msgid "Flower received many improvements under the hood, too many to list here."
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`Code <flwr.common.Code>`\\ \\(value\\)"
 msgstr ""
-"Flower a reçu de nombreuses améliorations sous le capot, trop nombreuses "
-"pour être énumérées ici."
 
-#: ../../source/ref-changelog.md:95 ../../source/ref-changelog.md:159
-#: ../../source/ref-changelog.md:217 ../../source/ref-changelog.md:286
-#: ../../source/ref-changelog.md:348
-msgid "None"
-msgstr "Aucun"
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.Code:1 of
+msgid "Client status codes."
+msgstr "Codes d'état du client."
 
-#: ../../source/ref-changelog.md:97
-msgid "v1.4.0 (2023-04-21)"
-msgstr "v1.4.0 (2023-04-21)"
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`DisconnectRes <flwr.common.DisconnectRes>`\\ \\(reason\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.DisconnectRes:1 of
+msgid "DisconnectRes message from client to server."
+msgstr "Message DisconnectRes envoyé par le client au serveur."
 
-#: ../../source/ref-changelog.md:103
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
 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:`EvaluateIns <flwr.common.EvaluateIns>`\\ \\(parameters\\, "
+"config\\)"
 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:107
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.EvaluateIns:1 of
+msgid "Evaluate instructions for a client."
+msgstr "Évaluer les instructions pour un client."
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
 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:`EvaluateRes <flwr.common.EvaluateRes>`\\ \\(status\\, loss\\, "
+"num\\_examples\\, metrics\\)"
 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))"
 
-#: ../../source/ref-changelog.md:109
-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/quickstart_xgboost_horizontal)"
-" that demonstrates the usage of this new strategy in an XGBoost project."
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.EvaluateRes:1 of
+msgid "Evaluate response from a client."
+msgstr "Évaluer la réponse d'un client."
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`EventType <flwr.common.EventType>`\\ \\(value\\)"
 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/quickstart_xgboost_horizontal)"
-" qui démontre l'utilisation de cette nouvelle stratégie dans un projet "
-"XGBoost."
 
-#: ../../source/ref-changelog.md:111
-msgid ""
-"**Introduce iOS SDK (preview)** "
-"([#1621](https://github.com/adap/flower/pull/1621), "
-"[#1764](https://github.com/adap/flower/pull/1764))"
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.telemetry.EventType:1 of
+msgid "Types of telemetry events."
+msgstr "Types d'événements télémétriques."
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`FitIns <flwr.common.FitIns>`\\ \\(parameters\\, config\\)"
 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:113
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.FitIns:1 of
+msgid "Fit instructions for a client."
+msgstr "Instructions d'ajustement pour un client."
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
 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:`FitRes <flwr.common.FitRes>`\\ \\(status\\, parameters\\, "
+"num\\_examples\\, metrics\\)"
 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:115
-msgid ""
-"**Introduce new \"What is Federated Learning?\" tutorial** "
-"([#1657](https://github.com/adap/flower/pull/1657), "
-"[#1721](https://github.com/adap/flower/pull/1721))"
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.FitRes:1 of
+msgid "Fit response from a client."
+msgstr "Réponse adaptée d'un client."
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`GetParametersIns <flwr.common.GetParametersIns>`\\ \\(config\\)"
 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:117
-#, fuzzy
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.GetParametersIns:1 of
+msgid "Parameters request for a client."
+msgstr "Demande de paramètres pour un client."
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
 msgid ""
-"A new [entry-level tutorial](https://flower.dev/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!"
+":py:obj:`GetParametersRes <flwr.common.GetParametersRes>`\\ \\(status\\, "
+"parameters\\)"
 msgstr ""
-"Un nouveau [tutoriel d'entrée de gamme] "
-"(https://flower.dev/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:119
-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))"
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.GetParametersRes:1 of
+msgid "Response when asked to return parameters."
+msgstr "Réponse lorsqu'on te demande de renvoyer des paramètres."
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`GetPropertiesIns <flwr.common.GetPropertiesIns>`\\ \\(config\\)"
 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))"
 
-#: ../../source/ref-changelog.md:121
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.GetPropertiesIns:1 of
+msgid "Properties request for a client."
+msgstr "Demande de propriétés pour un client."
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
 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 heterogenous settings."
+":py:obj:`GetPropertiesRes <flwr.common.GetPropertiesRes>`\\ \\(status\\, "
+"properties\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.GetPropertiesRes:1 of
+msgid "Properties response from a client."
+msgstr "Réponse des propriétés d'un client."
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`NDArray <flwr.common.NDArray>`\\"
 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:123
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
 msgid ""
-"**Introduce new Flower Baseline: FedAvg FEMNIST** "
-"([#1655](https://github.com/adap/flower/pull/1655))"
+"alias of :py:class:`~numpy.ndarray`\\ [:py:obj:`~typing.Any`, "
+":py:class:`~numpy.dtype`\\ [:py:obj:`~typing.Any`]]"
 msgstr ""
-"**Introduire une nouvelle ligne de base pour les fleurs : FedAvg "
-"FEMNIST** ([#1655](https://github.com/adap/flower/pull/1655))"
 
-#: ../../source/ref-changelog.md:125
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
 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:`Parameters <flwr.common.Parameters>`\\ \\(tensors\\, "
+"tensor\\_type\\)"
 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:127
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.Parameters:1 of
+msgid "Model parameters."
+msgstr "Paramètres du modèle."
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`ReconnectIns <flwr.common.ReconnectIns>`\\ \\(seconds\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.ReconnectIns:1 of
+msgid "ReconnectIns message from server to client."
+msgstr "Message de reconnexion du serveur au client."
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
 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:`ServerMessage <flwr.common.ServerMessage>`\\ "
+"\\(\\[get\\_properties\\_ins\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.ServerMessage:1 of
+msgid "ServerMessage is a container used to hold one instruction message."
+msgstr ""
+"ServerMessage est un conteneur utilisé pour contenir un message "
+"d'instruction."
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`Status <flwr.common.Status>`\\ \\(code\\, message\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.Status:1 of
+msgid "Client status."
+msgstr "Statut du client."
+
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:2
+#, fuzzy
+msgid "ClientMessage"
+msgstr "Côté client"
+
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:31:<autosummary>:1
+msgid ":py:obj:`evaluate_res <flwr.common.ClientMessage.evaluate_res>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:31:<autosummary>:1
+msgid ":py:obj:`fit_res <flwr.common.ClientMessage.fit_res>`\\"
 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))"
 
-#: ../../source/ref-changelog.md:129
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:31:<autosummary>:1
 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:`get_parameters_res "
+"<flwr.common.ClientMessage.get_parameters_res>`\\"
 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:131
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:31:<autosummary>:1
 msgid ""
-"Please note: The REST API is still experimental and will likely change "
-"significantly over time."
+":py:obj:`get_properties_res "
+"<flwr.common.ClientMessage.get_properties_res>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Code.rst:2
+msgid "Code"
+msgstr ""
+
+#: flwr.common.typing.Code:1 of
+msgid "Bases: :py:class:`~enum.Enum`"
 msgstr ""
-"Remarque : l'API REST est encore expérimentale et est susceptible de "
-"changer de manière significative au fil du temps."
 
-#: ../../source/ref-changelog.md:133
+#: ../../source/ref-api/flwr.common.Code.rst:26:<autosummary>:1
+msgid ":py:obj:`OK <flwr.common.Code.OK>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Code.rst:26:<autosummary>:1
 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:`GET_PROPERTIES_NOT_IMPLEMENTED "
+"<flwr.common.Code.GET_PROPERTIES_NOT_IMPLEMENTED>`\\"
 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:135
+#: ../../source/ref-api/flwr.common.Code.rst:26:<autosummary>:1
 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:`GET_PARAMETERS_NOT_IMPLEMENTED "
+"<flwr.common.Code.GET_PARAMETERS_NOT_IMPLEMENTED>`\\"
 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."
 
-#: ../../source/ref-changelog.md:137
+#: ../../source/ref-api/flwr.common.Code.rst:26:<autosummary>:1
+msgid ":py:obj:`FIT_NOT_IMPLEMENTED <flwr.common.Code.FIT_NOT_IMPLEMENTED>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Code.rst:26:<autosummary>:1
 msgid ""
-"**Fix spilling issues related to Ray during simulations** "
-"([#1698](https://github.com/adap/flower/pull/1698))"
+":py:obj:`EVALUATE_NOT_IMPLEMENTED "
+"<flwr.common.Code.EVALUATE_NOT_IMPLEMENTED>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.DisconnectRes.rst:2
+msgid "DisconnectRes"
 msgstr ""
-"**Répare les problèmes de déversement liés à Ray pendant les "
-"simulations** ([#1698](https://github.com/adap/flower/pull/1698))"
 
-#: ../../source/ref-changelog.md:139
+#: ../../source/ref-api/flwr.common.DisconnectRes.rst:28:<autosummary>:1
+msgid ":py:obj:`reason <flwr.common.DisconnectRes.reason>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EvaluateIns.rst:2
 #, fuzzy
+msgid "EvaluateIns"
+msgstr "Explications"
+
+#: ../../source/ref-api/flwr.common.EvaluateIns.rst:29:<autosummary>:1
+msgid ":py:obj:`parameters <flwr.common.EvaluateIns.parameters>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EvaluateIns.rst:29:<autosummary>:1
+msgid ":py:obj:`config <flwr.common.EvaluateIns.config>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EvaluateRes.rst:2
+msgid "EvaluateRes"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31:<autosummary>:1
+msgid ":py:obj:`status <flwr.common.EvaluateRes.status>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31:<autosummary>:1
+msgid ":py:obj:`loss <flwr.common.EvaluateRes.loss>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31:<autosummary>:1
+msgid ":py:obj:`num_examples <flwr.common.EvaluateRes.num_examples>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31:<autosummary>:1
+msgid ":py:obj:`metrics <flwr.common.EvaluateRes.metrics>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:2
+msgid "EventType"
+msgstr ""
+
+#: flwr.common.telemetry.EventType:1 of
+msgid "Bases: :py:class:`str`, :py:class:`~enum.Enum`"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`PING <flwr.common.EventType.PING>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_CLIENT_ENTER <flwr.common.EventType.START_CLIENT_ENTER>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_CLIENT_LEAVE <flwr.common.EventType.START_CLIENT_LEAVE>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_SERVER_ENTER <flwr.common.EventType.START_SERVER_ENTER>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_SERVER_LEAVE <flwr.common.EventType.START_SERVER_LEAVE>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
 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! 🎉"
+":py:obj:`RUN_DRIVER_API_ENTER "
+"<flwr.common.EventType.RUN_DRIVER_API_ENTER>`\\"
 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 ! 🎉"
 
-#: ../../source/ref-changelog.md:141
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
 msgid ""
-"**Add new example using** `TabNet` **and Flower** "
-"([#1725](https://github.com/adap/flower/pull/1725))"
+":py:obj:`RUN_DRIVER_API_LEAVE "
+"<flwr.common.EventType.RUN_DRIVER_API_LEAVE>`\\"
 msgstr ""
-"**Ajouter un nouvel exemple utilisant** `TabNet` **et Flower** "
-"([#1725](https://github.com/adap/flower/pull/1725))"
 
-#: ../../source/ref-changelog.md:143
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
 msgid ""
-"TabNet is a powerful and flexible framework for training machine learning"
-" models on tabular data. We now have a federated example using Flower: "
-"[https://github.com/adap/flower/tree/main/examples/tabnet](https://github.com/adap/flower/tree/main/examples/quickstart_tabnet)."
+":py:obj:`RUN_FLEET_API_ENTER "
+"<flwr.common.EventType.RUN_FLEET_API_ENTER>`\\"
 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 : "
-"[https://github.com/adap/flower/tree/main/examples/tabnet](https://github.com/adap/flower/tree/main/examples/quickstart_tabnet)."
 
-#: ../../source/ref-changelog.md:145
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
 msgid ""
-"**Add new how-to guide for monitoring simulations** "
-"([#1649](https://github.com/adap/flower/pull/1649))"
+":py:obj:`RUN_FLEET_API_LEAVE "
+"<flwr.common.EventType.RUN_FLEET_API_LEAVE>`\\"
 msgstr ""
-"**Ajouter un nouveau guide pratique pour le suivi des simulations** "
-"([#1649](https://github.com/adap/flower/pull/1649))"
 
-#: ../../source/ref-changelog.md:147
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
 msgid ""
-"We now have a documentation guide to help users monitor their performance"
-" during simulations."
+":py:obj:`RUN_SUPERLINK_ENTER "
+"<flwr.common.EventType.RUN_SUPERLINK_ENTER>`\\"
 msgstr ""
-"Nous avons maintenant un guide de documentation pour aider les "
-"utilisateurs à surveiller leurs performances pendant les simulations."
 
-#: ../../source/ref-changelog.md:149
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
 msgid ""
-"**Add training metrics to** `History` **object during simulations** "
-"([#1696](https://github.com/adap/flower/pull/1696))"
+":py:obj:`RUN_SUPERLINK_LEAVE "
+"<flwr.common.EventType.RUN_SUPERLINK_LEAVE>`\\"
 msgstr ""
-"**Ajouter des mesures de formation à** `History` **objet pendant les "
-"simulations** ([#1696](https://github.com/adap/flower/pull/1696))"
 
-#: ../../source/ref-changelog.md:151
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
 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:`START_SIMULATION_ENTER "
+"<flwr.common.EventType.START_SIMULATION_ENTER>`\\"
 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 !"
 
-#: ../../source/ref-changelog.md:153
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
 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:`START_SIMULATION_LEAVE "
+"<flwr.common.EventType.START_SIMULATION_LEAVE>`\\"
 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:161
-msgid "v1.3.0 (2023-02-06)"
-msgstr "v1.3.0 (2023-02-06)"
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`DRIVER_CONNECT <flwr.common.EventType.DRIVER_CONNECT>`\\"
+msgstr ""
 
-#: ../../source/ref-changelog.md:167
-msgid ""
-"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, "
-"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`"
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`DRIVER_DISCONNECT <flwr.common.EventType.DRIVER_DISCONNECT>`\\"
 msgstr ""
-"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, "
-"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`"
 
-#: ../../source/ref-changelog.md:171
-msgid ""
-"**Add support for** `workload_id` **and** `group_id` **in Driver API** "
-"([#1595](https://github.com/adap/flower/pull/1595))"
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_DRIVER_ENTER <flwr.common.EventType.START_DRIVER_ENTER>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_DRIVER_LEAVE <flwr.common.EventType.START_DRIVER_LEAVE>`\\"
 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))"
 
-#: ../../source/ref-changelog.md:173
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
 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."
+":py:obj:`RUN_CLIENT_APP_ENTER "
+"<flwr.common.EventType.RUN_CLIENT_APP_ENTER>`\\"
 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:175
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
 msgid ""
-"**Make Driver API and Fleet API address configurable** "
-"([#1637](https://github.com/adap/flower/pull/1637))"
+":py:obj:`RUN_CLIENT_APP_LEAVE "
+"<flwr.common.EventType.RUN_CLIENT_APP_LEAVE>`\\"
 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:177
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
 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:`RUN_SERVER_APP_ENTER "
+"<flwr.common.EventType.RUN_SERVER_APP_ENTER>`\\"
 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:179
-#, fuzzy
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
 msgid ""
-"`flower-server --driver-api-address \"0.0.0.0:8081\" --fleet-api-address "
-"\"0.0.0.0:8086\"`"
+":py:obj:`RUN_SERVER_APP_LEAVE "
+"<flwr.common.EventType.RUN_SERVER_APP_LEAVE>`\\"
 msgstr ""
-"``flower-server --driver-api-address \"0.0.0.0:8081\" --fleet-api-address"
-" \"0.0.0.0:8086\" ``"
 
-#: ../../source/ref-changelog.md:181
-msgid "Both IPv4 and IPv6 addresses are supported."
-msgstr "Les adresses IPv4 et IPv6 sont toutes deux prises en charge."
+#: ../../source/ref-api/flwr.common.FitIns.rst:2
+msgid "FitIns"
+msgstr ""
 
-#: ../../source/ref-changelog.md:183
-msgid ""
-"**Add new example of Federated Learning using fastai and Flower** "
-"([#1598](https://github.com/adap/flower/pull/1598))"
+#: ../../source/ref-api/flwr.common.FitIns.rst:29:<autosummary>:1
+msgid ":py:obj:`parameters <flwr.common.FitIns.parameters>`\\"
 msgstr ""
-"**Ajouter un nouvel exemple d'apprentissage fédéré utilisant fastai et "
-"Flower** ([#1598](https://github.com/adap/flower/pull/1598))"
 
-#: ../../source/ref-changelog.md:185
-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)."
+#: ../../source/ref-api/flwr.common.FitIns.rst:29:<autosummary>:1
+msgid ":py:obj:`config <flwr.common.FitIns.config>`\\"
 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)."
 
-#: ../../source/ref-changelog.md:187
-msgid ""
-"**Make Android example compatible with** `flwr >= 1.0.0` **and the latest"
-" versions of Android** "
-"([#1603](https://github.com/adap/flower/pull/1603))"
+#: ../../source/ref-api/flwr.common.FitRes.rst:2
+msgid "FitRes"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.FitRes.rst:31:<autosummary>:1
+msgid ":py:obj:`status <flwr.common.FitRes.status>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.FitRes.rst:31:<autosummary>:1
+msgid ":py:obj:`parameters <flwr.common.FitRes.parameters>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.FitRes.rst:31:<autosummary>:1
+msgid ":py:obj:`num_examples <flwr.common.FitRes.num_examples>`\\"
 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:189
+#: ../../source/ref-api/flwr.common.FitRes.rst:31:<autosummary>:1
+msgid ":py:obj:`metrics <flwr.common.FitRes.metrics>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.GetParametersIns.rst:2
 #, 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."
+msgid "GetParametersIns"
+msgstr ":code:`get_parameters`"
+
+#: ../../source/ref-api/flwr.common.GetParametersIns.rst:28:<autosummary>:1
+msgid ":py:obj:`config <flwr.common.GetParametersIns.config>`\\"
 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."
 
-#: ../../source/ref-changelog.md:191
-msgid ""
-"**Add new `FedProx` strategy** "
-"([#1619](https://github.com/adap/flower/pull/1619))"
+#: ../../source/ref-api/flwr.common.GetParametersRes.rst:2
+#, fuzzy
+msgid "GetParametersRes"
+msgstr ":code:`get_parameters`"
+
+#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29:<autosummary>:1
+msgid ":py:obj:`status <flwr.common.GetParametersRes.status>`\\"
 msgstr ""
-"**Ajouter une nouvelle stratégie `FedProx`** "
-"([#1619](https://github.com/adap/flower/pull/1619))"
 
-#: ../../source/ref-changelog.md:193
-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."
+#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29:<autosummary>:1
+msgid ":py:obj:`parameters <flwr.common.GetParametersRes.parameters>`\\"
 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."
 
-#: ../../source/ref-changelog.md:195
-msgid ""
-"**Add new metrics to telemetry events** "
-"([#1640](https://github.com/adap/flower/pull/1640))"
+#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:2
+msgid "GetPropertiesIns"
 msgstr ""
-"**Ajouter de nouvelles métriques aux événements de télémétrie** "
-"([#1640](https://github.com/adap/flower/pull/1640))"
 
-#: ../../source/ref-changelog.md:197
-msgid ""
-"An updated event structure allows, for example, the clustering of events "
-"within the same workload."
+#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:28:<autosummary>:1
+msgid ":py:obj:`config <flwr.common.GetPropertiesIns.config>`\\"
 msgstr ""
-"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:199
-msgid ""
-"**Add new custom strategy tutorial section** "
-"[#1623](https://github.com/adap/flower/pull/1623)"
+#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:2
+msgid "GetPropertiesRes"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29:<autosummary>:1
+msgid ":py:obj:`status <flwr.common.GetPropertiesRes.status>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29:<autosummary>:1
+msgid ":py:obj:`properties <flwr.common.GetPropertiesRes.properties>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.NDArray.rst:2
+msgid "NDArray"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Parameters.rst:29:<autosummary>:1
+msgid ":py:obj:`tensors <flwr.common.Parameters.tensors>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Parameters.rst:29:<autosummary>:1
+msgid ":py:obj:`tensor_type <flwr.common.Parameters.tensor_type>`\\"
 msgstr ""
-"**Ajouter une nouvelle section de tutoriel sur les stratégies "
-"personnalisées** [#1623](https://github.com/adap/flower/pull/1623)"
 
-#: ../../source/ref-changelog.md:201
+#: ../../source/ref-api/flwr.common.ReconnectIns.rst:2
 #, fuzzy
+msgid "ReconnectIns"
+msgstr "Collecte centralisée des données"
+
+#: ../../source/ref-api/flwr.common.ReconnectIns.rst:28:<autosummary>:1
+msgid ":py:obj:`seconds <flwr.common.ReconnectIns.seconds>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:2
+#, fuzzy
+msgid "ServerMessage"
+msgstr "Côté serveur"
+
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:31:<autosummary>:1
+msgid ":py:obj:`evaluate_ins <flwr.common.ServerMessage.evaluate_ins>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:31:<autosummary>:1
+msgid ":py:obj:`fit_ins <flwr.common.ServerMessage.fit_ins>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:31:<autosummary>:1
 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:`get_parameters_ins "
+"<flwr.common.ServerMessage.get_parameters_ins>`\\"
 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)"
 
-#: ../../source/ref-changelog.md:203
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:31:<autosummary>:1
 msgid ""
-"**Add new custom serialization tutorial section** "
-"([#1622](https://github.com/adap/flower/pull/1622))"
+":py:obj:`get_properties_ins "
+"<flwr.common.ServerMessage.get_properties_ins>`\\"
 msgstr ""
-"**Ajouter une nouvelle section de tutoriel sur la sérialisation "
-"personnalisée** ([#1622](https://github.com/adap/flower/pull/1622))"
 
-#: ../../source/ref-changelog.md:205
+#: ../../source/ref-api/flwr.common.Status.rst:2
 #, 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)"
+msgid "Status"
+msgstr "Statut du client."
+
+#: ../../source/ref-api/flwr.common.Status.rst:29:<autosummary>:1
+msgid ":py:obj:`code <flwr.common.Status.code>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Status.rst:29:<autosummary>:1
+msgid ":py:obj:`message <flwr.common.Status.message>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.bytes_to_ndarray.rst:2
+msgid "bytes\\_to\\_ndarray"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.configure.rst:2
+#, fuzzy
+msgid "configure"
+msgstr "Configurer les clients"
+
+#: ../../source/ref-api/flwr.common.event.rst:2
+msgid "event"
 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)"
 
-#: ../../source/ref-changelog.md:207
+#: ../../source/ref-api/flwr.common.log.rst:2
+msgid "log"
+msgstr ""
+
+#: logging.Logger.log:3 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))"
+"To pass exception information, use the keyword argument exc_info with a "
+"true value, e.g."
 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"
+"Pour transmettre des informations sur les exceptions, utilise l'argument "
+"mot-clé exc_info avec une valeur vraie, par ex."
 
-#: ../../source/ref-changelog.md:211
-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))"
+#: 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)"
+
+#: ../../source/ref-api/flwr.common.ndarray_to_bytes.rst:2
+msgid "ndarray\\_to\\_bytes"
 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:213 ../../source/ref-changelog.md:280
-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!"
+#: ../../source/ref-api/flwr.common.ndarrays_to_parameters.rst:2
+msgid "ndarrays\\_to\\_parameters"
 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:219
-msgid "v1.2.0 (2023-01-13)"
-msgstr "v1.2.0 (2023-01-13)"
+#: ../../source/ref-api/flwr.common.now.rst:2
+msgid "now"
+msgstr ""
 
-#: ../../source/ref-changelog.md:225
-msgid ""
-"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L."
-" Jiang`, `Ragy`, `Taner Topal`, `dannymcy`"
+#: ../../source/ref-api/flwr.common.parameters_to_ndarrays.rst:2
+msgid "parameters\\_to\\_ndarrays"
 msgstr ""
-"adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L. "
-"Jiang`, `Ragy`, `Taner Topal`, `dannymcy`"
 
-#: ../../source/ref-changelog.md:229
-msgid ""
-"**Introduce new Flower Baseline: FedAvg MNIST** "
-"([#1497](https://github.com/adap/flower/pull/1497), "
-"[#1552](https://github.com/adap/flower/pull/1552))"
+#: ../../source/ref-api/flwr.server.rst:2
+msgid "server"
+msgstr "serveur"
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+msgid ":py:obj:`run_driver_api <flwr.server.run_driver_api>`\\ \\(\\)"
 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:231
-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.dev/blog/2023-01-12-fl-starter-"
-"pack-fedavg-mnist-cnn/)"
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+#: flwr.server.app.run_driver_api:1 of
+#, fuzzy
+msgid "Run Flower server (Driver API)."
+msgstr "flower-driver-api"
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+msgid ":py:obj:`run_fleet_api <flwr.server.run_fleet_api>`\\ \\(\\)"
 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.dev/blog/2023-01-12-fl-starter-pack-fedavg-mnist-"
-"cnn/)"
 
-#: ../../source/ref-changelog.md:233
-msgid ""
-"**Improve GPU support in simulations** "
-"([#1555](https://github.com/adap/flower/pull/1555))"
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+#: flwr.server.app.run_fleet_api:1 of
+#, fuzzy
+msgid "Run Flower server (Fleet API)."
+msgstr "flower-fleet-api"
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+msgid ":py:obj:`run_server_app <flwr.server.run_server_app>`\\ \\(\\)"
 msgstr ""
-"**Améliorer la prise en charge des GPU dans les simulations** "
-"([#1555](https://github.com/adap/flower/pull/1555))"
 
-#: ../../source/ref-changelog.md:235
-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."
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+#: flwr.server.app.run_server_app:1 of
+#, fuzzy
+msgid "Run Flower server app."
+msgstr "Serveur de Flower"
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+msgid ":py:obj:`run_superlink <flwr.server.run_superlink>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+#: flwr.server.app.run_superlink:1 of
+msgid "Run Flower server (Driver API and Fleet API)."
 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."
 
-#: ../../source/ref-changelog.md:237
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
 msgid ""
-"**Improve GPU support in Jupyter Notebook tutorials** "
-"([#1527](https://github.com/adap/flower/pull/1527), "
-"[#1558](https://github.com/adap/flower/pull/1558))"
+":py:obj:`start_server <flwr.server.start_server>`\\ \\(\\*\\[\\, "
+"server\\_address\\, server\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>: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:37:<autosummary>:1
+msgid ":py:obj:`ClientManager <flwr.server.ClientManager>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+#: flwr.server.client_manager.ClientManager:1 of
+msgid "Abstract base class for managing Flower clients."
 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))"
 
-#: ../../source/ref-changelog.md:239
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+msgid ":py:obj:`History <flwr.server.History>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+#: flwr.server.history.History:1 of
+msgid "History class for training and/or evaluation metrics collection."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
 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:`Server <flwr.server.Server>`\\ \\(\\*\\, client\\_manager\\[\\, "
+"strategy\\]\\)"
 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:241
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
 #, fuzzy
 msgid ""
-"[An Introduction to Federated Learning](https://flower.dev/docs/framework"
-"/tutorial-get-started-with-flower-pytorch.html)"
+":py:obj:`ServerConfig <flwr.server.ServerConfig>`\\ \\(\\[num\\_rounds\\,"
+" round\\_timeout\\]\\)"
 msgstr ""
-"[Une introduction à l'apprentissage fédéré] "
-"(https://flower.dev/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html)"
+"Flower 1.0 : ``start_server(..., "
+"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), "
+"...)``"
 
-#: ../../source/ref-changelog.md:242
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+#: flwr.server.app.ServerConfig:1 of
 #, fuzzy
-msgid ""
-"[Strategies in Federated Learning](https://flower.dev/docs/framework"
-"/tutorial-use-a-federated-learning-strategy-pytorch.html)"
+msgid "Flower server config."
+msgstr "Serveur de Flower"
+
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+msgid ":py:obj:`SimpleClientManager <flwr.server.SimpleClientManager>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager:1 of
+msgid "Provides a pool of available clients."
 msgstr ""
-"[Stratégies d'apprentissage fédéré] "
-"(https://flower.dev/docs/tutorial/Flower-2-Strategies-in-FL-PyTorch.html)"
 
-#: ../../source/ref-changelog.md:243
+#: ../../source/ref-api/flwr.server.rst:56:<autosummary>:1
+msgid ":py:obj:`flwr.server.driver <flwr.server.driver>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:56:<autosummary>:1 flwr.server.driver:1
+#: of
 #, fuzzy
-msgid ""
-"[Building a Strategy](https://flower.dev/docs/framework/tutorial-build-a"
-"-strategy-from-scratch-pytorch.html)"
+msgid "Flower driver SDK."
+msgstr "Serveur de Flower"
+
+#: ../../source/ref-api/flwr.server.rst:56:<autosummary>:1
+#, fuzzy
+msgid ":py:obj:`flwr.server.strategy <flwr.server.strategy>`\\"
+msgstr "serveur.stratégie.Stratégie"
+
+#: ../../source/ref-api/flwr.server.rst:56:<autosummary>:1
+#: flwr.server.strategy:1 of
+msgid "Contains the strategy abstraction and different implementations."
 msgstr ""
-"[Construire une stratégie] "
-"(https://flower.dev/docs/tutorial/Flower-3-Building-a-Strategy-"
-"PyTorch.html)"
 
-#: ../../source/ref-changelog.md:244
+#: ../../source/ref-api/flwr.server.ClientManager.rst:2
 #, fuzzy
-msgid ""
-"[Client and NumPyClient](https://flower.dev/docs/framework/tutorial-"
-"customize-the-client-pytorch.html)"
+msgid "ClientManager"
+msgstr "client"
+
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
+msgid ":py:obj:`all <flwr.server.ClientManager.all>`\\ \\(\\)"
 msgstr ""
-"[Client et NumPyClient] (https://flower.dev/docs/tutorial/Flower-4"
-"-Client-and-NumPyClient-PyTorch.html)"
 
-#: ../../source/ref-changelog.md:246
-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))"
+#: flwr.server.client_manager.ClientManager.all:1
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.all:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
+msgid "Return all available clients."
 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:248
-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."
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
+msgid ":py:obj:`num_available <flwr.server.ClientManager.num_available>`\\ \\(\\)"
 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."
 
-#: ../../source/ref-changelog.md:250
-#, 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.dev/docs/telemetry.html)."
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.ClientManager.num_available:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.num_available:1 of
+msgid "Return the number of available clients."
 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.dev/docs/telemetry.html)."
 
-#: ../../source/ref-changelog.md:252
-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))"
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
+msgid ":py:obj:`register <flwr.server.ClientManager.register>`\\ \\(client\\)"
 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:254
-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."
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.ClientManager.register:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.register:1 of
+msgid "Register Flower ClientProxy instance."
 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:256
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
 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."
+":py:obj:`sample <flwr.server.ClientManager.sample>`\\ "
+"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)"
 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:258
-msgid ""
-"To learn more, check out the `mt-pytorch` code example. We look forward "
-"to you feedback!"
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.ClientManager.sample:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.sample:1 of
+msgid "Sample a number of Flower ClientProxy instances."
 msgstr ""
-"Pour en savoir plus, consulte l'exemple de code `mt-pytorch`. Nous "
-"attendons tes commentaires avec impatience !"
 
-#: ../../source/ref-changelog.md:260
-msgid ""
-"Please note: *The Driver API is still experimental and will likely change"
-" significantly over time.*"
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
+msgid ":py:obj:`unregister <flwr.server.ClientManager.unregister>`\\ \\(client\\)"
 msgstr ""
-"Remarque : *L'API du pilote est encore expérimentale et est susceptible "
-"de changer de manière significative au fil du temps.*"
 
-#: ../../source/ref-changelog.md:262
-msgid ""
-"**Add new Federated Analytics with Pandas example** "
-"([#1469](https://github.com/adap/flower/pull/1469), "
-"[#1535](https://github.com/adap/flower/pull/1535))"
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.ClientManager.unregister:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.unregister:1 of
+msgid "Unregister Flower ClientProxy instance."
 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:264
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
 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)."
+":py:obj:`wait_for <flwr.server.ClientManager.wait_for>`\\ "
+"\\(num\\_clients\\, timeout\\)"
 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)."
 
-#: ../../source/ref-changelog.md:266
-msgid ""
-"**Add new strategies: Krum and MultiKrum** "
-"([#1481](https://github.com/adap/flower/pull/1481))"
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.ClientManager.wait_for:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.wait_for:1 of
+msgid "Wait until at least `num_clients` are available."
 msgstr ""
-"**Ajouter de nouvelles stratégies : Krum et MultiKrum** "
-"([#1481](https://github.com/adap/flower/pull/1481))"
 
-#: ../../source/ref-changelog.md:268
-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."
+#: 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 ""
-"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:270
+#: flwr.server.client_manager.ClientManager.register:6
+#: flwr.server.client_manager.SimpleClientManager.register:6 of
 msgid ""
-"**Update C++ example to be compatible with Flower v1.2.0** "
-"([#1495](https://github.com/adap/flower/pull/1495))"
+"**success** -- Indicating if registration was successful. False if "
+"ClientProxy is already registered or can not be registered for any "
+"reason."
 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))"
 
-#: ../../source/ref-changelog.md:272
-msgid ""
-"The C++ code example has received a substantial update to make it "
-"compatible with the latest version of Flower."
+#: flwr.server.client_manager.ClientManager.unregister:3
+#: flwr.server.client_manager.SimpleClientManager.unregister:3 of
+msgid "This method is idempotent."
 msgstr ""
-"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:274
-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))"
+#: ../../source/ref-api/flwr.server.History.rst:2
+msgid "History"
 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:278
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1 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))"
+":py:obj:`add_loss_centralized "
+"<flwr.server.History.add_loss_centralized>`\\ \\(server\\_round\\, "
+"loss\\)"
 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:282
+#: flwr.server.history.History.add_loss_centralized:1
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1 of
+#, fuzzy
+msgid "Add one loss entry (from centralized evaluation)."
+msgstr "Évaluation centralisée"
+
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1 of
 msgid ""
-"One highlight is the new [first time contributor "
-"guide](https://flower.dev/docs/first-time-contributors.html): if you've "
-"never contributed on GitHub before, this is the perfect place to start!"
+":py:obj:`add_loss_distributed "
+"<flwr.server.History.add_loss_distributed>`\\ \\(server\\_round\\, "
+"loss\\)"
 msgstr ""
-"L'un des points forts est le nouveau [guide du premier contributeur] "
-"(https://flower.dev/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:288
-msgid "v1.1.0 (2022-10-31)"
-msgstr "v1.1.0 (2022-10-31)"
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1
+#: flwr.server.history.History.add_loss_distributed:1 of
+msgid "Add one loss entry (from distributed evaluation)."
+msgstr ""
 
-#: ../../source/ref-changelog.md:292
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>: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:`add_metrics_centralized "
+"<flwr.server.History.add_metrics_centralized>`\\ \\(server\\_round\\, "
+"metrics\\)"
 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:294
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>: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:<autosummary>:1 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`"
+":py:obj:`add_metrics_distributed "
+"<flwr.server.History.add_metrics_distributed>`\\ \\(server\\_round\\, "
+"metrics\\)"
 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`"
 
-#: ../../source/ref-changelog.md:298
-msgid ""
-"**Introduce Differential Privacy wrappers (preview)** "
-"([#1357](https://github.com/adap/flower/pull/1357), "
-"[#1460](https://github.com/adap/flower/pull/1460))"
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1
+#: flwr.server.history.History.add_metrics_distributed:1 of
+msgid "Add metrics entries (from distributed evaluation)."
 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:300
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1 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."
+":py:obj:`add_metrics_distributed_fit "
+"<flwr.server.History.add_metrics_distributed_fit>`\\ \\(server\\_round\\,"
+" ...\\)"
 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:302
-msgid ""
-"**New iOS CoreML code example** "
-"([#1289](https://github.com/adap/flower/pull/1289))"
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1
+#: flwr.server.history.History.add_metrics_distributed_fit:1 of
+msgid "Add metrics entries (from distributed fit)."
 msgstr ""
-"**Nouvel exemple de code CoreML pour iOS** "
-"([#1289](https://github.com/adap/flower/pull/1289))"
 
-#: ../../source/ref-changelog.md:304
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
+msgid ":py:obj:`client_manager <flwr.server.Server.client_manager>`\\ \\(\\)"
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
+msgid "Return ClientManager."
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 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."
+":py:obj:`disconnect_all_clients "
+"<flwr.server.Server.disconnect_all_clients>`\\ \\(timeout\\)"
 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:306
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1
+#: flwr.server.server.Server.disconnect_all_clients:1 of
+msgid "Send shutdown signal to all clients."
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
 msgid ""
-"**New FedMedian strategy** "
-"([#1461](https://github.com/adap/flower/pull/1461))"
+":py:obj:`evaluate_round <flwr.server.Server.evaluate_round>`\\ "
+"\\(server\\_round\\, timeout\\)"
 msgstr ""
-"**Nouvelle stratégie de FedMedian** "
-"([#1461](https://github.com/adap/flower/pull/1461))"
 
-#: ../../source/ref-changelog.md:308
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1
+#: flwr.server.server.Server.evaluate_round:1 of
+msgid "Validate current global model on a number of clients."
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
+msgid ":py:obj:`fit <flwr.server.Server.fit>`\\ \\(num\\_rounds\\, timeout\\)"
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1
+#: flwr.server.server.Server.fit:1 of
+msgid "Run federated averaging for a number of rounds."
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
 msgid ""
-"The new `FedMedian` strategy implements Federated Median (FedMedian) by "
-"[Yin et al., 2018](https://arxiv.org/pdf/1803.01498v1.pdf)."
+":py:obj:`fit_round <flwr.server.Server.fit_round>`\\ \\(server\\_round\\,"
+" timeout\\)"
 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:310
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1
+#: flwr.server.server.Server.fit_round:1 of
+msgid "Perform a single round of federated averaging."
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
 msgid ""
-"**Log** `Client` **exceptions in Virtual Client Engine** "
-"([#1493](https://github.com/adap/flower/pull/1493))"
+":py:obj:`set_max_workers <flwr.server.Server.set_max_workers>`\\ "
+"\\(max\\_workers\\)"
 msgstr ""
-"**Log** `Client` **exceptions dans le moteur de client virtuel** "
-"([#1493](https://github.com/adap/flower/pull/1493))"
 
-#: ../../source/ref-changelog.md:312
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1
+#: flwr.server.server.Server.set_max_workers:1 of
+msgid "Set the max_workers used by ThreadPoolExecutor."
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
+msgid ":py:obj:`set_strategy <flwr.server.Server.set_strategy>`\\ \\(strategy\\)"
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1
+#: flwr.server.server.Server.set_strategy:1 of
+#, fuzzy
+msgid "Replace server strategy."
+msgstr "stratégie.du.serveur"
+
+#: ../../source/ref-api/flwr.server.ServerConfig.rst:2
+#, fuzzy
+msgid "ServerConfig"
+msgstr "serveur"
+
+#: flwr.server.app.ServerConfig:3 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)."
+"All attributes have default values which allows users to configure just "
+"the ones they care about."
 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:314
+#: ../../source/ref-api/flwr.server.ServerConfig.rst:29:<autosummary>:1
+msgid ":py:obj:`num_rounds <flwr.server.ServerConfig.num_rounds>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.ServerConfig.rst:29:<autosummary>:1
+msgid ":py:obj:`round_timeout <flwr.server.ServerConfig.round_timeout>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.SimpleClientManager.rst:2
+msgid "SimpleClientManager"
+msgstr ""
+
+#: flwr.server.client_manager.SimpleClientManager:1 of
+msgid "Bases: :py:class:`~flwr.server.client_manager.ClientManager`"
+msgstr ""
+
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
+msgid ":py:obj:`all <flwr.server.SimpleClientManager.all>`\\ \\(\\)"
+msgstr ""
+
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
 msgid ""
-"**Improve Virtual Client Engine internals** "
-"([#1401](https://github.com/adap/flower/pull/1401), "
-"[#1453](https://github.com/adap/flower/pull/1453))"
+":py:obj:`num_available <flwr.server.SimpleClientManager.num_available>`\\"
+" \\(\\)"
 msgstr ""
-"**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:316
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
 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."
+":py:obj:`register <flwr.server.SimpleClientManager.register>`\\ "
+"\\(client\\)"
 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."
 
-#: ../../source/ref-changelog.md:318
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
 msgid ""
-"**Support optional** `Client`**/**`NumPyClient` **methods in Virtual "
-"Client Engine**"
+":py:obj:`sample <flwr.server.SimpleClientManager.sample>`\\ "
+"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)"
 msgstr ""
-"**Support optional** `Client`**/**`NumPyClient` **methods in Virtual "
-"Client Engine**"
 
-#: ../../source/ref-changelog.md:320
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
 msgid ""
-"The Virtual Client Engine now has full support for optional `Client` (and"
-" `NumPyClient`) methods."
+":py:obj:`unregister <flwr.server.SimpleClientManager.unregister>`\\ "
+"\\(client\\)"
 msgstr ""
-"Le moteur de client virtuel prend désormais en charge les méthodes "
-"optionnelles `Client` (et `NumPyClient`)."
 
-#: ../../source/ref-changelog.md:322
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
 msgid ""
-"**Provide type information to packages using** `flwr` "
-"([#1377](https://github.com/adap/flower/pull/1377))"
+":py:obj:`wait_for <flwr.server.SimpleClientManager.wait_for>`\\ "
+"\\(num\\_clients\\[\\, timeout\\]\\)"
 msgstr ""
-"**Fournir des informations de type aux paquets en utilisant** `flwr` "
-"([#1377](https://github.com/adap/flower/pull/1377))"
 
-#: ../../source/ref-changelog.md:324
+#: flwr.server.client_manager.SimpleClientManager.wait_for:3 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`."
+"Blocks until the requested number of clients is available or until a "
+"timeout is reached. Current timeout default: 1 day."
 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:326
-msgid ""
-"**Updated code example** "
-"([#1344](https://github.com/adap/flower/pull/1344), "
-"[#1347](https://github.com/adap/flower/pull/1347))"
+#: flwr.server.client_manager.SimpleClientManager.wait_for:6 of
+msgid "The number of clients to wait for."
 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:328
-msgid ""
-"The code examples covering scikit-learn and PyTorch Lightning have been "
-"updated to work with the latest version of Flower."
+#: flwr.server.client_manager.SimpleClientManager.wait_for:8 of
+msgid "The time in seconds to wait for, defaults to 86400 (24h)."
 msgstr ""
-"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:330
-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))"
+#: flwr.server.client_manager.SimpleClientManager.wait_for:11 of
+msgid "**success**"
 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))"
 
-#: ../../source/ref-changelog.md:332
+#: ../../source/ref-api/flwr.server.driver.rst:2
+#, fuzzy
+msgid "driver"
+msgstr "serveur"
+
+#: ../../source/ref-api/flwr.server.driver.rst:22:<autosummary>:1
 msgid ""
-"There have been so many documentation updates that it doesn't even make "
-"sense to list them individually."
+":py:obj:`start_driver <flwr.server.driver.start_driver>`\\ \\(\\*\\[\\, "
+"server\\_address\\, server\\, ...\\]\\)"
 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."
 
-#: ../../source/ref-changelog.md:334
+#: ../../source/ref-api/flwr.server.driver.rst:22:<autosummary>:1
+#: flwr.server.driver.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.driver.rst:30:<autosummary>:1
 msgid ""
-"**Restructured documentation** "
-"([#1387](https://github.com/adap/flower/pull/1387))"
+":py:obj:`Driver <flwr.server.driver.Driver>`\\ "
+"\\(\\[driver\\_service\\_address\\, ...\\]\\)"
 msgstr ""
-"**Documentation restructurée** "
-"([#1387](https://github.com/adap/flower/pull/1387))"
 
-#: ../../source/ref-changelog.md:336
-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!"
+#: ../../source/ref-api/flwr.server.driver.rst:30:<autosummary>:1
+#: flwr.server.driver.driver.Driver:1 of
+msgid "`Driver` class provides an interface to the Driver API."
 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"
 
-#: ../../source/ref-changelog.md:338
+#: ../../source/ref-api/flwr.server.driver.rst:30:<autosummary>:1
 msgid ""
-"**Open in Colab button** "
-"([#1389](https://github.com/adap/flower/pull/1389))"
+":py:obj:`GrpcDriver <flwr.server.driver.GrpcDriver>`\\ "
+"\\(\\[driver\\_service\\_address\\, ...\\]\\)"
 msgstr ""
-"**Ouvrir dans le bouton Colab** "
-"([#1389](https://github.com/adap/flower/pull/1389))"
 
-#: ../../source/ref-changelog.md:340
+#: ../../source/ref-api/flwr.server.driver.rst:30:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver:1 of
+msgid "`GrpcDriver` provides access to the gRPC Driver API/service."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.driver.Driver.rst:2
+#, fuzzy
+msgid "Driver"
+msgstr "serveur"
+
+#: flwr.server.driver.driver.Driver:3 of
 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 IPv4 or IPv6 address of the Driver API server. Defaults to "
+"`\"[::]:9091\"`."
 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."
 
-#: ../../source/ref-changelog.md:342
+#: flwr.server.app.start_server:28 flwr.server.driver.driver.Driver:6 of
 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))"
+"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 ""
-"**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:344
+#: flwr.server.app.start_server:28 flwr.server.driver.driver.Driver:6 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)."
+"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 ""
-"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:350
-msgid "v1.0.0 (2022-07-28)"
-msgstr "v1.0.0 (2022-07-28)"
+#: flwr.server.app.start_server:32 flwr.server.driver.driver.Driver:10 of
+#, fuzzy
+msgid "CA certificate."
+msgstr "Certificats"
 
-#: ../../source/ref-changelog.md:352
-msgid "Highlights"
-msgstr "Points forts"
+#: flwr.server.app.start_server:33 flwr.server.driver.driver.Driver:11 of
+#, fuzzy
+msgid "server certificate."
+msgstr "Certificats"
 
-#: ../../source/ref-changelog.md:354
-msgid "Stable **Virtual Client Engine** (accessible via `start_simulation`)"
-msgstr "Moteur de client virtuel stable** (accessible via `start_simulation`)"
+#: flwr.server.app.start_server:34 flwr.server.driver.driver.Driver:12 of
+#, fuzzy
+msgid "server private key."
+msgstr "stratégie.du.serveur"
 
-#: ../../source/ref-changelog.md:355
-msgid "All `Client`/`NumPyClient` methods are now optional"
-msgstr "Toutes les méthodes `Client`/`NumPyClient` sont maintenant optionnelles"
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1 of
+msgid ":py:obj:`get_nodes <flwr.server.driver.Driver.get_nodes>`\\ \\(\\)"
+msgstr ""
 
-#: ../../source/ref-changelog.md:356
-msgid "Configurable `get_parameters`"
-msgstr "`get_parameters` configurable"
+#: flwr.server.driver.driver.Driver.get_nodes:1
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1 of
+msgid "Get node IDs."
+msgstr ""
 
-#: ../../source/ref-changelog.md:357
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1 of
 msgid ""
-"Tons of small API cleanups resulting in a more coherent developer "
-"experience"
+":py:obj:`pull_task_res <flwr.server.driver.Driver.pull_task_res>`\\ "
+"\\(task\\_ids\\)"
 msgstr ""
-"Des tonnes de petits nettoyages d'API résultant en une expérience plus "
-"cohérente pour les développeurs"
 
-#: ../../source/ref-changelog.md:361
-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):"
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1
+#: flwr.server.driver.driver.Driver.pull_task_res:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.pull_task_res:1 of
+msgid "Get task results."
 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)) :"
 
-#: ../../source/ref-changelog.md:363
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1 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), "
-"[@sandracl72](https://github.com/sandracl72), "
-"[@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)."
+":py:obj:`push_task_ins <flwr.server.driver.Driver.push_task_ins>`\\ "
+"\\(task\\_ins\\_list\\)"
 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]("
 
-#: ../../source/ref-changelog.md:367
-msgid ""
-"**All arguments must be passed as keyword arguments** "
-"([#1338](https://github.com/adap/flower/pull/1338))"
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1
+#: flwr.server.driver.driver.Driver.push_task_ins:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.push_task_ins:1 of
+msgid "Schedule tasks."
 msgstr ""
-"**Tous les arguments doivent être passés comme des arguments de mot-clé**"
-" ([#1338](https://github.com/adap/flower/pull/1338))"
 
-#: ../../source/ref-changelog.md:369
-#, 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())`)."
+#: ../../source/ref-api/flwr.server.driver.GrpcDriver.rst:2
+msgid "GrpcDriver"
 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())`)."
 
-#: ../../source/ref-changelog.md:371
-msgid ""
-"**Introduce configuration object** `ServerConfig` **in** `start_server` "
-"**and** `start_simulation` "
-"([#1317](https://github.com/adap/flower/pull/1317))"
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid ":py:obj:`connect <flwr.server.driver.GrpcDriver.connect>`\\ \\(\\)"
 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:373
-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."
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid "Connect to the Driver API."
 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."
 
-#: ../../source/ref-changelog.md:375
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
 msgid ""
-"**Rename built-in strategy parameters for clarity** "
-"([#1334](https://github.com/adap/flower/pull/1334))"
+":py:obj:`create_run <flwr.server.driver.GrpcDriver.create_run>`\\ "
+"\\(req\\)"
 msgstr ""
-"**Renommer les paramètres de la stratégie intégrée pour plus de clarté** "
-"([#1334](https://github.com/adap/flower/pull/1334))"
 
-#: ../../source/ref-changelog.md:377
-msgid ""
-"The following built-in strategy parameters were renamed to improve "
-"readability and consistency with other API's:"
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.create_run:1 of
+#, fuzzy
+msgid "Request for run ID."
+msgstr "Demande pour une nouvelle Flower Baseline"
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid ":py:obj:`disconnect <flwr.server.driver.GrpcDriver.disconnect>`\\ \\(\\)"
 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:379
-msgid "`fraction_eval` --> `fraction_evaluate`"
-msgstr "`fraction_eval` --> `fraction_evaluate`"
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.disconnect:1 of
+msgid "Disconnect from the Driver API."
+msgstr ""
 
-#: ../../source/ref-changelog.md:380
-msgid "`min_eval_clients` --> `min_evaluate_clients`"
-msgstr "`min_eval_clients` --> `min_evaluate_clients`"
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid ":py:obj:`get_nodes <flwr.server.driver.GrpcDriver.get_nodes>`\\ \\(req\\)"
+msgstr ""
 
-#: ../../source/ref-changelog.md:381
-msgid "`eval_fn` --> `evaluate_fn`"
-msgstr "`eval_fn` --> `evaluate_fn`"
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.get_nodes:1 of
+#, fuzzy
+msgid "Get client IDs."
+msgstr "Moteur client Edge"
 
-#: ../../source/ref-changelog.md:383
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
 msgid ""
-"**Update default arguments of built-in strategies** "
-"([#1278](https://github.com/adap/flower/pull/1278))"
+":py:obj:`pull_task_res <flwr.server.driver.GrpcDriver.pull_task_res>`\\ "
+"\\(req\\)"
 msgstr ""
-"**Mettre à jour les arguments par défaut des stratégies intégrées** "
-"([#1278](https://github.com/adap/flower/pull/1278))"
 
-#: ../../source/ref-changelog.md:385
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
 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:"
+":py:obj:`push_task_ins <flwr.server.driver.GrpcDriver.push_task_ins>`\\ "
+"\\(req\\)"
 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:387
-msgid "`strategy = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`"
-msgstr "`stratégie = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`"
+#: ../../source/ref-api/flwr.server.driver.start_driver.rst:2
+#, fuzzy
+msgid "start\\_driver"
+msgstr "start_client"
 
-#: ../../source/ref-changelog.md:389
+#: flwr.server.driver.app.start_driver:3 of
 msgid ""
-"**Add** `server_round` **to** `Strategy.evaluate` "
-"([#1334](https://github.com/adap/flower/pull/1334))"
+"The IPv4 or IPv6 address of the Driver API server. Defaults to "
+"`\"[::]:8080\"`."
 msgstr ""
-"**Ajouter** `server_round` **à** `Strategy.evaluate` "
-"([#1334](https://github.com/adap/flower/pull/1334))"
 
-#: ../../source/ref-changelog.md:391
+#: flwr.server.driver.app.start_driver:6 of
 msgid ""
-"The `Strategy` method `evaluate` now receives the current round of "
-"federated learning/evaluation as the first parameter."
+"A server implementation, either `flwr.server.Server` or a subclass "
+"thereof. If no instance is provided, then `start_driver` will create one."
 msgstr ""
-"La méthode `Stratégie` `évaluer` reçoit maintenant le cycle actuel "
-"d'apprentissage/évaluation fédéré comme premier paramètre."
 
-#: ../../source/ref-changelog.md:393
+#: flwr.server.app.start_server:9 flwr.server.driver.app.start_driver:10
+#: flwr.simulation.app.start_simulation:28 of
 msgid ""
-"**Add** `server_round` **and** `config` **parameters to** `evaluate_fn` "
-"([#1334](https://github.com/adap/flower/pull/1334))"
+"Currently supported values are `num_rounds` (int, default: 1) and "
+"`round_timeout` in seconds (float, default: None)."
 msgstr ""
-"**Ajouter** `server_round` **et** `config` **paramètres à** `evaluate_fn`"
-" ([#1334](https://github.com/adap/flower/pull/1334))"
 
-#: ../../source/ref-changelog.md:395
+#: flwr.server.app.start_server:12 flwr.server.driver.app.start_driver:13 of
 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`)."
+"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 ""
-"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:397
+#: flwr.server.driver.app.start_driver:17 of
 msgid ""
-"**Rename** `rnd` **to** `server_round` "
-"([#1321](https://github.com/adap/flower/pull/1321))"
+"An implementation of the class `flwr.server.ClientManager`. If no "
+"implementation is provided, then `start_driver` will use "
+"`flwr.server.SimpleClientManager`."
 msgstr ""
-"**Rename** `rnd` **to** `server_round` "
-"([#1321](https://github.com/adap/flower/pull/1321))"
 
-#: ../../source/ref-changelog.md:399
-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`."
+#: flwr.server.app.start_server:37 flwr.server.driver.app.start_driver:26 of
+msgid "**hist** -- Object containing training and evaluation metrics."
 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:401
-msgid ""
-"**Move** `flwr.dataset` **to** `flwr_baselines` "
-"([#1273](https://github.com/adap/flower/pull/1273))"
+#: flwr.server.driver.app.start_driver:31 of
+msgid "Starting a driver that connects to an insecure server:"
 msgstr ""
-"**Déplacer** `flwr.dataset` **vers** `flwr_baselines` "
-"([#1273](https://github.com/adap/flower/pull/1273))"
 
-#: ../../source/ref-changelog.md:403
-msgid "The experimental package `flwr.dataset` was migrated to Flower Baselines."
-msgstr "Le paquet expérimental `flwr.dataset` a été migré vers Flower Baselines."
+#: flwr.server.driver.app.start_driver:35 of
+msgid "Starting a driver that connects to an SSL-enabled server:"
+msgstr ""
 
-#: ../../source/ref-changelog.md:405
-msgid ""
-"**Remove experimental strategies** "
-"([#1280](https://github.com/adap/flower/pull/1280))"
+#: ../../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"
 msgstr ""
-"**Supprimer les stratégies expérimentales** "
-"([#1280](https://github.com/adap/flower/pull/1280))"
 
-#: ../../source/ref-changelog.md:407
-msgid ""
-"Remove unmaintained experimental strategies (`FastAndSlow`, `FedFSv0`, "
-"`FedFSv1`)."
+#: ../../source/ref-api/flwr.server.run_server_app.rst:2
+msgid "run\\_server\\_app"
 msgstr ""
-"Supprimer les stratégies expérimentales non maintenues (`FastAndSlow`, "
-"`FedFSv0`, `FedFSv1`)."
 
-#: ../../source/ref-changelog.md:409
-msgid ""
-"**Rename** `Weights` **to** `NDArrays` "
-"([#1258](https://github.com/adap/flower/pull/1258), "
-"[#1259](https://github.com/adap/flower/pull/1259))"
+#: ../../source/ref-api/flwr.server.run_superlink.rst:2
+#, fuzzy
+msgid "run\\_superlink"
+msgstr "flower-superlink"
+
+#: ../../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 ""
-"**Rename** `Weights` **to** `NDArrays` "
-"([#1258](https://github.com/adap/flower/pull/1258), "
-"[#1259](https://github.com/adap/flower/pull/1259))"
 
-#: ../../source/ref-changelog.md:411
+#: flwr.server.app.start_server:5 of
 msgid ""
-"`flwr.common.Weights` was renamed to `flwr.common.NDArrays` to better "
-"capture what this type is all about."
+"A server implementation, either `flwr.server.Server` or a subclass "
+"thereof. If no instance is provided, then `start_server` will create one."
 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:413
+#: flwr.server.app.start_server:16 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.ClientManager`."
+" If no implementation is provided, then `start_server` will use "
+"`flwr.server.client_manager.SimpleClientManager`."
 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))"
 
-#: ../../source/ref-changelog.md:415
+#: flwr.server.app.start_server:21 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."
+"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 ""
-"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:417
+#: flwr.server.app.start_server:42 of
+#, fuzzy
+msgid "Starting an insecure server:"
+msgstr "Démarrer le serveur"
+
+#: flwr.server.app.start_server:46 of
+#, fuzzy
+msgid "Starting an SSL-enabled server:"
+msgstr "Démarrer le serveur"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:2
+#, fuzzy
+msgid "strategy"
+msgstr "stratégie.du.serveur"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
 msgid ""
-"**Make** `get_parameters` **configurable** "
-"([#1242](https://github.com/adap/flower/pull/1242))"
+":py:obj:`FaultTolerantFedAvg "
+"<flwr.server.strategy.FaultTolerantFedAvg>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
 msgstr ""
-"**Make** `get_parameters` **configurable** "
-"([#1242](https://github.com/adap/flower/pull/1242))"
 
-#: ../../source/ref-changelog.md:419
-msgid ""
-"The `get_parameters` method now accepts a configuration dictionary, just "
-"like `get_properties`, `fit`, and `evaluate`."
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 of
+msgid "Configurable fault-tolerant FedAvg strategy implementation."
 msgstr ""
-"La méthode `get_parameters` accepte maintenant un dictionnaire de "
-"configuration, tout comme `get_properties`, `fit`, et `evaluate`."
 
-#: ../../source/ref-changelog.md:421
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
 msgid ""
-"**Replace** `num_rounds` **in** `start_simulation` **with new** `config` "
-"**parameter** ([#1281](https://github.com/adap/flower/pull/1281))"
+":py:obj:`FedAdagrad <flwr.server.strategy.FedAdagrad>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
 msgstr ""
-"**Remplace** `num_rounds` **dans** `start_simulation` **avec le nouveau**"
-" `config` **paramètre** "
-"([#1281](https://github.com/adap/flower/pull/1281))"
 
-#: ../../source/ref-changelog.md:423
-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."
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedadagrad.FedAdagrad:1 of
+#, fuzzy
+msgid "FedAdagrad strategy - Adaptive Federated Optimization using Adagrad."
 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."
+"`FedAdam` et `FedAdam` correspondent à la dernière version de l'article "
+"sur l'optimisation fédérée adaptative."
 
-#: ../../source/ref-changelog.md:427
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
 msgid ""
-"**Support Python 3.10** "
-"([#1320](https://github.com/adap/flower/pull/1320))"
+":py:obj:`FedAdam <flwr.server.strategy.FedAdam>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
 msgstr ""
-"**Support Python 3.10** "
-"([#1320](https://github.com/adap/flower/pull/1320))"
 
-#: ../../source/ref-changelog.md:429
-msgid ""
-"The previous Flower release introduced experimental support for Python "
-"3.10, this release declares Python 3.10 support as stable."
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedadam.FedAdam:1 of
+msgid "FedAdam - Adaptive Federated Optimization using Adam."
 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."
 
-#: ../../source/ref-changelog.md:431
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
 msgid ""
-"**Make all** `Client` **and** `NumPyClient` **methods optional** "
-"([#1260](https://github.com/adap/flower/pull/1260), "
-"[#1277](https://github.com/adap/flower/pull/1277))"
+":py:obj:`FedAvg <flwr.server.strategy.FedAvg>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)"
 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:433
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>: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:41:<autosummary>:1
 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!"
+":py:obj:`FedXgbNnAvg <flwr.server.strategy.FedXgbNnAvg>`\\ \\(\\*args\\, "
+"\\*\\*kwargs\\)"
 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 !"
 
-#: ../../source/ref-changelog.md:435
-msgid ""
-"**Enable passing a** `Server` **instance to** `start_simulation` "
-"([#1281](https://github.com/adap/flower/pull/1281))"
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 of
+msgid "Configurable FedXgbNnAvg strategy implementation."
 msgstr ""
-"**Autoriser le passage d'une **instance `Server` à** `start_simulation` "
-"([#1281](https://github.com/adap/flower/pull/1281))"
 
-#: ../../source/ref-changelog.md:437
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
 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."
+":py:obj:`FedXgbBagging <flwr.server.strategy.FedXgbBagging>`\\ "
+"\\(\\[evaluate\\_function\\]\\)"
 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."
 
-#: ../../source/ref-changelog.md:439
-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-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging:1 of
+msgid "Configurable FedXgbBagging strategy implementation."
 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:441
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
 msgid ""
-"Many code examples received small or even large maintenance updates, "
-"among them are"
+":py:obj:`FedXgbCyclic <flwr.server.strategy.FedXgbCyclic>`\\ "
+"\\(\\*\\*kwargs\\)"
 msgstr ""
-"De nombreux exemples de code ont reçu de petites ou même de grandes mises"
-" à jour de maintenance"
 
-#: ../../source/ref-changelog.md:443
-msgid "`scikit-learn`"
-msgstr "`scikit-learn`"
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic:1 of
+msgid "Configurable FedXgbCyclic strategy implementation."
+msgstr ""
 
-#: ../../source/ref-changelog.md:444
-msgid "`simulation_pytorch`"
-msgstr "`simulation_pytorch`"
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedAvgAndroid <flwr.server.strategy.FedAvgAndroid>`\\ "
+"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)"
+msgstr ""
 
-#: ../../source/ref-changelog.md:445
-msgid "`quickstart_pytorch`"
-msgstr "`quickstart_pytorch` (démarrage rapide)"
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedAvgM <flwr.server.strategy.FedAvgM>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
+msgstr ""
 
-#: ../../source/ref-changelog.md:446
-msgid "`quickstart_simulation`"
-msgstr "`quickstart_simulation`"
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedavgm.FedAvgM:1 of
+#, fuzzy
+msgid "Federated Averaging with Momentum strategy."
+msgstr "Stratégie de moyenne fédérée."
 
-#: ../../source/ref-changelog.md:447
-msgid "`quickstart_tensorflow`"
-msgstr "`quickstart_tensorflow`"
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedOpt <flwr.server.strategy.FedOpt>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)"
+msgstr ""
 
-#: ../../source/ref-changelog.md:448
-msgid "`advanced_tensorflow`"
-msgstr "`advanced_tensorflow` (en anglais)"
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedopt.FedOpt:1 of
+#, fuzzy
+msgid "Federated Optim strategy."
+msgstr "Stratégie de moyenne fédérée."
 
-#: ../../source/ref-changelog.md:450
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
 msgid ""
-"**Remove the obsolete simulation example** "
-"([#1328](https://github.com/adap/flower/pull/1328))"
+":py:obj:`FedProx <flwr.server.strategy.FedProx>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
 msgstr ""
-"**Supprime l'exemple de simulation obsolète** "
-"([#1328](https://github.com/adap/flower/pull/1328))"
 
-#: ../../source/ref-changelog.md:452
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>: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:41:<autosummary>:1
 msgid ""
-"Removes the obsolete `simulation` example and renames "
-"`quickstart_simulation` to `simulation_tensorflow` so it fits withs the "
-"naming of `simulation_pytorch`"
+":py:obj:`FedYogi <flwr.server.strategy.FedYogi>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
 msgstr ""
-"Supprime l'exemple obsolète `simulation` et renomme "
-"`quickstart_simulation` en `simulation_tensorflow` pour qu'il corresponde"
-" au nom de `simulation_pytorch`"
 
-#: ../../source/ref-changelog.md:454
-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))"
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedyogi.FedYogi:1 of
+msgid "FedYogi [Reddi et al., 2020] strategy."
 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:456
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
 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!"
+":py:obj:`QFedAvg <flwr.server.strategy.QFedAvg>`\\ \\(\\*\\[\\, "
+"q\\_param\\, qffl\\_learning\\_rate\\, ...\\]\\)"
 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:458 ../../source/ref-changelog.md:513
-#: ../../source/ref-changelog.md:582 ../../source/ref-changelog.md:621
-msgid "**Minor updates**"
-msgstr "**Mises à jour mineures**"
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg:1 of
+msgid "Configurable QFedAvg strategy implementation."
+msgstr ""
 
-#: ../../source/ref-changelog.md:460
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
 msgid ""
-"Add round number to fit and evaluate log messages "
-"([#1266](https://github.com/adap/flower/pull/1266))"
+":py:obj:`FedMedian <flwr.server.strategy.FedMedian>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
 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:461
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>: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:41:<autosummary>:1
 msgid ""
-"Add secure gRPC connection to the `advanced_tensorflow` code example "
-"([#847](https://github.com/adap/flower/pull/847))"
+":py:obj:`FedTrimmedAvg <flwr.server.strategy.FedTrimmedAvg>`\\ "
+"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)"
 msgstr ""
-"Ajouter une connexion gRPC sécurisée à l'exemple de code "
-"`advanced_tensorflow` ([#847](https://github.com/adap/flower/pull/847))"
 
-#: ../../source/ref-changelog.md:462
-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))"
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1 of
+msgid "Federated Averaging with Trimmed Mean [Dong Yin, et al., 2021]."
 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:463
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
 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))"
+":py:obj:`Krum <flwr.server.strategy.Krum>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)"
 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:465
-msgid "v0.19.0 (2022-05-18)"
-msgstr "v0.19.0 (2022-05-18)"
-
-#: ../../source/ref-changelog.md:469
-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))"
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.krum.Krum:1 of
+msgid "Krum [Blanchard et al., 2017] strategy."
 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:471
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
 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.dev/docs/using-baselines.html). "
-"With this first preview release we're also inviting the community to "
-"[contribute their own baselines](https://flower.dev/docs/contributing-"
-"baselines.html)."
+":py:obj:`Bulyan <flwr.server.strategy.Bulyan>`\\ \\(\\*\\, "
+"fraction\\_fit\\, fraction\\_evaluate\\, ...\\)"
 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.dev/docs/using-"
-"baselines.html). Avec cette première version préliminaire, nous invitons "
-"également la communauté à [contribuer à leurs propres lignes de "
-"base](https://flower.dev/docs/contributing-baselines.html)."
 
-#: ../../source/ref-changelog.md:473
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.bulyan.Bulyan:1 of
+#, fuzzy
+msgid "Bulyan strategy."
+msgstr "Stratégies intégrées"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
 msgid ""
-"**C++ client SDK (preview) and code example** "
-"([#1111](https://github.com/adap/flower/pull/1111))"
+":py:obj:`DPFedAvgAdaptive <flwr.server.strategy.DPFedAvgAdaptive>`\\ "
+"\\(strategy\\, num\\_sampled\\_clients\\)"
 msgstr ""
-"**SDK client C++ (aperçu) et exemple de code** "
-"([#1111](https://github.com/adap/flower/pull/1111))"
 
-#: ../../source/ref-changelog.md:475
-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."
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of
+msgid "Wrapper for configuring a Strategy for DP with Adaptive Clipping."
 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."
 
-#: ../../source/ref-changelog.md:477
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
 msgid ""
-"**Add experimental support for Python 3.10 and Python 3.11** "
-"([#1135](https://github.com/adap/flower/pull/1135))"
+":py:obj:`DPFedAvgFixed <flwr.server.strategy.DPFedAvgFixed>`\\ "
+"\\(strategy\\, num\\_sampled\\_clients\\, ...\\)"
 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:479
-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."
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:1 of
+msgid "Wrapper for configuring a Strategy for DP with Fixed Clipping."
 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."
 
-#: ../../source/ref-changelog.md:481
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#, fuzzy
+msgid ":py:obj:`Strategy <flwr.server.strategy.Strategy>`\\ \\(\\)"
+msgstr "serveur.stratégie.Stratégie"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.strategy.Strategy:1 of
+msgid "Abstract base class for server strategy implementations."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.Bulyan.rst:2
+msgid "Bulyan"
+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`"
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan:3 of
+msgid "Implementation based on https://arxiv.org/abs/1802.07927."
+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."
+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."
+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 ""
+
+#: 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.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.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.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.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.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.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.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 ""
-"**Aggregate custom metrics through user-provided functions** "
-"([#1144](https://github.com/adap/flower/pull/1144))"
+"Byzantine resilient aggregation rule that is used as the first step of "
+"the Bulyan (e.g., Krum)"
 msgstr ""
-"**Agréger des mesures personnalisées grâce à des fonctions fournies par "
-"l'utilisateur** ([#1144](https://github.com/adap/flower/pull/1144))"
 
-#: ../../source/ref-changelog.md:483
-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."
+#: flwr.server.strategy.bulyan.Bulyan:29 of
+msgid "arguments to the first_aggregation rule"
 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."
 
-#: ../../source/ref-changelog.md:485
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**User-configurable round timeout** "
-"([#1162](https://github.com/adap/flower/pull/1162))"
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.Bulyan.aggregate_evaluate>`\\ \\(server\\_round\\, "
+"results\\, ...\\)"
 msgstr ""
-"**Temps d'attente configurable par l'utilisateur** "
-"([#1162](https://github.com/adap/flower/pull/1162))"
 
-#: ../../source/ref-changelog.md:487
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+#, fuzzy
+msgid "Aggregate evaluation losses using weighted average."
+msgstr "Résultats globaux de l'évaluation."
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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."
+":py:obj:`aggregate_fit <flwr.server.strategy.Bulyan.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
 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."
 
-#: ../../source/ref-changelog.md:489
+#: flwr.server.strategy.bulyan.Bulyan.aggregate_fit:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+#, fuzzy
+msgid "Aggregate fit results using Bulyan."
+msgstr "Résultats globaux de l'évaluation."
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.Bulyan.configure_evaluate>`\\ \\(server\\_round\\, "
+"parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.configure_evaluate:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.configure_evaluate:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_evaluate:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.configure_evaluate:1
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.Bulyan.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.configure_fit:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.configure_fit:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_fit:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.configure_fit:1
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.Bulyan.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.evaluate:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.evaluate:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.evaluate:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.evaluate:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid "Evaluate model parameters using an evaluation function."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.Bulyan.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.initialize_parameters:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+#, fuzzy
+msgid "Initialize global model parameters."
+msgstr "Initialise le modèle global"
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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))"
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.Bulyan.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 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))"
 
-#: ../../source/ref-changelog.md:491
-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`."
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.num_evaluation_clients:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_evaluation_clients:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.num_evaluation_clients:1 of
+msgid "Use a fraction of available clients for evaluation."
 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`."
 
-#: ../../source/ref-changelog.md:493
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**Two new Jupyter Notebook tutorials** "
-"([#1141](https://github.com/adap/flower/pull/1141))"
+":py:obj:`num_fit_clients <flwr.server.strategy.Bulyan.num_fit_clients>`\\"
+" \\(num\\_available\\_clients\\)"
 msgstr ""
-"**Deux nouveaux tutoriels Jupyter Notebook** "
-"([#1141](https://github.com/adap/flower/pull/1141))"
 
-#: ../../source/ref-changelog.md:495
-msgid ""
-"Two Jupyter Notebook tutorials (compatible with Google Colab) explain "
-"basic and intermediate Flower features:"
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.num_fit_clients:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_fit_clients:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.num_fit_clients:1 of
+msgid "Return the sample size and the required number of available clients."
 msgstr ""
-"Deux tutoriels Jupyter Notebook (compatibles avec Google Colab) "
-"expliquent les fonctionnalités de base et intermédiaires de Flower :"
 
-#: ../../source/ref-changelog.md:497
-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)"
+#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of
+msgid "Bases: :py:class:`~flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed`"
 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)"
 
-#: ../../source/ref-changelog.md:499
-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)"
+#: 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 ""
-"*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-changelog.md:501
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"**New FedAvgM strategy (Federated Averaging with Server Momentum)** "
-"([#1076](https://github.com/adap/flower/pull/1076))"
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.DPFedAvgAdaptive.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
 msgstr ""
-"**Nouvelle stratégie FedAvgM (Federated Averaging with Server Momentum)**"
-" ([#1076](https://github.com/adap/flower/pull/1076))"
 
-#: ../../source/ref-changelog.md:503
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: of
 msgid ""
-"The new `FedAvgM` strategy implements Federated Averaging with Server "
-"Momentum \\[Hsu et al., 2019\\]."
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.DPFedAvgAdaptive.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
 msgstr ""
-"La nouvelle stratégie `FedAvgM` met en œuvre la moyenne fédérée avec le "
-"momentum du serveur [Hsu et al., 2019]."
 
-#: ../../source/ref-changelog.md:505
-msgid ""
-"**New advanced PyTorch code example** "
-"([#1007](https://github.com/adap/flower/pull/1007))"
+#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.aggregate_fit:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid "Aggregate training results as in DPFedAvgFixed and update clip norms."
 msgstr ""
-"**Nouvel exemple de code PyTorch avancé** "
-"([#1007](https://github.com/adap/flower/pull/1007))"
 
-#: ../../source/ref-changelog.md:507
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"A new code example (`advanced_pytorch`) demonstrates advanced Flower "
-"concepts with PyTorch."
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.DPFedAvgAdaptive.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"Un nouvel exemple de code (`advanced_pytorch`) démontre des concepts de "
-"fleur avancés avec PyTorch."
 
-#: ../../source/ref-changelog.md:509
-msgid ""
-"**New JAX code example** "
-"([#906](https://github.com/adap/flower/pull/906), "
-"[#1143](https://github.com/adap/flower/pull/1143))"
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:1 of
+msgid "Configure the next round of evaluation using the specified strategy."
 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:511
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"A new code example (`jax_from_centralized_to_federated`) shows federated "
-"learning with JAX and Flower."
+":py:obj:`configure_fit "
+"<flwr.server.strategy.DPFedAvgAdaptive.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"Un nouvel exemple de code (`jax_from_centralized_to_federated`) montre "
-"l'apprentissage fédéré avec JAX et Flower."
 
-#: ../../source/ref-changelog.md:515
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"New option to keep Ray running if Ray was already initialized in "
-"`start_simulation` ([#1177](https://github.com/adap/flower/pull/1177))"
+":py:obj:`evaluate <flwr.server.strategy.DPFedAvgAdaptive.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.evaluate:1 of
+msgid "Evaluate model parameters using an evaluation function from the strategy."
 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))"
 
-#: ../../source/ref-changelog.md:516
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"Add support for custom `ClientManager` as a `start_simulation` parameter "
-"([#1171](https://github.com/adap/flower/pull/1171))"
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.DPFedAvgAdaptive.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 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:517
-msgid ""
-"New documentation for [implementing strategies](https://flower.dev/docs"
-"/how-to-implement-strategies.html) "
-"([#1097](https://github.com/adap/flower/pull/1097), "
-"[#1175](https://github.com/adap/flower/pull/1175))"
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.initialize_parameters:1 of
+msgid "Initialize global model parameters using given strategy."
+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."
 msgstr ""
-"Nouvelle documentation pour [mettre en œuvre des "
-"stratégies](https://flower.dev/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-changelog.md:518
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:10
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate:10 of
 msgid ""
-"New mobile-friendly documentation theme "
-"([#1174](https://github.com/adap/flower/pull/1174))"
+"**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 ""
-"Nouveau thème de documentation adapté aux mobiles "
-"([#1174](https://github.com/adap/flower/pull/1174))"
 
-#: ../../source/ref-changelog.md:519
+#: 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.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
 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))"
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.DPFedAvgFixed.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
 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))"
 
-#: ../../source/ref-changelog.md:523
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"**Remove deprecated support for Python 3.6** "
-"([#871](https://github.com/adap/flower/pull/871))"
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.DPFedAvgFixed.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_fit:1 of
+msgid "Aggregate training results using unweighted aggregation."
 msgstr ""
-"**Supprime la prise en charge obsolète de Python 3.6** "
-"([#871](https://github.com/adap/flower/pull/871))"
 
-#: ../../source/ref-changelog.md:524
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"**Remove deprecated KerasClient** "
-"([#857](https://github.com/adap/flower/pull/857))"
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.DPFedAvgFixed.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"**Supprimez KerasClient** "
-"([#857](https://github.com/adap/flower/pull/857))"
 
-#: ../../source/ref-changelog.md:525
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"**Remove deprecated no-op extra installs** "
-"([#973](https://github.com/adap/flower/pull/973))"
+":py:obj:`configure_fit "
+"<flwr.server.strategy.DPFedAvgFixed.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"**Supprimer les installations supplémentaires no-op dépréciées** "
-"([#973](https://github.com/adap/flower/pull/973))"
 
-#: ../../source/ref-changelog.md:526
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:1 of
 msgid ""
-"**Remove deprecated proto fields from** `FitRes` **and** `EvaluateRes` "
-"([#869](https://github.com/adap/flower/pull/869))"
+"Configure the next round of training incorporating Differential Privacy "
+"(DP)."
 msgstr ""
-"**Supprimez les champs proto obsolètes de** `FitRes` **et** `EvaluateRes`"
-" ([#869](https://github.com/adap/flower/pull/869))"
 
-#: ../../source/ref-changelog.md:527
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"**Remove deprecated QffedAvg strategy (replaced by QFedAvg)** "
-"([#1107](https://github.com/adap/flower/pull/1107))"
+":py:obj:`evaluate <flwr.server.strategy.DPFedAvgFixed.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
 msgstr ""
-"**Supprime la stratégie QffedAvg (remplacée par QFedAvg)** "
-"([#1107](https://github.com/adap/flower/pull/1107))"
 
-#: ../../source/ref-changelog.md:528
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"**Remove deprecated DefaultStrategy strategy** "
-"([#1142](https://github.com/adap/flower/pull/1142))"
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.DPFedAvgFixed.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 msgstr ""
-"**Supprime la stratégie DefaultStrategy qui est obsolète** "
-"([#1142](https://github.com/adap/flower/pull/1142))"
 
-#: ../../source/ref-changelog.md:529
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:3 of
 msgid ""
-"**Remove deprecated support for eval_fn accuracy return value** "
-"([#1142](https://github.com/adap/flower/pull/1142))"
+"Configuration of the next training round includes information related to "
+"DP, such as clip norm and noise stddev."
 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))"
 
-#: ../../source/ref-changelog.md:530
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:13
+#: flwr.server.strategy.strategy.Strategy.configure_fit:10 of
 msgid ""
-"**Remove deprecated support for passing initial parameters as NumPy "
-"ndarrays** ([#1142](https://github.com/adap/flower/pull/1142))"
+"**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 ""
-"**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/ref-changelog.md:532
-msgid "v0.18.0 (2022-02-28)"
-msgstr "v0.18.0 (2022-02-28)"
+#: ../../source/ref-api/flwr.server.strategy.FaultTolerantFedAvg.rst:2
+#, fuzzy
+msgid "FaultTolerantFedAvg"
+msgstr "server.strategy.FaultTolerantFedAvg"
 
-#: ../../source/ref-changelog.md:536
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
 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))"
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FaultTolerantFedAvg.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
 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/ref-changelog.md:538
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
 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]`)."
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FaultTolerantFedAvg.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_fit:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_fit:1 of
+msgid "Aggregate fit results using weighted average."
 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]`)."
 
-#: ../../source/ref-changelog.md:540
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"**New Jupyter Notebook code example** "
-"([#833](https://github.com/adap/flower/pull/833))"
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FaultTolerantFedAvg.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"**Nouvel exemple de code Jupyter Notebook** "
-"([#833](https://github.com/adap/flower/pull/833))"
 
-#: ../../source/ref-changelog.md:542
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"A new code example (`quickstart_simulation`) demonstrates Flower "
-"simulations using the Virtual Client Engine through Jupyter Notebook "
-"(incl. Google Colab)."
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FaultTolerantFedAvg.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 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:544
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"**Client properties (feature preview)** "
-"([#795](https://github.com/adap/flower/pull/795))"
+":py:obj:`evaluate <flwr.server.strategy.FaultTolerantFedAvg.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
 msgstr ""
-"**Propriétés du client (aperçu des fonctionnalités)** "
-"([#795](https://github.com/adap/flower/pull/795))"
 
-#: ../../source/ref-changelog.md:546
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"Clients can implement a new method `get_properties` to enable server-side"
-" strategies to query client properties."
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FaultTolerantFedAvg.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 msgstr ""
-"Les clients peuvent implémenter une nouvelle méthode `get_properties` "
-"pour permettre aux stratégies côté serveur d'interroger les propriétés du"
-" client."
 
-#: ../../source/ref-changelog.md:548
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"**Experimental Android support with TFLite** "
-"([#865](https://github.com/adap/flower/pull/865))"
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FaultTolerantFedAvg.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 msgstr ""
-"**Support expérimental d'Android avec TFLite** "
-"([#865](https://github.com/adap/flower/pull/865))"
 
-#: ../../source/ref-changelog.md:550
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
 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."
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FaultTolerantFedAvg.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 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."
 
-#: ../../source/ref-changelog.md:552
+#: ../../source/ref-api/flwr.server.strategy.FedAdagrad.rst:2
+#: ../../source/ref-changelog.md:839
+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 ""
+
+#: 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.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.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:<autosummary>:1 of
 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`."
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedAdagrad.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
 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`."
 
-#: ../../source/ref-changelog.md:554
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**Make gRPC keepalive time user-configurable and decrease default "
-"keepalive time** ([#1069](https://github.com/adap/flower/pull/1069))"
+":py:obj:`aggregate_fit <flwr.server.strategy.FedAdagrad.aggregate_fit>`\\"
+" \\(server\\_round\\, results\\, failures\\)"
 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))"
 
-#: ../../source/ref-changelog.md:556
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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."
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedAdagrad.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 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."
 
-#: ../../source/ref-changelog.md:558
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**New differential privacy example using Opacus and PyTorch** "
-"([#805](https://github.com/adap/flower/pull/805))"
+":py:obj:`configure_fit <flwr.server.strategy.FedAdagrad.configure_fit>`\\"
+" \\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"**Nouvel exemple de confidentialité différentielle utilisant Opacus et "
-"PyTorch** ([#805](https://github.com/adap/flower/pull/805))"
 
-#: ../../source/ref-changelog.md:560
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"A new code example (`opacus`) demonstrates differentially-private "
-"federated learning with Opacus, PyTorch, and Flower."
+":py:obj:`evaluate <flwr.server.strategy.FedAdagrad.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
 msgstr ""
-"Un nouvel exemple de code (`opacus`) démontre l'apprentissage fédéré "
-"différentiellement privé avec Opacus, PyTorch et Flower."
 
-#: ../../source/ref-changelog.md:562
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**New Hugging Face Transformers code example** "
-"([#863](https://github.com/adap/flower/pull/863))"
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedAdagrad.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 msgstr ""
-"**Nouvel exemple de code pour les Transformers à visage embrassant** "
-"([#863](https://github.com/adap/flower/pull/863))"
 
-#: ../../source/ref-changelog.md:564
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"A new code example (`quickstart_huggingface`) demonstrates usage of "
-"Hugging Face Transformers with Flower."
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedAdagrad.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 msgstr ""
-"Un nouvel exemple de code (`quickstart_huggingface`) démontre "
-"l'utilisation des transformateurs Hugging Face avec Flower."
 
-#: ../../source/ref-changelog.md:566
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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))"
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedAdagrad.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 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:568
+#: ../../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.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:<autosummary>:1 of
 msgid ""
-"A new code example (`quickstart_mlcube`) demonstrates usage of MLCube "
-"with Flower."
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedAdam.aggregate_evaluate>`\\ \\(server\\_round\\,"
+" results\\, ...\\)"
 msgstr ""
-"Un nouvel exemple de code (`quickstart_mlcube`) démontre l'utilisation de"
-" MLCube avec Flower."
 
-#: ../../source/ref-changelog.md:570
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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))"
+":py:obj:`aggregate_fit <flwr.server.strategy.FedAdam.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
 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:572
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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."
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedAdam.configure_evaluate>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
 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."
 
-#: ../../source/ref-changelog.md:574
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**Updated** `FedAdam` **and** `FedYogi` **strategies** "
-"([#885](https://github.com/adap/flower/pull/885), "
-"[#895](https://github.com/adap/flower/pull/895))"
+":py:obj:`configure_fit <flwr.server.strategy.FedAdam.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"**Mise à jour** `FedAdam` **et** `FedYogi` **stratégies** "
-"([#885](https://github.com/adap/flower/pull/885), "
-"[#895](https://github.com/adap/flower/pull/895))"
 
-#: ../../source/ref-changelog.md:576
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"`FedAdam` and `FedAdam` match the latest version of the Adaptive "
-"Federated Optimization paper."
+":py:obj:`evaluate <flwr.server.strategy.FedAdam.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
 msgstr ""
-"`FedAdam` et `FedAdam` correspondent à la dernière version de l'article "
-"sur l'optimisation fédérée adaptative."
 
-#: ../../source/ref-changelog.md:578
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**Initialize** `start_simulation` **with a list of client IDs** "
-"([#860](https://github.com/adap/flower/pull/860))"
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedAdam.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 msgstr ""
-"**Initialise** `start_simulation` **avec une liste d'ID de clients** "
-"([#860](https://github.com/adap/flower/pull/860))"
 
-#: ../../source/ref-changelog.md:580
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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."
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedAdam.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 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/ref-changelog.md:584
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"Update `num_examples` calculation in PyTorch code examples in "
-"([#909](https://github.com/adap/flower/pull/909))"
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedAdam.num_fit_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 ""
-"Mettre à jour le calcul de `num_examples` dans les exemples de code "
-"PyTorch dans ([#909](https://github.com/adap/flower/pull/909))"
 
-#: ../../source/ref-changelog.md:585
+#: flwr.server.strategy.fedavg.FedAvg:5 flwr.server.strategy.fedprox.FedProx:37
+#: of
 msgid ""
-"Expose Flower version through `flwr.__version__` "
-"([#952](https://github.com/adap/flower/pull/952))"
+"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 ""
-"Exposer la version de Flower à travers `flwr.__version__` "
-"([#952](https://github.com/adap/flower/pull/952))"
 
-#: ../../source/ref-changelog.md:586
+#: flwr.server.strategy.fedavg.FedAvg:9 flwr.server.strategy.fedprox.FedProx:41
+#: of
 msgid ""
-"`start_server` in `app.py` now returns a `History` object containing "
-"metrics from training ([#974](https://github.com/adap/flower/pull/974))"
+"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 ""
-"`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/ref-changelog.md:587
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"Make `max_workers` (used by `ThreadPoolExecutor`) configurable "
-"([#978](https://github.com/adap/flower/pull/978))"
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedAvg.aggregate_evaluate>`\\ \\(server\\_round\\, "
+"results\\, ...\\)"
 msgstr ""
-"Rendre `max_workers` (utilisé par `ThreadPoolExecutor`) configurable "
-"([#978](https://github.com/adap/flower/pull/978))"
 
-#: ../../source/ref-changelog.md:588
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"Increase sleep time after server start to three seconds in all code "
-"examples ([#1086](https://github.com/adap/flower/pull/1086))"
+":py:obj:`aggregate_fit <flwr.server.strategy.FedAvg.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
 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/ref-changelog.md:589
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"Added a new FAQ section to the documentation "
-"([#948](https://github.com/adap/flower/pull/948))"
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedAvg.configure_evaluate>`\\ \\(server\\_round\\, "
+"parameters\\, ...\\)"
 msgstr ""
-"Ajout d'une nouvelle section FAQ à la documentation "
-"([#948](https://github.com/adap/flower/pull/948))"
 
-#: ../../source/ref-changelog.md:590
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"And many more under-the-hood changes, library updates, documentation "
-"changes, and tooling improvements!"
+":py:obj:`configure_fit <flwr.server.strategy.FedAvg.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 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/ref-changelog.md:594
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**Removed** `flwr_example` **and** `flwr_experimental` **from release "
-"build** ([#869](https://github.com/adap/flower/pull/869))"
+":py:obj:`evaluate <flwr.server.strategy.FedAvg.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
 msgstr ""
-"**Supprimé** `flwr_example` **et** `flwr_experimental` **de la version "
-"release build** ([#869](https://github.com/adap/flower/pull/869))"
 
-#: ../../source/ref-changelog.md:596
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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."
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedAvg.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 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:598
-msgid "v0.17.0 (2021-09-24)"
-msgstr "v0.17.0 (2021-09-24)"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedAvg.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
 
-#: ../../source/ref-changelog.md:602
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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))"
+":py:obj:`num_fit_clients <flwr.server.strategy.FedAvg.num_fit_clients>`\\"
+" \\(num\\_available\\_clients\\)"
 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))"
 
-#: ../../source/ref-changelog.md:604
+#: ../../source/ref-api/flwr.server.strategy.FedAvgAndroid.rst:2
+#, fuzzy
+msgid "FedAvgAndroid"
+msgstr "DPFedAvgAdaptive"
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
 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`."
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedAvgAndroid.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
 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/ref-changelog.md:606
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
 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."
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FedAvgAndroid.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
 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."
 
-#: ../../source/ref-changelog.md:608
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"**New built-in strategies** "
-"([#828](https://github.com/adap/flower/pull/828) "
-"[#822](https://github.com/adap/flower/pull/822))"
+":py:obj:`bytes_to_ndarray "
+"<flwr.server.strategy.FedAvgAndroid.bytes_to_ndarray>`\\ \\(tensor\\)"
 msgstr ""
-"**Nouvelles stratégies intégrées** "
-"([#828](https://github.com/adap/flower/pull/828) "
-"[#822](https://github.com/adap/flower/pull/822))"
 
-#: ../../source/ref-changelog.md:610
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: of
 msgid ""
-"FedYogi - Federated learning strategy using Yogi on server-side. "
-"Implementation based on https://arxiv.org/abs/2003.00295"
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedAvgAndroid.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"FedYogi - Stratégie d'apprentissage fédéré utilisant Yogi côté serveur. "
-"Mise en oeuvre basée sur https://arxiv.org/abs/2003.00295"
 
-#: ../../source/ref-changelog.md:611
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"FedAdam - Federated learning strategy using Adam on server-side. "
-"Implementation based on https://arxiv.org/abs/2003.00295"
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FedAvgAndroid.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"FedAdam - Stratégie d'apprentissage fédéré utilisant Adam côté serveur. "
-"Mise en œuvre basée sur https://arxiv.org/abs/2003.00295"
 
-#: ../../source/ref-changelog.md:613
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"**New PyTorch Lightning code example** "
-"([#617](https://github.com/adap/flower/pull/617))"
+":py:obj:`evaluate <flwr.server.strategy.FedAvgAndroid.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
 msgstr ""
-"**Nouvel exemple de code PyTorch Lightning** "
-"([#617](https://github.com/adap/flower/pull/617))"
 
-#: ../../source/ref-changelog.md:615
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"**New Variational Auto-Encoder code example** "
-"([#752](https://github.com/adap/flower/pull/752))"
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedAvgAndroid.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 msgstr ""
-"**Nouvel exemple de code d'autocodage variationnel** "
-"([#752](https://github.com/adap/flower/pull/752))"
 
-#: ../../source/ref-changelog.md:617
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"**New scikit-learn code example** "
-"([#748](https://github.com/adap/flower/pull/748))"
+":py:obj:`ndarray_to_bytes "
+"<flwr.server.strategy.FedAvgAndroid.ndarray_to_bytes>`\\ \\(ndarray\\)"
 msgstr ""
-"**Nouvel exemple de code scikit-learn** "
-"([#748](https://github.com/adap/flower/pull/748))"
 
-#: ../../source/ref-changelog.md:619
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: of
 msgid ""
-"**New experimental TensorBoard strategy** "
-"([#789](https://github.com/adap/flower/pull/789))"
+":py:obj:`ndarrays_to_parameters "
+"<flwr.server.strategy.FedAvgAndroid.ndarrays_to_parameters>`\\ "
+"\\(ndarrays\\)"
 msgstr ""
-"**Nouvelle stratégie expérimentale TensorBoard** "
-"([#789](https://github.com/adap/flower/pull/789))"
 
-#: ../../source/ref-changelog.md:623
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"Improved advanced TensorFlow code example "
-"([#769](https://github.com/adap/flower/pull/769))"
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedAvgAndroid.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 msgstr ""
-"Amélioration de l'exemple de code TensorFlow avancé "
-"([#769](https://github.com/adap/flower/pull/769))"
 
-#: ../../source/ref-changelog.md:624
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"Warning when `min_available_clients` is misconfigured "
-"([#830](https://github.com/adap/flower/pull/830))"
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedAvgAndroid.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 msgstr ""
-"Avertissement lorsque `min_available_clients` est mal configuré "
-"([#830](https://github.com/adap/flower/pull/830))"
 
-#: ../../source/ref-changelog.md:625
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"Improved gRPC server docs "
-"([#841](https://github.com/adap/flower/pull/841))"
+":py:obj:`parameters_to_ndarrays "
+"<flwr.server.strategy.FedAvgAndroid.parameters_to_ndarrays>`\\ "
+"\\(parameters\\)"
 msgstr ""
-"Amélioration de la documentation sur le serveur gRPC "
-"([#841](https://github.com/adap/flower/pull/841))"
 
-#: ../../source/ref-changelog.md:626
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>: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.fedavgm.FedAvgM:25 of
 msgid ""
-"Improved error message in `NumPyClient` "
-"([#851](https://github.com/adap/flower/pull/851))"
+"Server-side learning rate used in server-side optimization. Defaults to "
+"1.0."
+msgstr ""
+
+#: flwr.server.strategy.fedavgm.FedAvgM:28 of
+msgid "Server-side momentum factor used for FedAvgM. Defaults to 0.0."
 msgstr ""
-"Amélioration du message d'erreur dans `NumPyClient` "
-"([#851](https://github.com/adap/flower/pull/851))"
 
-#: ../../source/ref-changelog.md:627
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"Improved PyTorch quickstart code example "
-"([#852](https://github.com/adap/flower/pull/852))"
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedAvgM.aggregate_evaluate>`\\ \\(server\\_round\\,"
+" results\\, ...\\)"
 msgstr ""
-"Exemple de code de démarrage rapide PyTorch amélioré "
-"([#852](https://github.com/adap/flower/pull/852))"
 
-#: ../../source/ref-changelog.md:631
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**Disabled final distributed evaluation** "
-"([#800](https://github.com/adap/flower/pull/800))"
+":py:obj:`aggregate_fit <flwr.server.strategy.FedAvgM.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
 msgstr ""
-"**Désactivé l'évaluation finale distribuée** "
-"([#800](https://github.com/adap/flower/pull/800))"
 
-#: ../../source/ref-changelog.md:633
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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`."
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedAvgM.configure_evaluate>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
 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`."
 
-#: ../../source/ref-changelog.md:635
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**Renamed q-FedAvg strategy** "
-"([#802](https://github.com/adap/flower/pull/802))"
+":py:obj:`configure_fit <flwr.server.strategy.FedAvgM.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"**Renommé stratégie q-FedAvg** "
-"([#802](https://github.com/adap/flower/pull/802))"
 
-#: ../../source/ref-changelog.md:637
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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 the original (now "
-"deprecated) `QffedAvg` class is still available for compatibility reasons"
-" (it will be removed in a future release)."
+":py:obj:`evaluate <flwr.server.strategy.FedAvgM.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
 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)."
 
-#: ../../source/ref-changelog.md:639
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**Deprecated and renamed code example** `simulation_pytorch` **to** "
-"`simulation_pytorch_legacy` "
-"([#791](https://github.com/adap/flower/pull/791))"
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedAvgM.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 msgstr ""
-"**Exemple de code déprécié et renommé** `simulation_pytorch` **en** "
-"`simulation_pytorch_legacy` "
-"([#791](https://github.com/adap/flower/pull/791))"
 
-#: ../../source/ref-changelog.md:641
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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."
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedAvgM.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 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:643
-msgid "v0.16.0 (2021-05-11)"
-msgstr "v0.16.0 (2021-05-11)"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedAvgM.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedMedian.rst:2
+msgid "FedMedian"
+msgstr ""
 
-#: ../../source/ref-changelog.md:647
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**New built-in strategies** "
-"([#549](https://github.com/adap/flower/pull/549))"
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedMedian.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
 msgstr ""
-"**Nouvelles stratégies intégrées** "
-"([#549](https://github.com/adap/flower/pull/549))"
 
-#: ../../source/ref-changelog.md:649
-msgid "(abstract) FedOpt"
-msgstr "(résumé) FedOpt"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedMedian.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
 
-#: ../../source/ref-changelog.md:650
-msgid "FedAdagrad"
-msgstr "FedAdagrad"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedmedian.FedMedian.aggregate_fit:1 of
+#, fuzzy
+msgid "Aggregate fit results using median."
+msgstr "Résultats globaux de l'évaluation."
 
-#: ../../source/ref-changelog.md:652
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**Custom metrics for server and strategies** "
-"([#717](https://github.com/adap/flower/pull/717))"
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedMedian.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"**Métriques personnalisées pour le serveur et les stratégies** "
-"([#717](https://github.com/adap/flower/pull/717))"
 
-#: ../../source/ref-changelog.md:654
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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."
+":py:obj:`configure_fit <flwr.server.strategy.FedMedian.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 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."
 
-#: ../../source/ref-changelog.md:656
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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 build-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."
+":py:obj:`evaluate <flwr.server.strategy.FedMedian.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
 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."
 
-#: ../../source/ref-changelog.md:658
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"Stratey 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}`."
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedMedian.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 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}`."
 
-#: ../../source/ref-changelog.md:660
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"Flower 0.15-style return types are deprecated (but still supported), "
-"compatibility will be removed in a future release."
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedMedian.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 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."
 
-#: ../../source/ref-changelog.md:662
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**Migration warnings for deprecated functionality** "
-"([#690](https://github.com/adap/flower/pull/690))"
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedMedian.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 msgstr ""
-"**Avertissements de migration pour les fonctionnalités obsolètes** "
-"([#690](https://github.com/adap/flower/pull/690))"
 
-#: ../../source/ref-changelog.md:664
-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."
+#: ../../source/ref-api/flwr.server.strategy.FedOpt.rst:2
+msgid "FedOpt"
 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."
 
-#: ../../source/ref-changelog.md:666
-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))"
+#: flwr.server.strategy.fedopt.FedOpt:33 of
+msgid "Momentum parameter. Defaults to 0.0."
 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:668
-msgid "MXNet example and documentation"
-msgstr "Exemple et documentation MXNet"
+#: flwr.server.strategy.fedopt.FedOpt:35 of
+msgid "Second moment parameter. Defaults to 0.0."
+msgstr ""
 
-#: ../../source/ref-changelog.md:670
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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))"
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedOpt.aggregate_evaluate>`\\ \\(server\\_round\\, "
+"results\\, ...\\)"
 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))"
 
-#: ../../source/ref-changelog.md:674
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**Serialization-agnostic server** "
-"([#721](https://github.com/adap/flower/pull/721))"
+":py:obj:`aggregate_fit <flwr.server.strategy.FedOpt.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
 msgstr ""
-"**Serveur agnostique de sérialisation** "
-"([#721](https://github.com/adap/flower/pull/721))"
 
-#: ../../source/ref-changelog.md:676
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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)."
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedOpt.configure_evaluate>`\\ \\(server\\_round\\, "
+"parameters\\, ...\\)"
 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)."
 
-#: ../../source/ref-changelog.md:678
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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 slighly 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."
+":py:obj:`configure_fit <flwr.server.strategy.FedOpt.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 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."
 
-#: ../../source/ref-changelog.md:680
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"Deprecated `flwr.server.Server.evaluate`, use "
-"`flwr.server.Server.evaluate_round` instead "
-"([#717](https://github.com/adap/flower/pull/717))"
+":py:obj:`evaluate <flwr.server.strategy.FedOpt.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
 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:682
-msgid "v0.15.0 (2021-03-12)"
-msgstr "v0.15.0 (2021-03-12)"
-
-#: ../../source/ref-changelog.md:686
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**Server-side parameter initialization** "
-"([#658](https://github.com/adap/flower/pull/658))"
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedOpt.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 msgstr ""
-"**Initialisation des paramètres côté serveur** "
-"([#658](https://github.com/adap/flower/pull/658))"
 
-#: ../../source/ref-changelog.md:688
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"Model parameters can now be initialized on the server-side. Server-side "
-"parameter initialization works via a new `Strategy` method called "
-"`initialize_parameters`."
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedOpt.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 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`."
 
-#: ../../source/ref-changelog.md:690
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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."
+":py:obj:`num_fit_clients <flwr.server.strategy.FedOpt.num_fit_clients>`\\"
+" \\(num\\_available\\_clients\\)"
 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."
 
-#: ../../source/ref-changelog.md:709
-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)."
+#: ../../source/ref-api/flwr.server.strategy.FedProx.rst:2
+msgid "FedProx"
 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)."
 
-#: ../../source/ref-changelog.md:711
-msgid "Deprecations"
-msgstr "Dépréciations"
+#: flwr.server.strategy.fedprox.FedProx:3 of
+msgid "Implementation based on https://arxiv.org/abs/1812.06127"
+msgstr ""
 
-#: ../../source/ref-changelog.md:713
+#: flwr.server.strategy.fedprox.FedProx:5 of
 msgid ""
-"Deprecate `flwr.server.strategy.DefaultStrategy` (migrate to "
-"`flwr.server.strategy.FedAvg`, which is equivalent)"
+"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 ""
-"Déclasser `flwr.server.strategy.DefaultStrategy` (migrer vers "
-"`flwr.server.strategy.FedAvg`, qui est équivalent)"
 
-#: ../../source/ref-changelog.md:715
-msgid "v0.14.0 (2021-02-18)"
-msgstr "v0.14.0 (2021-02-18)"
+#: flwr.server.strategy.fedprox.FedProx:9 of
+msgid ""
+"\\\\frac{\\\\mu}{2} || w - w^t ||^2\n"
+"\n"
+msgstr ""
 
-#: ../../source/ref-changelog.md:719
+#: flwr.server.strategy.fedprox.FedProx:12 of
 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))"
+"Where $w^t$ are the global parameters and $w$ are the local weights the "
+"function will be optimized with."
 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))"
 
-#: ../../source/ref-changelog.md:721
+#: 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 ""
+
+#: flwr.server.strategy.fedprox.FedProx:30 of
 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!"
+"With `global_params` being a copy of the parameters before the training "
+"takes place."
 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 !"
 
-#: ../../source/ref-changelog.md:723
+#: flwr.server.strategy.fedprox.FedProx:65 of
 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."
+"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 ""
-"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/ref-changelog.md:725
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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."
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedProx.aggregate_evaluate>`\\ \\(server\\_round\\,"
+" results\\, ...\\)"
 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."
 
-#: ../../source/ref-changelog.md:727
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"*Code example:* note the additional dictionary return values in both "
-"`FlwrClient.fit` and `FlwrClient.evaluate`:"
+":py:obj:`aggregate_fit <flwr.server.strategy.FedProx.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
 msgstr ""
-"*Exemple de code:* note les valeurs de retour du dictionnaire "
-"supplémentaires dans `FlwrClient.fit` et `FlwrClient.evaluate` :"
 
-#: ../../source/ref-changelog.md:742
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"**Generalized** `config` **argument in** `Client.fit` **and** "
-"`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))"
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedProx.configure_evaluate>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
 msgstr ""
-"**Généralisé** `config` **argument dans** `Client.fit` **et** "
-"`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))"
 
-#: ../../source/ref-changelog.md:744
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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`."
+":py:obj:`configure_fit <flwr.server.strategy.FedProx.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 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`."
 
-#: ../../source/ref-changelog.md:746
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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!"
+":py:obj:`evaluate <flwr.server.strategy.FedProx.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
 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 !"
 
-#: ../../source/ref-changelog.md:748
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"*Code example:* note that the `config` dictionary now contains non-`str` "
-"values in both `Client.fit` and `Client.evaluate`:"
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedProx.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 msgstr ""
-"*Exemple de code:* Notez que le dictionnaire `config` contient maintenant"
-" des valeurs autres que `str` dans `Client.fit` et `Client.evaluate` :"
 
-#: ../../source/ref-changelog.md:765
-msgid "v0.13.0 (2021-01-08)"
-msgstr "v0.13.0 (2021-01-08)"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedProx.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
 
-#: ../../source/ref-changelog.md:769
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"New example: PyTorch From Centralized To Federated "
-"([#549](https://github.com/adap/flower/pull/549))"
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedProx.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 msgstr ""
-"Nouvel exemple : PyTorch de centralisé à fédéré "
-"([#549](https://github.com/adap/flower/pull/549))"
 
-#: ../../source/ref-changelog.md:770
-msgid "Improved documentation"
-msgstr "Amélioration de la documentation"
+#: flwr.server.strategy.fedprox.FedProx.configure_fit:3 of
+msgid "Sends the proximal factor mu to the clients"
+msgstr ""
 
-#: ../../source/ref-changelog.md:771
-msgid "New documentation theme ([#551](https://github.com/adap/flower/pull/551))"
+#: ../../source/ref-api/flwr.server.strategy.FedTrimmedAvg.rst:2
+msgid "FedTrimmedAvg"
 msgstr ""
-"Nouveau thème de documentation "
-"([#551](https://github.com/adap/flower/pull/551))"
 
-#: ../../source/ref-changelog.md:772
-msgid "New API reference ([#554](https://github.com/adap/flower/pull/554))"
-msgstr "Nouvelle référence API ([#554](https://github.com/adap/flower/pull/554))"
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:3 of
+msgid "Implemented based on: https://arxiv.org/abs/1803.01498"
+msgstr ""
 
-#: ../../source/ref-changelog.md:773
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:25 of
+msgid "Fraction to cut off of both tails of the distribution. Defaults to 0.2."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"Updated examples documentation "
-"([#549](https://github.com/adap/flower/pull/549))"
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedTrimmedAvg.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
 msgstr ""
-"Mise à jour de la documentation des exemples "
-"([#549](https://github.com/adap/flower/pull/549))"
 
-#: ../../source/ref-changelog.md:774
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"Removed obsolete documentation "
-"([#548](https://github.com/adap/flower/pull/548))"
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FedTrimmedAvg.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
 msgstr ""
-"Suppression de la documentation obsolète "
-"([#548](https://github.com/adap/flower/pull/548))"
 
-#: ../../source/ref-changelog.md:776
-msgid "Bugfix:"
-msgstr "Correction de bogues :"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.aggregate_fit:1 of
+msgid "Aggregate fit results using trimmed average."
+msgstr ""
 
-#: ../../source/ref-changelog.md:778
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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))."
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedTrimmedAvg.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 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:780
-msgid "v0.12.0 (2020-12-07)"
-msgstr "v0.12.0 (2020-12-07)"
 
-#: ../../source/ref-changelog.md:782 ../../source/ref-changelog.md:798
-msgid "Important changes:"
-msgstr "Changements importants :"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FedTrimmedAvg.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
 
-#: ../../source/ref-changelog.md:784
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"Added an example for embedded devices "
-"([#507](https://github.com/adap/flower/pull/507))"
+":py:obj:`evaluate <flwr.server.strategy.FedTrimmedAvg.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
 msgstr ""
-"Ajout d'un exemple pour les périphériques embarqués "
-"([#507](https://github.com/adap/flower/pull/507))"
 
-#: ../../source/ref-changelog.md:785
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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))"
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedTrimmedAvg.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 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))"
 
-#: ../../source/ref-changelog.md:786
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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))"
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedTrimmedAvg.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 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:788
-msgid "v0.11.0 (2020-11-30)"
-msgstr "v0.11.0 (2020-11-30)"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedTrimmedAvg.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
 
-#: ../../source/ref-changelog.md:790
-msgid "Incompatible changes:"
-msgstr "Changements incompatibles :"
+#: ../../source/ref-api/flwr.server.strategy.FedXgbBagging.rst:2
+msgid "FedXgbBagging"
+msgstr ""
 
-#: ../../source/ref-changelog.md:792
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
 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:"
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedXgbBagging.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
 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:793
-msgid "`on_configure_evaluate` => `configure_evaluate`"
-msgstr "`on_configure_evaluate` => `configure_evaluate`"
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+#, fuzzy
+msgid "Aggregate evaluation metrics using average."
+msgstr "Résultats globaux de l'évaluation."
 
-#: ../../source/ref-changelog.md:794
-msgid "`on_aggregate_evaluate` => `aggregate_evaluate`"
-msgstr "`on_aggregate_evaluate` => `aggregate_evaluate`"
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FedXgbBagging.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
 
-#: ../../source/ref-changelog.md:795
-msgid "`on_configure_fit` => `configure_fit`"
-msgstr "`on_configure_fit` => `configure_fit`"
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_fit:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_fit:1 of
+msgid "Aggregate fit results using bagging."
+msgstr ""
 
-#: ../../source/ref-changelog.md:796
-msgid "`on_aggregate_fit` => `aggregate_fit`"
-msgstr "`on_aggregate_fit` => `aggregate_fit`"
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedXgbBagging.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
 
-#: ../../source/ref-changelog.md:800
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"Deprecated `DefaultStrategy` "
-"([#479](https://github.com/adap/flower/pull/479)). To migrate use "
-"`FedAvg` instead."
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FedXgbBagging.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"Déclassé `DefaultStrategy` "
-"([#479](https://github.com/adap/flower/pull/479)). Pour migrer, utilisez "
-"`FedAvg` à la place."
 
-#: ../../source/ref-changelog.md:801
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"Simplified examples and baselines "
-"([#484](https://github.com/adap/flower/pull/484))."
+":py:obj:`evaluate <flwr.server.strategy.FedXgbBagging.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
 msgstr ""
-"Exemples simplifiés et lignes de base "
-"([#484](https://github.com/adap/flower/pull/484))."
 
-#: ../../source/ref-changelog.md:802
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"Removed presently unused `on_conclude_round` from strategy interface "
-"([#483](https://github.com/adap/flower/pull/483))."
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedXgbBagging.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 msgstr ""
-"Suppression de `on_conclude_round` actuellement inutilisé de l'interface "
-"de stratégie ([#483](https://github.com/adap/flower/pull/483))."
 
-#: ../../source/ref-changelog.md:803
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"Set minimal Python version to 3.6.1 instead of 3.6.9 "
-"([#471](https://github.com/adap/flower/pull/471))."
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedXgbBagging.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 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))."
 
-#: ../../source/ref-changelog.md:804
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"Improved `Strategy` docstrings "
-"([#470](https://github.com/adap/flower/pull/470))."
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedXgbBagging.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 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"
+#: ../../source/ref-api/flwr.server.strategy.FedXgbCyclic.rst:2
+msgid "FedXgbCyclic"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:4
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
 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 <https://pytorch.org/>`_ or `TensorFlow "
-"<https://www.tensorflow.org/>`_."
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedXgbCyclic.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
 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 <https://pytorch.org/>`_ ou `TensorFlow "
-"<https://www.tensorflow.org/>`_."
 
-#: ../../source/ref-example-projects.rst:11
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
 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 <https://github.com/adap/flower/tree/main/examples>`_."
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FedXgbCyclic.aggregate_fit>`\\ \\(server\\_round\\,"
+" results\\, failures\\)"
 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 "
-"<https://github.com/adap/flower/tree/main/examples>`_."
-
-#: ../../source/ref-example-projects.rst:16
-msgid "The following examples are available as standalone projects."
-msgstr "Les exemples suivants sont disponibles sous forme de projets autonomes."
-
-#: ../../source/ref-example-projects.rst:20
-msgid "Quickstart TensorFlow/Keras"
-msgstr "Démarrage rapide de TensorFlow/Keras"
 
-#: ../../source/ref-example-projects.rst:22
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"The TensorFlow/Keras quickstart example shows CIFAR-10 image "
-"classification with MobileNetV2:"
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedXgbCyclic.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"L'exemple de démarrage rapide TensorFlow/Keras montre la classification "
-"d'images CIFAR-10 avec MobileNetV2 :"
 
-#: ../../source/ref-example-projects.rst:25
-#, fuzzy
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"`Quickstart TensorFlow (Code) "
-"<https://github.com/adap/flower/tree/main/examples/quickstart-"
-"tensorflow>`_"
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FedXgbCyclic.configure_fit>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
 msgstr ""
-"`Quickstart TensorFlow (Code) "
-"<https://github.com/adap/flower/tree/main/examples/quickstart-"
-"tensorflow>`_"
 
-#: ../../source/ref-example-projects.rst:26
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"`Quickstart TensorFlow (Tutorial) <https://flower.dev/docs/quickstart-"
-"tensorflow.html>`_"
+":py:obj:`evaluate <flwr.server.strategy.FedXgbCyclic.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
 msgstr ""
-"`Quickstart TensorFlow (Tutorial) <https://flower.dev/docs/quickstart-"
-"tensorflow.html>`_"
 
-#: ../../source/ref-example-projects.rst:27
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"`Quickstart TensorFlow (Blog Post) <https://flower.dev/blog/2020-12-11"
-"-federated-learning-in-less-than-20-lines-of-code>`_"
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedXgbCyclic.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 msgstr ""
-"`Quickstart TensorFlow (Blog Post) <https://flower.dev/blog/2020-12-11"
-"-federated-learning-in-less-than-20-lines-of-code>`_"
 
-#: ../../source/ref-example-projects.rst:31
-#: ../../source/tutorial-quickstart-pytorch.rst:5
-msgid "Quickstart PyTorch"
-msgstr "Démarrage rapide de PyTorch"
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedXgbCyclic.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:33
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"The PyTorch quickstart example shows CIFAR-10 image classification with a"
-" simple Convolutional Neural Network:"
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedXgbCyclic.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 msgstr ""
-"L'exemple de démarrage rapide PyTorch montre la classification d'images "
-"CIFAR-10 avec un simple réseau neuronal convolutif :"
 
-#: ../../source/ref-example-projects.rst:36
+#: ../../source/ref-api/flwr.server.strategy.FedXgbNnAvg.rst:2
 #, fuzzy
+msgid "FedXgbNnAvg"
+msgstr "DP-FedAvg"
+
+#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:5 of
 msgid ""
-"`Quickstart PyTorch (Code) "
-"<https://github.com/adap/flower/tree/main/examples/quickstart-pytorch>`_"
+"This strategy is deprecated, but a copy of it is available in Flower "
+"Baselines: "
+"https://github.com/adap/flower/tree/main/baselines/hfedxgboost."
 msgstr ""
-"`Quickstart PyTorch (Code) "
-"<https://github.com/adap/flower/tree/main/examples/quickstart-pytorch>`_"
 
-#: ../../source/ref-example-projects.rst:37
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"`Quickstart PyTorch (Tutorial) <https://flower.dev/docs/quickstart-"
-"pytorch.html>`_"
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedXgbNnAvg.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
 msgstr ""
-"`Quickstart PyTorch (Tutorial) <https://flower.dev/docs/quickstart-"
-"pytorch.html>`_"
-
-#: ../../source/ref-example-projects.rst:41
-msgid "PyTorch: From Centralized To Federated"
-msgstr "PyTorch : De la centralisation à la fédération"
 
-#: ../../source/ref-example-projects.rst:43
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"This example shows how a regular PyTorch project can be federated using "
-"Flower:"
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FedXgbNnAvg.aggregate_fit>`\\ \\(server\\_round\\, "
+"results\\, failures\\)"
 msgstr ""
-"Cet exemple montre comment un projet PyTorch ordinaire peut être fédéré à"
-" l'aide de Flower :"
 
-#: ../../source/ref-example-projects.rst:45
-#, fuzzy
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"`PyTorch: From Centralized To Federated (Code) "
-"<https://github.com/adap/flower/tree/main/examples/pytorch-from-"
-"centralized-to-federated>`_"
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedXgbNnAvg.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"`PyTorch : De la centralisation à la fédération (Code) "
-"<https://github.com/adap/flower/tree/main/examples/pytorch-from-"
-"centralized-to-federated>`_"
 
-#: ../../source/ref-example-projects.rst:46
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"`PyTorch: From Centralized To Federated (Tutorial) "
-"<https://flower.dev/docs/example-pytorch-from-centralized-to-"
-"federated.html>`_"
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FedXgbNnAvg.configure_fit>`\\ \\(server\\_round\\, "
+"parameters\\, ...\\)"
 msgstr ""
-"`PyTorch : De la centralisation à la fédération (Tutoriel) "
-"<https://flower.dev/docs/example-pytorch-from-centralized-to-"
-"federated.html>`_"
 
-#: ../../source/ref-example-projects.rst:50
-msgid "Federated Learning on Raspberry Pi and Nvidia Jetson"
-msgstr "Apprentissage fédéré sur Raspberry Pi et Nvidia Jetson"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedXgbNnAvg.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:52
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"This example shows how Flower can be used to build a federated learning "
-"system that run across Raspberry Pi and Nvidia Jetson:"
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedXgbNnAvg.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 msgstr ""
-"Cet exemple montre comment Flower peut être utilisé pour construire un "
-"système d'apprentissage fédéré qui fonctionne sur Raspberry Pi et Nvidia "
-"Jetson :"
 
-#: ../../source/ref-example-projects.rst:54
-#, fuzzy
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"`Federated Learning on Raspberry Pi and Nvidia Jetson (Code) "
-"<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_"
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedXgbNnAvg.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 msgstr ""
-"`L'apprentissage fédéré sur Raspberry Pi et Nvidia Jetson (Code) "
-"<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_"
 
-#: ../../source/ref-example-projects.rst:55
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedXgbNnAvg.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 msgstr ""
-"`L'apprentissage fédéré sur Raspberry Pi et Nvidia Jetson (Blog Post) "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
 
-#: ../../source/ref-example-projects.rst:60
-msgid "Legacy Examples (`flwr_example`)"
-msgstr "Exemples hérités (`flwr_example`)"
+#: ../../source/ref-api/flwr.server.strategy.FedYogi.rst:2
+msgid "FedYogi"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:63
-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 <https://github.com/adap/flower/tree/main/examples>`_."
+#: flwr.server.strategy.fedyogi.FedYogi:32 of
+msgid "Server-side learning rate. Defaults to 1e-2."
 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 "
-"<https://github.com/adap/flower/tree/main/examples>`_."
 
-#: ../../source/ref-example-projects.rst:69
-msgid "Extra Dependencies"
-msgstr "Dépendances supplémentaires"
+#: flwr.server.strategy.fedyogi.FedYogi:34 of
+msgid "Client-side learning rate. Defaults to 0.0316."
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:71
+#: flwr.server.strategy.fedyogi.FedYogi:40 of
+msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-3."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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."
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedYogi.aggregate_evaluate>`\\ \\(server\\_round\\,"
+" results\\, ...\\)"
 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."
 
-#: ../../source/ref-example-projects.rst:75
-msgid "For PyTorch examples::"
-msgstr "Pour les exemples de PyTorch: :"
-
-#: ../../source/ref-example-projects.rst:79
-msgid "For TensorFlow examples::"
-msgstr "Pour les exemples de TensorFlow : :"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedYogi.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:83
-msgid "For both PyTorch and TensorFlow examples::"
-msgstr "Pour les exemples PyTorch et TensorFlow: :"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedYogi.configure_evaluate>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:87
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"Please consult :code:`pyproject.toml` for a full list of possible extras "
-"(section :code:`[tool.poetry.extras]`)."
+":py:obj:`configure_fit <flwr.server.strategy.FedYogi.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"Tu peux consulter :code:`pyproject.toml` pour une liste complète des "
-"extras possibles (section :code:`[tool.poetry.extras]`)."
 
-#: ../../source/ref-example-projects.rst:92
-msgid "PyTorch Examples"
-msgstr "Exemples de PyTorch"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedYogi.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:94
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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."
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedYogi.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 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."
 
-#: ../../source/ref-example-projects.rst:98
-msgid "CIFAR-10 Image Classification"
-msgstr "Classification d'images CIFAR-10"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedYogi.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:100
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"`CIFAR-10 and CIFAR-100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ "
-"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."
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedYogi.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 msgstr ""
-"`CIFAR-10 et CIFAR-100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ "
-"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."
 
-#: ../../source/ref-example-projects.rst:104
-#: ../../source/ref-example-projects.rst:121
-#: ../../source/ref-example-projects.rst:146
-msgid "First, start a Flower server:"
-msgstr "Tout d'abord, démarre un serveur Flower :"
+#: ../../source/ref-api/flwr.server.strategy.Krum.rst:2
+msgid "Krum"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:106
-msgid "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh"
-msgstr "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh"
+#: flwr.server.strategy.krum.Krum:3 of
+msgid "Implementation based on https://arxiv.org/abs/1703.02757"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:108
-#: ../../source/ref-example-projects.rst:125
-#: ../../source/ref-example-projects.rst:150
-msgid "Then, start the two clients in a new terminal window:"
-msgstr "Ensuite, démarre les deux clients dans une nouvelle fenêtre de terminal :"
+#: 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."
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:110
-msgid "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh"
-msgstr "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.Krum.aggregate_evaluate>`\\ \\(server\\_round\\, "
+"results\\, ...\\)"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:112
-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`."
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.Krum.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:115
-msgid "ImageNet-2012 Image Classification"
-msgstr "ImageNet-2012 Classification des images"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.krum.Krum.aggregate_fit:1 of
+#, fuzzy
+msgid "Aggregate fit results using Krum."
+msgstr "Résultats globaux de l'évaluation."
 
-#: ../../source/ref-example-projects.rst:117
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"`ImageNet-2012 <http://www.image-net.org/>`_ 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."
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.Krum.configure_evaluate>`\\ \\(server\\_round\\, "
+"parameters\\, ...\\)"
 msgstr ""
-"`ImageNet-2012 <http://www.image-net.org/>`_ 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."
 
-#: ../../source/ref-example-projects.rst:123
-msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh"
-msgstr "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.Krum.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:127
-msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh"
-msgstr "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.Krum.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:129
-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`."
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.Krum.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:133
-msgid "TensorFlow Examples"
-msgstr "Exemples de TensorFlow"
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.Krum.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:135
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"Our TensorFlow examples are based on TensorFlow 2.0 or newer. So far, we "
-"provide the following examples."
+":py:obj:`num_fit_clients <flwr.server.strategy.Krum.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 msgstr ""
-"Nos exemples TensorFlow sont basés sur TensorFlow 2.0 ou une version plus"
-" récente. Jusqu'à présent, nous te proposons les exemples suivants."
 
-#: ../../source/ref-example-projects.rst:139
-msgid "Fashion-MNIST Image Classification"
-msgstr "Classification d'images Fashion-MNIST"
+#: ../../source/ref-api/flwr.server.strategy.QFedAvg.rst:2
+#, fuzzy
+msgid "QFedAvg"
+msgstr "DP-FedAvg"
 
-#: ../../source/ref-example-projects.rst:141
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"`Fashion-MNIST <https://github.com/zalandoresearch/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."
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.QFedAvg.aggregate_evaluate>`\\ \\(server\\_round\\,"
+" results\\, ...\\)"
 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."
 
-#: ../../source/ref-example-projects.rst:148
-msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh"
-msgstr "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh"
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.QFedAvg.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:152
-msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh"
-msgstr "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh"
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.QFedAvg.configure_evaluate>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
+msgstr ""
 
-#: ../../source/ref-example-projects.rst:154
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"For more details, see "
-":code:`src/py/flwr_example/tensorflow_fashion_mnist`."
+":py:obj:`configure_fit <flwr.server.strategy.QFedAvg.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"Pour plus de détails, voir "
-":code:`src/py/flwr_example/tensorflow_fashion_mnist`."
 
-#: ../../source/ref-faq.rst:4
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"This page collects answers to commonly asked questions about Federated "
-"Learning with Flower."
+":py:obj:`evaluate <flwr.server.strategy.QFedAvg.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
 msgstr ""
-"Cette page rassemble les réponses aux questions les plus fréquemment "
-"posées sur l'apprentissage fédéré avec Flower."
 
-#: ../../source/ref-faq.rst
-msgid ":fa:`eye,mr-1` Can Flower run on Juptyter Notebooks / Google Colab?"
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.QFedAvg.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 msgstr ""
-":fa:`eye,mr-1` Flower peut-il fonctionner sur les ordinateurs portables "
-"Juptyter / Google Colab ?"
 
-#: ../../source/ref-faq.rst:8
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
 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:"
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.QFedAvg.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 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/ref-faq.rst:10
-#, fuzzy
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
 msgid ""
-"`Flower simulation PyTorch "
-"<https://colab.research.google.com/github/adap/flower/blob/main/examples"
-"/simulation-pytorch/sim.ipynb>`_"
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.QFedAvg.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
 msgstr ""
-"`Flower Quickstart (TensorFlow/Keras) "
-"<https://colab.research.google.com/github/adap/flower/blob/main/examples/simulation_tensorflow/sim.ipynb>`_"
 
-#: ../../source/ref-faq.rst:11
+#: ../../source/ref-api/flwr.server.strategy.Strategy.rst:2
 #, fuzzy
+msgid "Strategy"
+msgstr "stratégie.du.serveur"
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"`Flower simulation TensorFlow/Keras "
-"<https://colab.research.google.com/github/adap/flower/blob/main/examples"
-"/simulation-tensorflow/sim.ipynb>`_"
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.Strategy.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
 msgstr ""
-"`Flower Quickstart (TensorFlow/Keras) "
-"<https://colab.research.google.com/github/adap/flower/blob/main/examples/simulation_tensorflow/sim.ipynb>`_"
 
-#: ../../source/ref-faq.rst
-msgid ":fa:`eye,mr-1` How can I run Federated Learning on a Raspberry Pi?"
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
+#, fuzzy
+msgid "Aggregate evaluation results."
+msgstr "Résultats globaux de l'évaluation."
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.Strategy.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
 msgstr ""
-":fa:`eye,mr-1` Comment puis-je faire fonctionner l'apprentissage fédéré "
-"sur un Raspberry Pi ?"
 
-#: ../../source/ref-faq.rst:15
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: of
 msgid ""
-"Find the `blog post about federated learning on embedded device here "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
-" and the corresponding `GitHub code example "
-"<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_."
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.Strategy.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-"Trouve le `blog post about federated learning on embedded device ici "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
-" et l'exemple de code GitHub correspondant "
-"<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_."
 
-#: ../../source/ref-faq.rst
-msgid ":fa:`eye,mr-1` Does Flower support federated learning on Android devices?"
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.Strategy.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
 msgstr ""
-":fa:`eye,mr-1` Est-ce que Flower prend en charge l'apprentissage fédéré "
-"sur les appareils Android ?"
 
-#: ../../source/ref-faq.rst:19
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.Strategy.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>: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.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
 msgid ""
-"Yes, it does. Please take a look at our `blog post "
-"<https://flower.dev/blog/2021-12-15-federated-learning-on-android-"
-"devices-with-flower>`_ or check out the code examples:"
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.Strategy.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
 msgstr ""
-"Oui. Jetez un coup d'œil à notre `blog post "
-"<https://flower.dev/blog/2021-12-15-federated-learning-on-android-"
-"devices-with-flower>`_ ou consultez l'`exemple de code Android sur GitHub"
-" <https://github.com/adap/flower/tree/main/examples/android>`_."
 
-#: ../../source/ref-faq.rst:21
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>: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 ""
-"`Android Kotlin example <https://flower.dev/docs/examples/android-"
-"kotlin.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-faq.rst:22
-msgid "`Android Java example <https://flower.dev/docs/examples/android.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-faq.rst
-msgid ":fa:`eye,mr-1` Can I combine federated learning with blockchain?"
+#: 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 ""
-":fa:`eye,mr-1` Puis-je combiner l'apprentissage fédéré avec la blockchain"
-" ?"
 
-#: ../../source/ref-faq.rst:26
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit:5 of
 msgid ""
-"Yes, of course. A list of available examples using Flower within a "
-"blockchain environment is available here:"
+"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 ""
-"Oui, bien sûr, une liste d'exemples disponibles utilisant Flower dans un "
-"environnement blockchain est disponible ici :"
 
-#: ../../source/ref-faq.rst:28
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit:17 of
 msgid ""
-"`Flower meets Nevermined GitHub Repository <https://github.com"
-"/nevermined-io/fl-demo/tree/master/image-classification-flower>`_."
+"**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 ""
-"`Flower meets Nevermined GitHub Repository <https://github.com"
-"/nevermined-io/fl-demo/tree/master/image-classification-flower>`_."
 
-#: ../../source/ref-faq.rst:29
+#: flwr.server.strategy.strategy.Strategy.evaluate:3 of
 msgid ""
-"`Flower meets Nevermined YouTube video "
-"<https://www.youtube.com/watch?v=A0A9hSlPhKI>`_."
+"This function can be used to perform centralized (i.e., server-side) "
+"evaluation of model parameters."
 msgstr ""
-"`Flower rencontre Nevermined vidéo YouTube "
-"<https://www.youtube.com/watch?v=A0A9hSlPhKI>`_."
 
-#: ../../source/ref-faq.rst:30
+#: flwr.server.strategy.strategy.Strategy.evaluate:11 of
 msgid ""
-"`Flower meets KOSMoS <https://www.kosmos-bmbf.de/wp-"
-"content/uploads/sites/13/2021/05/Talk-Flower-Summit-2021.pdf>`_."
+"**evaluation_result** -- The evaluation result, usually a Tuple "
+"containing loss and a dictionary containing task-specific metrics (e.g., "
+"accuracy)."
 msgstr ""
-"`Flower rencontre KOSMoS <https://www.kosmos-bmbf.de/wp-"
-"content/uploads/sites/13/2021/05/Talk-Flower-Summit-2021.pdf>`_."
 
-#: ../../source/ref-faq.rst:31
+#: flwr.server.strategy.strategy.Strategy.initialize_parameters:6 of
 msgid ""
-"`Flower meets Talan blog post <https://www.linkedin.com/pulse/federated-"
-"learning-same-mask-different-faces-imen-"
-"ayari/?trackingId=971oIlxLQ9%2BA9RB0IQ73XQ%3D%3D>`_ ."
+"**parameters** -- If parameters are returned, then the server will treat "
+"these as the initial global model parameters."
 msgstr ""
-"`Flower meets Talan blog post <https://www.linkedin.com/pulse/federated-"
-"learning-same-mask-different-faces-imen-"
-"ayari/?trackingId=971oIlxLQ9%2BA9RB0IQ73XQ%3D%3D>`_ ."
 
-#: ../../source/ref-faq.rst:32
+#: ../../source/ref-api/flwr.simulation.rst:2
+#, fuzzy
+msgid "simulation"
+msgstr "Simulation de moniteur"
+
+#: ../../source/ref-api/flwr.simulation.rst:17:<autosummary>:1
 msgid ""
-"`Flower meets Talan GitHub Repository "
-"<https://gitlab.com/Talan_Innovation_Factory/food-waste-prevention>`_ ."
+":py:obj:`start_simulation <flwr.simulation.start_simulation>`\\ \\(\\*\\,"
+" client\\_fn\\[\\, ...\\]\\)"
 msgstr ""
-"`Flower rencontre Talan Dépôt GitHub "
-"<https://gitlab.com/Talan_Innovation_Factory/food-waste-prevention>`_ ."
 
-#: ../../source/ref-telemetry.md:1
-msgid "Telemetry"
-msgstr "Télémétrie"
+#: ../../source/ref-api/flwr.simulation.rst:17:<autosummary>:1
+#: flwr.simulation.app.start_simulation:1 of
+#, fuzzy
+msgid "Start a Ray-based Flower simulation server."
+msgstr "Simulation de moniteur"
 
-#: ../../source/ref-telemetry.md:3
+#: ../../source/ref-api/flwr.simulation.start_simulation.rst:2
+#, fuzzy
+msgid "start\\_simulation"
+msgstr "démarrer_simulation"
+
+#: flwr.simulation.app.start_simulation:3 of
 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."
+"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 ""
-"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/ref-telemetry.md:5
+#: flwr.simulation.app.start_simulation:13 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 that do not want to share anonymous usage metrics."
+"The total number of clients in this simulation. This must be set if "
+"`clients_ids` is not set and vice-versa."
 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"
 
-#: ../../source/ref-telemetry.md:9
-msgid "We follow strong principles guarding anonymous usage metrics collection:"
+#: 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 ""
-"Nous suivons des principes stricts concernant la collecte de données "
-"anonymes sur l'utilisation :"
 
-#: ../../source/ref-telemetry.md:11
+#: flwr.simulation.app.start_simulation:20 of
 msgid ""
-"**Optional:** You will always be able to disable telemetry; read on to "
-"learn “[How to opt-out](#how-to-opt-out)”."
+"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 ""
-"**Optionnel:** Tu pourras toujours désactiver la télémétrie ; lis la "
-"suite pour apprendre \"[Comment se désengager](#how-to-opt-out)\"."
 
-#: ../../source/ref-telemetry.md:12
+#: flwr.simulation.app.start_simulation:25 of
 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 implementation of the abstract base class `flwr.server.Server`. If no "
+"instance is provided, then `start_server` will create one."
 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/ref-telemetry.md:13
+#: flwr.simulation.app.start_simulation:31 of
 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)”"
+"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 ""
-"**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/ref-telemetry.md:14
-#, fuzzy
+#: flwr.simulation.app.start_simulation:35 of
 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."
+"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 ""
-"**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"
-
-#: ../../source/ref-telemetry.md:18
+#: flwr.simulation.app.start_simulation:39 of
 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:"
+"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 ""
-"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/ref-telemetry.md:24
+#: flwr.simulation.app.start_simulation:39 of
 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."
+"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 ""
-"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 :"
+#: flwr.simulation.app.start_simulation:43 of
+msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }"
+msgstr ""
 
-#: ../../source/ref-telemetry.md:30
+#: flwr.simulation.app.start_simulation:45 of
 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."
+"An empty dictionary can be used (ray_init_args={}) to prevent any "
+"arguments from being passed to ray.init."
 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/ref-telemetry.md:32
+#: flwr.simulation.app.start_simulation:48 of
 msgid ""
-"**Operating system.** Enables us to answer questions such as: *Should we "
-"create more guides for Linux, macOS, or Windows?*"
+"Set to True to prevent `ray.shutdown()` in case "
+"`ray.is_initialized()=True`."
 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/ref-telemetry.md:34
+#: flwr.simulation.app.start_simulation:50 of
 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."
+"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 ""
-"**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/ref-telemetry.md:36
+#: flwr.simulation.app.start_simulation:54 of
 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."
+"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 ""
-"**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/ref-telemetry.md:38
+#: flwr.simulation.app.start_simulation:57 of
 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."
+"(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 ""
-"**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/ref-telemetry.md:40
-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."
+#: flwr.simulation.app.start_simulation:66 of
+msgid "**hist** -- Object containing metrics from training."
 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/ref-telemetry.md:42
+#: ../../source/ref-changelog.md:1
+msgid "Changelog"
+msgstr "Changelog"
+
+#: ../../source/ref-changelog.md:3
+#, 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 ?"
+
+#: ../../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"
+
+#: ../../source/ref-changelog.md:9
+#, 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"
+
+#: ../../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
 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."
+"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.** 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."
+"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-telemetry.md:44
+#: ../../source/ref-changelog.md:15
 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.dev`. All events "
-"related to that source ID will then be permanently deleted."
+"`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` <!---TOKEN_v1.7.0-->"
 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.dev`."
-" Tous les événements liés à cet identifiant de source seront alors "
-"définitivement supprimés."
 
-#: ../../source/ref-telemetry.md:46
+#: ../../source/ref-changelog.md:19
+#, fuzzy
 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."
+"**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 ""
-"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)."
+"**([#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-telemetry.md:48
+#: ../../source/ref-changelog.md:21
 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."
+"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 ""
-"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"
 
-#: ../../source/ref-telemetry.md:52
+#: ../../source/ref-changelog.md:23
+#, fuzzy
 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."
+"**Improve performance** "
+"([#2293](https://github.com/adap/flower/pull/2293))"
 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 stratégies expérimentales** "
+"([#1280](https://github.com/adap/flower/pull/1280))"
 
-#: ../../source/ref-telemetry.md:58
+#: ../../source/ref-changelog.md:25
 msgid ""
-"The inspect Flower telemetry without sending any anonymous usage metrics,"
-" use both environment variables:"
+"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 ""
-"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"
-
-#: ../../source/ref-telemetry.md:66
+#: ../../source/ref-changelog.md:27
+#, fuzzy
 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.dev/join-slack/) (channel `#telemetry`) or email "
-"(`telemetry@flower.dev`)."
+"**Support Federated Learning with Apple MLX and Flower** "
+"([#2693](https://github.com/adap/flower/pull/2693))"
 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.dev/join-slack/) (canal `#telemetry`) ou par "
-"courriel (`telemetry@flower.dev`)."
-
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:9
-#, fuzzy
-msgid "Build a strategy from scratch"
-msgstr "Élaborer une stratégie à partir de zéro"
+"**Ajouter un nouvel exemple d'apprentissage fédéré utilisant fastai et "
+"Flower** ([#1598](https://github.com/adap/flower/pull/1598))"
 
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:11
-#, fuzzy
+#: ../../source/ref-changelog.md:29
 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 <https://flower.dev/docs/framework/tutorial-"
-"get-started-with-flower-pytorch.html>`__) and we learned how strategies "
-"can be used to customize the execution on both the server and the clients"
-" (`part 2 <https://flower.dev/docs/framework/tutorial-use-a-federated-"
-"learning-strategy-pytorch.html>`__)."
+"Flower has official support for federated learning using [Apple "
+"MLX](https://ml-explore.github.io/mlx) via the new `quickstart-mlx` code "
+"example."
 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 "
-"<https://flower.dev/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html>`__) "
-"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 <https://flower.dev/docs/tutorial/Flower-2-Strategies-in-FL-"
-"PyTorch.html>`__)."
 
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:13
+#: ../../source/ref-changelog.md:31
+#, 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 <https://flower.dev/>`__ and `PyTorch "
-"<https://pytorch.org/>`__)."
+"**Introduce new XGBoost cyclic strategy** "
+"([#2666](https://github.com/adap/flower/pull/2666), "
+"[#2668](https://github.com/adap/flower/pull/2668))"
 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 <https://flower.dev/>`__ et `PyTorch <https://pytorch.org/>`__)."
+"**Introduction du SDK iOS (aperçu)** "
+"([#1621](https://github.com/adap/flower/pull/1621), "
+"[#1764](https://github.com/adap/flower/pull/1764))"
 
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:15
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:16
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:15
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:15
+#: ../../source/ref-changelog.md:33
 msgid ""
-"`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ and join "
-"the Flower community on Slack to connect, ask questions, and get help: "
-"`Join Slack <https://flower.dev/join-slack>`__ 🌼 We'd love to hear from "
-"you in the ``#introductions`` channel! And if anything is unclear, head "
-"over to the ``#questions`` channel."
+"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 ""
-"`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ et "
-"rejoignez la communauté Flower sur Slack pour vous connecter, poser des "
-"questions et obtenir de l'aide : `Join Slack <https://flower.dev/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-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-build-a-strategy-from-scratch-pytorch.ipynb:29
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:29
-msgid "Preparation"
-msgstr "Préparation"
-
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:31
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:32
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:31
+#: ../../source/ref-changelog.md:35
+#, fuzzy
 msgid ""
-"Before we begin with the actual code, let's make sure that we have "
-"everything we need."
+"**Support Python 3.11** "
+"([#2394](https://github.com/adap/flower/pull/2394))"
 msgstr ""
-"Avant de commencer le code proprement dit, assurons-nous que nous "
-"disposons de tout ce dont nous avons besoin."
-
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:43
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:44
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:43
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:43
-msgid "Installing dependencies"
-msgstr "Installation des dépendances"
-
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:45
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:46
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:45
-msgid "First, we install the necessary packages:"
-msgstr "Tout d'abord, nous installons les paquets nécessaires :"
+"**Support Python 3.10** "
+"([#1320](https://github.com/adap/flower/pull/1320))"
 
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:65
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:66
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:65
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:65
+#: ../../source/ref-changelog.md:37
 msgid ""
-"Now that we have all dependencies installed, we can import everything we "
-"need for this tutorial:"
+"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 ""
-"Maintenant que toutes les dépendances sont installées, nous pouvons "
-"importer tout ce dont nous avons besoin pour ce tutoriel :"
 
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:101
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:102
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:104
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:101
+#: ../../source/ref-changelog.md:39
+#, fuzzy
 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``."
+"**Update gRPC and ProtoBuf dependencies** "
+"([#2814](https://github.com/adap/flower/pull/2814))"
 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-build-a-strategy-from-scratch-pytorch.ipynb:114
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:115
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:114
-msgid "Data loading"
-msgstr "Chargement des données"
+"**Ajouter une nouvelle stratégie `FedProx`** "
+"([#1619](https://github.com/adap/flower/pull/1619))"
 
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:116
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:116
+#: ../../source/ref-changelog.md:41
 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."
+"The `grpcio` and `protobuf` dependencies were updated to their latest "
+"versions for improved security and performance."
 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-build-a-strategy-from-scratch-pytorch.ipynb:167
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:168
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:167
-msgid "Model training/evaluation"
-msgstr "Formation/évaluation du modèle"
 
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:169
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:170
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:169
+#: ../../source/ref-changelog.md:43
+#, fuzzy
 msgid ""
-"Let's continue with the usual model definition (including "
-"``set_parameters`` and ``get_parameters``), training and test functions:"
+"**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 ""
-"Continuons avec la définition habituelle du modèle (y compris "
-"``set_parameters`` et ``get_parameters``), les fonctions d'entraînement "
-"et de test :"
-
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:258
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:258
-msgid "Flower client"
-msgstr "Client de Flower"
+"**([#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-build-a-strategy-from-scratch-pytorch.ipynb:260
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:260
+#: ../../source/ref-changelog.md:45
 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:"
+"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 ""
-"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-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-build-a-strategy-from-scratch-pytorch.ipynb:339
-msgid "Build a Strategy from scratch"
-msgstr "Élaborer une stratégie à partir de zéro"
 
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:341
+#: ../../source/ref-changelog.md:47
+#, fuzzy
 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)."
+"**Introduce** `flower-via-docker-compose` **example** "
+"([#2626](https://github.com/adap/flower/pull/2626))"
 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``)."
+"**Introduire une nouvelle ligne de base pour les fleurs : FedAvg "
+"FEMNIST** ([#1655](https://github.com/adap/flower/pull/1655))"
 
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:507
+#: ../../source/ref-changelog.md:49
+#, fuzzy
 msgid ""
-"The only thing left is to use the newly created custom Strategy "
-"``FedCustom`` when starting the experiment:"
+"**Introduce** `quickstart-sklearn-tabular` **example** "
+"([#2719](https://github.com/adap/flower/pull/2719))"
 msgstr ""
-"Il ne reste plus qu'à utiliser la stratégie personnalisée nouvellement "
-"créée ``FedCustom`` lors du démarrage de l'expérience :"
-
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:534
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:932
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:697
-msgid "Recap"
-msgstr "Récapitulation"
+"**Ajouter une nouvelle stratégie `FedProx`** "
+"([#1619](https://github.com/adap/flower/pull/1619))"
 
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:536
+#: ../../source/ref-changelog.md:51
+#, fuzzy
 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."
+"**Introduce** `custom-metrics` **example** "
+"([#1958](https://github.com/adap/flower/pull/1958))"
 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."
+"**Ajouter une nouvelle stratégie `FedProx`** "
+"([#1619](https://github.com/adap/flower/pull/1619))"
 
-#: ../../source/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:550
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:948
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:749
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:715
-#: ../../source/tutorial-what-is-federated-learning.ipynb:369
+#: ../../source/ref-changelog.md:53
+#, fuzzy
 msgid ""
-"Before you continue, make sure to join the Flower community on Slack: "
-"`Join Slack <https://flower.dev/join-slack/>`__"
+"**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 ""
-"Avant de continuer, n'oublie pas de rejoindre la communauté Flower sur "
-"Slack : `Join Slack <https://flower.dev/join-slack/>`__"
+"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/tutorial-build-a-strategy-from-scratch-pytorch.ipynb:552
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:950
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:751
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:717
-#: ../../source/tutorial-what-is-federated-learning.ipynb:371
+#: ../../source/ref-changelog.md:55
 msgid ""
-"There's a dedicated ``#questions`` channel if you need help, but we'd "
-"also love to hear who you are in ``#introductions``!"
+"Several code examples were updated to use [Flower "
+"Datasets](https://flower.ai/docs/datasets/)."
 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-build-a-strategy-from-scratch-pytorch.ipynb:554
+#: ../../source/ref-changelog.md:57
 #, fuzzy
 msgid ""
-"The `Flower Federated Learning Tutorial - Part 4 "
-"<https://flower.dev/docs/framework/tutorial-customize-the-client-"
-"pytorch.html>`__ introduces ``Client``, the flexible API underlying "
-"``NumPyClient``."
+"**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 ""
-"Le `Tutoriel d'apprentissage fédéré Flower - Partie 4 "
-"<https://flower.dev/docs/tutorial/Flower-4-Client-and-NumPyClient-"
-"PyTorch.html>`__ présente ``Client``, l'API flexible qui sous-tend "
-"``NumPyClient``."
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:9
+#: ../../source/ref-changelog.md:61 ../../source/ref-changelog.md:154
 #, fuzzy
-msgid "Customize the client"
-msgstr "Création du client IMDBC"
+msgid "**Update Flower Baselines**"
+msgstr "Demande pour une nouvelle Flower Baseline"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:11
+#: ../../source/ref-changelog.md:63
 #, 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 <https://flower.dev/docs/framework"
-"/tutorial-get-started-with-flower-pytorch.html>`__), we learned how "
-"strategies can be used to customize the execution on both the server and "
-"the clients (`part 2 <https://flower.dev/docs/framework/tutorial-use-a"
-"-federated-learning-strategy-pytorch.html>`__), and we built our own "
-"custom strategy from scratch (`part 3 <https://flower.dev/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 ""
-"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 "
-"<https://flower.dev/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html>`__),"
-" nous avons appris comment les stratégies peuvent être utilisées pour "
-"personnaliser l'exécution à la fois sur le serveur et les clients "
-"(`partie 2 <https://flower.dev/docs/tutorial/Flower-2-Strategies-in-FL-"
-"PyTorch.html>`__), et nous avons construit notre propre stratégie "
-"personnalisée à partir de zéro (`partie 3 - WIP "
-"<https://flower.dev/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/tutorial-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:64
+#, fuzzy
+msgid "FedVSSL ([#2412](https://github.com/adap/flower/pull/2412))"
 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."
+"Amélioration de la documentation sur le serveur gRPC "
+"([#841](https://github.com/adap/flower/pull/841))"
 
-#: ../../source/tutorial-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:65
+#, fuzzy
+msgid "FedNova ([#2179](https://github.com/adap/flower/pull/2179))"
 msgstr ""
-"Allons plus loin et voyons ce qu'il faut faire pour passer de "
-"``NumPyClient`` à ``Client`` !"
+"**Ajouter une nouvelle stratégie `FedProx`** "
+"([#1619](https://github.com/adap/flower/pull/1619))"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:30
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:29
-msgid "Step 0: Preparation"
-msgstr "Étape 0 : Préparation"
+#: ../../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))"
+
+#: ../../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))"
+
+#: ../../source/ref-changelog.md:68
+#, fuzzy
+msgid "FedPara ([#2722](https://github.com/adap/flower/pull/2722))"
+msgstr ""
+"**Renommé stratégie q-FedAvg** "
+"([#802](https://github.com/adap/flower/pull/802))"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:117
+#: ../../source/ref-changelog.md:70
+#, 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``."
+"**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 ""
-"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``."
+"**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/tutorial-customize-the-client-pytorch.ipynb:259
-msgid "Step 1: Revisiting NumPyClient"
-msgstr "Étape 1 : Revoir NumPyClient"
+#: ../../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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:76
+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: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 ""
+
+#: ../../source/ref-changelog.md:82
+#, fuzzy
+msgid ""
+"**Deprecate** `start_numpy_client` "
+"([#2563](https://github.com/adap/flower/pull/2563), "
+"[#2718](https://github.com/adap/flower/pull/2718))"
+msgstr ""
+"**Nouvelles stratégies intégrées** "
+"([#828](https://github.com/adap/flower/pull/828) "
+"[#822](https://github.com/adap/flower/pull/822))"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:261
+#: ../../source/ref-changelog.md:84
 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``:"
+"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 ""
-"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-customize-the-client-pytorch.ipynb:309
+#: ../../source/ref-changelog.md:86
+#, fuzzy
 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:"
+"**Deprecate legacy DP wrappers** "
+"([#2749](https://github.com/adap/flower/pull/2749))"
 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 :"
+"**Supprimez KerasClient** "
+"([#857](https://github.com/adap/flower/pull/857))"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:339
+#: ../../source/ref-changelog.md:88
 msgid ""
-"This works as expected, two clients are training for three rounds of "
-"federated learning."
+"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 ""
-"Cela fonctionne comme prévu, deux clients s'entraînent pour trois tours "
-"d'apprentissage fédéré."
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:341
+#: ../../source/ref-changelog.md:90
+#, fuzzy
 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)."
+"**Make optional arg** `--callable` **in** `flower-client` **a required "
+"positional arg** ([#2673](https://github.com/adap/flower/pull/2673))"
 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)."
+"**Log** `Client` **exceptions dans le moteur de client virtuel** "
+"([#1493](https://github.com/adap/flower/pull/1493))"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:343
+#: ../../source/ref-changelog.md:92
+#, fuzzy
 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``."
+"**Rename** `certificates` **to** `root_certificates` **in** `Driver` "
+"([#2890](https://github.com/adap/flower/pull/2890))"
 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`."
+"**Rename** `rnd` **to** `server_round` "
+"([#1321](https://github.com/adap/flower/pull/1321))"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:345
+#: ../../source/ref-changelog.md:94
+#, fuzzy
 msgid ""
-"Instead of building on top of ``NumPyClient``, we can directly build on "
-"top of ``Client``."
+"**Drop experimental** `Task` **fields** "
+"([#2866](https://github.com/adap/flower/pull/2866), "
+"[#2865](https://github.com/adap/flower/pull/2865))"
 msgstr ""
-"Au lieu de construire par-dessus `NumPyClient``, nous pouvons construire "
-"directement par-dessus `Client``."
+"**Rename** `Weights` **to** `NDArrays` "
+"([#1258](https://github.com/adap/flower/pull/1258), "
+"[#1259](https://github.com/adap/flower/pull/1259))"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:357
-msgid "Step 2: Moving from ``NumPyClient`` to ``Client``"
-msgstr "Étape 2 : Passer de ``NumPyClient`` à ``Client``"
+#: ../../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 ""
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:359
+#: ../../source/ref-changelog.md:98
+#, fuzzy
 msgid ""
-"Let's try to do the same thing using ``Client`` instead of "
-"``NumPyClient``."
+"**Retire MXNet examples** "
+"([#2724](https://github.com/adap/flower/pull/2724))"
 msgstr ""
-"Essayons de faire la même chose en utilisant ``Client`` au lieu de "
-"``NumPyClient``."
+"**Nouvel exemple de code scikit-learn** "
+"([#748](https://github.com/adap/flower/pull/748))"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:465
+#: ../../source/ref-changelog.md:100
 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?"
+"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 ""
-"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-customize-the-client-pytorch.ipynb:490
+#: ../../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
 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?"
+"`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` <!---TOKEN_v1.6.0-->"
 msgstr ""
-"Voilà, nous utilisons maintenant ``Client``. Cela ressemble probablement "
-"à ce que nous avons fait avec ``NumPyClient``. Alors quelle est la "
-"différence ?"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:492
+#: ../../source/ref-changelog.md:112
+#, fuzzy
 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."
+"**Add experimental support for Python 3.12** "
+"([#2565](https://github.com/adap/flower/pull/2565))"
 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."
+"**Ajouter la prise en charge expérimentale de Python 3.10 et Python "
+"3.11** ([#1135](https://github.com/adap/flower/pull/1135))"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:495
+#: ../../source/ref-changelog.md:114
+#, fuzzy
 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."
+"**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 ""
-"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."
+"**([#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/tutorial-customize-the-client-pytorch.ipynb:497
+#: ../../source/ref-changelog.md:116
 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``."
+"We have added a new `xgboost-quickstart` example alongside a new "
+"`xgboost-comprehensive` example that goes more in-depth."
 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-customize-the-client-pytorch.ipynb:510
-msgid "Step 3: Custom serialization"
-msgstr "Étape 3 : Sérialisation personnalisée"
+#: ../../source/ref-changelog.md:118
+#, fuzzy
+msgid ""
+"**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/tutorial-customize-the-client-pytorch.ipynb:512
+#: ../../source/ref-changelog.md:120
 msgid ""
-"Here we will explore how to implement custom serialization with a simple "
-"example."
+"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 ""
-"Nous allons ici explorer comment mettre en œuvre une sérialisation "
-"personnalisée à l'aide d'un exemple simple."
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:514
+#: ../../source/ref-changelog.md:122
+#, fuzzy
 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."
+"**Support custom** `ClientManager` **in** `start_driver()` "
+"([#2292](https://github.com/adap/flower/pull/2292))"
 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."
+"Ajout de la prise en charge d'un `ClientManager` personnalisé comme "
+"paramètre de `start_simulation` "
+"([#1171](https://github.com/adap/flower/pull/1171))"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:516
+#: ../../source/ref-changelog.md:124
+#, fuzzy
 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."
+"**Update REST API to support create and delete nodes** "
+"([#2283](https://github.com/adap/flower/pull/2283))"
 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."
+"**Nouvelle stratégie expérimentale TensorBoard** "
+"([#789](https://github.com/adap/flower/pull/789))"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:518
+#: ../../source/ref-changelog.md:126
+#, fuzzy
 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 Android SDK** "
+"([#2187](https://github.com/adap/flower/pull/2187))"
 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-customize-the-client-pytorch.ipynb:521
-msgid "Our custom serialization/deserialization functions"
-msgstr "Nos fonctions de sérialisation/désérialisation personnalisées"
+"**Introduire une nouvelle ligne de base pour les fleurs : FedAvg "
+"FEMNIST** ([#1655](https://github.com/adap/flower/pull/1655))"
 
-#: ../../source/tutorial-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."
+#: ../../source/ref-changelog.md:128
+msgid "Add gRPC request-response capability to the Android SDK."
 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-customize-the-client-pytorch.ipynb:525
+#: ../../source/ref-changelog.md:130
+#, fuzzy
 msgid ""
-"Note that we imported the ``scipy.sparse`` library in order to convert "
-"our arrays."
+"**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 ""
-"Notez que nous avons importé la bibliothèque ``scipy.sparse`` afin de "
-"convertir nos tableaux."
+"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/tutorial-customize-the-client-pytorch.ipynb:613
-msgid "Client-side"
-msgstr "Côté client"
+#: ../../source/ref-changelog.md:132
+msgid "Add gRPC request-response capability to the C++ SDK."
+msgstr ""
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:615
+#: ../../source/ref-changelog.md:134
+#, fuzzy
 msgid ""
-"To be able to able to serialize our ``ndarray``\\ s into sparse "
-"parameters, we will just have to call our custom functions in our "
-"``flwr.client.Client``."
+"**Make HTTPS the new default** "
+"([#2591](https://github.com/adap/flower/pull/2591), "
+"[#2636](https://github.com/adap/flower/pull/2636))"
 msgstr ""
-"Pour pouvoir sérialiser nos ``ndarray`` en paramètres sparse, il nous "
-"suffira d'appeler nos fonctions personnalisées dans notre "
-"``flwr.client.Client``."
+"**Exemple de code mis à jour** "
+"([#1344](https://github.com/adap/flower/pull/1344), "
+"[#1347](https://github.com/adap/flower/pull/1347))"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:617
+#: ../../source/ref-changelog.md:136
 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."
+"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 ""
-"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-customize-the-client-pytorch.ipynb:619
+#: ../../source/ref-changelog.md:138
 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``."
+"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 ""
-"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-customize-the-client-pytorch.ipynb:621
+#: ../../source/ref-changelog.md:140
+#, fuzzy
 msgid ""
-"In ``evaluate``, we will only need to deserialize the global parameters "
-"with our custom function."
+"**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 ""
-"Dans ``evaluate``, nous n'aurons besoin que de désérialiser les "
-"paramètres globaux avec notre fonction personnalisée."
-
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:725
-msgid "Server-side"
-msgstr "Côté serveur"
+"**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/tutorial-customize-the-client-pytorch.ipynb:727
+#: ../../source/ref-changelog.md:142
 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``."
+"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 ""
-"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-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-customize-the-client-pytorch.ipynb:735
+#: ../../source/ref-changelog.md:144
+#, fuzzy
 msgid ""
-"And for ``aggregate_fit``, we will first deserialize every result we "
-"received:"
+"**Add new** `Bulyan` **strategy** "
+"([#1817](https://github.com/adap/flower/pull/1817), "
+"[#1891](https://github.com/adap/flower/pull/1891))"
 msgstr ""
-"Et pour ``aggregate_fit``, nous allons d'abord désérialiser chaque "
-"résultat que nous avons reçu :"
-
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:744
-msgid "And then serialize the aggregated result:"
-msgstr "Puis sérialise le résultat agrégé :"
+"**Nouvelles stratégies intégrées** "
+"([#828](https://github.com/adap/flower/pull/828) "
+"[#822](https://github.com/adap/flower/pull/822))"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:903
-msgid "We can now run our custom serialization example!"
+#: ../../source/ref-changelog.md:146
+#, fuzzy
+msgid ""
+"The new `Bulyan` strategy implements Bulyan by [El Mhamdi et al., "
+"2018](https://arxiv.org/abs/1802.07927)"
 msgstr ""
-"Nous pouvons maintenant exécuter notre exemple de sérialisation "
-"personnalisée !"
+"La nouvelle stratégie `FedMedian` met en œuvre Federated Median "
+"(FedMedian) par [Yin et al., 2018] "
+"(https://arxiv.org/pdf/1803.01498v1.pdf)."
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:934
+#: ../../source/ref-changelog.md:148
+#, 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."
+"**Add new** `XGB Bagging` **strategy** "
+"([#2611](https://github.com/adap/flower/pull/2611))"
 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."
+"**Ajouter une nouvelle stratégie `FedProx`** "
+"([#1619](https://github.com/adap/flower/pull/1619))"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:952
+#: ../../source/ref-changelog.md:150 ../../source/ref-changelog.md:152
+#, 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:"
+"**Introduce `WorkloadState`** "
+"([#2564](https://github.com/adap/flower/pull/2564), "
+"[#2632](https://github.com/adap/flower/pull/2632))"
 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-customize-the-client-pytorch.ipynb:954
-msgid "`Read Flower Docs <https://flower.dev/docs/>`__"
-msgstr "`Lire les docs sur les fleurs <https://flower.dev/docs/>`__"
+"**Nouvelles stratégies intégrées** "
+"([#828](https://github.com/adap/flower/pull/828) "
+"[#822](https://github.com/adap/flower/pull/822))"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:955
+#: ../../source/ref-changelog.md:156
+#, fuzzy
 msgid ""
-"`Check out Flower Code Examples "
-"<https://github.com/adap/flower/tree/main/examples>`__"
+"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 ""
-"`Check out Flower Code Examples "
-"<https://github.com/adap/flower/tree/main/examples>`__"
+"**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/tutorial-customize-the-client-pytorch.ipynb:956
+#: ../../source/ref-changelog.md:158
 #, fuzzy
 msgid ""
-"`Use Flower Baselines for your research "
-"<https://flower.dev/docs/baselines/>`__"
+"Baselines Docs ([#2290](https://github.com/adap/flower/pull/2290), "
+"[#2400](https://github.com/adap/flower/pull/2400))"
 msgstr ""
-"`Utilise les lignes de base des fleurs pour ta recherche "
-"<https://flower.dev/docs/using-baselines.html>`__"
+"**Nouvel exemple de code JAX** "
+"([#906](https://github.com/adap/flower/pull/906), "
+"[#1143](https://github.com/adap/flower/pull/1143))"
 
-#: ../../source/tutorial-customize-the-client-pytorch.ipynb:957
+#: ../../source/ref-changelog.md:160
 #, fuzzy
 msgid ""
-"`Watch Flower Summit 2023 videos <https://flower.dev/conf/flower-"
-"summit-2023/>`__"
+"FedMLB ([#2340](https://github.com/adap/flower/pull/2340), "
+"[#2507](https://github.com/adap/flower/pull/2507))"
 msgstr ""
-"`Regardez les vidéos du Flower Summit 2022 <https://flower.dev/conf"
-"/flower-summit-2022/>`__"
+"**Exemple de code mis à jour** "
+"([#1344](https://github.com/adap/flower/pull/1344), "
+"[#1347](https://github.com/adap/flower/pull/1347))"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:9
-msgid "Get started with Flower"
+#: ../../source/ref-changelog.md:162
+#, fuzzy
+msgid ""
+"TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), "
+"[#2508](https://github.com/adap/flower/pull/2508))"
 msgstr ""
+"**Nouvelles stratégies intégrées** "
+"([#828](https://github.com/adap/flower/pull/828) "
+"[#822](https://github.com/adap/flower/pull/822))"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:11
-#: ../../source/tutorial-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/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/tutorial-get-started-with-flower-pytorch.ipynb:13
-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."
+#: ../../source/ref-changelog.md:166
+#, fuzzy
+msgid "FjORD [#2431](https://github.com/adap/flower/pull/2431)"
 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-get-started-with-flower-pytorch.ipynb:17
-#: ../../source/tutorial-what-is-federated-learning.ipynb:19
-msgid "Let's get stated!"
-msgstr "Allons-y, déclarons-le !"
+"Amélioration de la documentation sur le serveur gRPC "
+"([#841](https://github.com/adap/flower/pull/841))"
 
-#: ../../source/tutorial-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/ref-changelog.md:168
+#, fuzzy
+msgid "MOON [#2421](https://github.com/adap/flower/pull/2421)"
 msgstr ""
-"Avant de commencer à coder, assurons-nous que nous disposons de tout ce "
-"dont nous avons besoin."
+"**Ajouter une nouvelle stratégie `FedProx`** "
+"([#1619](https://github.com/adap/flower/pull/1619))"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:45
-msgid ""
-"Next, we install the necessary packages for PyTorch (``torch`` and "
-"``torchvision``) and Flower (``flwr``):"
+#: ../../source/ref-changelog.md:170
+#, fuzzy
+msgid "DepthFL [#2295](https://github.com/adap/flower/pull/2295)"
 msgstr ""
-"Ensuite, nous installons les paquets nécessaires pour PyTorch (``torch`` "
-"et ``torchvision``) et Flower (``flwr``) :"
+"**Ajouter une nouvelle stratégie `FedProx`** "
+"([#1619](https://github.com/adap/flower/pull/1619))"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:117
-msgid "Loading the data"
-msgstr "Chargement des données"
+#: ../../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/tutorial-get-started-with-flower-pytorch.ipynb:119
-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 ""
-"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/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))"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:150
+#: ../../source/ref-changelog.md:178
+#, fuzzy
 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)."
+"FedBN ([#2608](https://github.com/adap/flower/pull/2608), "
+"[#2615](https://github.com/adap/flower/pull/2615))"
 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)."
+"**Nouvelles stratégies intégrées** "
+"([#828](https://github.com/adap/flower/pull/828) "
+"[#822](https://github.com/adap/flower/pull/822))"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:152
+#: ../../source/ref-changelog.md:180
+#, 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:"
+"**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 ""
-"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 :"
+"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/tutorial-get-started-with-flower-pytorch.ipynb:172
+#: ../../source/ref-changelog.md:182
+#, 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 the resulting partitions by creating a PyTorch ``DataLoader`` for "
-"each of them:"
+"**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 ""
-"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 "
-":"
+"**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/tutorial-get-started-with-flower-pytorch.ipynb:222
+#: ../../source/ref-changelog.md:184
+#, 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"
-" 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."
+"**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 ""
-"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."
+"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/tutorial-get-started-with-flower-pytorch.ipynb:225
+#: ../../source/ref-changelog.md:186
+#, fuzzy
 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:"
+"**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 ""
-"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 :"
+"**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/tutorial-get-started-with-flower-pytorch.ipynb:264
+#: ../../source/ref-changelog.md:188
 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."
+"**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: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 ""
-"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-get-started-with-flower-pytorch.ipynb:276
-msgid "Step 1: Centralized Training with PyTorch"
-msgstr "Étape 1 : Formation centralisée avec PyTorch"
+"Flower a reçu de nombreuses améliorations sous le capot, trop nombreuses "
+"pour être énumérées ici."
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:287
+#: ../../source/ref-changelog.md:194
+#, fuzzy
 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 "
-"<https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html>`__."
+"**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 ""
-"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 "
-"<https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html>`__."
-
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:299
-msgid "Defining the model"
-msgstr "Définir le modèle"
+"**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-get-started-with-flower-pytorch.ipynb:301
+#: ../../source/ref-changelog.md:196
 msgid ""
-"We use the simple CNN described in the `PyTorch tutorial "
-"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#define-a"
-"-convolutional-neural-network>`__:"
+"Python 3.7 support was deprecated in Flower 1.5, and this release removes"
+" support. Flower now requires Python 3.8."
 msgstr ""
-"Nous utilisons le CNN simple décrit dans le tutoriel `PyTorch "
-"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#define-a"
-"-convolutional-neural-network>`__ :"
-
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:338
-msgid "Let's continue with the usual training and test functions:"
-msgstr "Poursuivons avec les fonctions habituelles de formation et de test :"
-
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:398
-msgid "Training the model"
-msgstr "Entraîne le modèle"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:400
+#: ../../source/ref-changelog.md:198
+#, fuzzy
 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:"
+"**Remove experimental argument** `rest` **from** `start_client` "
+"([#2324](https://github.com/adap/flower/pull/2324))"
 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 :"
+"**Supprimer les stratégies expérimentales** "
+"([#1280](https://github.com/adap/flower/pull/1280))"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:430
+#: ../../source/ref-changelog.md:200
 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!"
+"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 ""
-"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-get-started-with-flower-pytorch.ipynb:442
-msgid "Step 2: Federated Learning with Flower"
-msgstr "Étape 2 : Apprentissage fédéré avec Flower"
+#: ../../source/ref-changelog.md:202
+#, fuzzy
+msgid "v1.5.0 (2023-08-31)"
+msgstr "v1.4.0 (2023-04-21)"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:444
+#: ../../source/ref-changelog.md:208
 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."
+"`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` <!---"
+"TOKEN_v1.5.0-->"
 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-get-started-with-flower-pytorch.ipynb:456
-msgid "Updating model parameters"
-msgstr "Mise à jour des paramètres du modèle"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:458
+#: ../../source/ref-changelog.md:212
+#, fuzzy
 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)."
+"**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 ""
-"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)."
+"**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/tutorial-get-started-with-flower-pytorch.ipynb:460
+#: ../../source/ref-changelog.md:214
 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."
+"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 ""
-"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-get-started-with-flower-pytorch.ipynb:462
+#: ../../source/ref-changelog.md:216
 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):"
+"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 ""
-"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-get-started-with-flower-pytorch.ipynb:490
-msgid "Implementing a Flower client"
-msgstr "Mise en place d'un client Flower"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:492
+#: ../../source/ref-changelog.md:218
 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."
+"**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 ""
-"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-get-started-with-flower-pytorch.ipynb:494
+#: ../../source/ref-changelog.md:220
 msgid ""
-"To implement the Flower client, we create a subclass of "
-"``flwr.client.NumPyClient`` and implement the three methods "
-"``get_parameters``, ``fit``, and ``evaluate``:"
+"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 ""
-"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-get-started-with-flower-pytorch.ipynb:496
-msgid "``get_parameters``: Return the current local model parameters"
-msgstr "``get_parameters`` : renvoie les paramètres du modèle local actuel"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:497
+#: ../../source/ref-changelog.md:222
+#, fuzzy
 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"
+"**Introduce Flower Swift SDK** "
+"([#1858](https://github.com/adap/flower/pull/1858), "
+"[#1897](https://github.com/adap/flower/pull/1897))"
 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"
+"**Introduction du SDK iOS (aperçu)** "
+"([#1621](https://github.com/adap/flower/pull/1621), "
+"[#1764](https://github.com/adap/flower/pull/1764))"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:498
+#: ../../source/ref-changelog.md:224
 msgid ""
-"``evaluate``: Receive model parameters from the server, evaluate the "
-"model parameters on the local data, and return the evaluation result to "
-"the server"
+"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 ""
-"``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-get-started-with-flower-pytorch.ipynb:500
+#: ../../source/ref-changelog.md:226
+#, fuzzy
 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:"
+"**Introduce Flower Android SDK** "
+"([#2131](https://github.com/adap/flower/pull/2131))"
 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 :"
+"**Introduire une nouvelle ligne de base pour les fleurs : FedAvg "
+"FEMNIST** ([#1655](https://github.com/adap/flower/pull/1655))"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:537
+#: ../../source/ref-changelog.md:228
 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)."
+"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 ""
-"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-get-started-with-flower-pytorch.ipynb:541
-msgid "Using the Virtual Client Engine"
-msgstr "Utilisation du moteur du client virtuel"
-
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:543
+#: ../../source/ref-changelog.md:230
+#, fuzzy
 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."
+"**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 ""
-"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."
+"**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/tutorial-get-started-with-flower-pytorch.ipynb:545
+#: ../../source/ref-changelog.md:232
 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:"
+"A new testing infrastructure ensures that new changes stay compatible "
+"with existing framework integrations or strategies."
 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-get-started-with-flower-pytorch.ipynb:580
-msgid "Starting the training"
-msgstr "Commencer la formation"
+#: ../../source/ref-changelog.md:234
+#, fuzzy
+msgid "**Deprecate Python 3.7**"
+msgstr "**Créer le PR**"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:582
+#: ../../source/ref-changelog.md:236
 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``."
+"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 ""
-"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-get-started-with-flower-pytorch.ipynb:584
+#: ../../source/ref-changelog.md:238
+#, fuzzy
 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)."
+"**Add new** `FedTrimmedAvg` **strategy** "
+"([#1769](https://github.com/adap/flower/pull/1769), "
+"[#1853](https://github.com/adap/flower/pull/1853))"
 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)."
+"**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/tutorial-get-started-with-flower-pytorch.ipynb:586
+#: ../../source/ref-changelog.md:240
+#, fuzzy
 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:"
+"The new `FedTrimmedAvg` strategy implements Trimmed Mean by [Dong Yin, "
+"2018](https://arxiv.org/abs/1803.01498)."
 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-get-started-with-flower-pytorch.ipynb:628
-msgid "Behind the scenes"
-msgstr "Dans les coulisses"
+"La nouvelle stratégie `FedMedian` met en œuvre Federated Median "
+"(FedMedian) par [Yin et al., 2018] "
+"(https://arxiv.org/pdf/1803.01498v1.pdf)."
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:630
-msgid "So how does this work? How does Flower execute this simulation?"
+#: ../../source/ref-changelog.md:242
+#, fuzzy
+msgid ""
+"**Introduce start_driver** "
+"([#1697](https://github.com/adap/flower/pull/1697))"
 msgstr ""
-"Alors, comment cela fonctionne-t-il ? Comment Flower exécute-t-il cette "
-"simulation ?"
+"**Ajouter une nouvelle stratégie `FedProx`** "
+"([#1619](https://github.com/adap/flower/pull/1619))"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:632
-#, python-format
+#: ../../source/ref-changelog.md:244
 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)."
+"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 ""
-"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-get-started-with-flower-pytorch.ipynb:634
+#: ../../source/ref-changelog.md:246
+#, 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."
+"**Add parameter aggregation to** `mt-pytorch` **code example** "
+"([#1785](https://github.com/adap/flower/pull/1785))"
 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-get-started-with-flower-pytorch.ipynb:646
-msgid "Where's the accuracy?"
-msgstr "Où est la précision ?"
+"**Nouvel exemple de code PyTorch avancé** "
+"([#1007](https://github.com/adap/flower/pull/1007))"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:648
+#: ../../source/ref-changelog.md:248
 msgid ""
-"You may have noticed that all metrics except for ``losses_distributed`` "
-"are empty. Where did the ``{\"accuracy\": float(accuracy)}`` go?"
+"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 ""
-"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-get-started-with-flower-pytorch.ipynb:650
+#: ../../source/ref-changelog.md:250
+#, 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."
+"**Migrate experimental REST API to Starlette** "
+"([2171](https://github.com/adap/flower/pull/2171))"
 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."
+"**Nouvelle stratégie expérimentale TensorBoard** "
+"([#789](https://github.com/adap/flower/pull/789))"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:652
+#: ../../source/ref-changelog.md:252
 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``."
+"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 ""
-"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-get-started-with-flower-pytorch.ipynb:654
+#: ../../source/ref-changelog.md:254
+#, fuzzy
 msgid ""
-"Let's create a simple weighted averaging function to aggregate the "
-"``accuracy`` metric we return from ``evaluate``:"
+"Please note: The REST request-response API is still experimental and will"
+" likely change significantly over time."
 msgstr ""
-"Créons une simple fonction de calcul de la moyenne pondérée pour agréger "
-"la mesure de \"précision\" que nous renvoie ``evaluate`` :"
+"Remarque : l'API REST est encore expérimentale et est susceptible de "
+"changer de manière significative au fil du temps."
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:680
+#: ../../source/ref-changelog.md:256
+#, fuzzy
 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:"
+"**Introduce experimental gRPC request-response API** "
+"([#1867](https://github.com/adap/flower/pull/1867), "
+"[#1901](https://github.com/adap/flower/pull/1901))"
 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 :"
+"**Introduire les enveloppes de confidentialité différentielle (aperçu)** "
+"([#1357](https://github.com/adap/flower/pull/1357), "
+"[#1460](https://github.com/adap/flower/pull/1460))"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:717
+#: ../../source/ref-changelog.md:258
 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."
+"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 ""
-"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-get-started-with-flower-pytorch.ipynb:719
+#: ../../source/ref-changelog.md:260
+#, fuzzy
 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."
+"Please note: The gRPC request-response API is still experimental and will"
+" likely change significantly over time."
 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-get-started-with-flower-pytorch.ipynb:731
-#: ../../source/tutorial-what-is-federated-learning.ipynb:351
-msgid "Final remarks"
-msgstr "Remarques finales"
+"Remarque : l'API REST est encore expérimentale et est susceptible de "
+"changer de manière significative au fil du temps."
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:733
+#: ../../source/ref-changelog.md:262
+#, fuzzy
 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."
+"**Replace the experimental** `start_client(rest=True)` **with the new** "
+"`start_client(transport=\"rest\")` "
+"([#1880](https://github.com/adap/flower/pull/1880))"
 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."
+"**Initialise** `start_simulation` **avec une liste d'ID de clients** "
+"([#860](https://github.com/adap/flower/pull/860))"
 
-#: ../../source/tutorial-get-started-with-flower-pytorch.ipynb:735
+#: ../../source/ref-changelog.md:264
 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 (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 ""
-"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-get-started-with-flower-pytorch.ipynb:753
+#: ../../source/ref-changelog.md:266
 #, fuzzy
 msgid ""
-"The `Flower Federated Learning Tutorial - Part 2 "
-"<https://flower.dev/docs/framework/tutorial-use-a-federated-learning-"
-"strategy-pytorch.html>`__ goes into more depth about strategies and all "
-"the advanced things you can build with them."
+"**Add a new gRPC option** "
+"([#2197](https://github.com/adap/flower/pull/2197))"
 msgstr ""
-"Le `Tutoriel d'apprentissage fédéré Flower - Partie 2 "
-"<https://flower.dev/docs/tutorial/Flower-2-Strategies-in-FL-"
-"PyTorch.html>`__ va plus en profondeur sur les stratégies et toutes les "
-"choses avancées que tu peux construire avec elles."
-
-#: ../../source/tutorial-quickstart-android.rst:5
-#, fuzzy
-msgid "Quickstart Android"
-msgstr "Démarrage rapide des Pandas"
+"**Ajouter une nouvelle stratégie `FedProx`** "
+"([#1619](https://github.com/adap/flower/pull/1619))"
 
-#: ../../source/tutorial-quickstart-android.rst:7
-#, fuzzy
+#: ../../source/ref-changelog.md:268
 msgid ""
-"Let's build a federated learning system using TFLite and Flower on "
-"Android!"
+"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 ""
-"Construisons un système d'apprentissage fédéré en utilisant fastai et "
-"Flower !"
 
-#: ../../source/tutorial-quickstart-android.rst:9
+#: ../../source/ref-changelog.md:270
 #, fuzzy
 msgid ""
-"Please refer to the `full code example "
-"<https://github.com/adap/flower/tree/main/examples/android>`_ to learn "
-"more."
-msgstr ""
-"Réfère-toi à l'exemple de code complet "
-"<https://github.com/adap/flower/tree/main/examples/quickstart-pandas>`_ "
-"pour en savoir plus."
-
-#: ../../source/tutorial-quickstart-fastai.rst:5
-msgid "Quickstart fastai"
-msgstr "Démarrage rapide fastai"
-
-#: ../../source/tutorial-quickstart-fastai.rst:7
-msgid "Let's build a federated learning system using fastai and Flower!"
+"**Improve example notebooks** "
+"([#2005](https://github.com/adap/flower/pull/2005))"
 msgstr ""
-"Construisons un système d'apprentissage fédéré en utilisant fastai et "
-"Flower !"
+"**Supprimer les stratégies expérimentales** "
+"([#1280](https://github.com/adap/flower/pull/1280))"
 
-#: ../../source/tutorial-quickstart-fastai.rst:9
+#: ../../source/ref-changelog.md:272
 #, fuzzy
-msgid ""
-"Please refer to the `full code example "
-"<https://github.com/adap/flower/tree/main/examples/quickstart-fastai>`_ "
-"to learn more."
-msgstr ""
-"Réfère-toi à l'exemple de code complet "
-"<https://github.com/adap/flower/tree/main/examples/quickstart-fastai>`_ "
-"pour en savoir plus."
-
-#: ../../source/tutorial-quickstart-huggingface.rst:5
-msgid "Quickstart 🤗 Transformers"
-msgstr "Démarrage rapide 🤗 Transformateurs"
+msgid "There's a new 30min Federated Learning PyTorch tutorial!"
+msgstr "Bienvenue au tutoriel sur l'apprentissage fédéré de la fleur !"
 
-#: ../../source/tutorial-quickstart-huggingface.rst:7
+#: ../../source/ref-changelog.md:274
 msgid ""
-"Let's build a federated learning system using Hugging Face Transformers "
-"and Flower!"
+"**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 ""
-"Construisons un système d'apprentissage fédéré à l'aide des "
-"transformateurs Hugging Face et de Flower !"
 
-#: ../../source/tutorial-quickstart-huggingface.rst:9
+#: ../../source/ref-changelog.md:276
 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."
+"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 ""
-"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:15
-msgid "Dependencies"
-msgstr "Dépendances"
-
-#: ../../source/tutorial-quickstart-huggingface.rst:17
+#: ../../source/ref-changelog.md:278
+#, fuzzy
 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`:"
+"**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 ""
-"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` :"
+"**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/tutorial-quickstart-huggingface.rst:27
-msgid "Standard Hugging Face workflow"
-msgstr "Flux de travail standard pour le visage"
+#: ../../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/tutorial-quickstart-huggingface.rst:30
-msgid "Handling the data"
-msgstr "Traitement des données"
+#: ../../source/ref-changelog.md:286
+msgid "v1.4.0 (2023-04-21)"
+msgstr "v1.4.0 (2023-04-21)"
 
-#: ../../source/tutorial-quickstart-huggingface.rst:32
+#: ../../source/ref-changelog.md:292
 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:"
+"`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 ""
-"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:78
-msgid "Training and testing the model"
-msgstr "Former et tester le modèle"
+"`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/tutorial-quickstart-huggingface.rst:80
+#: ../../source/ref-changelog.md:296
 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:"
-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:118
-msgid "Creating the model itself"
-msgstr "Créer le modèle lui-même"
-
-#: ../../source/tutorial-quickstart-huggingface.rst:120
-msgid ""
-"To create the model itself, we will just load the pre-trained distillBERT"
-" model using Hugging Face’s :code:`AutoModelForSequenceClassification` :"
+"**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 ""
-"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:133
-msgid "Federating the example"
-msgstr "Fédérer l'exemple"
-
-#: ../../source/tutorial-quickstart-huggingface.rst:136
-msgid "Creating the IMDBClient"
-msgstr "Création du client IMDBC"
+"**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/tutorial-quickstart-huggingface.rst:138
+#: ../../source/ref-changelog.md:298
 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:"
+"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 ""
-"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 :"
+"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/tutorial-quickstart-huggingface.rst:166
+#: ../../source/ref-changelog.md:300
 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."
+"**Introduce iOS SDK (preview)** "
+"([#1621](https://github.com/adap/flower/pull/1621), "
+"[#1764](https://github.com/adap/flower/pull/1764))"
 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:172
-msgid "Starting the server"
-msgstr "Démarrer le serveur"
+"**Introduction du SDK iOS (aperçu)** "
+"([#1621](https://github.com/adap/flower/pull/1621), "
+"[#1764](https://github.com/adap/flower/pull/1764))"
 
-#: ../../source/tutorial-quickstart-huggingface.rst:174
+#: ../../source/ref-changelog.md:302
 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:"
+"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 ""
-"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` :"
+"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/tutorial-quickstart-huggingface.rst:202
+#: ../../source/ref-changelog.md:304
 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)."
+"**Introduce new \"What is Federated Learning?\" tutorial** "
+"([#1657](https://github.com/adap/flower/pull/1657), "
+"[#1721](https://github.com/adap/flower/pull/1721))"
 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:206
-msgid "Putting everything together"
-msgstr "Tout assembler"
-
-#: ../../source/tutorial-quickstart-huggingface.rst:208
-msgid "We can now start client instances using:"
-msgstr "Nous pouvons maintenant démarrer des instances de clients en utilisant :"
-
-#: ../../source/tutorial-quickstart-huggingface.rst:218
-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."
+"**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/tutorial-quickstart-huggingface.rst:220
+#: ../../source/ref-changelog.md:306
 #, fuzzy
 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)."
+"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 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 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/tutorial-quickstart-huggingface.rst:224
+#: ../../source/ref-changelog.md:308
 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 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 ""
-"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."
+"**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/tutorial-quickstart-huggingface.rst:227
+#: ../../source/ref-changelog.md:310
 msgid ""
-"Note that in this example we used :code:`PyTorch`, but we could have very"
-" well used :code:`TensorFlow`."
+"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 ""
-"Notez que dans cet exemple, nous avons utilisé :code:`PyTorch`, mais nous"
-" aurions très bien pu utiliser :code:`TensorFlow`."
-
-#: ../../source/tutorial-quickstart-ios.rst:5
-#, fuzzy
-msgid "Quickstart iOS"
-msgstr "Démarrage rapide XGBoost"
+"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/tutorial-quickstart-ios.rst:7
-#, fuzzy
+#: ../../source/ref-changelog.md:312
 msgid ""
-"In this tutorial we will learn how to train a Neural Network on MNIST "
-"using Flower and CoreML on iOS devices."
+"**Introduce new Flower Baseline: FedAvg FEMNIST** "
+"([#1655](https://github.com/adap/flower/pull/1655))"
 msgstr ""
-"Dans ce tutoriel, nous allons apprendre, comment former un réseau "
-"neuronal convolutif sur MNIST en utilisant Flower et PyTorch."
+"**Introduire une nouvelle ligne de base pour les fleurs : FedAvg "
+"FEMNIST** ([#1655](https://github.com/adap/flower/pull/1655))"
 
-#: ../../source/tutorial-quickstart-ios.rst:9
-#, fuzzy
+#: ../../source/ref-changelog.md:314
 msgid ""
-"First of all, for running the Flower Python server, it is recommended to "
-"create a virtual environment and run everything within a `virtualenv "
-"<https://flower.dev/docs/recommended-env-setup.html>`_. For the Flower "
-"client implementation in iOS, it is recommended to use Xcode as our IDE."
+"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 ""
-"Tout d'abord, il est recommandé de créer un environnement virtuel et de "
-"tout exécuter au sein d'un `virtualenv <https://flower.dev/docs"
-"/recommended-env-setup.html>`_."
+"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/tutorial-quickstart-ios.rst:12
-#, fuzzy
+#: ../../source/ref-changelog.md:316
 msgid ""
-"Our example consists of one Python *server* and two iPhone *clients* that"
-" all have the same model."
+"**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 ""
-"Notre exemple consiste en un *serveur* et deux *clients* ayant tous le "
-"même modèle."
+"**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/tutorial-quickstart-ios.rst:14
-#, fuzzy
+#: ../../source/ref-changelog.md:318
 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*."
+"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 ""
-"*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*."
+"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/tutorial-quickstart-ios.rst:18
-#, fuzzy
+#: ../../source/ref-changelog.md:320
 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:"
+"Please note: The REST API is still experimental and will likely change "
+"significantly over time."
 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 :"
+"Remarque : l'API REST est encore expérimentale et est susceptible de "
+"changer de manière significative au fil du temps."
 
-#: ../../source/tutorial-quickstart-ios.rst:24
-msgid "Or Poetry:"
+#: ../../source/ref-changelog.md:322
+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 ""
+"**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/tutorial-quickstart-ios.rst:33
+#: ../../source/ref-changelog.md:324
 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:"
+"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 ""
+"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/tutorial-quickstart-ios.rst:69
+#: ../../source/ref-changelog.md:326
 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 "
-"<https://github.com/adap/flower/tree/main/examples/ios>`_ to learn more "
-"about the app."
+"**Fix spilling issues related to Ray during simulations** "
+"([#1698](https://github.com/adap/flower/pull/1698))"
 msgstr ""
+"**Répare les problèmes de déversement liés à Ray pendant les "
+"simulations** ([#1698](https://github.com/adap/flower/pull/1698))"
 
-#: ../../source/tutorial-quickstart-ios.rst:72
-msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:"
+#: ../../source/ref-changelog.md:328
+#, 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! 🎉"
 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 ! 🎉"
 
-#: ../../source/tutorial-quickstart-ios.rst:80
+#: ../../source/ref-changelog.md:330
 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`."
+"**Add new example using** `TabNet` **and Flower** "
+"([#1725](https://github.com/adap/flower/pull/1725))"
 msgstr ""
+"**Ajouter un nouvel exemple utilisant** `TabNet` **et Flower** "
+"([#1725](https://github.com/adap/flower/pull/1725))"
 
-#: ../../source/tutorial-quickstart-ios.rst:96
+#: ../../source/ref-changelog.md:332
 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`."
+"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 ""
+"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/tutorial-quickstart-ios.rst:99
+#: ../../source/ref-changelog.md:334
 msgid ""
-"After we have all of the necessary informations, let's create our Flower "
-"client."
+"**Add new how-to guide for monitoring simulations** "
+"([#1649](https://github.com/adap/flower/pull/1649))"
 msgstr ""
+"**Ajouter un nouveau guide pratique pour le suivi des simulations** "
+"([#1649](https://github.com/adap/flower/pull/1649))"
 
-#: ../../source/tutorial-quickstart-ios.rst:114
+#: ../../source/ref-changelog.md:336
 msgid ""
-"Then start the Flower gRPC client and start communicating to the server "
-"by passing our Flower client to the function :code:`startFlwrGRPC`."
+"We now have a documentation guide to help users monitor their performance"
+" during simulations."
 msgstr ""
+"Nous avons maintenant un guide de documentation pour aider les "
+"utilisateurs à surveiller leurs performances pendant les simulations."
 
-#: ../../source/tutorial-quickstart-ios.rst:121
+#: ../../source/ref-changelog.md:338
 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."
+"**Add training metrics to** `History` **object during simulations** "
+"([#1696](https://github.com/adap/flower/pull/1696))"
 msgstr ""
+"**Ajouter des mesures de formation à** `History` **objet pendant les "
+"simulations** ([#1696](https://github.com/adap/flower/pull/1696))"
 
-#: ../../source/tutorial-quickstart-ios.rst:128
-#: ../../source/tutorial-quickstart-mxnet.rst:223
-#: ../../source/tutorial-quickstart-pytorch.rst:202
-#: ../../source/tutorial-quickstart-tensorflow.rst:97
+#: ../../source/ref-changelog.md:340
 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 `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 ""
-"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-quickstart-ios.rst:139
-#: ../../source/tutorial-quickstart-mxnet.rst:234
-#: ../../source/tutorial-quickstart-pytorch.rst:213
-#: ../../source/tutorial-quickstart-scikitlearn.rst:212
-#: ../../source/tutorial-quickstart-tensorflow.rst:109
-msgid "Train the model, federated!"
-msgstr "Entraîne le modèle, fédéré !"
+"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/tutorial-quickstart-ios.rst:141
-#: ../../source/tutorial-quickstart-pytorch.rst:215
-#: ../../source/tutorial-quickstart-tensorflow.rst:111
+#: ../../source/ref-changelog.md:342
 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:"
+"**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 ""
-"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 :"
+"**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/tutorial-quickstart-ios.rst:149
+#: ../../source/ref-changelog.md:356
 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 "
-"<https://developer.apple.com/documentation/xcode/running-your-app-in-"
-"simulator-or-on-a-device>`_."
+"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, "
+"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`"
 msgstr ""
+"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, "
+"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`"
 
-#: ../../source/tutorial-quickstart-ios.rst:153
-#, fuzzy
+#: ../../source/ref-changelog.md:360
 msgid ""
-"Congratulations! You've successfully built and run your first federated "
-"learning system in your ios device. The full `source code "
-"<https://github.com/adap/flower/blob/main/examples/ios>`_ for this "
-"example can be found in :code:`examples/ios`."
+"**Add support for** `workload_id` **and** `group_id` **in Driver API** "
+"([#1595](https://github.com/adap/flower/pull/1595))"
 msgstr ""
-"Félicitations ! Tu as réussi à construire et à faire fonctionner ton "
-"premier système d'apprentissage fédéré. Le code source complet "
-"<https://github.com/adap/flower/blob/main/examples/quickstart-"
-"mxnet/client.py>`_ de cet exemple se trouve dans :code:`examples"
-"/quickstart-mxnet`."
-
-#: ../../source/tutorial-quickstart-jax.rst:5
-msgid "Quickstart JAX"
-msgstr "Démarrage rapide de JAX"
+"**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/tutorial-quickstart-mxnet.rst:5
-msgid "Quickstart MXNet"
-msgstr "Démarrage rapide de MXNet"
-
-#: ../../source/tutorial-quickstart-mxnet.rst:7
+#: ../../source/ref-changelog.md:362
 msgid ""
-"In this tutorial, we will learn how to train a :code:`Sequential` model "
-"on MNIST using Flower and MXNet."
+"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 ""
-"Dans ce tutoriel, nous allons apprendre à former un modèle "
-":code:`Sequential` sur MNIST à l'aide de Flower et de MXNet."
+"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/tutorial-quickstart-mxnet.rst:9
-#: ../../source/tutorial-quickstart-scikitlearn.rst:9
+#: ../../source/ref-changelog.md:364
 msgid ""
-"It is recommended to create a virtual environment and run everything "
-"within this `virtualenv <https://flower.dev/docs/recommended-env-"
-"setup.html>`_."
+"**Make Driver API and Fleet API address configurable** "
+"([#1637](https://github.com/adap/flower/pull/1637))"
 msgstr ""
-"Il est recommandé de créer un environnement virtuel et de tout exécuter "
-"dans ce `virtualenv <https://flower.dev/docs/recommended-env-"
-"setup.html>`_."
+"**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/tutorial-quickstart-mxnet.rst:13
-#: ../../source/tutorial-quickstart-scikitlearn.rst:13
+#: ../../source/ref-changelog.md:366
 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*."
+"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 ""
-"*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*."
+"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/tutorial-quickstart-mxnet.rst:17
-#: ../../source/tutorial-quickstart-scikitlearn.rst:17
+#: ../../source/ref-changelog.md:368
+#, fuzzy
 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:"
+"`flower-server --driver-api-address \"0.0.0.0:8081\" --fleet-api-address "
+"\"0.0.0.0:8086\"`"
 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 :"
+"``flower-superlink --driver-api-address \"0.0.0.0:8081\" --fleet-api-"
+"address \"0.0.0.0:8086\" ``"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:23
-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 :"
+#: ../../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."
 
-#: ../../source/tutorial-quickstart-mxnet.rst:33
+#: ../../source/ref-changelog.md:372
 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 "
-"<https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html>`_."
+"**Add new example of Federated Learning using fastai and Flower** "
+"([#1598](https://github.com/adap/flower/pull/1598))"
 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 "
-"<https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html>`_."
+"**Ajouter un nouvel exemple d'apprentissage fédéré utilisant fastai et "
+"Flower** ([#1598](https://github.com/adap/flower/pull/1598))"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:35
+#: ../../source/ref-changelog.md:374
 msgid ""
-"In a file called :code:`client.py`, import Flower and MXNet related "
-"packages:"
+"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 ""
-"Dans un fichier appelé :code:`client.py`, importe Flower et les paquets "
-"liés au MXNet :"
-
-#: ../../source/tutorial-quickstart-mxnet.rst:50
-msgid "In addition, define the device allocation in MXNet with:"
-msgstr "En outre, définis l'attribution de l'appareil dans MXNet avec :"
+"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/tutorial-quickstart-mxnet.rst:56
+#: ../../source/ref-changelog.md:376
 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 Android example compatible with** `flwr >= 1.0.0` **and the latest"
+" versions of Android** "
+"([#1603](https://github.com/adap/flower/pull/1603))"
 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."
+"**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/tutorial-quickstart-mxnet.rst:70
+#: ../../source/ref-changelog.md:378
+#, fuzzy
 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 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 ""
-"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."
+"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/tutorial-quickstart-mxnet.rst:108
+#: ../../source/ref-changelog.md:380
 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."
+"**Add new `FedProx` strategy** "
+"([#1619](https://github.com/adap/flower/pull/1619))"
 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."
+"**Ajouter une nouvelle stratégie `FedProx`** "
+"([#1619](https://github.com/adap/flower/pull/1619))"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:132
+#: ../../source/ref-changelog.md:382
 msgid ""
-"After defining the training and testing of a MXNet machine learning "
-"model, we use these functions to implement a Flower client."
+"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 ""
-"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:134
-msgid "Our Flower clients will use a simple :code:`Sequential` model:"
-msgstr "Nos clients Flower utiliseront un modèle simple :code:`Sequential` :"
+"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/tutorial-quickstart-mxnet.rst:153
+#: ../../source/ref-changelog.md:384
 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."
+"**Add new metrics to telemetry events** "
+"([#1640](https://github.com/adap/flower/pull/1640))"
 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."
+"**Ajouter de nouvelles métriques aux événements de télémétrie** "
+"([#1640](https://github.com/adap/flower/pull/1640))"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:155
-#: ../../source/tutorial-quickstart-pytorch.rst:141
-#: ../../source/tutorial-quickstart-tensorflow.rst:51
+#: ../../source/ref-changelog.md:386
 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)."
+"An updated event structure allows, for example, the clustering of events "
+"within the same workload."
 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)."
+"Une structure d'événements mise à jour permet, par exemple, de regrouper "
+"des événements au sein d'une même charge de travail."
 
-#: ../../source/tutorial-quickstart-mxnet.rst:161
+#: ../../source/ref-changelog.md:388
 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):"
+"**Add new custom strategy tutorial section** "
+"[#1623](https://github.com/adap/flower/pull/1623)"
 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) :"
-
-#: ../../source/tutorial-quickstart-mxnet.rst:167
-#: ../../source/tutorial-quickstart-pytorch.rst:153
-#: ../../source/tutorial-quickstart-scikitlearn.rst:106
-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:168
-#: ../../source/tutorial-quickstart-pytorch.rst:154
-#: ../../source/tutorial-quickstart-scikitlearn.rst:108
-msgid ":code:`set_parameters` (optional)"
-msgstr ":code:`set_parameters` (optionnel)"
+"**Ajouter une nouvelle section de tutoriel sur les stratégies "
+"personnalisées** [#1623](https://github.com/adap/flower/pull/1623)"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:169
-#: ../../source/tutorial-quickstart-pytorch.rst:155
-#: ../../source/tutorial-quickstart-scikitlearn.rst:108
+#: ../../source/ref-changelog.md:390
+#, fuzzy
 msgid ""
-"update the local model weights with the parameters received from the "
-"server"
+"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 ""
-"mettre à jour les poids du modèle local avec les paramètres reçus du "
-"serveur"
-
-#: ../../source/tutorial-quickstart-mxnet.rst:171
-#: ../../source/tutorial-quickstart-pytorch.rst:157
-#: ../../source/tutorial-quickstart-scikitlearn.rst:111
-msgid "set the local model weights"
-msgstr "fixe les poids du modèle local"
-
-#: ../../source/tutorial-quickstart-mxnet.rst:172
-#: ../../source/tutorial-quickstart-pytorch.rst:158
-#: ../../source/tutorial-quickstart-scikitlearn.rst:112
-msgid "train the local model"
-msgstr "entraîne le modèle local"
-
-#: ../../source/tutorial-quickstart-mxnet.rst:173
-#: ../../source/tutorial-quickstart-pytorch.rst:159
-#: ../../source/tutorial-quickstart-scikitlearn.rst:113
-msgid "receive the updated local model weights"
-msgstr "recevoir les poids du modèle local mis à jour"
-
-#: ../../source/tutorial-quickstart-mxnet.rst:175
-#: ../../source/tutorial-quickstart-pytorch.rst:161
-#: ../../source/tutorial-quickstart-scikitlearn.rst:115
-msgid "test the local model"
-msgstr "teste le modèle local"
-
-#: ../../source/tutorial-quickstart-mxnet.rst:177
-msgid "They can be implemented in the following way:"
-msgstr "Ils peuvent être mis en œuvre de la manière suivante :"
+"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/tutorial-quickstart-mxnet.rst:207
+#: ../../source/ref-changelog.md:392
 msgid ""
-"We can now create an instance of our class :code:`MNISTClient` and add "
-"one line to actually run this client:"
+"**Add new custom serialization tutorial section** "
+"([#1622](https://github.com/adap/flower/pull/1622))"
 msgstr ""
-"Nous pouvons maintenant créer une instance de notre classe "
-":code:`MNISTClient` et ajouter une ligne pour exécuter ce client :"
+"**Ajouter une nouvelle section de tutoriel sur la sérialisation "
+"personnalisée** ([#1622](https://github.com/adap/flower/pull/1622))"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:214
-#: ../../source/tutorial-quickstart-scikitlearn.rst:147
+#: ../../source/ref-changelog.md:394
+#, 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."
+"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 ""
-"C'est tout pour le client. Il nous suffit d'implémenter :code:`Client` ou"
-" :code:`NumPyClient` et d'appeler :code:`fl.client.start_client()` ou "
-":code:`fl.client.start_numpy_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-quickstart-mxnet.rst:236
-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 :"
+"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/tutorial-quickstart-mxnet.rst:244
-#: ../../source/tutorial-quickstart-pytorch.rst:223
-#: ../../source/tutorial-quickstart-scikitlearn.rst:221
-#: ../../source/tutorial-quickstart-tensorflow.rst:119
+#: ../../source/ref-changelog.md:396
 msgid ""
-"Once the server is running we can start the clients in different "
-"terminals. Open a new terminal and start the first client:"
+"**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 ""
-"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:251
-#: ../../source/tutorial-quickstart-pytorch.rst:230
-#: ../../source/tutorial-quickstart-scikitlearn.rst:228
-#: ../../source/tutorial-quickstart-tensorflow.rst:126
-msgid "Open another terminal and start the second client:"
-msgstr "Ouvre un autre terminal et démarre le deuxième client :"
+"**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/tutorial-quickstart-mxnet.rst:257
-#: ../../source/tutorial-quickstart-pytorch.rst:236
-#: ../../source/tutorial-quickstart-scikitlearn.rst:234
+#: ../../source/ref-changelog.md:400
 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):"
+"**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 ""
-"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) :"
+"**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/tutorial-quickstart-mxnet.rst:289
-#, fuzzy
+#: ../../source/ref-changelog.md:402 ../../source/ref-changelog.md:469
 msgid ""
-"Congratulations! You've successfully built and run your first federated "
-"learning system. The full `source code "
-"<https://github.com/adap/flower/blob/main/examples/quickstart-"
-"mxnet/client.py>`_ for this example can be found in :code:`examples"
-"/quickstart-mxnet`."
+"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 ""
-"Félicitations ! Tu as réussi à construire et à faire fonctionner ton "
-"premier système d'apprentissage fédéré. Le code source complet "
-"<https://github.com/adap/flower/blob/main/examples/quickstart-"
-"mxnet/client.py>`_ de cet exemple se trouve dans :code:`examples"
-"/quickstart-mxnet`."
-
-#: ../../source/tutorial-quickstart-pandas.rst:5
-msgid "Quickstart Pandas"
-msgstr "Démarrage rapide des Pandas"
+"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/tutorial-quickstart-pandas.rst:7
-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:408
+msgid "v1.2.0 (2023-01-13)"
+msgstr "v1.2.0 (2023-01-13)"
 
-#: ../../source/tutorial-quickstart-pandas.rst:9
-#, fuzzy
+#: ../../source/ref-changelog.md:414
 msgid ""
-"Please refer to the `full code example "
-"<https://github.com/adap/flower/tree/main/examples/quickstart-pandas>`_ "
-"to learn more."
+"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L."
+" Jiang`, `Ragy`, `Taner Topal`, `dannymcy`"
 msgstr ""
-"Réfère-toi à l'exemple de code complet "
-"<https://github.com/adap/flower/tree/main/examples/quickstart-pandas>`_ "
-"pour en savoir plus."
+"adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L. "
+"Jiang`, `Ragy`, `Taner Topal`, `dannymcy`"
 
-#: ../../source/tutorial-quickstart-pytorch.rst:10
+#: ../../source/ref-changelog.md:418
 msgid ""
-"In this tutorial we will learn how to train a Convolutional Neural "
-"Network on CIFAR10 using Flower and PyTorch."
+"**Introduce new Flower Baseline: FedAvg MNIST** "
+"([#1497](https://github.com/adap/flower/pull/1497), "
+"[#1552](https://github.com/adap/flower/pull/1552))"
 msgstr ""
-"Dans ce tutoriel, nous allons apprendre à entraîner un réseau neuronal "
-"convolutif sur CIFAR10 à l'aide de Flower et PyTorch."
+"**Introduire une nouvelle fleur Référence : FedAvg MNIST** "
+"([#1497](https://github.com/adap/flower/pull/1497), "
+"[#1552](https://github.com/adap/flower/pull/1552))"
 
-#: ../../source/tutorial-quickstart-pytorch.rst:12
+#: ../../source/ref-changelog.md:420
 msgid ""
-"First of all, it is recommended to create a virtual environment and run "
-"everything within a `virtualenv <https://flower.dev/docs/recommended-env-"
-"setup.html>`_."
+"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 ""
-"Tout d'abord, il est recommandé de créer un environnement virtuel et de "
-"tout exécuter au sein d'un `virtualenv <https://flower.dev/docs"
-"/recommended-env-setup.html>`_."
+"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/tutorial-quickstart-pytorch.rst:26
+#: ../../source/ref-changelog.md:422
 msgid ""
-"Since we want to use PyTorch to solve a computer vision task, let's go "
-"ahead and install PyTorch and the **torchvision** library:"
+"**Improve GPU support in simulations** "
+"([#1555](https://github.com/adap/flower/pull/1555))"
 msgstr ""
-"Puisque nous voulons utiliser PyTorch pour résoudre une tâche de vision "
-"par ordinateur, allons-y et installons PyTorch et la bibliothèque "
-"**torchvision** :"
+"**Améliorer la prise en charge des GPU dans les simulations** "
+"([#1555](https://github.com/adap/flower/pull/1555))"
 
-#: ../../source/tutorial-quickstart-pytorch.rst:36
+#: ../../source/ref-changelog.md:424
 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 "
-"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_."
+"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 ""
-"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 "
-"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_ de"
-" PyTorch."
+"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/tutorial-quickstart-pytorch.rst:38
+#: ../../source/ref-changelog.md:426
 msgid ""
-"In a file called :code:`client.py`, import Flower and PyTorch related "
-"packages:"
+"**Improve GPU support in Jupyter Notebook tutorials** "
+"([#1527](https://github.com/adap/flower/pull/1527), "
+"[#1558](https://github.com/adap/flower/pull/1558))"
 msgstr ""
-"Dans un fichier appelé :code:`client.py`, importe Flower et les paquets "
-"liés à PyTorch :"
-
-#: ../../source/tutorial-quickstart-pytorch.rst:53
-msgid "In addition, we define the device allocation in PyTorch with:"
-msgstr "En outre, nous définissons l'attribution des appareils dans PyTorch avec :"
+"**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/tutorial-quickstart-pytorch.rst:59
+#: ../../source/ref-changelog.md:428
 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."
+"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 ""
-"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."
+"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/tutorial-quickstart-pytorch.rst:75
+#: ../../source/ref-changelog.md:430
+#, fuzzy
 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."
+"[An Introduction to Federated Learning](https://flower.ai/docs/framework"
+"/tutorial-get-started-with-flower-pytorch.html)"
 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."
+"[Une introduction à l'apprentissage fédéré] "
+"(https://flower.ai/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html)"
 
-#: ../../source/tutorial-quickstart-pytorch.rst:91
+#: ../../source/ref-changelog.md:431
+#, fuzzy
 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."
+"[Strategies in Federated Learning](https://flower.ai/docs/framework"
+"/tutorial-use-a-federated-learning-strategy-pytorch.html)"
 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."
+"[Stratégies d'apprentissage fédéré] "
+"(https://flower.ai/docs/tutorial/Flower-2-Strategies-in-FL-PyTorch.html)"
 
-#: ../../source/tutorial-quickstart-pytorch.rst:110
+#: ../../source/ref-changelog.md:432
+#, fuzzy
 msgid ""
-"After defining the training and testing of a PyTorch machine learning "
-"model, we use the functions for the Flower clients."
+"[Building a Strategy](https://flower.ai/docs/framework/tutorial-build-a"
+"-strategy-from-scratch-pytorch.html)"
 msgstr ""
-"Après avoir défini l'entraînement et le test d'un modèle d'apprentissage "
-"automatique PyTorch, nous utilisons les fonctions pour les clients "
-"Flower."
+"[Construire une stratégie] "
+"(https://flower.ai/docs/tutorial/Flower-3-Building-a-Strategy-"
+"PyTorch.html)"
 
-#: ../../source/tutorial-quickstart-pytorch.rst:112
+#: ../../source/ref-changelog.md:433
+#, fuzzy
 msgid ""
-"The Flower clients will use a simple CNN adapted from 'PyTorch: A 60 "
-"Minute Blitz':"
+"[Client and NumPyClient](https://flower.ai/docs/framework/tutorial-"
+"customize-the-client-pytorch.html)"
 msgstr ""
-"Les clients de Flower utiliseront un CNN simple adapté de \"PyTorch : A "
-"60 Minute Blitz\" :"
+"[Client et NumPyClient] (https://flower.ai/docs/tutorial/Flower-4"
+"-Client-and-NumPyClient-PyTorch.html)"
 
-#: ../../source/tutorial-quickstart-pytorch.rst:139
+#: ../../source/ref-changelog.md:435
 msgid ""
-"After loading the data set with :code:`load_data()` we define the Flower "
-"interface."
+"**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 ""
-"Après avoir chargé l'ensemble des données avec :code:`load_data()`, nous "
-"définissons l'interface Flower."
+"**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/tutorial-quickstart-pytorch.rst:147
+#: ../../source/ref-changelog.md:437
 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):"
+"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 ""
-"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:163
-msgid "which can be implemented in the following way:"
-msgstr "qui peut être mis en œuvre de la manière suivante :"
+"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/tutorial-quickstart-pytorch.rst:186
-#: ../../source/tutorial-quickstart-tensorflow.rst:79
+#: ../../source/ref-changelog.md:439
+#, fuzzy
 msgid ""
-"We can now create an instance of our class :code:`CifarClient` and add "
-"one line to actually run this client:"
+"**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 ""
-"Nous pouvons maintenant créer une instance de notre classe "
-":code:`CifarClient` et ajouter une ligne pour exécuter ce client :"
+"**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/tutorial-quickstart-pytorch.rst:193
-#: ../../source/tutorial-quickstart-tensorflow.rst:87
+#: ../../source/ref-changelog.md:441
 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:`\"[::]: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."
+"**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 ""
-"C'est tout pour le client. Il nous suffit d'implémenter :code:`Client` ou"
-" :code:`NumPyClient` et d'appeler :code:`fl.client.start_client()` ou "
-":code:`fl.client.start_numpy_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."
+"**([#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/tutorial-quickstart-pytorch.rst:268
-#, fuzzy
+#: ../../source/ref-changelog.md:443
 msgid ""
-"Congratulations! You've successfully built and run your first federated "
-"learning system. The full `source code "
-"<https://github.com/adap/flower/blob/main/examples/quickstart-"
-"pytorch/client.py>`_ for this example can be found in :code:`examples"
-"/quickstart-pytorch`."
+"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 ""
-"Félicitations ! Tu as réussi à construire et à faire fonctionner ton "
-"premier système d'apprentissage fédéré. Le code source complet "
-"<https://github.com/adap/flower/blob/main/examples/quickstart-"
-"pytorch/client.py>`_ de cet exemple se trouve dans :code:`examples"
-"/quickstart-pytorch`."
-
-#: ../../source/tutorial-quickstart-pytorch-lightning.rst:5
-msgid "Quickstart PyTorch Lightning"
-msgstr "Démarrage rapide de PyTorch Lightning"
+"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/tutorial-quickstart-pytorch-lightning.rst:7
+#: ../../source/ref-changelog.md:445
 msgid ""
-"Let's build a federated learning system using PyTorch Lightning and "
-"Flower!"
+"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 ""
-"Construisons un système d'apprentissage fédéré en utilisant PyTorch "
-"Lightning et Flower !"
+"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/tutorial-quickstart-pytorch-lightning.rst:9
-#, fuzzy
+#: ../../source/ref-changelog.md:447
 msgid ""
-"Please refer to the `full code example "
-"<https://github.com/adap/flower/tree/main/examples/quickstart-pytorch-"
-"lightning>`_ to learn more."
+"To learn more, check out the `mt-pytorch` code example. We look forward "
+"to you feedback!"
 msgstr ""
-"Réfère-toi à l'exemple de code complet "
-"<https://github.com/adap/flower/tree/main/examples/quickstart-pytorch-"
-"lightning>`_ pour en savoir plus."
-
-#: ../../source/tutorial-quickstart-scikitlearn.rst:5
-msgid "Quickstart scikit-learn"
-msgstr "Démarrage rapide de scikit-learn"
+"Pour en savoir plus, consulte l'exemple de code `mt-pytorch`. Nous "
+"attendons tes commentaires avec impatience !"
 
-#: ../../source/tutorial-quickstart-scikitlearn.rst:7
+#: ../../source/ref-changelog.md:449
 msgid ""
-"In this tutorial, we will learn how to train a :code:`Logistic "
-"Regression` model on MNIST using Flower and scikit-learn."
+"Please note: *The Driver API is still experimental and will likely change"
+" significantly over time.*"
 msgstr ""
-"Dans ce tutoriel, nous allons apprendre à former un :code:`modèle de "
-"régression logistique` sur MNIST en utilisant Flower et scikit-learn."
-
-#: ../../source/tutorial-quickstart-scikitlearn.rst:23
-msgid "Since we want to use scikt-learn, let's go ahead and install it:"
-msgstr "Puisque nous voulons utiliser scikt-learn, allons-y et installons-le :"
-
-#: ../../source/tutorial-quickstart-scikitlearn.rst:29
-msgid "Or simply install all dependencies using Poetry:"
-msgstr "Ou installe simplement toutes les dépendances à l'aide de Poetry :"
+"Remarque : *L'API du pilote est encore expérimentale et est susceptible "
+"de changer de manière significative au fil du temps.*"
 
-#: ../../source/tutorial-quickstart-scikitlearn.rst:39
+#: ../../source/ref-changelog.md:451
 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:"
+"**Add new Federated Analytics with Pandas example** "
+"([#1469](https://github.com/adap/flower/pull/1469), "
+"[#1535](https://github.com/adap/flower/pull/1535))"
 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:42
-msgid ":code:`get_model_parameters()`"
-msgstr ":code:`get_model_parameters()`"
+"**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/tutorial-quickstart-scikitlearn.rst:43
-msgid "Returns the paramters of a :code:`sklearn` LogisticRegression model"
+#: ../../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 ""
-"Renvoie les paramètres d'un modèle de régression logistique "
-":code:`sklearn`"
-
-#: ../../source/tutorial-quickstart-scikitlearn.rst:44
-msgid ":code:`set_model_params()`"
-msgstr ":code:`set_model_params()`"
-
-#: ../../source/tutorial-quickstart-scikitlearn.rst:45
-msgid "Sets the parameters of a :code:`sklean` LogisticRegression model"
-msgstr "Définit les paramètres d'un modèle de régression logistique :code:`sklean`"
-
-#: ../../source/tutorial-quickstart-scikitlearn.rst:46
-msgid ":code:`set_initial_params()`"
-msgstr ":code:`set_initial_params()`"
-
-#: ../../source/tutorial-quickstart-scikitlearn.rst:47
-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:48
-msgid ":code:`load_mnist()`"
-msgstr ":code:`load_mnist()`"
-
-#: ../../source/tutorial-quickstart-scikitlearn.rst:49
-msgid "Loads the MNIST dataset using OpenML"
-msgstr "Charge l'ensemble de données MNIST à l'aide d'OpenML"
-
-#: ../../source/tutorial-quickstart-scikitlearn.rst:50
-msgid ":code:`shuffle()`"
-msgstr ":code:`shuffle()`"
-
-#: ../../source/tutorial-quickstart-scikitlearn.rst:51
-msgid "Shuffles data and its label"
-msgstr "Mélange les données et leur étiquette"
-
-#: ../../source/tutorial-quickstart-scikitlearn.rst:53
-msgid ":code:`partition()`"
-msgstr ":code:`partition()`"
-
-#: ../../source/tutorial-quickstart-scikitlearn.rst:53
-msgid "Splits datasets into a number of partitions"
-msgstr "Divise les ensembles de données en un certain nombre de partitions"
+"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/tutorial-quickstart-scikitlearn.rst:55
+#: ../../source/ref-changelog.md:455
 msgid ""
-"Please check out :code:`utils.py` `here "
-"<https://github.com/adap/flower/blob/main/examples/sklearn-logreg-"
-"mnist/utils.py>`_ 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:"
+"**Add new strategies: Krum and MultiKrum** "
+"([#1481](https://github.com/adap/flower/pull/1481))"
 msgstr ""
-"Tu peux consulter :code:`utils.py` `ici "
-"<https://github.com/adap/flower/blob/main/examples/sklearn-logreg-"
-"mnist/utils.py>`_ 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 :"
+"**Ajouter de nouvelles stratégies : Krum et MultiKrum** "
+"([#1481](https://github.com/adap/flower/pull/1481))"
 
-#: ../../source/tutorial-quickstart-scikitlearn.rst:70
+#: ../../source/ref-changelog.md:457
 msgid ""
-"We load the MNIST dataset from `OpenML <https://www.openml.org/d/554>`_, "
-"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()`."
+"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 ""
-"Nous chargeons l'ensemble de données MNIST de `OpenML "
-"<https://www.openml.org/d/554>`_, 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()`."
+"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/tutorial-quickstart-scikitlearn.rst:82
+#: ../../source/ref-changelog.md:459
 msgid ""
-"Next, the logistic regression model is defined and initialized with "
-":code:`utils.set_initial_params()`."
+"**Update C++ example to be compatible with Flower v1.2.0** "
+"([#1495](https://github.com/adap/flower/pull/1495))"
 msgstr ""
-"Ensuite, le modèle de régression logistique est défini et initialisé avec"
-" :code:`utils.set_initial_params()`."
+"**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/tutorial-quickstart-scikitlearn.rst:94
+#: ../../source/ref-changelog.md:461
 msgid ""
-"The Flower server interacts with clients through an interface called "
-":code:`Client`. When the server selects a particular client for training,"
+"The C++ code example has received a substantial update to make it "
+"compatible with the latest version of Flower."
+msgstr ""
+"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: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 ""
+"**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
+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 ""
+"**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: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!"
+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: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 ""
+"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
+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`"
+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`"
+
+#: ../../source/ref-changelog.md:487
+msgid ""
+"**Introduce Differential Privacy wrappers (preview)** "
+"([#1357](https://github.com/adap/flower/pull/1357), "
+"[#1460](https://github.com/adap/flower/pull/1460))"
+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:489
+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."
+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
+msgid ""
+"**New iOS CoreML code example** "
+"([#1289](https://github.com/adap/flower/pull/1289))"
+msgstr ""
+"**Nouvel exemple de code CoreML pour iOS** "
+"([#1289](https://github.com/adap/flower/pull/1289))"
+
+#: ../../source/ref-changelog.md:493
+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."
+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
+msgid ""
+"**New FedMedian strategy** "
+"([#1461](https://github.com/adap/flower/pull/1461))"
+msgstr ""
+"**Nouvelle stratégie de FedMedian** "
+"([#1461](https://github.com/adap/flower/pull/1461))"
+
+#: ../../source/ref-changelog.md:497
+msgid ""
+"The new `FedMedian` strategy implements Federated Median (FedMedian) by "
+"[Yin et al., 2018](https://arxiv.org/pdf/1803.01498v1.pdf)."
+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:499
+msgid ""
+"**Log** `Client` **exceptions in Virtual Client Engine** "
+"([#1493](https://github.com/adap/flower/pull/1493))"
+msgstr ""
+"**Log** `Client` **exceptions dans le moteur de client virtuel** "
+"([#1493](https://github.com/adap/flower/pull/1493))"
+
+#: ../../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)."
+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
+msgid ""
+"**Improve Virtual Client Engine internals** "
+"([#1401](https://github.com/adap/flower/pull/1401), "
+"[#1453](https://github.com/adap/flower/pull/1453))"
+msgstr ""
+"**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: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."
+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."
+
+#: ../../source/ref-changelog.md:507
+msgid ""
+"**Support optional** `Client`**/**`NumPyClient` **methods in Virtual "
+"Client Engine**"
+msgstr ""
+"**Support optional** `Client`**/**`NumPyClient` **methods in Virtual "
+"Client Engine**"
+
+#: ../../source/ref-changelog.md:509
+msgid ""
+"The Virtual Client Engine now has full support for optional `Client` (and"
+" `NumPyClient`) methods."
+msgstr ""
+"Le moteur de client virtuel prend désormais en charge les méthodes "
+"optionnelles `Client` (et `NumPyClient`)."
+
+#: ../../source/ref-changelog.md:511
+msgid ""
+"**Provide type information to packages using** `flwr` "
+"([#1377](https://github.com/adap/flower/pull/1377))"
+msgstr ""
+"**Fournir des informations de type aux paquets en utilisant** `flwr` "
+"([#1377](https://github.com/adap/flower/pull/1377))"
+
+#: ../../source/ref-changelog.md:513
+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`."
+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
+msgid ""
+"**Updated code example** "
+"([#1344](https://github.com/adap/flower/pull/1344), "
+"[#1347](https://github.com/adap/flower/pull/1347))"
+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:517
+msgid ""
+"The code examples covering scikit-learn and PyTorch Lightning have been "
+"updated to work with the latest version of Flower."
+msgstr ""
+"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:519
+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 ""
+"**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:521
+msgid ""
+"There have been so many documentation updates that it doesn't even make "
+"sense to list them individually."
+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."
+
+#: ../../source/ref-changelog.md:523
+msgid ""
+"**Restructured documentation** "
+"([#1387](https://github.com/adap/flower/pull/1387))"
+msgstr ""
+"**Documentation restructurée** "
+"([#1387](https://github.com/adap/flower/pull/1387))"
+
+#: ../../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!"
+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"
+
+#: ../../source/ref-changelog.md:527
+msgid ""
+"**Open in Colab button** "
+"([#1389](https://github.com/adap/flower/pull/1389))"
+msgstr ""
+"**Ouvrir dans le bouton Colab** "
+"([#1389](https://github.com/adap/flower/pull/1389))"
+
+#: ../../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."
+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."
+
+#: ../../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: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)."
+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)."
+
+#: ../../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 "Points forts"
+
+#: ../../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:544
+msgid "All `Client`/`NumPyClient` methods are now optional"
+msgstr "Toutes les méthodes `Client`/`NumPyClient` sont maintenant optionnelles"
+
+#: ../../source/ref-changelog.md:545
+msgid "Configurable `get_parameters`"
+msgstr "`get_parameters` configurable"
+
+#: ../../source/ref-changelog.md:546
+msgid ""
+"Tons of small API cleanups resulting in a more coherent developer "
+"experience"
+msgstr ""
+"Des tonnes de petits nettoyages d'API résultant en une expérience plus "
+"cohérente pour les développeurs"
+
+#: ../../source/ref-changelog.md:550
+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 ""
+"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:552
+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 ""
+"[@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:556
+msgid ""
+"**All arguments must be passed as keyword arguments** "
+"([#1338](https://github.com/adap/flower/pull/1338))"
+msgstr ""
+"**Tous les arguments doivent être passés comme des arguments de mot-clé**"
+" ([#1338](https://github.com/adap/flower/pull/1338))"
+
+#: ../../source/ref-changelog.md:558
+#, 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())`)."
+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())`)."
+
+#: ../../source/ref-changelog.md:560
+msgid ""
+"**Introduce configuration object** `ServerConfig` **in** `start_server` "
+"**and** `start_simulation` "
+"([#1317](https://github.com/adap/flower/pull/1317))"
+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."
+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."
+
+#: ../../source/ref-changelog.md:564
+msgid ""
+"**Rename built-in strategy parameters for clarity** "
+"([#1334](https://github.com/adap/flower/pull/1334))"
+msgstr ""
+"**Renommer les paramètres de la stratégie intégrée pour plus de clarté** "
+"([#1334](https://github.com/adap/flower/pull/1334))"
+
+#: ../../source/ref-changelog.md:566
+msgid ""
+"The following built-in strategy parameters were renamed to improve "
+"readability and consistency with other API's:"
+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
+msgid ""
+"**Update default arguments of built-in strategies** "
+"([#1278](https://github.com/adap/flower/pull/1278))"
+msgstr ""
+"**Mettre à jour les arguments par défaut des stratégies intégrées** "
+"([#1278](https://github.com/adap/flower/pull/1278))"
+
+#: ../../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:"
+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:580
+msgid ""
+"The `Strategy` method `evaluate` now receives the current round of "
+"federated learning/evaluation as the first parameter."
+msgstr ""
+"La méthode `Stratégie` `évaluer` reçoit maintenant le cycle actuel "
+"d'apprentissage/évaluation fédéré comme premier paramètre."
+
+#: ../../source/ref-changelog.md:582
+msgid ""
+"**Add** `server_round` **and** `config` **parameters to** `evaluate_fn` "
+"([#1334](https://github.com/adap/flower/pull/1334))"
+msgstr ""
+"**Ajouter** `server_round` **et** `config` **paramètres à** `evaluate_fn`"
+" ([#1334](https://github.com/adap/flower/pull/1334))"
+
+#: ../../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`)."
+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
+msgid ""
+"**Rename** `rnd` **to** `server_round` "
+"([#1321](https://github.com/adap/flower/pull/1321))"
+msgstr ""
+"**Rename** `rnd` **to** `server_round` "
+"([#1321](https://github.com/adap/flower/pull/1321))"
+
+#: ../../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 ""
+"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
+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: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
+msgid ""
+"**Remove experimental strategies** "
+"([#1280](https://github.com/adap/flower/pull/1280))"
+msgstr ""
+"**Supprimer les stratégies expérimentales** "
+"([#1280](https://github.com/adap/flower/pull/1280))"
+
+#: ../../source/ref-changelog.md:596
+msgid ""
+"Remove unmaintained experimental strategies (`FastAndSlow`, `FedFSv0`, "
+"`FedFSv1`)."
+msgstr ""
+"Supprimer les stratégies expérimentales non maintenues (`FastAndSlow`, "
+"`FedFSv0`, `FedFSv1`)."
+
+#: ../../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 ""
+"**Rename** `Weights` **to** `NDArrays` "
+"([#1258](https://github.com/adap/flower/pull/1258), "
+"[#1259](https://github.com/adap/flower/pull/1259))"
+
+#: ../../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` a été renommé en `flwr.common.NDArys` pour mieux "
+"rendre compte de la nature de ce type."
+
+#: ../../source/ref-changelog.md:602
+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))"
+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))"
+
+#: ../../source/ref-changelog.md:604
+msgid ""
+"The `start_server` parameter `force_final_distributed_eval` has long been"
+" a historic artefact, in this release it is finally gone for good."
+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
+msgid ""
+"**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-changelog.md:608
+msgid ""
+"The `get_parameters` method now accepts a configuration dictionary, just "
+"like `get_properties`, `fit`, and `evaluate`."
+msgstr ""
+"La méthode `get_parameters` accepte maintenant un dictionnaire de "
+"configuration, tout comme `get_properties`, `fit`, et `evaluate`."
+
+#: ../../source/ref-changelog.md:610
+msgid ""
+"**Replace** `num_rounds` **in** `start_simulation` **with new** `config` "
+"**parameter** ([#1281](https://github.com/adap/flower/pull/1281))"
+msgstr ""
+"**Remplace** `num_rounds` **dans** `start_simulation` **avec le nouveau**"
+" `config` **paramètre** "
+"([#1281](https://github.com/adap/flower/pull/1281))"
+
+#: ../../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."
+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."
+
+#: ../../source/ref-changelog.md:616
+msgid ""
+"**Support Python 3.10** "
+"([#1320](https://github.com/adap/flower/pull/1320))"
+msgstr ""
+"**Support 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 ""
+"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-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 ""
+"**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
+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 ""
+"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-changelog.md:624
+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-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."
+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."
+
+#: ../../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))"
+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:630
+msgid ""
+"Many code examples received small or even large maintenance updates, "
+"among them are"
+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
+msgid ""
+"**Remove the obsolete simulation example** "
+"([#1328](https://github.com/adap/flower/pull/1328))"
+msgstr ""
+"**Supprime l'exemple de simulation obsolète** "
+"([#1328](https://github.com/adap/flower/pull/1328))"
+
+#: ../../source/ref-changelog.md:641
+msgid ""
+"Removes the obsolete `simulation` example and renames "
+"`quickstart_simulation` to `simulation_tensorflow` so it fits withs the "
+"naming of `simulation_pytorch`"
+msgstr ""
+"Supprime l'exemple obsolète `simulation` et renomme "
+"`quickstart_simulation` en `simulation_tensorflow` pour qu'il corresponde"
+" au nom de `simulation_pytorch`"
+
+#: ../../source/ref-changelog.md:643
+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 ""
+"**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
+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!"
+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**"
+
+#: ../../source/ref-changelog.md:649
+msgid ""
+"Add round number to fit and evaluate log messages "
+"([#1266](https://github.com/adap/flower/pull/1266))"
+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
+msgid ""
+"Add secure gRPC connection to the `advanced_tensorflow` code example "
+"([#847](https://github.com/adap/flower/pull/847))"
+msgstr ""
+"Ajouter une connexion gRPC sécurisée à l'exemple de code "
+"`advanced_tensorflow` ([#847](https://github.com/adap/flower/pull/847))"
+
+#: ../../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:652
+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 ""
+"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
+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 ""
+"**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
+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)."
+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-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:664
+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 ""
+"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-changelog.md:666
+msgid ""
+"**Add experimental support for Python 3.10 and Python 3.11** "
+"([#1135](https://github.com/adap/flower/pull/1135))"
+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:668
+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 ""
+"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-changelog.md:670
+msgid ""
+"**Aggregate custom metrics through user-provided functions** "
+"([#1144](https://github.com/adap/flower/pull/1144))"
+msgstr ""
+"**Agréger des mesures personnalisées grâce à des fonctions fournies par "
+"l'utilisateur** ([#1144](https://github.com/adap/flower/pull/1144))"
+
+#: ../../source/ref-changelog.md:672
+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 ""
+"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-changelog.md:674
+msgid ""
+"**User-configurable round timeout** "
+"([#1162](https://github.com/adap/flower/pull/1162))"
+msgstr ""
+"**Temps d'attente configurable par l'utilisateur** "
+"([#1162](https://github.com/adap/flower/pull/1162))"
+
+#: ../../source/ref-changelog.md:676
+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."
+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."
+
+#: ../../source/ref-changelog.md:678
+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 ""
+"**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-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`."
+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`."
+
+#: ../../source/ref-changelog.md:682
+msgid ""
+"**Two new Jupyter Notebook tutorials** "
+"([#1141](https://github.com/adap/flower/pull/1141))"
+msgstr ""
+"**Deux nouveaux tutoriels Jupyter Notebook** "
+"([#1141](https://github.com/adap/flower/pull/1141))"
+
+#: ../../source/ref-changelog.md:684
+msgid ""
+"Two Jupyter Notebook tutorials (compatible with Google Colab) explain "
+"basic and intermediate Flower features:"
+msgstr ""
+"Deux tutoriels Jupyter Notebook (compatibles avec Google Colab) "
+"expliquent les fonctionnalités de base et intermédiaires de Flower :"
+
+#: ../../source/ref-changelog.md:686
+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)"
+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)"
+
+#: ../../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)"
+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)"
+
+#: ../../source/ref-changelog.md:690
+msgid ""
+"**New FedAvgM strategy (Federated Averaging with Server Momentum)** "
+"([#1076](https://github.com/adap/flower/pull/1076))"
+msgstr ""
+"**Nouvelle stratégie FedAvgM (Federated Averaging with Server Momentum)**"
+" ([#1076](https://github.com/adap/flower/pull/1076))"
+
+#: ../../source/ref-changelog.md:692
+#, fuzzy
+msgid ""
+"The new `FedAvgM` strategy implements Federated Averaging with Server "
+"Momentum \\[Hsu et al., 2019\\]."
+msgstr ""
+"La nouvelle stratégie `FedAvgM` met en œuvre la moyenne fédérée avec le "
+"momentum du serveur [Hsu et al., 2019]."
+
+#: ../../source/ref-changelog.md:694
+msgid ""
+"**New advanced PyTorch code example** "
+"([#1007](https://github.com/adap/flower/pull/1007))"
+msgstr ""
+"**Nouvel exemple de code PyTorch avancé** "
+"([#1007](https://github.com/adap/flower/pull/1007))"
+
+#: ../../source/ref-changelog.md:696
+msgid ""
+"A new code example (`advanced_pytorch`) demonstrates advanced Flower "
+"concepts with PyTorch."
+msgstr ""
+"Un nouvel exemple de code (`advanced_pytorch`) démontre des concepts de "
+"fleur avancés avec PyTorch."
+
+#: ../../source/ref-changelog.md:698
+msgid ""
+"**New JAX code example** "
+"([#906](https://github.com/adap/flower/pull/906), "
+"[#1143](https://github.com/adap/flower/pull/1143))"
+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:700
+msgid ""
+"A new code example (`jax_from_centralized_to_federated`) shows federated "
+"learning with JAX and Flower."
+msgstr ""
+"Un nouvel exemple de code (`jax_from_centralized_to_federated`) montre "
+"l'apprentissage fédéré avec JAX et Flower."
+
+#: ../../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 ""
+"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-changelog.md:705
+msgid ""
+"Add support for custom `ClientManager` as a `start_simulation` parameter "
+"([#1171](https://github.com/adap/flower/pull/1171))"
+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:706
+#, fuzzy
+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 ""
+"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-changelog.md:707
+msgid ""
+"New mobile-friendly documentation theme "
+"([#1174](https://github.com/adap/flower/pull/1174))"
+msgstr ""
+"Nouveau thème de documentation adapté aux mobiles "
+"([#1174](https://github.com/adap/flower/pull/1174))"
+
+#: ../../source/ref-changelog.md:708
+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 ""
+"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-changelog.md:712
+msgid ""
+"**Remove deprecated support for Python 3.6** "
+"([#871](https://github.com/adap/flower/pull/871))"
+msgstr ""
+"**Supprime la prise en charge obsolète de Python 3.6** "
+"([#871](https://github.com/adap/flower/pull/871))"
+
+#: ../../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:714
+msgid ""
+"**Remove deprecated no-op extra installs** "
+"([#973](https://github.com/adap/flower/pull/973))"
+msgstr ""
+"**Supprimer les installations supplémentaires no-op dépréciées** "
+"([#973](https://github.com/adap/flower/pull/973))"
+
+#: ../../source/ref-changelog.md:715
+msgid ""
+"**Remove deprecated proto fields from** `FitRes` **and** `EvaluateRes` "
+"([#869](https://github.com/adap/flower/pull/869))"
+msgstr ""
+"**Supprimez les champs proto obsolètes de** `FitRes` **et** `EvaluateRes`"
+" ([#869](https://github.com/adap/flower/pull/869))"
+
+#: ../../source/ref-changelog.md:716
+msgid ""
+"**Remove deprecated QffedAvg strategy (replaced by QFedAvg)** "
+"([#1107](https://github.com/adap/flower/pull/1107))"
+msgstr ""
+"**Supprime la stratégie QffedAvg (remplacée par QFedAvg)** "
+"([#1107](https://github.com/adap/flower/pull/1107))"
+
+#: ../../source/ref-changelog.md:717
+msgid ""
+"**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/ref-changelog.md:718
+msgid ""
+"**Remove deprecated support for eval_fn accuracy return value** "
+"([#1142](https://github.com/adap/flower/pull/1142))"
+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))"
+
+#: ../../source/ref-changelog.md:719
+msgid ""
+"**Remove deprecated support for passing initial parameters as NumPy "
+"ndarrays** ([#1142](https://github.com/adap/flower/pull/1142))"
+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))"
+
+#: ../../source/ref-changelog.md:721
+msgid "v0.18.0 (2022-02-28)"
+msgstr "v0.18.0 (2022-02-28)"
+
+#: ../../source/ref-changelog.md:725
+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))"
+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/ref-changelog.md:727
+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 ""
+"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/ref-changelog.md:729
+msgid ""
+"**New Jupyter Notebook code example** "
+"([#833](https://github.com/adap/flower/pull/833))"
+msgstr ""
+"**Nouvel exemple de code Jupyter Notebook** "
+"([#833](https://github.com/adap/flower/pull/833))"
+
+#: ../../source/ref-changelog.md:731
+msgid ""
+"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/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))"
+
+#: ../../source/ref-changelog.md:735
+msgid ""
+"Clients can implement a new method `get_properties` to enable server-side"
+" strategies to query client properties."
+msgstr ""
+"Les clients peuvent implémenter une nouvelle méthode `get_properties` "
+"pour permettre aux stratégies côté serveur d'interroger les propriétés du"
+" client."
+
+#: ../../source/ref-changelog.md:737
+msgid ""
+"**Experimental Android support with TFLite** "
+"([#865](https://github.com/adap/flower/pull/865))"
+msgstr ""
+"**Support expérimental d'Android avec TFLite** "
+"([#865](https://github.com/adap/flower/pull/865))"
+
+#: ../../source/ref-changelog.md:739
+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."
+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."
+
+#: ../../source/ref-changelog.md:741
+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`."
+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`."
+
+#: ../../source/ref-changelog.md:743
+msgid ""
+"**Make gRPC keepalive time user-configurable and decrease default "
+"keepalive time** ([#1069](https://github.com/adap/flower/pull/1069))"
+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))"
+
+#: ../../source/ref-changelog.md:745
+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 ""
+"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/ref-changelog.md:747
+msgid ""
+"**New differential privacy example using Opacus and PyTorch** "
+"([#805](https://github.com/adap/flower/pull/805))"
+msgstr ""
+"**Nouvel exemple de confidentialité différentielle utilisant Opacus et "
+"PyTorch** ([#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 ""
+"Un nouvel exemple de code (`opacus`) démontre l'apprentissage fédéré "
+"différentiellement privé avec Opacus, PyTorch et Flower."
+
+#: ../../source/ref-changelog.md:751
+msgid ""
+"**New Hugging Face Transformers code example** "
+"([#863](https://github.com/adap/flower/pull/863))"
+msgstr ""
+"**Nouvel exemple de code pour les Transformers à visage embrassant** "
+"([#863](https://github.com/adap/flower/pull/863))"
+
+#: ../../source/ref-changelog.md:753
+msgid ""
+"A new code example (`quickstart_huggingface`) demonstrates usage of "
+"Hugging Face Transformers with Flower."
+msgstr ""
+"Un nouvel exemple de code (`quickstart_huggingface`) démontre "
+"l'utilisation des transformateurs Hugging Face avec Flower."
+
+#: ../../source/ref-changelog.md:755
+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 ""
+"**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: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:759
+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))"
+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:761
+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 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/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))"
+msgstr ""
+"**Mise à jour** `FedAdam` **et** `FedYogi` **stratégies** "
+"([#885](https://github.com/adap/flower/pull/885), "
+"[#895](https://github.com/adap/flower/pull/895))"
+
+#: ../../source/ref-changelog.md:765
+msgid ""
+"`FedAdam` and `FedAdam` match the latest version of the Adaptive "
+"Federated Optimization paper."
+msgstr ""
+"`FedAdam` et `FedAdam` correspondent à la dernière version de l'article "
+"sur l'optimisation fédérée adaptative."
+
+#: ../../source/ref-changelog.md:767
+msgid ""
+"**Initialize** `start_simulation` **with a list of client IDs** "
+"([#860](https://github.com/adap/flower/pull/860))"
+msgstr ""
+"**Initialise** `start_simulation` **avec une liste d'ID de clients** "
+"([#860](https://github.com/adap/flower/pull/860))"
+
+#: ../../source/ref-changelog.md:769
+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."
+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/ref-changelog.md:773
+msgid ""
+"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/ref-changelog.md:774
+msgid ""
+"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/ref-changelog.md:775
+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/ref-changelog.md:776
+msgid ""
+"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/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 ""
+"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/ref-changelog.md:778
+msgid ""
+"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/ref-changelog.md:779
+msgid ""
+"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/ref-changelog.md:783
+msgid ""
+"**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/ref-changelog.md:785
+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."
+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)"
+
+#: ../../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))"
+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))"
+
+#: ../../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`."
+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/ref-changelog.md:795
+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 ""
+"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/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))"
+msgstr ""
+"**Nouvelles stratégies intégrées** "
+"([#828](https://github.com/adap/flower/pull/828) "
+"[#822](https://github.com/adap/flower/pull/822))"
+
+#: ../../source/ref-changelog.md:799
+msgid ""
+"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/ref-changelog.md:800
+msgid ""
+"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/ref-changelog.md:802
+msgid ""
+"**New PyTorch Lightning code example** "
+"([#617](https://github.com/adap/flower/pull/617))"
+msgstr ""
+"**Nouvel exemple de code PyTorch Lightning** "
+"([#617](https://github.com/adap/flower/pull/617))"
+
+#: ../../source/ref-changelog.md:804
+msgid ""
+"**New Variational Auto-Encoder code example** "
+"([#752](https://github.com/adap/flower/pull/752))"
+msgstr ""
+"**Nouvel exemple de code d'autocodage variationnel** "
+"([#752](https://github.com/adap/flower/pull/752))"
+
+#: ../../source/ref-changelog.md:806
+msgid ""
+"**New scikit-learn code example** "
+"([#748](https://github.com/adap/flower/pull/748))"
+msgstr ""
+"**Nouvel exemple de code scikit-learn** "
+"([#748](https://github.com/adap/flower/pull/748))"
+
+#: ../../source/ref-changelog.md:808
+msgid ""
+"**New experimental TensorBoard strategy** "
+"([#789](https://github.com/adap/flower/pull/789))"
+msgstr ""
+"**Nouvelle stratégie expérimentale TensorBoard** "
+"([#789](https://github.com/adap/flower/pull/789))"
+
+#: ../../source/ref-changelog.md:812
+msgid ""
+"Improved advanced TensorFlow code example "
+"([#769](https://github.com/adap/flower/pull/769))"
+msgstr ""
+"Amélioration de l'exemple de code TensorFlow avancé "
+"([#769](https://github.com/adap/flower/pull/769))"
+
+#: ../../source/ref-changelog.md:813
+msgid ""
+"Warning when `min_available_clients` is misconfigured "
+"([#830](https://github.com/adap/flower/pull/830))"
+msgstr ""
+"Avertissement lorsque `min_available_clients` est mal configuré "
+"([#830](https://github.com/adap/flower/pull/830))"
+
+#: ../../source/ref-changelog.md:814
+msgid ""
+"Improved gRPC server docs "
+"([#841](https://github.com/adap/flower/pull/841))"
+msgstr ""
+"Amélioration de la documentation sur le serveur gRPC "
+"([#841](https://github.com/adap/flower/pull/841))"
+
+#: ../../source/ref-changelog.md:815
+msgid ""
+"Improved error message in `NumPyClient` "
+"([#851](https://github.com/adap/flower/pull/851))"
+msgstr ""
+"Amélioration du message d'erreur dans `NumPyClient` "
+"([#851](https://github.com/adap/flower/pull/851))"
+
+#: ../../source/ref-changelog.md:816
+msgid ""
+"Improved PyTorch quickstart code example "
+"([#852](https://github.com/adap/flower/pull/852))"
+msgstr ""
+"Exemple de code de démarrage rapide PyTorch amélioré "
+"([#852](https://github.com/adap/flower/pull/852))"
+
+#: ../../source/ref-changelog.md:820
+msgid ""
+"**Disabled final distributed evaluation** "
+"([#800](https://github.com/adap/flower/pull/800))"
+msgstr ""
+"**Désactivé l'évaluation finale distribuée** "
+"([#800](https://github.com/adap/flower/pull/800))"
+
+#: ../../source/ref-changelog.md:822
+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`."
+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`."
+
+#: ../../source/ref-changelog.md:824
+msgid ""
+"**Renamed q-FedAvg strategy** "
+"([#802](https://github.com/adap/flower/pull/802))"
+msgstr ""
+"**Renommé stratégie q-FedAvg** "
+"([#802](https://github.com/adap/flower/pull/802))"
+
+#: ../../source/ref-changelog.md:826
+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)."
+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)."
+
+#: ../../source/ref-changelog.md:828
+msgid ""
+"**Deprecated and renamed code example** `simulation_pytorch` **to** "
+"`simulation_pytorch_legacy` "
+"([#791](https://github.com/adap/flower/pull/791))"
+msgstr ""
+"**Exemple de code déprécié et renommé** `simulation_pytorch` **en** "
+"`simulation_pytorch_legacy` "
+"([#791](https://github.com/adap/flower/pull/791))"
+
+#: ../../source/ref-changelog.md:830
+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 ""
+"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)"
+
+#: ../../source/ref-changelog.md:836
+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/ref-changelog.md:838
+msgid "(abstract) FedOpt"
+msgstr "(résumé) FedOpt"
+
+#: ../../source/ref-changelog.md:841
+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/ref-changelog.md:843
+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."
+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."
+
+#: ../../source/ref-changelog.md:845
+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 build-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 ""
+"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/ref-changelog.md:847
+msgid ""
+"Stratey 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 ""
+"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/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 ""
+"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/ref-changelog.md:851
+msgid ""
+"**Migration warnings for deprecated functionality** "
+"([#690](https://github.com/adap/flower/pull/690))"
+msgstr ""
+"**Avertissements de migration pour les fonctionnalités obsolètes** "
+"([#690](https://github.com/adap/flower/pull/690))"
+
+#: ../../source/ref-changelog.md:853
+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 ""
+"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/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 ""
+"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"
+
+#: ../../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))"
+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))"
+
+#: ../../source/ref-changelog.md:863
+msgid ""
+"**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/ref-changelog.md:865
+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 ""
+"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/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."
+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."
+
+#: ../../source/ref-changelog.md:869
+msgid ""
+"Deprecated `flwr.server.Server.evaluate`, use "
+"`flwr.server.Server.evaluate_round` instead "
+"([#717](https://github.com/adap/flower/pull/717))"
+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)"
+
+#: ../../source/ref-changelog.md:875
+msgid ""
+"**Server-side parameter initialization** "
+"([#658](https://github.com/adap/flower/pull/658))"
+msgstr ""
+"**Initialisation des paramètres côté serveur** "
+"([#658](https://github.com/adap/flower/pull/658))"
+
+#: ../../source/ref-changelog.md:877
+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 ""
+"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/ref-changelog.md:879
+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 ""
+"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/ref-changelog.md:898
+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 ""
+"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/ref-changelog.md:900
+msgid "Deprecations"
+msgstr "Dépréciations"
+
+#: ../../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:904
+msgid "v0.14.0 (2021-02-18)"
+msgstr "v0.14.0 (2021-02-18)"
+
+#: ../../source/ref-changelog.md:908
+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))"
+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))"
+
+#: ../../source/ref-changelog.md:910
+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!"
+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 !"
+
+#: ../../source/ref-changelog.md:912
+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 ""
+"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/ref-changelog.md:914
+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 ""
+"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/ref-changelog.md:916
+msgid ""
+"*Code example:* note the additional dictionary return values in both "
+"`FlwrClient.fit` and `FlwrClient.evaluate`:"
+msgstr ""
+"*Exemple de code:* note les valeurs de retour du dictionnaire "
+"supplémentaires dans `FlwrClient.fit` et `FlwrClient.evaluate` :"
+
+#: ../../source/ref-changelog.md:931
+msgid ""
+"**Generalized** `config` **argument in** `Client.fit` **and** "
+"`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))"
+msgstr ""
+"**Généralisé** `config` **argument dans** `Client.fit` **et** "
+"`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))"
+
+#: ../../source/ref-changelog.md:933
+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 ""
+"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/ref-changelog.md:935
+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!"
+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 !"
+
+#: ../../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`:"
+msgstr ""
+"*Exemple de code:* Notez que le dictionnaire `config` contient maintenant"
+" des valeurs autres que `str` dans `Client.fit` et `Client.evaluate` :"
+
+#: ../../source/ref-changelog.md:954
+msgid "v0.13.0 (2021-01-08)"
+msgstr "v0.13.0 (2021-01-08)"
+
+#: ../../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: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:962
+msgid ""
+"Updated examples documentation "
+"([#549](https://github.com/adap/flower/pull/549))"
+msgstr ""
+"Mise à jour de la documentation des exemples "
+"([#549](https://github.com/adap/flower/pull/549))"
+
+#: ../../source/ref-changelog.md:963
+msgid ""
+"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/ref-changelog.md:965
+msgid "Bugfix:"
+msgstr "Correction de bogues :"
+
+#: ../../source/ref-changelog.md:967
+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 ""
+"`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 :"
+
+#: ../../source/ref-changelog.md:973
+msgid ""
+"Added an example for embedded devices "
+"([#507](https://github.com/adap/flower/pull/507))"
+msgstr ""
+"Ajout d'un exemple pour les périphériques embarqués "
+"([#507](https://github.com/adap/flower/pull/507))"
+
+#: ../../source/ref-changelog.md:974
+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 ""
+"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/ref-changelog.md:975
+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/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 :"
+
+#: ../../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 ""
+"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`"
+
+#: ../../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
+msgid ""
+"Deprecated `DefaultStrategy` "
+"([#479](https://github.com/adap/flower/pull/479)). To migrate use "
+"`FedAvg` instead."
+msgstr ""
+"Déclassé `DefaultStrategy` "
+"([#479](https://github.com/adap/flower/pull/479)). Pour migrer, utilisez "
+"`FedAvg` à la place."
+
+#: ../../source/ref-changelog.md:990
+msgid ""
+"Simplified examples and baselines "
+"([#484](https://github.com/adap/flower/pull/484))."
+msgstr ""
+"Exemples simplifiés et lignes de base "
+"([#484](https://github.com/adap/flower/pull/484))."
+
+#: ../../source/ref-changelog.md:991
+msgid ""
+"Removed presently unused `on_conclude_round` from strategy interface "
+"([#483](https://github.com/adap/flower/pull/483))."
+msgstr ""
+"Suppression de `on_conclude_round` actuellement inutilisé de l'interface "
+"de stratégie ([#483](https://github.com/adap/flower/pull/483))."
+
+#: ../../source/ref-changelog.md:992
+msgid ""
+"Set minimal Python version to 3.6.1 instead of 3.6.9 "
+"([#471](https://github.com/adap/flower/pull/471))."
+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))."
+
+#: ../../source/ref-changelog.md:993
+msgid ""
+"Improved `Strategy` docstrings "
+"([#470](https://github.com/adap/flower/pull/470))."
+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"
+
+#: ../../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 <https://pytorch.org/>`_ or `TensorFlow "
+"<https://www.tensorflow.org/>`_."
+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 <https://pytorch.org/>`_ ou `TensorFlow "
+"<https://www.tensorflow.org/>`_."
+
+#: ../../source/ref-example-projects.rst:11
+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 <https://github.com/adap/flower/tree/main/examples>`_."
+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 "
+"<https://github.com/adap/flower/tree/main/examples>`_."
+
+#: ../../source/ref-example-projects.rst:16
+msgid "The following examples are available as standalone projects."
+msgstr "Les exemples suivants sont disponibles sous forme de projets autonomes."
+
+#: ../../source/ref-example-projects.rst:20
+msgid "Quickstart TensorFlow/Keras"
+msgstr "Démarrage rapide de TensorFlow/Keras"
+
+#: ../../source/ref-example-projects.rst:22
+msgid ""
+"The TensorFlow/Keras quickstart example shows CIFAR-10 image "
+"classification with MobileNetV2:"
+msgstr ""
+"L'exemple de démarrage rapide TensorFlow/Keras montre la classification "
+"d'images CIFAR-10 avec MobileNetV2 :"
+
+#: ../../source/ref-example-projects.rst:25
+#, fuzzy
+msgid ""
+"`Quickstart TensorFlow (Code) "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-"
+"tensorflow>`_"
+msgstr ""
+"`Quickstart TensorFlow (Code) "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-"
+"tensorflow>`_"
+
+#: ../../source/ref-example-projects.rst:26
+#, fuzzy
+msgid ""
+"`Quickstart TensorFlow (Tutorial) <https://flower.ai/docs/framework"
+"/tutorial-quickstart-tensorflow.html>`_"
+msgstr ""
+"`Quickstart TensorFlow (Tutorial) <https://flower.ai/docs/quickstart-"
+"tensorflow.html>`_"
+
+#: ../../source/ref-example-projects.rst:27
+msgid ""
+"`Quickstart TensorFlow (Blog Post) <https://flower.ai/blog/2020-12-11"
+"-federated-learning-in-less-than-20-lines-of-code>`_"
+msgstr ""
+"`Quickstart TensorFlow (Blog Post) <https://flower.ai/blog/2020-12-11"
+"-federated-learning-in-less-than-20-lines-of-code>`_"
+
+#: ../../source/ref-example-projects.rst:31
+#: ../../source/tutorial-quickstart-pytorch.rst:5
+msgid "Quickstart PyTorch"
+msgstr "Démarrage rapide de PyTorch"
+
+#: ../../source/ref-example-projects.rst:33
+msgid ""
+"The PyTorch quickstart example shows CIFAR-10 image classification with a"
+" simple Convolutional Neural Network:"
+msgstr ""
+"L'exemple de démarrage rapide PyTorch montre la classification d'images "
+"CIFAR-10 avec un simple réseau neuronal convolutif :"
+
+#: ../../source/ref-example-projects.rst:36
+#, fuzzy
+msgid ""
+"`Quickstart PyTorch (Code) "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-pytorch>`_"
+msgstr ""
+"`Quickstart PyTorch (Code) "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-pytorch>`_"
+
+#: ../../source/ref-example-projects.rst:37
+#, fuzzy
+msgid ""
+"`Quickstart PyTorch (Tutorial) <https://flower.ai/docs/framework"
+"/tutorial-quickstart-pytorch.html>`_"
+msgstr ""
+"`Quickstart PyTorch (Tutorial) <https://flower.ai/docs/quickstart-"
+"pytorch.html>`_"
+
+#: ../../source/ref-example-projects.rst:41
+msgid "PyTorch: From Centralized To Federated"
+msgstr "PyTorch : De la centralisation à la fédération"
+
+#: ../../source/ref-example-projects.rst:43
+msgid ""
+"This example shows how a regular PyTorch project can be federated using "
+"Flower:"
+msgstr ""
+"Cet exemple montre comment un projet PyTorch ordinaire peut être fédéré à"
+" l'aide de Flower :"
+
+#: ../../source/ref-example-projects.rst:45
+#, fuzzy
+msgid ""
+"`PyTorch: From Centralized To Federated (Code) "
+"<https://github.com/adap/flower/tree/main/examples/pytorch-from-"
+"centralized-to-federated>`_"
+msgstr ""
+"`PyTorch : De la centralisation à la fédération (Code) "
+"<https://github.com/adap/flower/tree/main/examples/pytorch-from-"
+"centralized-to-federated>`_"
+
+#: ../../source/ref-example-projects.rst:46
+#, fuzzy
+msgid ""
+"`PyTorch: From Centralized To Federated (Tutorial) "
+"<https://flower.ai/docs/framework/example-pytorch-from-centralized-to-"
+"federated.html>`_"
+msgstr ""
+"`PyTorch : De la centralisation à la fédération (Tutoriel) "
+"<https://flower.ai/docs/example-pytorch-from-centralized-to-"
+"federated.html>`_"
+
+#: ../../source/ref-example-projects.rst:50
+msgid "Federated Learning on Raspberry Pi and Nvidia Jetson"
+msgstr "Apprentissage fédéré sur Raspberry Pi et Nvidia Jetson"
+
+#: ../../source/ref-example-projects.rst:52
+msgid ""
+"This example shows how Flower can be used to build a federated learning "
+"system that run across Raspberry Pi and Nvidia Jetson:"
+msgstr ""
+"Cet exemple montre comment Flower peut être utilisé pour construire un "
+"système d'apprentissage fédéré qui fonctionne sur Raspberry Pi et Nvidia "
+"Jetson :"
+
+#: ../../source/ref-example-projects.rst:54
+#, fuzzy
+msgid ""
+"`Federated Learning on Raspberry Pi and Nvidia Jetson (Code) "
+"<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_"
+msgstr ""
+"`L'apprentissage fédéré sur Raspberry Pi et Nvidia Jetson (Code) "
+"<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_"
+
+#: ../../source/ref-example-projects.rst:55
+msgid ""
+"`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) "
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+msgstr ""
+"`L'apprentissage fédéré sur Raspberry Pi et Nvidia Jetson (Blog Post) "
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+
+#: ../../source/ref-example-projects.rst:60
+msgid "Legacy Examples (`flwr_example`)"
+msgstr "Exemples hérités (`flwr_example`)"
+
+#: ../../source/ref-example-projects.rst:63
+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 <https://github.com/adap/flower/tree/main/examples>`_."
+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 "
+"<https://github.com/adap/flower/tree/main/examples>`_."
+
+#: ../../source/ref-example-projects.rst:69
+msgid "Extra Dependencies"
+msgstr "Dépendances supplémentaires"
+
+#: ../../source/ref-example-projects.rst:71
+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 ""
+"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."
+
+#: ../../source/ref-example-projects.rst:75
+msgid "For PyTorch examples::"
+msgstr "Pour les exemples de PyTorch: :"
+
+#: ../../source/ref-example-projects.rst:79
+msgid "For TensorFlow examples::"
+msgstr "Pour les exemples de TensorFlow : :"
+
+#: ../../source/ref-example-projects.rst:83
+msgid "For both PyTorch and TensorFlow examples::"
+msgstr "Pour les exemples PyTorch et TensorFlow: :"
+
+#: ../../source/ref-example-projects.rst:87
+msgid ""
+"Please consult :code:`pyproject.toml` for a full list of possible extras "
+"(section :code:`[tool.poetry.extras]`)."
+msgstr ""
+"Tu peux consulter :code:`pyproject.toml` pour une liste complète des "
+"extras possibles (section :code:`[tool.poetry.extras]`)."
+
+#: ../../source/ref-example-projects.rst:92
+msgid "PyTorch Examples"
+msgstr "Exemples de PyTorch"
+
+#: ../../source/ref-example-projects.rst:94
+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."
+
+#: ../../source/ref-example-projects.rst:98
+msgid "CIFAR-10 Image Classification"
+msgstr "Classification d'images CIFAR-10"
+
+#: ../../source/ref-example-projects.rst:100
+msgid ""
+"`CIFAR-10 and CIFAR-100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ "
+"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 et CIFAR-100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ "
+"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."
+
+#: ../../source/ref-example-projects.rst:104
+#: ../../source/ref-example-projects.rst:121
+#: ../../source/ref-example-projects.rst:146
+msgid "First, start a Flower server:"
+msgstr "Tout d'abord, démarre un serveur Flower :"
+
+#: ../../source/ref-example-projects.rst:106
+msgid "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh"
+msgstr "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh"
+
+#: ../../source/ref-example-projects.rst:108
+#: ../../source/ref-example-projects.rst:125
+#: ../../source/ref-example-projects.rst:150
+msgid "Then, start the two clients in a new terminal window:"
+msgstr "Ensuite, démarre les deux clients dans une nouvelle fenêtre de terminal :"
+
+#: ../../source/ref-example-projects.rst:110
+msgid "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh"
+msgstr "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh"
+
+#: ../../source/ref-example-projects.rst:112
+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`."
+
+#: ../../source/ref-example-projects.rst:115
+msgid "ImageNet-2012 Image Classification"
+msgstr "ImageNet-2012 Classification des images"
+
+#: ../../source/ref-example-projects.rst:117
+msgid ""
+"`ImageNet-2012 <http://www.image-net.org/>`_ 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 <http://www.image-net.org/>`_ 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."
+
+#: ../../source/ref-example-projects.rst:123
+msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh"
+msgstr "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh"
+
+#: ../../source/ref-example-projects.rst:127
+msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh"
+msgstr "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh"
+
+#: ../../source/ref-example-projects.rst:129
+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`."
+
+#: ../../source/ref-example-projects.rst:133
+msgid "TensorFlow Examples"
+msgstr "Exemples de TensorFlow"
+
+#: ../../source/ref-example-projects.rst:135
+msgid ""
+"Our TensorFlow examples are based on TensorFlow 2.0 or newer. So far, we "
+"provide the following examples."
+msgstr ""
+"Nos exemples TensorFlow sont basés sur TensorFlow 2.0 ou une version plus"
+" récente. Jusqu'à présent, nous te proposons les exemples suivants."
+
+#: ../../source/ref-example-projects.rst:139
+msgid "Fashion-MNIST Image Classification"
+msgstr "Classification d'images Fashion-MNIST"
+
+#: ../../source/ref-example-projects.rst:141
+msgid ""
+"`Fashion-MNIST <https://github.com/zalandoresearch/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 ""
+"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."
+
+#: ../../source/ref-example-projects.rst:148
+msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh"
+msgstr "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh"
+
+#: ../../source/ref-example-projects.rst:152
+msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh"
+msgstr "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh"
+
+#: ../../source/ref-example-projects.rst:154
+msgid ""
+"For more details, see "
+":code:`src/py/flwr_example/tensorflow_fashion_mnist`."
+msgstr ""
+"Pour plus de détails, voir "
+":code:`src/py/flwr_example/tensorflow_fashion_mnist`."
+
+#: ../../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/ref-faq.rst
+msgid ":fa:`eye,mr-1` Can Flower run on Juptyter Notebooks / Google Colab?"
+msgstr ""
+":fa:`eye,mr-1` Flower peut-il fonctionner sur les ordinateurs portables "
+"Juptyter / Google Colab ?"
+
+#: ../../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:"
+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/ref-faq.rst:10
+#, fuzzy
+msgid ""
+"`Flower simulation PyTorch "
+"<https://colab.research.google.com/github/adap/flower/blob/main/examples"
+"/simulation-pytorch/sim.ipynb>`_"
+msgstr ""
+"`Flower Quickstart (TensorFlow/Keras) "
+"<https://colab.research.google.com/github/adap/flower/blob/main/examples/simulation_tensorflow/sim.ipynb>`_"
+
+#: ../../source/ref-faq.rst:11
+#, fuzzy
+msgid ""
+"`Flower simulation TensorFlow/Keras "
+"<https://colab.research.google.com/github/adap/flower/blob/main/examples"
+"/simulation-tensorflow/sim.ipynb>`_"
+msgstr ""
+"`Flower Quickstart (TensorFlow/Keras) "
+"<https://colab.research.google.com/github/adap/flower/blob/main/examples/simulation_tensorflow/sim.ipynb>`_"
+
+#: ../../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/ref-faq.rst:15
+#, fuzzy
+msgid ""
+"Find the `blog post about federated learning on embedded device here "
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+" and the corresponding `GitHub code example "
+"<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_."
+msgstr ""
+"Trouve le `blog post about federated learning on embedded device ici "
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+" et l'exemple de code GitHub correspondant "
+"<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_."
+
+#: ../../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-faq.rst:19
+#, fuzzy
+msgid ""
+"Yes, it does. Please take a look at our `blog post "
+"<https://flower.ai/blog/2021-12-15-federated-learning-on-android-"
+"devices-with-flower>`_ or check out the code examples:"
+msgstr ""
+"Oui. Jetez un coup d'œil à notre `blog post "
+"<https://flower.ai/blog/2021-12-15-federated-learning-on-android-"
+"devices-with-flower>`_ ou consultez l'`exemple de code Android sur GitHub"
+" <https://github.com/adap/flower/tree/main/examples/android>`_."
+
+#: ../../source/ref-faq.rst:21
+msgid ""
+"`Android Kotlin example <https://flower.ai/docs/examples/android-"
+"kotlin.html>`_"
+msgstr ""
+
+#: ../../source/ref-faq.rst:22
+msgid "`Android Java example <https://flower.ai/docs/examples/android.html>`_"
+msgstr ""
+
+#: ../../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/ref-faq.rst:26
+msgid ""
+"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/ref-faq.rst:28
+msgid ""
+"`Flower meets Nevermined GitHub Repository <https://github.com"
+"/nevermined-io/fl-demo/tree/master/image-classification-flower>`_."
+msgstr ""
+"`Flower meets Nevermined GitHub Repository <https://github.com"
+"/nevermined-io/fl-demo/tree/master/image-classification-flower>`_."
+
+#: ../../source/ref-faq.rst:29
+msgid ""
+"`Flower meets Nevermined YouTube video "
+"<https://www.youtube.com/watch?v=A0A9hSlPhKI>`_."
+msgstr ""
+"`Flower rencontre Nevermined vidéo YouTube "
+"<https://www.youtube.com/watch?v=A0A9hSlPhKI>`_."
+
+#: ../../source/ref-faq.rst:30
+msgid ""
+"`Flower meets KOSMoS <https://www.kosmos-bmbf.de/wp-"
+"content/uploads/sites/13/2021/05/Talk-Flower-Summit-2021.pdf>`_."
+msgstr ""
+"`Flower rencontre KOSMoS <https://www.kosmos-bmbf.de/wp-"
+"content/uploads/sites/13/2021/05/Talk-Flower-Summit-2021.pdf>`_."
+
+#: ../../source/ref-faq.rst:31
+msgid ""
+"`Flower meets Talan blog post <https://www.linkedin.com/pulse/federated-"
+"learning-same-mask-different-faces-imen-"
+"ayari/?trackingId=971oIlxLQ9%2BA9RB0IQ73XQ%3D%3D>`_ ."
+msgstr ""
+"`Flower meets Talan blog post <https://www.linkedin.com/pulse/federated-"
+"learning-same-mask-different-faces-imen-"
+"ayari/?trackingId=971oIlxLQ9%2BA9RB0IQ73XQ%3D%3D>`_ ."
+
+#: ../../source/ref-faq.rst:32
+msgid ""
+"`Flower meets Talan GitHub Repository "
+"<https://gitlab.com/Talan_Innovation_Factory/food-waste-prevention>`_ ."
+msgstr ""
+"`Flower rencontre Talan Dépôt GitHub "
+"<https://gitlab.com/Talan_Innovation_Factory/food-waste-prevention>`_ ."
+
+#: ../../source/ref-telemetry.md:1
+msgid "Telemetry"
+msgstr "Télémétrie"
+
+#: ../../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."
+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/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."
+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"
+
+#: ../../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/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)”."
+msgstr ""
+"**Optionnel:** Tu pourras toujours désactiver la télémétrie ; lis la "
+"suite pour apprendre \"[Comment se désengager](#how-to-opt-out)\"."
+
+#: ../../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."
+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/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)”"
+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/ref-telemetry.md:14
+#, fuzzy
+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 ""
+"**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"
+
+#: ../../source/ref-telemetry.md:18
+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:"
+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 :"
+
+#: ../../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."
+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 :"
+
+#: ../../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 ""
+"**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/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/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 ""
+"**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/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 ""
+"**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/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 ""
+"**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/ref-telemetry.md:40
+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 ""
+"**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/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.** 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/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/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 ""
+"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/ref-telemetry.md:48
+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 ""
+"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"
+
+#: ../../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/ref-telemetry.md:58
+msgid ""
+"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/ref-telemetry.md:64
+msgid "How to contact us"
+msgstr "Comment nous contacter"
+
+#: ../../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 ""
+"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-android.rst:-1
+msgid ""
+"Read this Federated Learning quickstart tutorial for creating an Android "
+"app using Flower."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-android.rst:5
+#, fuzzy
+msgid "Quickstart Android"
+msgstr "Démarrage rapide des Pandas"
+
+#: ../../source/tutorial-quickstart-android.rst:10
+#, fuzzy
+msgid ""
+"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-android.rst:12
+#, fuzzy
+msgid ""
+"Please refer to the `full code example "
+"<https://github.com/adap/flower/tree/main/examples/android>`_ to learn "
+"more."
+msgstr ""
+"Réfère-toi à l'exemple de code complet "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-pandas>`_ "
+"pour en savoir plus."
+
+#: ../../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."
+msgstr ""
+
+#: ../../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!"
+msgstr ""
+"Construisons un système d'apprentissage fédéré en utilisant fastai et "
+"Flower !"
+
+#: ../../source/tutorial-quickstart-fastai.rst:12
+#, fuzzy
+msgid ""
+"Please refer to the `full code example "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-fastai>`_ "
+"to learn more."
+msgstr ""
+"Réfère-toi à l'exemple de code complet "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-fastai>`_ "
+"pour en savoir plus."
+
+#: ../../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."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-huggingface.rst:5
+msgid "Quickstart 🤗 Transformers"
+msgstr "Démarrage rapide 🤗 Transformateurs"
+
+#: ../../source/tutorial-quickstart-huggingface.rst:10
+msgid ""
+"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-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."
+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"
+
+#: ../../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 ""
+"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"
+
+#: ../../source/tutorial-quickstart-huggingface.rst:35
+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:"
+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"
+
+#: ../../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:"
+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"
+
+#: ../../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` :"
+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"
+
+#: ../../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:"
+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-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."
+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"
+
+#: ../../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:"
+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-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-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."
+
+#: ../../source/tutorial-quickstart-huggingface.rst:223
+#, fuzzy
+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)."
+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-huggingface.rst:227
+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-huggingface.rst:230
+msgid ""
+"Note that in this example we used :code:`PyTorch`, but we could have very"
+" well used :code:`TensorFlow`."
+msgstr ""
+"Notez que dans cet exemple, nous avons utilisé :code:`PyTorch`, mais nous"
+" aurions très bien pu utiliser :code:`TensorFlow`."
+
+#: ../../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 "Quickstart iOS"
+msgstr "Démarrage rapide XGBoost"
+
+#: ../../source/tutorial-quickstart-ios.rst:10
+#, fuzzy
+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, comment former un réseau "
+"neuronal convolutif sur MNIST en utilisant Flower et PyTorch."
+
+#: ../../source/tutorial-quickstart-ios.rst:12
+#, fuzzy
+msgid ""
+"First of all, for running the Flower Python server, it is recommended to "
+"create a virtual environment and run everything within a `virtualenv "
+"<https://flower.ai/docs/recommended-env-setup.html>`_. For the Flower "
+"client implementation in iOS, it is recommended to use Xcode as our IDE."
+msgstr ""
+"Tout d'abord, il est recommandé de créer un environnement virtuel et de "
+"tout exécuter au sein d'un `virtualenv <https://flower.ai/docs"
+"/recommended-env-setup.html>`_."
+
+#: ../../source/tutorial-quickstart-ios.rst:15
+#, fuzzy
+msgid ""
+"Our example consists of one Python *server* and two iPhone *clients* that"
+" all have the same model."
+msgstr ""
+"Notre exemple consiste en un *serveur* et deux *clients* ayant tous le "
+"même modèle."
+
+#: ../../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-quickstart-ios.rst:21
+#, fuzzy
+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:"
+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:"
+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 "
+"<https://github.com/adap/flower/tree/main/examples/ios>`_ to learn more "
+"about the app."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:75
+msgid "Import Flower and CoreML related packages in :code:`FLiOSModel.swift`:"
+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`."
+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 "
+"informations 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
+msgid ""
+"After we have all of the necessary informations, let's create our Flower "
+"client."
+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`."
+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."
+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
+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:"
+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 :"
+
+#: ../../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/tutorial-quickstart-ios.rst:144
+#: ../../source/tutorial-quickstart-pytorch.rst:218
+#: ../../source/tutorial-quickstart-tensorflow.rst:114
+#: ../../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:"
+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 :"
+
+#: ../../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 "
+"<https://developer.apple.com/documentation/xcode/running-your-app-in-"
+"simulator-or-on-a-device>`_."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-ios.rst:156
+#, fuzzy
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"learning system in your ios device. The full `source code "
+"<https://github.com/adap/flower/blob/main/examples/ios>`_ for this "
+"example can be found in :code:`examples/ios`."
+msgstr ""
+"Félicitations ! Tu as réussi à construire et à faire fonctionner ton "
+"premier système d'apprentissage fédéré. Le code source complet "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"mxnet/client.py>`_ de cet exemple se trouve dans :code:`examples"
+"/quickstart-mxnet`."
+
+#: ../../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."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-jax.rst:5
+msgid "Quickstart JAX"
+msgstr "Démarrage rapide de JAX"
+
+#: ../../source/tutorial-quickstart-mxnet.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with MXNet to train a Sequential model on MNIST."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:5
+msgid "Quickstart MXNet"
+msgstr "Démarrage rapide de MXNet"
+
+#: ../../source/tutorial-quickstart-mxnet.rst:7
+msgid ""
+"MXNet is no longer maintained and has been moved into `Attic "
+"<https://attic.apache.org/projects/mxnet.html>`_. 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 ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:12
+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."
+
+#: ../../source/tutorial-quickstart-mxnet.rst:14
+#: ../../source/tutorial-quickstart-scikitlearn.rst:12
+msgid ""
+"It is recommended to create a virtual environment and run everything "
+"within this `virtualenv <https://flower.ai/docs/recommended-env-"
+"setup.html>`_."
+msgstr ""
+"Il est recommandé de créer un environnement virtuel et de tout exécuter "
+"dans ce `virtualenv <https://flower.ai/docs/recommended-env-"
+"setup.html>`_."
+
+#: ../../source/tutorial-quickstart-mxnet.rst:18
+#: ../../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 ""
+"*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-quickstart-mxnet.rst:22
+#: ../../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 ""
+"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 :"
+
+#: ../../source/tutorial-quickstart-mxnet.rst:38
+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 "
+"<https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html>`_."
+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 "
+"<https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html>`_."
+
+#: ../../source/tutorial-quickstart-mxnet.rst:40
+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 :"
+
+#: ../../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 :"
+
+#: ../../source/tutorial-quickstart-mxnet.rst:61
+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."
+
+#: ../../source/tutorial-quickstart-mxnet.rst:75
+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."
+
+#: ../../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."
+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."
+
+#: ../../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."
+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` :"
+
+#: ../../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."
+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."
+
+#: ../../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)."
+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)."
+
+#: ../../source/tutorial-quickstart-mxnet.rst:166
+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) :"
+
+#: ../../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
+msgid ""
+"update the local model weights with the parameters received from the "
+"server"
+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"
+
+#: ../../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/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/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/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/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:"
+msgstr ""
+"Nous pouvons maintenant créer une instance de notre classe "
+":code:`MNISTClient` et ajouter une ligne pour exécuter ce client :"
+
+#: ../../source/tutorial-quickstart-mxnet.rst:219
+#, 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."
+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."
+
+#: ../../source/tutorial-quickstart-mxnet.rst:241
+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 :"
+
+#: ../../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
+msgid ""
+"Once the server is running we can start the clients in different "
+"terminals. Open a new terminal and start the first client:"
+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 :"
+
+#: ../../source/tutorial-quickstart-mxnet.rst:262
+#: ../../source/tutorial-quickstart-pytorch.rst:239
+#: ../../source/tutorial-quickstart-scikitlearn.rst:237
+#: ../../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):"
+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) :"
+
+#: ../../source/tutorial-quickstart-mxnet.rst:294
+#, fuzzy
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"learning system. The full `source code "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"mxnet/client.py>`_ 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 "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"mxnet/client.py>`_ de cet exemple se trouve dans :code:`examples"
+"/quickstart-mxnet`."
+
+#: ../../source/tutorial-quickstart-pandas.rst:-1
+msgid ""
+"Check out this Federated Learning quickstart tutorial for using Flower "
+"with Pandas to perform Federated Analytics."
+msgstr ""
+
+#: ../../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/tutorial-quickstart-pandas.rst:12
+#, fuzzy
+msgid ""
+"Please refer to the `full code example "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-pandas>`_ "
+"to learn more."
+msgstr ""
+"Réfère-toi à l'exemple de code complet "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-pandas>`_ "
+"pour en savoir plus."
+
+#: ../../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 ""
+
+#: ../../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 ""
+"Dans ce tutoriel, nous allons apprendre à entraîner un réseau neuronal "
+"convolutif sur CIFAR10 à l'aide de Flower et PyTorch."
+
+#: ../../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 `virtualenv <https://flower.ai/docs/recommended-env-"
+"setup.html>`_."
+msgstr ""
+"Tout d'abord, il est recommandé de créer un environnement virtuel et de "
+"tout exécuter au sein d'un `virtualenv <https://flower.ai/docs"
+"/recommended-env-setup.html>`_."
+
+#: ../../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:"
+msgstr ""
+"Puisque nous voulons utiliser PyTorch pour résoudre une tâche de vision "
+"par ordinateur, allons-y et installons PyTorch et la bibliothèque "
+"**torchvision** :"
+
+#: ../../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 "
+"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_."
+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 "
+"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_ de"
+" PyTorch."
+
+#: ../../source/tutorial-quickstart-pytorch.rst:41
+msgid ""
+"In a file called :code:`client.py`, import Flower and PyTorch related "
+"packages:"
+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 :"
+
+#: ../../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."
+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."
+
+#: ../../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 ""
+"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-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 ""
+"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-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."
+msgstr ""
+"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-quickstart-pytorch.rst:115
+msgid ""
+"The Flower clients will use a simple CNN adapted from 'PyTorch: A 60 "
+"Minute Blitz':"
+msgstr ""
+"Les clients de Flower utiliseront un CNN simple adapté de \"PyTorch : A "
+"60 Minute Blitz\" :"
+
+#: ../../source/tutorial-quickstart-pytorch.rst:142
+msgid ""
+"After loading the data set with :code:`load_data()` we define the Flower "
+"interface."
+msgstr ""
+"Après avoir chargé l'ensemble des données avec :code:`load_data()`, nous "
+"définissons l'interface Flower."
+
+#: ../../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 ""
+"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 :"
+
+#: ../../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:"
+msgstr ""
+"Nous pouvons maintenant créer une instance de notre classe "
+":code:`CifarClient` et ajouter une ligne pour exécuter ce client :"
+
+#: ../../source/tutorial-quickstart-pytorch.rst:196
+#: ../../source/tutorial-quickstart-tensorflow.rst:90
+#, 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:`\"[::]: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 ""
+"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-quickstart-pytorch.rst:271
+#, fuzzy
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"learning system. The full `source code "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"pytorch/client.py>`_ for this example can be found in :code:`examples"
+"/quickstart-pytorch`."
+msgstr ""
+"Félicitations ! Tu as réussi à construire et à faire fonctionner ton "
+"premier système d'apprentissage fédéré. Le code source complet "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"pytorch/client.py>`_ de cet exemple se trouve dans :code:`examples"
+"/quickstart-pytorch`."
+
+#: ../../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-quickstart-pytorch-lightning.rst:5
+msgid "Quickstart PyTorch Lightning"
+msgstr "Démarrage rapide de PyTorch Lightning"
+
+#: ../../source/tutorial-quickstart-pytorch-lightning.rst:10
+#, fuzzy
+msgid ""
+"Let's build a horizontal federated learning system using PyTorch "
+"Lightning and Flower!"
+msgstr ""
+"Construisons un système d'apprentissage fédéré en utilisant PyTorch "
+"Lightning et Flower !"
+
+#: ../../source/tutorial-quickstart-pytorch-lightning.rst:12
+#, fuzzy
+msgid ""
+"Please refer to the `full code example "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-pytorch-"
+"lightning>`_ to learn more."
+msgstr ""
+"Réfère-toi à l'exemple de code complet "
+"<https://github.com/adap/flower/tree/main/examples/quickstart-pytorch-"
+"lightning>`_ pour en savoir plus."
+
+#: ../../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."
+msgstr ""
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:5
+msgid "Quickstart scikit-learn"
+msgstr "Démarrage rapide de scikit-learn"
+
+#: ../../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 ""
+"Dans ce tutoriel, nous allons apprendre à former un :code:`modèle de "
+"régression logistique` sur MNIST en utilisant Flower et scikit-learn."
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:26
+msgid "Since we want to use scikt-learn, let's go ahead and install it:"
+msgstr "Puisque nous voulons utiliser scikt-learn, allons-y et installons-le :"
+
+#: ../../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-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 ""
+"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()`"
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:46
+msgid "Returns the parameters of a :code:`sklearn` LogisticRegression model"
+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`"
+
+#: ../../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 "Initialise les paramètres du modèle que le serveur de Flower demandera"
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:51
+msgid ":code:`load_mnist()`"
+msgstr ":code:`load_mnist()`"
+
+#: ../../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/tutorial-quickstart-scikitlearn.rst:53
+msgid ":code:`shuffle()`"
+msgstr ":code:`shuffle()`"
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:54
+msgid "Shuffles data and its label"
+msgstr "Mélange les données et leur étiquette"
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:56
+msgid ":code:`partition()`"
+msgstr ":code:`partition()`"
+
+#: ../../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/tutorial-quickstart-scikitlearn.rst:58
+msgid ""
+"Please check out :code:`utils.py` `here "
+"<https://github.com/adap/flower/blob/main/examples/sklearn-logreg-"
+"mnist/utils.py>`_ 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 ""
+"Tu peux consulter :code:`utils.py` `ici "
+"<https://github.com/adap/flower/blob/main/examples/sklearn-logreg-"
+"mnist/utils.py>`_ 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-quickstart-scikitlearn.rst:73
+msgid ""
+"We load the MNIST dataset from `OpenML <https://www.openml.org/d/554>`_, "
+"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 "
+"<https://www.openml.org/d/554>`_, 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()`."
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:85
+msgid ""
+"Next, the logistic regression model is defined and initialized with "
+":code:`utils.set_initial_params()`."
+msgstr ""
+"Ensuite, le modèle de régression logistique est défini et initialisé avec"
+" :code:`utils.set_initial_params()`."
+
+#: ../../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)."
 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)."
+"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-quickstart-scikitlearn.rst:103
+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):"
+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()`"
+
+#: ../../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/tutorial-quickstart-scikitlearn.rst:143
+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 :"
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:150
+#, 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."
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:159
+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."
+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 :"
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:173
+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."
+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."
+
+#: ../../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))`."
+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-quickstart-scikitlearn.rst:217
+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-quickstart-scikitlearn.rst:271
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"learning system. The full `source code "
+"<https://github.com/adap/flower/tree/main/examples/sklearn-logreg-"
+"mnist>`_ 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 "
+"<https://github.com/adap/flower/tree/main/examples/sklearn-logreg-"
+"mnist>`_ de cet exemple se trouve dans :code:`examples/sklearn-logreg-"
+"mnist`."
+
+#: ../../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."
+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!"
+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 :"
+
+#: ../../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:"
+msgstr ""
+"Comme nous voulons utiliser l'API Keras de TensorFlow (TF), nous devons "
+"également installer TF :"
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:31
+msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:"
+msgstr ""
+"Ensuite, dans un fichier appelé :code:`client.py`, importe Flower et "
+"TensorFlow :"
+
+#: ../../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."
+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."
+
+#: ../../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 ""
+"Ensuite, nous avons besoin d'un modèle. Pour les besoins de ce tutoriel, "
+"nous utilisons MobilNetV2 avec 10 classes de sortie :"
+
+#: ../../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 ""
+"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-quickstart-tensorflow.rst:135
+msgid "Each client will have its own dataset."
+msgstr "Chaque client aura son propre ensemble de données."
+
+#: ../../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 ""
+"Tu devrais maintenant voir comment la formation se déroule dans le tout "
+"premier terminal (celui qui a démarré le serveur) :"
+
+#: ../../source/tutorial-quickstart-tensorflow.rst:169
+#, fuzzy
+msgid ""
+"Congratulations! You've successfully built and run your first federated "
+"learning system. The full `source code "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"tensorflow/client.py>`_ for this can be found in :code:`examples"
+"/quickstart-tensorflow/client.py`."
+msgstr ""
+"Félicitations ! Tu as réussi à construire et à faire fonctionner ton "
+"premier système d'apprentissage fédéré. Le `code source complet "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"tensorflow/client.py>`_ pour cela se trouve dans :code:`examples"
+"/quickstart-tensorflow/client.py`."
+
+#: ../../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 "Démarrage rapide XGBoost"
+
+#: ../../source/tutorial-quickstart-xgboost.rst:14
+#, fuzzy
+msgid "Federated XGBoost"
+msgstr "Formation fédérée"
+
+#: ../../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
+#, 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."
+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 "
+"<https://github.com/adap/flower/tree/main/examples/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 <https://github.com/adap/flower/tree/main/examples/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
+#, 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 :"
+
+#: ../../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
+#, fuzzy
+msgid ""
+"In a file called :code:`client.py`, import xgboost, Flower, Flower "
+"Datasets and other related functions:"
+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"
+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-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
+#, fuzzy
+msgid ""
+"Now, we can create an instance of our class :code:`XgbClient` 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 :"
+
+#: ../../source/tutorial-quickstart-xgboost.rst:300
+#, fuzzy
+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 ""
+"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-quickstart-xgboost.rst:311
+#, fuzzy
+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 ""
+"*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-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/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
+#, fuzzy
+msgid "Then, we start the server:"
+msgstr "Démarrer le serveur"
+
+#: ../../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
+#, fuzzy
+msgid ""
+"The full `source code <https://github.com/adap/flower/blob/main/examples"
+"/xgboost-quickstart/>`_ for this example can be found in :code:`examples"
+"/xgboost-quickstart`."
+msgstr ""
+"Félicitations ! Tu as réussi à construire et à faire fonctionner ton "
+"premier système d'apprentissage fédéré. Le code source complet "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"mxnet/client.py>`_ de cet exemple se trouve dans :code:`examples"
+"/quickstart-mxnet`."
+
+#: ../../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 "
+"<https://github.com/adap/flower/tree/main/examples/xgboost-"
+"comprehensive>`_), we provide more options to define various experimental"
+" setups, including aggregation strategies, data partitioning and "
+"centralised/distributed evaluation. We also support `Flower simulation "
+"<https://flower.ai/docs/framework/how-to-run-simulations.html>`_ 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
+#, fuzzy
+msgid "Cyclic training"
+msgstr "Formation centralisée"
+
+#: ../../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
+#, 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`:"
+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
+#, fuzzy
+msgid "Flower simulation"
+msgstr "Simulation de moniteur"
+
+#: ../../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
+#, fuzzy
+msgid "Example commands"
+msgstr "Exemples de PyTorch"
+
+#: ../../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
+#, fuzzy
+msgid "Then, on each client terminal, we start the clients:"
+msgstr "Ouvre un autre terminal et démarre le deuxième client :"
+
+#: ../../source/tutorial-quickstart-xgboost.rst:1244
+msgid "To run the same experiment with Flower simulation:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:1250
+#, fuzzy
+msgid ""
+"The full `code <https://github.com/adap/flower/blob/main/examples"
+"/xgboost-comprehensive/>`_ for this comprehensive example can be found in"
+" :code:`examples/xgboost-comprehensive`."
+msgstr ""
+"Félicitations ! Tu as réussi à construire et à faire fonctionner ton "
+"premier système d'apprentissage fédéré. Le code source complet "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"mxnet/client.py>`_ de cet exemple se trouve dans :code:`examples"
+"/quickstart-mxnet`."
+
+#: ../../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-build-a-strategy-from-scratch-pytorch.ipynb:11
+#, 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 <https://flower.ai/docs/framework/tutorial-"
+"get-started-with-flower-pytorch.html>`__) and we learned how strategies "
+"can be used to customize the execution on both the server and the clients"
+" (`part 2 <https://flower.ai/docs/framework/tutorial-use-a-federated-"
+"learning-strategy-pytorch.html>`__)."
+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 "
+"<https://flower.ai/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html>`__) "
+"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 <https://flower.ai/docs/tutorial/Flower-2-Strategies-in-FL-"
+"PyTorch.html>`__)."
+
+#: ../../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 <https://flower.ai/>`__ and `PyTorch "
+"<https://pytorch.org/>`__)."
+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 <https://flower.ai/>`__ et `PyTorch <https://pytorch.org/>`__)."
+
+#: ../../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 <https://github.com/adap/flower>`__ ⭐️ and join "
+"the Flower community on Slack to connect, ask questions, and get help: "
+"`Join Slack <https://flower.ai/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 <https://github.com/adap/flower>`__ ⭐️ et "
+"rejoignez la communauté Flower sur Slack pour vous connecter, poser des "
+"questions et obtenir de l'aide : `Join Slack <https://flower.ai/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 !"
+
+#: ../../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 ""
+"Before we begin with the actual code, let's make sure that we have "
+"everything we need."
+msgstr ""
+"Avant de commencer le code proprement dit, assurons-nous que nous "
+"disposons de tout ce dont nous avons besoin."
+
+#: ../../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-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-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 ""
+"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
+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 ""
+"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-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 ""
+"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
+msgid ""
+"Let's continue with the usual model definition (including "
+"``set_parameters`` and ``get_parameters``), training and test functions:"
+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-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 ""
+"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-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-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 ""
+"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-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 ""
+"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"
+
+#: ../../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-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 <https://flower.ai/join-slack/>`__"
+msgstr ""
+"Avant de continuer, n'oublie pas de rejoindre la communauté Flower sur "
+"Slack : `Join Slack <https://flower.ai/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 ""
+"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
+msgid ""
+"The `Flower Federated Learning Tutorial - Part 4 "
+"<https://flower.ai/docs/framework/tutorial-customize-the-client-"
+"pytorch.html>`__ introduces ``Client``, the flexible API underlying "
+"``NumPyClient``."
+msgstr ""
+"Le `Tutoriel d'apprentissage fédéré Flower - Partie 4 "
+"<https://flower.ai/docs/tutorial/Flower-4-Client-and-NumPyClient-"
+"PyTorch.html>`__ 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-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 <https://flower.ai/docs/framework"
+"/tutorial-get-started-with-flower-pytorch.html>`__), we learned how "
+"strategies can be used to customize the execution on both the server and "
+"the clients (`part 2 <https://flower.ai/docs/framework/tutorial-use-a"
+"-federated-learning-strategy-pytorch.html>`__), and we built our own "
+"custom strategy from scratch (`part 3 <https://flower.ai/docs/framework"
+"/tutorial-build-a-strategy-from-scratch-pytorch.html>`__)."
+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 "
+"<https://flower.ai/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html>`__),"
+" nous avons appris comment les stratégies peuvent être utilisées pour "
+"personnaliser l'exécution à la fois sur le serveur et les clients "
+"(`partie 2 <https://flower.ai/docs/tutorial/Flower-2-Strategies-in-FL-"
+"PyTorch.html>`__), et nous avons construit notre propre stratégie "
+"personnalisée à partir de zéro (`partie 3 - WIP "
+"<https://flower.ai/docs/tutorial/Flower-3-Building-a-Strategy-"
+"PyTorch.html>`__)."
+
+#: ../../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 ""
+"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
+msgid ""
+"Let's go deeper and see what it takes to move from ``NumPyClient`` to "
+"``Client``!"
+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"
+
+#: ../../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-customize-the-client-pytorch.ipynb:259
+msgid "Step 1: Revisiting NumPyClient"
+msgstr "Étape 1 : Revoir 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 ""
+"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-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 ""
+"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-customize-the-client-pytorch.ipynb:339
+msgid ""
+"This works as expected, two clients are training for three rounds of "
+"federated learning."
+msgstr ""
+"Cela fonctionne comme prévu, deux clients s'entraînent pour trois tours "
+"d'apprentissage fédéré."
+
+#: ../../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 ""
+"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-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 ""
+"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-customize-the-client-pytorch.ipynb:345
+msgid ""
+"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-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-customize-the-client-pytorch.ipynb:359
+msgid ""
+"Let's try to do the same thing using ``Client`` instead of "
+"``NumPyClient``."
+msgstr ""
+"Essayons de faire la même chose en utilisant ``Client`` au lieu de "
+"``NumPyClient``."
+
+#: ../../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-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 ""
+"Voilà, nous utilisons maintenant ``Client``. Cela ressemble probablement "
+"à ce que nous avons fait avec ``NumPyClient``. Alors quelle est la "
+"différence ?"
+
+#: ../../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 ""
+"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-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 ""
+"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-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 ""
+"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"
+
+#: ../../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."
+
+#: ../../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 <https://flower.ai/docs/>`__"
+msgstr "`Lire les docs sur les fleurs <https://flower.ai/docs/>`__"
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:955
+msgid ""
+"`Check out Flower Code Examples "
+"<https://github.com/adap/flower/tree/main/examples>`__"
+msgstr ""
+"`Check out Flower Code Examples "
+"<https://github.com/adap/flower/tree/main/examples>`__"
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:956
+#, fuzzy
+msgid ""
+"`Use Flower Baselines for your research "
+"<https://flower.ai/docs/baselines/>`__"
+msgstr ""
+"`Utilise les lignes de base des fleurs pour ta recherche "
+"<https://flower.ai/docs/using-baselines.html>`__"
+
+#: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:957
+#, fuzzy
+msgid ""
+"`Watch Flower Summit 2023 videos <https://flower.ai/conf/flower-"
+"summit-2023/>`__"
+msgstr ""
+"`Regardez les vidéos du Flower Summit 2022 <https://flower.ai/conf"
+"/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 <https://flower.ai/docs/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
+msgid "Let's get stated!"
+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
+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."
+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 "
+"<https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html>`__."
+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 "
+"<https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html>`__."
+
+#: ../../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 "
+"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#define-a"
+"-convolutional-neural-network>`__:"
+msgstr ""
+"Nous utilisons le CNN simple décrit dans le tutoriel `PyTorch "
+"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#define-a"
+"-convolutional-neural-network>`__ :"
+
+#: ../../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-quickstart-scikitlearn.rst:100
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:438
 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):"
+"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 ""
-"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) :"
+"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-quickstart-scikitlearn.rst:109
-msgid "is directly imported with :code:`utils.set_model_params()`"
-msgstr "est directement importé avec :code:`utils.set_model_params()`"
+#: ../../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-quickstart-scikitlearn.rst:117
-msgid "The methods can be implemented in the following way:"
-msgstr "Les méthodes peuvent être mises en œuvre de la manière suivante :"
+#: ../../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-quickstart-scikitlearn.rst:140
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:470
 msgid ""
-"We can now create an instance of our class :code:`MnistClient` and add "
-"one line to actually run this client:"
+"To implement the Flower client, we create a subclass of "
+"``flwr.client.NumPyClient`` and implement the three methods "
+"``get_parameters``, ``fit``, and ``evaluate``:"
 msgstr ""
-"Nous pouvons maintenant créer une instance de notre classe "
-":code:`MnistClient` et ajouter une ligne pour exécuter ce client :"
+"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-quickstart-scikitlearn.rst:156
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:473
 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."
+"``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 ""
-"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."
+"``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-quickstart-scikitlearn.rst:159
-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: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-quickstart-scikitlearn.rst:170
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:476
 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."
+"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 ""
-"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."
+"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-quickstart-scikitlearn.rst:195
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:513
 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))`."
+"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 ""
-"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))`."
+"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-quickstart-scikitlearn.rst:214
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:519
 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:"
+"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 ""
-"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 :"
+"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-quickstart-scikitlearn.rst:268
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:612
+#, python-format
 msgid ""
-"Congratulations! You've successfully built and run your first federated "
-"learning system. The full `source code "
-"<https://github.com/adap/flower/tree/main/examples/sklearn-logreg-"
-"mnist>`_ for this example can be found in :code:`examples/sklearn-logreg-"
-"mnist`."
+"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 ""
-"Félicitations ! Tu as réussi à construire et à faire fonctionner ton "
-"premier système d'apprentissage fédéré. Le code source complet "
-"<https://github.com/adap/flower/tree/main/examples/sklearn-logreg-"
-"mnist>`_ de cet exemple se trouve dans :code:`examples/sklearn-logreg-"
-"mnist`."
-
-#: ../../source/tutorial-quickstart-tensorflow.rst:5
-msgid "Quickstart TensorFlow"
-msgstr "Démarrage rapide de TensorFlow"
+"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-quickstart-tensorflow.rst:10
-msgid "Let's build a federated learning system in less than 20 lines of code!"
+#: ../../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 ""
-"Construisons un système d'apprentissage fédéré en moins de 20 lignes de "
-"code !"
+"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-quickstart-tensorflow.rst:12
-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:626
+msgid "Where's the accuracy?"
+msgstr "Où est la précision ?"
 
-#: ../../source/tutorial-quickstart-tensorflow.rst:18
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:628
 msgid ""
-"Since we want to use the Keras API of TensorFlow (TF), we have to install"
-" TF as well:"
+"You may have noticed that all metrics except for ``losses_distributed`` "
+"are empty. Where did the ``{\"accuracy\": float(accuracy)}`` go?"
 msgstr ""
-"Comme nous voulons utiliser l'API Keras de TensorFlow (TF), nous devons "
-"également installer TF :"
+"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-quickstart-tensorflow.rst:28
-msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:"
+#: ../../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 ""
-"Ensuite, dans un fichier appelé :code:`client.py`, importe Flower et "
-"TensorFlow :"
+"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-quickstart-tensorflow.rst:35
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:632
 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."
+"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 ""
-"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."
+"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-quickstart-tensorflow.rst:44
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:634
 msgid ""
-"Next, we need a model. For the purpose of this tutorial, we use "
-"MobilNetV2 with 10 output classes:"
+"Let's create a simple weighted averaging function to aggregate the "
+"``accuracy`` metric we return from ``evaluate``:"
 msgstr ""
-"Ensuite, nous avons besoin d'un modèle. Pour les besoins de ce tutoriel, "
-"nous utilisons MobilNetV2 avec 10 classes de sortie :"
+"Créons une simple fonction de calcul de la moyenne pondérée pour agréger "
+"la mesure de \"précision\" que nous renvoie ``evaluate`` :"
 
-#: ../../source/tutorial-quickstart-tensorflow.rst:57
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:660
 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:"
+"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 ""
-"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-quickstart-tensorflow.rst:132
-msgid "Each client will have its own dataset."
-msgstr "Chaque client aura son propre ensemble de données."
+"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-quickstart-tensorflow.rst:134
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:697
 msgid ""
-"You should now see how the training does in the very first terminal (the "
-"one that started the server):"
+"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 ""
-"Tu devrais maintenant voir comment la formation se déroule dans le tout "
-"premier terminal (celui qui a démarré le serveur) :"
+"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-quickstart-tensorflow.rst:166
-#, fuzzy
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:699
 msgid ""
-"Congratulations! You've successfully built and run your first federated "
-"learning system. The full `source code "
-"<https://github.com/adap/flower/blob/main/examples/quickstart-"
-"tensorflow/client.py>`_ for this can be found in :code:`examples"
-"/quickstart-tensorflow/client.py`."
+"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 ""
-"Félicitations ! Tu as réussi à construire et à faire fonctionner ton "
-"premier système d'apprentissage fédéré. Le `code source complet "
-"<https://github.com/adap/flower/blob/main/examples/quickstart-"
-"tensorflow/client.py>`_ pour cela se trouve dans :code:`examples"
-"/quickstart-tensorflow/client.py`."
+"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-quickstart-xgboost.rst:5
-msgid "Quickstart XGBoost"
-msgstr "Démarrage rapide XGBoost"
+#: ../../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-quickstart-xgboost.rst:7
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:715
 msgid ""
-"Let's build a horizontal federated learning system using XGBoost and "
-"Flower!"
+"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 ""
-"Construisons un système d'apprentissage fédéré horizontal en utilisant "
-"XGBoost et Flower !"
+"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-quickstart-xgboost.rst:9
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:733
 #, fuzzy
 msgid ""
-"Please refer to the `full code example "
-"<https://github.com/adap/flower/tree/main/examples/quickstart-xgboost-"
-"horizontal>`_ to learn more."
+"The `Flower Federated Learning Tutorial - Part 2 "
+"<https://flower.ai/docs/framework/tutorial-use-a-federated-learning-"
+"strategy-pytorch.html>`__ goes into more depth about strategies and all "
+"the advanced things you can build with them."
 msgstr ""
-"Réfère-toi à l'exemple de code complet "
-"<https://github.com/adap/flower/tree/main/examples/quickstart-xgboost-"
-"horizontal>`_ pour en savoir plus."
+"Le `Tutoriel d'apprentissage fédéré Flower - Partie 2 "
+"<https://flower.ai/docs/tutorial/Flower-2-Strategies-in-FL-"
+"PyTorch.html>`__ va plus en profondeur sur les stratégies et toutes les "
+"choses avancées que tu peux construire avec elles."
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:9
+#: ../../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-use-a-federated-learning-strategy-pytorch.ipynb:11
+#: ../../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 <https://flower.dev/docs/framework/tutorial-get-started-"
+" Flower (`part 1 <https://flower.ai/docs/framework/tutorial-get-started-"
 "with-flower-pytorch.html>`__)."
 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 "
-"<https://flower.dev/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html>`__)."
+"<https://flower.ai/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html>`__)."
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:13
+#: ../../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 "
-"<https://flower.dev/>`__ and `PyTorch <https://pytorch.org/>`__)."
+"<https://flower.ai/>`__ and `PyTorch <https://pytorch.org/>`__)."
 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 <https://flower.dev/>`__ et"
+"d'introduction (toujours en utilisant `Flower <https://flower.ai/>`__ et"
 " `PyTorch <https://pytorch.org/>`__)."
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:17
+#: ../../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-use-a-federated-learning-strategy-pytorch.ipynb:309
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:309
 msgid "Strategy customization"
 msgstr "Personnalisation de la stratégie"
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:311
+#: ../../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 "
@@ -14785,11 +21441,11 @@ msgstr ""
 "le cahier d'introduction. Avec cela, nous sommes prêts à présenter un "
 "certain nombre de nouvelles fonctionnalités."
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:323
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:323
 msgid "Server-side parameter **initialization**"
 msgstr "Paramètres côté serveur **initialisation**"
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:325
+#: ../../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 "
@@ -14802,7 +21458,7 @@ msgstr ""
 "Flower te permet donc de passer directement les paramètres initiaux à la "
 "Stratégie :"
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:370
+#: ../../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 "
@@ -14814,11 +21470,11 @@ msgstr ""
 " nous regardons de près, nous pouvons voir que les journaux ne montrent "
 "aucun appel à la méthode ``FlowerClient.get_parameters``."
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:382
+#: ../../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-use-a-federated-learning-strategy-pytorch.ipynb:384
+#: ../../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 "
@@ -14830,7 +21486,7 @@ msgstr ""
 "nombre de clients à simuler ``num_clients``, le nombre de rounds "
 "``num_rounds``, et la stratégie."
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:386
+#: ../../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 "
@@ -14840,11 +21496,11 @@ msgstr ""
 "exemple, ``FedAvg`` ou ``FedAdagrad``. Essayons d'utiliser une stratégie "
 "différente cette fois-ci :"
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:424
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:424
 msgid "Server-side parameter **evaluation**"
 msgstr "Paramètre côté serveur **évaluation**"
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:426
+#: ../../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 "
@@ -14854,7 +21510,7 @@ msgstr ""
 "évaluations côté client et côté serveur sont similaires à certains "
 "égards, mais différentes à d'autres."
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:428
+#: ../../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 "
@@ -14873,7 +21529,7 @@ msgstr ""
 "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-use-a-federated-learning-strategy-pytorch.ipynb:430
+#: ../../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 "
@@ -14904,7 +21560,7 @@ msgstr ""
 "le modèle, nous verrions nos résultats d'évaluation fluctuer au cours des"
 " cycles consécutifs."
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:433
+#: ../../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 "
@@ -14915,11 +21571,11 @@ msgstr ""
 "``FlowerClient``). Voyons maintenant comment nous pouvons évaluer les "
 "paramètres du modèle agrégé du côté serveur :"
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:490
+#: ../../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-use-a-federated-learning-strategy-pytorch.ipynb:492
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:492
 #, fuzzy
 msgid ""
 "In some situations, we want to configure client-side execution (training,"
@@ -14946,7 +21602,7 @@ msgstr ""
 " 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-use-a-federated-learning-strategy-pytorch.ipynb:546
+#: ../../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"
@@ -14961,7 +21617,7 @@ msgstr ""
 "stratégie appelle cette fonction pour chaque cycle d'apprentissage fédéré"
 " :"
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:576
+#: ../../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:"
@@ -14969,7 +21625,7 @@ msgstr ""
 "Ensuite, nous allons simplement passer cette fonction à la stratégie "
 "FedAvg avant de commencer la simulation :"
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:613
+#: ../../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 "
@@ -14984,7 +21640,7 @@ msgstr ""
 " premier et du deuxième cycle d'apprentissage fédéré, puis pendant deux "
 "époques au cours du troisième cycle."
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:615
+#: ../../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 "
@@ -14999,11 +21655,11 @@ msgstr ""
 "renvoie un dictionnaire contenant une paire clé/valeur personnalisée en "
 "tant que troisième valeur de retour dans ``evaluate``."
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:627
+#: ../../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-use-a-federated-learning-strategy-pytorch.ipynb:629
+#: ../../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."
@@ -15011,7 +21667,7 @@ msgstr ""
 "Comme dernière étape de ce carnet, voyons comment nous pouvons utiliser "
 "Flower pour expérimenter avec un grand nombre de clients."
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:651
+#: ../../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 "
@@ -15036,7 +21692,7 @@ msgstr ""
 " disponibles (donc 50 clients) seront sélectionnés pour l'entraînement à "
 "chaque tour :"
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:699
+#: ../../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, "
@@ -15049,7 +21705,7 @@ msgstr ""
 "en évaluant les modèles côté serveur. C'est une sacrée flexibilité avec "
 "si peu de code, n'est-ce pas ?"
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:701
+#: ../../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. "
@@ -15065,24 +21721,24 @@ msgstr ""
 "avons mené une expérience impliquant 1000 clients dans la même charge de "
 "travail - le tout dans un carnet Jupyter !"
 
-#: ../../source/tutorial-use-a-federated-learning-strategy-pytorch.ipynb:719
+#: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:719
 #, fuzzy
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 3 "
-"<https://flower.dev/docs/framework/tutorial-build-a-strategy-from-"
+"<https://flower.ai/docs/framework/tutorial-build-a-strategy-from-"
 "scratch-pytorch.html>`__ shows how to build a fully custom ``Strategy`` "
 "from scratch."
 msgstr ""
 "Le `Tutoriel d'apprentissage fédéré Flower - Partie 3 [WIP] "
-"<https://flower.dev/docs/tutorial/Flower-3-Building-a-Strategy-"
+"<https://flower.ai/docs/tutorial/Flower-3-Building-a-Strategy-"
 "PyTorch.html>`__ montre comment construire une ``Stratégie`` entièrement "
 "personnalisée à partir de zéro."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:9
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:9
 msgid "What is Federated Learning?"
 msgstr "Qu'est-ce que l'apprentissage fédéré ?"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:13
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:13
 #, fuzzy
 msgid ""
 "In this tutorial, you will learn what federated learning is, build your "
@@ -15096,7 +21752,7 @@ msgstr ""
 "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-what-is-federated-learning.ipynb:15
+#: ../../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"
@@ -15106,27 +21762,32 @@ msgstr ""
 "l'apprentissage fédéré. Seule une compréhension de base de la science des"
 " données et de la programmation Python est supposée."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:17
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:17
 #, fuzzy
 msgid ""
 "`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ and join "
 "the open-source Flower community on Slack to connect, ask questions, and "
-"get help: `Join Slack <https://flower.dev/join-slack>`__ 🌼 We'd love to "
+"get help: `Join Slack <https://flower.ai/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 <https://github.com/adap/flower>`__ ⭐️ et "
 "rejoignez la communauté Flower sur Slack pour vous connecter, poser des "
-"questions et obtenir de l'aide : `Join Slack <https://flower.dev/join-"
+"questions et obtenir de l'aide : `Join Slack <https://flower.ai/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-what-is-federated-learning.ipynb:31
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:19
+#, 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"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:33
+#: ../../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."
@@ -15135,7 +21796,7 @@ msgstr ""
 "rapidement la façon dont la plupart des apprentissages automatiques "
 "fonctionnent aujourd'hui."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:35
+#: ../../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"
@@ -15145,15 +21806,15 @@ msgstr ""
 " modèle peut être un réseau neuronal (comme illustré ici), ou quelque "
 "chose d'autre, comme la régression linéaire classique."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:41
-msgid "|3ff4c820a01d4a5abb022617de537c54|"
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:41
+msgid "|31e4b1afa87c4b968327bbeafbf184d4|"
 msgstr ""
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:109
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:109
 msgid "Model and data"
 msgstr "Modèle et données"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:47
+#: ../../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 "
@@ -15164,15 +21825,15 @@ msgstr ""
 "images, à transcrire un enregistrement audio ou à jouer à un jeu comme le"
 " Go."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:53
-msgid "|7f1889391ad448e2a65920165f0d798c|"
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:53
+msgid "|c9d935b4284e4c389a33d86b33e07c0a|"
 msgstr ""
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:111
+#: ../../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-what-is-federated-learning.ipynb:59
+#: ../../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"
@@ -15182,7 +21843,7 @@ msgstr ""
 "travaillons ne proviennent pas de la machine sur laquelle nous entraînons"
 " le modèle. Elles sont créées ailleurs."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:61
+#: ../../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 "
@@ -15195,15 +21856,15 @@ msgstr ""
 "parleur intelligent qui écoute quelqu'un qui essaie de chanter une "
 "chanson."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:67
-msgid "|a171dc4a0d044e70b5d585cc10ace0e0|"
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:67
+msgid "|00727b5faffb468f84dd1b03ded88638|"
 msgstr ""
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:113
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:113
 msgid "Data on a phone"
 msgstr "Données sur un téléphone"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:73
+#: ../../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"
@@ -15216,15 +21877,15 @@ msgstr ""
 "peut également s'agir de plusieurs organisations, qui génèrent toutes des"
 " données pour la même tâche."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:79
-msgid "|fe518aa0d86341f7b2fc87bd6e3bbf0c|"
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:79
+msgid "|daf0cf0ff4c24fd29439af78416cf47b|"
 msgstr ""
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:115
+#: ../../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-what-is-federated-learning.ipynb:85
+#: ../../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 "
@@ -15237,15 +21898,15 @@ msgstr ""
 "trouver quelque part dans un centre de données, ou quelque part dans le "
 "cloud."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:91
-msgid "|6abfdf0dade44469ae9f08c8dc7d148c|"
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:91
+msgid "|9f093007080d471d94ca90d3e9fde9b6|"
 msgstr ""
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:117
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:117
 msgid "Central data collection"
 msgstr "Collecte centralisée des données"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:97
+#: ../../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 "
@@ -15258,19 +21919,19 @@ msgstr ""
 "automatique sur laquelle nous nous sommes fondamentalement toujours "
 "appuyés."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:103
-msgid "|b4f147db24bb4da9a786e1d6676a1c2d|"
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:103
+msgid "|46a26e6150e0479fbd3dfd655f36eb13|"
 msgstr ""
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:119
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:119
 msgid "Central model training"
 msgstr "Formation au modèle central"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:130
+#: ../../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-what-is-federated-learning.ipynb:132
+#: ../../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 "
@@ -15283,15 +21944,15 @@ msgstr ""
 "trafic web. Des cas, où toutes les données sont naturellement disponibles"
 " sur un serveur centralisé."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:138
-msgid "|5c62032f589a457bb37b5fee5b2adbde|"
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:138
+msgid "|3daba297595c4c7fb845d90404a6179a|"
 msgstr ""
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:173
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:173
 msgid "Centralized possible"
 msgstr "Possibilité de centralisation"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:144
+#: ../../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 "
@@ -15302,15 +21963,15 @@ msgstr ""
 " ou lorsque les données disponibles sur un serveur ne sont pas "
 "suffisantes pour former un bon modèle."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:150
-msgid "|f154df1846dd44f79a94f1dc3ae8b088|"
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:150
+msgid "|5769874fa9c4455b80b2efda850d39d7|"
 msgstr ""
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:175
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:175
 msgid "Centralized impossible"
 msgstr "Impossible de centraliser"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:156
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:156
 #, fuzzy
 msgid ""
 "There are many reasons why the classic centralized machine learning "
@@ -15322,7 +21983,7 @@ msgstr ""
 "grand nombre de cas d'utilisation très importants dans le monde réel, "
 "notamment :"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:158
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:158
 #, fuzzy
 msgid ""
 "**Regulations**: GDPR (Europe), CCPA (California), PIPEDA (Canada), LGPD "
@@ -15347,7 +22008,7 @@ msgstr ""
 "régies par des réglementations différentes en matière de protection des "
 "données."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:160
+#: ../../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 "
@@ -15365,7 +22026,7 @@ msgstr ""
 "'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-what-is-federated-learning.ipynb:161
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:161
 #, fuzzy
 msgid ""
 "**Data volume**: Some sensors, like cameras, produce such a high data "
@@ -15388,13 +22049,13 @@ msgstr ""
 "excessivement coûteuse pour les traiter et les stocker. Et la plupart de "
 "ces données ne sont même pas utiles."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:164
+#: ../../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-what-is-federated-learning.ipynb:166
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:166
 #, fuzzy
 msgid ""
 "Sensitive healthcare records from multiple hospitals to train cancer "
@@ -15403,7 +22064,7 @@ msgstr ""
 "Des dossiers médicaux sensibles provenant de plusieurs hôpitaux pour "
 "former des modèles de détection du cancer"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:167
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:167
 msgid ""
 "Financial information from different organizations to detect financial "
 "fraud"
@@ -15411,25 +22072,25 @@ msgstr ""
 "Informations financières provenant de différentes organisations pour "
 "détecter les fraudes financières"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:168
+#: ../../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-what-is-federated-learning.ipynb:169
+#: ../../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-what-is-federated-learning.ipynb:171
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:171
 #, fuzzy
 msgid ""
 "The popularity of privacy-enhancing systems like the `Brave "
 "<https://brave.com/>`__ browser or the `Signal <https://signal.org/>`__ "
 "messenger shows that users care about privacy. In fact, they choose the "
-"privacy-enhancing version over other alternatives, if such an alernative "
+"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."
@@ -15445,11 +22106,11 @@ msgstr ""
 "qui bénéficieraient de manière significative des récentes avancées en "
 "matière d'IA."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:186
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:186
 msgid "Federated learning"
 msgstr "Apprentissage fédéré"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:188
+#: ../../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 "
@@ -15461,15 +22122,15 @@ msgstr ""
 "formation vers les données, au lieu de déplacer les données vers la "
 "formation. Voici l'explication en une seule phrase :"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:190
+#: ../../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-what-is-federated-learning.ipynb:191
+#: ../../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-what-is-federated-learning.ipynb:193
+#: ../../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 "
@@ -15497,7 +22158,7 @@ msgstr ""
 " réinventés parce qu'ils ont maintenant accès à de vastes quantités de "
 "données auparavant inaccessibles."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:196
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:196
 msgid ""
 "So how does federated learning work, exactly? Let's start with an "
 "intuitive explanation."
@@ -15505,15 +22166,15 @@ msgstr ""
 "Comment fonctionne l'apprentissage fédéré ? Commençons par une "
 "explication intuitive."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:199
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:199
 msgid "Federated learning in five steps"
 msgstr "L'apprentissage fédéré en cinq étapes"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:202
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:202
 msgid "Step 0: Initialize global model"
 msgstr "Étape 0 : Initialisation du modèle global"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:204
+#: ../../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,"
@@ -15524,15 +22185,15 @@ msgstr ""
 " initialisons les paramètres du modèle, soit de façon aléatoire, soit à "
 "partir d'un point de contrôle précédemment sauvegardé."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:210
-msgid "|9d20be8160f7451fb0f33b194506503f|"
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:210
+msgid "|ba47ffb421814b0f8f9fa5719093d839|"
 msgstr ""
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:307
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:307
 msgid "Initialize global model"
 msgstr "Initialise le modèle global"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:217
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:217
 msgid ""
 "Step 1: Send model to a number of connected organizations/devices (client"
 " nodes)"
@@ -15540,7 +22201,7 @@ msgstr ""
 "Étape 1 : envoyer le modèle à un certain nombre d'organisations/appareils"
 " connectés (nœuds clients)"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:219
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:219
 #, fuzzy
 msgid ""
 "Next, we send the parameters of the global model to the connected client "
@@ -15559,15 +22220,15 @@ msgstr ""
 "est que la sélection d'un nombre croissant de nœuds clients a des "
 "rendements décroissants."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:225
-msgid "|3d949f76988443c59990d2e64f05c386|"
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:225
+msgid "|aeac5bf79cbf497082e979834717e01b|"
 msgstr ""
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:309
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:309
 msgid "Send global model"
 msgstr "Envoyer le modèle global"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:232
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:232
 msgid ""
 "Step 2: Train model locally on the data of each organization/device "
 "(client node)"
@@ -15575,7 +22236,7 @@ msgstr ""
 "Étape 2 : Entraîne le modèle localement sur les données de chaque "
 "organisation/appareil (nœud client)"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:234
+#: ../../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 "
@@ -15592,19 +22253,19 @@ msgstr ""
 "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-what-is-federated-learning.ipynb:240
-msgid "|526c6d9140f6404f8a226d9056327b3b|"
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:240
+msgid "|ce27ed4bbe95459dba016afc42486ba2|"
 msgstr ""
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:311
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:311
 msgid "Train on local data"
 msgstr "Forme-toi aux données locales"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:247
+#: ../../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-what-is-federated-learning.ipynb:249
+#: ../../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 "
@@ -15623,21 +22284,21 @@ msgstr ""
 "du modèle, soit seulement les gradients qui ont été accumulés au cours de"
 " l'entraînement local."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:255
-msgid "|a5f6af14cd7c4550929b17f83b4f63c7|"
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:255
+msgid "|ae94a7f71dda443cbec2385751427d41|"
 msgstr ""
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:313
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:313
 msgid "Send model updates"
 msgstr "Envoyer les mises à jour du modèle"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:262
+#: ../../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-what-is-federated-learning.ipynb:264
+#: ../../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 "
@@ -15652,7 +22313,7 @@ msgstr ""
 "voulions-nous pas avoir un seul modèle qui contienne les apprentissages "
 "des données de l'ensemble des 100 nœuds clients ?"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:266
+#: ../../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 "
@@ -15682,19 +22343,19 @@ msgstr ""
 "weighting - each of the 10 examples would influence the global model ten "
 "times as much as each of the 100 examples."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:273
-msgid "|bcd571c4f4ee4803a54f71b5c20448cb|"
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:273
+msgid "|e61fce4d43d243e7bb08bdde97d81ce6|"
 msgstr ""
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:315
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:315
 msgid "Aggregate model updates"
 msgstr "Mises à jour globales du modèle"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:280
+#: ../../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-what-is-federated-learning.ipynb:282
+#: ../../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 "
@@ -15709,7 +22370,7 @@ msgstr ""
 " 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-what-is-federated-learning.ipynb:284
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:284
 #, fuzzy
 msgid ""
 "During a single round, each client node that participates in that "
@@ -15729,7 +22390,7 @@ msgstr ""
 "modèle entièrement entraîné qui fonctionne bien sur l'ensemble des "
 "données de tous les nœuds clients."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:289
+#: ../../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"
@@ -15748,7 +22409,7 @@ msgstr ""
 "meilleure façon d'agréger les mises à jour du modèle ? Comment pouvons-"
 "nous gérer les nœuds clients qui échouent (stragglers) ?"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:294
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:294
 #, fuzzy
 msgid ""
 "Just like we can train a model on the decentralized data of different "
@@ -15764,11 +22425,11 @@ msgstr ""
 "fédérée fait partie intégrante de la plupart des systèmes d'apprentissage"
 " fédéré."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:297
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:297
 msgid "Federated analytics"
 msgstr "Analyses fédérées"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:299
+#: ../../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"
@@ -15790,12 +22451,12 @@ msgstr ""
 "empêcher le serveur de voir les résultats soumis par les nœuds clients "
 "individuels."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:303
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:303
 #, fuzzy
 msgid "Differential Privacy"
 msgstr "Confidentialité différentielle"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:305
+#: ../../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 "
@@ -15806,11 +22467,11 @@ msgid ""
 "optimization that provides a quantifiable privacy protection measure."
 msgstr ""
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:326
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:326
 msgid "Flower"
 msgstr "Fleur"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:328
+#: ../../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"
@@ -15830,11 +22491,11 @@ msgstr ""
 "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-what-is-federated-learning.ipynb:334
-msgid "|c76452ae1ed84965be7ef23c72b95845|"
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:334
+msgid "|08cb60859b07461588fe44e55810b050|"
 msgstr ""
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:340
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:340
 msgid ""
 "Flower federated learning server and client nodes (car, scooter, personal"
 " computer, roomba, and phone)"
@@ -15842,7 +22503,7 @@ msgstr ""
 "Serveur d'apprentissage fédéré de Flower et nœuds clients (voiture, "
 "scooter, ordinateur personnel, roomba et téléphone)"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:353
+#: ../../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!"
@@ -15850,7 +22511,7 @@ msgstr ""
 "Félicitations, tu viens d'apprendre les bases de l'apprentissage fédéré "
 "et son rapport avec l'apprentissage automatique classique (centralisé) !"
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:355
+#: ../../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."
@@ -15858,16 +22519,16 @@ msgstr ""
 "Dans la prochaine partie de ce tutoriel, nous allons construire un "
 "premier système d'apprentissage fédéré avec Flower."
 
-#: ../../source/tutorial-what-is-federated-learning.ipynb:373
+#: ../../source/tutorial-series-what-is-federated-learning.ipynb:373
 #, fuzzy
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 1 "
-"<https://flower.dev/docs/framework/tutorial-get-started-with-flower-"
+"<https://flower.ai/docs/framework/tutorial-get-started-with-flower-"
 "pytorch.html>`__ shows how to build a simple federated learning system "
 "with PyTorch and Flower."
 msgstr ""
 "Le `Tutoriel d'apprentissage fédéré Flower - Partie 1 "
-"<https://flower.dev/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html>`__ "
+"<https://flower.ai/docs/tutorial/Flower-1-Intro-to-FL-PyTorch.html>`__ "
 "montre comment construire un système d'apprentissage fédéré simple avec "
 "PyTorch et Flower."
 
@@ -17153,7 +23814,7 @@ msgstr ""
 #~ "that in Flower simulation, the server"
 #~ " automatically displays all logs. You "
 #~ "can still specify a :code:`HTTPHandler` "
-#~ "should you whish to backup or "
+#~ "should you wish to backup or "
 #~ "analyze the logs somewhere else."
 #~ msgstr ""
 #~ "La fonction :code:`fl.common.logger.configure` "
@@ -17225,14 +23886,13 @@ msgstr ""
 #~ " 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"
-#~ " <ref-api-flwr.html#start-numpy-client>`_)"
-#~ " in the sense that they can be"
-#~ " configure by creating a class "
-#~ "inheriting, for example, from "
-#~ "`flwr.client.NumPyClient <ref-api-"
-#~ "flwr.html#flwr.client.NumPyClient>`_ and therefore "
-#~ "behave in an identical way. In "
+#~ " via the command `flwr.client.start_client "
+#~ "<ref-api-flwr.html#start-client>`_) in the"
+#~ " sense that they can be configure "
+#~ "by creating a class inheriting, for "
+#~ "example, from `flwr.client.NumPyClient <ref-"
+#~ "api-flwr.html#flwr.client.NumPyClient>`_ and therefore"
+#~ " behave in an identical way. In "
 #~ "addition to that, clients managed by "
 #~ "the :code:`VirtualClientEngine` are:"
 #~ msgstr ""
@@ -17709,7 +24369,7 @@ msgstr ""
 #~ msgstr "Documentation de Flower"
 
 #~ msgid "PyTorch"
-#~ msgstr "PyTorch"
+#~ msgstr "Exemples de PyTorch"
 
 #~ msgid "TensorFlow"
 #~ msgstr "TensorFlow"
@@ -17718,7 +24378,7 @@ msgstr ""
 #~ msgstr "Client de Flower"
 
 #~ msgid "flwr (Python API reference)"
-#~ msgstr "flwr (paquet Python)"
+#~ msgstr "Référence pour l'API"
 
 #~ msgid "Unreleased"
 #~ msgstr "Inédit"
@@ -17913,10 +24573,10 @@ msgstr ""
 
 #~ msgid ""
 #~ "`Android Kotlin example "
-#~ "<https://flower.dev/docs/examples/android-kotlin.html>`_"
+#~ "<https://flower.ai/docs/examples/android-kotlin.html>`_"
 #~ msgstr ""
 
-#~ msgid "`Android Java example <https://flower.dev/docs/examples/android.html>`_"
+#~ msgid "`Android Java example <https://flower.ai/docs/examples/android.html>`_"
 #~ msgstr ""
 
 #~ msgid "Build a strategy from scratch"
@@ -17969,7 +24629,7 @@ msgstr ""
 #~ "Flower Python server, it is recommended"
 #~ " to create a virtual environment and"
 #~ " run everything within a `virtualenv "
-#~ "<https://flower.dev/docs/recommended-env-setup.html>`_."
+#~ "<https://flower.ai/docs/recommended-env-setup.html>`_."
 #~ " For the Flower client implementation "
 #~ "in iOS, it is recommended to use"
 #~ " Xcode as our IDE."
@@ -17978,7 +24638,7 @@ msgstr ""
 #~ "serveur Flower Python, il est recommandé"
 #~ " de créer un environnement virtuel et"
 #~ " de tout exécuter au sein d'un "
-#~ "`virtualenv <https://flower.dev/docs/recommended-env-"
+#~ "`virtualenv <https://flower.ai/docs/recommended-env-"
 #~ "setup.html>`_. Pour l'implémentation du client"
 #~ " Flower dans iOS, il est recommandé"
 #~ " d'utiliser Xcode comme notre IDE."
@@ -18241,7 +24901,7 @@ msgstr ""
 #~ "<https://github.com/adap/flower>`__ ⭐️ and join "
 #~ "the open-source Flower community on "
 #~ "Slack to connect, ask questions, and "
-#~ "get help: `Join Slack <https://flower.dev"
+#~ "get help: `Join Slack <https://flower.ai"
 #~ "/join-slack>`__ 🌼 We'd love to hear"
 #~ " from you in the ``#introductions`` "
 #~ "channel! And if anything is unclear, "
@@ -18252,7 +24912,7 @@ msgstr ""
 #~ " la communauté open-source Flower sur"
 #~ " Slack pour vous connecter, poser des"
 #~ " questions et obtenir de l'aide : "
-#~ "`Join Slack <https://flower.dev/join-slack>`__ "
+#~ "`Join Slack <https://flower.ai/join-slack>`__ "
 #~ "🌼 Nous serions ravis d'avoir de "
 #~ "vos nouvelles dans le canal "
 #~ "``#introductions`` ! Et si quelque chose"
@@ -18582,7 +25242,7 @@ msgstr ""
 #~ "Please make sure to add your "
 #~ "baseline or experiment to the "
 #~ "corresponding directory as explained in "
-#~ "`Executing Baseline <https://flower.dev/docs/using-"
+#~ "`Executing Baseline <https://flower.ai/docs/using-"
 #~ "baselines.html>`_. Give your baseline the "
 #~ "unique identifier. For example, :code:`fedbn`"
 #~ " refers to the paper \"FedBN: "
@@ -18814,7 +25474,7 @@ msgstr ""
 #~ " papers. If you want to add a"
 #~ " new baseline or experiment, please "
 #~ "check the `Contributing Baselines "
-#~ "<https://flower.dev/docs/contributing-baselines.html>`_ "
+#~ "<https://flower.ai/docs/baselines/how-to-contribute-baselines.html>`_ "
 #~ "section."
 #~ msgstr ""
 
@@ -18913,3 +25573,228 @@ msgstr ""
 #~ msgid "|025f0a6f7a6145cba4bf8fa0e2495851|"
 #~ 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:"
+#~ 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 "
+#~ "<https://github.com/adap/flower/compare/v1.2.0...main>`_"
+#~ msgstr ""
+#~ "`GitHub : Compare v1.2.0...main "
+#~ "<https://github.com/adap/flower/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``."
+#~ 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 ""
+#~ "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 ""
+#~ "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 ""
+#~ "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 ""
+#~ "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 ""
+#~ "Build the release with ``./dev/build.sh``, "
+#~ "then publish it with ``./dev/publish.sh``"
+#~ msgstr ""
+#~ "Construisez la version avec "
+#~ "``./dev/build.sh``, puis publiez-la avec "
+#~ "``./dev/publish.sh``"
+
+#~ 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`)."
+#~ 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`)."
+
+#~ 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:`<version>`)::"
+#~ 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:`<version>` spécifique)::"
+
+#~ msgid "server.strategy.FedAvg"
+#~ msgstr "serveur.stratégie.FedAvg"
+
+#~ msgid "server.strategy.FedAvgM"
+#~ msgstr "stratégie.serveur.FedAvgM"
+
+#~ msgid "server.strategy.QFedAvg"
+#~ msgstr "server.strategy.QFedAvg"
+
+#~ msgid "server.strategy.FedOpt"
+#~ msgstr "serveur.stratégie.FedOpt"
+
+#~ msgid "server.strategy.FedProx"
+#~ msgstr "serveur.stratégie.FedProx"
+
+#~ msgid "server.strategy.FedAdagrad"
+#~ msgstr "serveur.stratégie.FedAdagrad"
+
+#~ 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 ""
+#~ "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 ""
+#~ "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 ""
+#~ "Please refer to the `full code "
+#~ "example <https://github.com/adap/flower/tree/main/examples"
+#~ "/quickstart-xgboost-horizontal>`_ to learn "
+#~ "more."
+#~ msgstr ""
+#~ "Réfère-toi à l'exemple de code "
+#~ "complet <https://github.com/adap/flower/tree/main/examples"
+#~ "/quickstart-xgboost-horizontal>`_ pour en "
+#~ "savoir plus."
+
+#~ msgid "|3ff4c820a01d4a5abb022617de537c54|"
+#~ msgstr ""
+
+#~ msgid "|7f1889391ad448e2a65920165f0d798c|"
+#~ msgstr ""
+
+#~ msgid "|a171dc4a0d044e70b5d585cc10ace0e0|"
+#~ msgstr ""
+
+#~ msgid "|fe518aa0d86341f7b2fc87bd6e3bbf0c|"
+#~ msgstr ""
+
+#~ msgid "|6abfdf0dade44469ae9f08c8dc7d148c|"
+#~ msgstr ""
+
+#~ msgid "|b4f147db24bb4da9a786e1d6676a1c2d|"
+#~ msgstr ""
+
+#~ msgid "|5c62032f589a457bb37b5fee5b2adbde|"
+#~ msgstr ""
+
+#~ msgid "|f154df1846dd44f79a94f1dc3ae8b088|"
+#~ msgstr ""
+
+#~ msgid "|9d20be8160f7451fb0f33b194506503f|"
+#~ msgstr ""
+
+#~ msgid "|3d949f76988443c59990d2e64f05c386|"
+#~ msgstr ""
+
+#~ msgid "|526c6d9140f6404f8a226d9056327b3b|"
+#~ msgstr ""
+
+#~ msgid "|a5f6af14cd7c4550929b17f83b4f63c7|"
+#~ msgstr ""
+
+#~ msgid "|bcd571c4f4ee4803a54f71b5c20448cb|"
+#~ msgstr ""
+
+#~ msgid "|c76452ae1ed84965be7ef23c72b95845|"
+#~ msgstr ""
+
diff --git a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
index 4bb195306670..5a5d736ece38 100644
--- a/doc/locales/pt_BR/LC_MESSAGES/framework-docs.po
+++ b/doc/locales/pt_BR/LC_MESSAGES/framework-docs.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: 2024-02-13 11:23+0100\n"
 "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
 "Last-Translator: FULL NAME <EMAIL@ADDRESS>\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 2.13.1\n"
 
 #: ../../source/contributor-explanation-architecture.rst:2
 msgid "Flower Architecture"
@@ -29,7 +29,7 @@ msgstr ""
 
 #: ../../source/contributor-explanation-architecture.rst:7
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with Edge "
+"`Flower <https://flower.ai>`_ core framework architecture with Edge "
 "Client Engine"
 msgstr ""
 
@@ -39,7 +39,7 @@ msgstr ""
 
 #: ../../source/contributor-explanation-architecture.rst:15
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with Virtual "
+"`Flower <https://flower.ai>`_ core framework architecture with Virtual "
 "Client Engine"
 msgstr ""
 
@@ -49,121 +49,229 @@ msgstr ""
 
 #: ../../source/contributor-explanation-architecture.rst:23
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with both "
+"`Flower <https://flower.ai>`_ core framework architecture with both "
 "Virtual Client Engine and Edge Client Engine"
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:2
-msgid "Creating New Messages"
+#: ../../source/contributor-how-to-build-docker-images.rst:2
+msgid "How to build Docker Flower images locally"
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:4
+#: ../../source/contributor-how-to-build-docker-images.rst:4
 msgid ""
-"This is a simple guide for creating a new type of message between the "
-"server and clients in Flower."
+"Flower provides pre-made docker images on `Docker Hub "
+"<https://hub.docker.com/r/flwr/server/tags>`_ 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-create-new-messages.rst:6
+#: ../../source/contributor-how-to-build-docker-images.rst:9
 msgid ""
-"Let's suppose we have the following example functions in "
-":code:`server.py` and :code:`numpy_client.py`..."
+"Before we can start, we need to meet a few prerequisites in our local "
+"development environment."
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:8
-msgid "Server's side:"
+#: ../../source/contributor-how-to-build-docker-images.rst:11
+msgid "Clone the flower repository."
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:17
-msgid "Client's side:"
+#: ../../source/contributor-how-to-build-docker-images.rst:17
+msgid "Verify the Docker daemon is running."
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:26
+#: ../../source/contributor-how-to-build-docker-images.rst:19
 msgid ""
-"Let's now see what we need to implement in order to get this simple "
-"function between the server and client to work!"
+"Please follow the first section on `Run Flower using Docker "
+"<https://flower.ai/docs/framework/how-to-run-flower-using-docker>`_ "
+"which covers this step in more detail."
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:30
-msgid "Message Types for Protocol Buffers"
+#: ../../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-create-new-messages.rst:32
+#: ../../source/contributor-how-to-build-docker-images.rst:28
 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://developers.google.com/protocol-buffers/docs/proto3>`_."
+"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-create-new-messages.rst:35
-msgid "Within the :code:`ServerMessage` block:"
+#: ../../source/contributor-how-to-build-docker-images.rst:31
+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 "
+"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-create-new-messages.rst:52
-msgid "Within the ClientMessage block:"
+#: ../../source/contributor-how-to-build-docker-images.rst:38
+msgid "Building the base image"
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:70
-msgid ""
-"Make sure to also add a field of the newly created message type in "
-":code:`oneof msg`."
+#: ../../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-create-new-messages.rst:72
-msgid "Once that is done, we will compile the file with:"
+#: ../../source/contributor-how-to-build-docker-images.rst:45
+#: ../../source/contributor-how-to-build-docker-images.rst:87
+msgid "Description"
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:78
-msgid "If it compiles succesfully, you should see the following message:"
+#: ../../source/contributor-how-to-build-docker-images.rst:46
+#: ../../source/contributor-how-to-build-docker-images.rst:88
+msgid "Required"
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:87
-msgid "Serialization and Deserialization Functions"
+#: ../../source/contributor-how-to-build-docker-images.rst:47
+#: ../../source/contributor-how-to-build-docker-images.rst:89
+msgid "Example"
 msgstr ""
 
-#: ../../source/contributor-how-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`."
+#: ../../source/contributor-how-to-build-docker-images.rst:48
+msgid "``PYTHON_VERSION``"
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:91
-msgid "The four functions:"
+#: ../../source/contributor-how-to-build-docker-images.rst:49
+msgid "Version of ``python`` to be installed."
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:112
-msgid "Sending the Message from the Server"
+#: ../../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``"
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:114
+#: ../../source/contributor-how-to-build-docker-images.rst:52
+msgid "``PIP_VERSION``"
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:53
+msgid "Version of ``pip`` to be installed."
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:55
+msgid "``23.0.1``"
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:56
+msgid "``SETUPTOOLS_VERSION``"
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:57
+msgid "Version of ``setuptools`` to be installed."
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:59
+msgid "``69.0.2``"
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:60
+msgid "``UBUNTU_VERSION``"
+msgstr ""
+
+#: ../../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:62
+msgid "Defaults to ``22.04``."
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:65
 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:"
+"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-create-new-messages.rst:128
-msgid "Receiving the Message by the Client"
+#: ../../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:80
+msgid "Building the server image"
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:90
+msgid "``BASE_REPOSITORY``"
+msgstr ""
+
+#: ../../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:92
+msgid "Defaults to ``flwr/server``."
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:94
+msgid "``BASE_IMAGE_TAG``"
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:130
+#: ../../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``"
+msgstr ""
+
+#: ../../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:101
+msgid "``1.7.0``"
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:103
 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!"
+"The following example creates a server image with the official Flower "
+"base image py3.11-ubuntu22.04 and Flower 1.7.0:"
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:132
-msgid "Within the handle function:"
+#: ../../source/contributor-how-to-build-docker-images.rst:114
+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 ""
 
-#: ../../source/contributor-how-create-new-messages.rst:139
-msgid "And add a new function:"
+#: ../../source/contributor-how-to-build-docker-images.rst:117
+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 ""
 
-#: ../../source/contributor-how-create-new-messages.rst:149
-msgid "Hopefully, when you run your program you will get the intended result!"
+#: ../../source/contributor-how-to-build-docker-images.rst:131
+msgid "After creating the image, we can test whether the image is working:"
 msgstr ""
 
 #: ../../source/contributor-how-to-contribute-translations.rst:2
@@ -172,7 +280,7 @@ msgstr ""
 
 #: ../../source/contributor-how-to-contribute-translations.rst:4
 msgid ""
-"Since `Flower 1.5 <https://flower.dev/docs/framework/ref-"
+"Since `Flower 1.5 <https://flower.ai/docs/framework/ref-"
 "changelog.html#v1-5-0-2023-08-31>`_ 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 "
@@ -268,10 +376,123 @@ msgstr ""
 #: ../../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 <https://flower.dev/join-slack>`_, or by opening an "
+"either on `Slack <https://flower.ai/join-slack>`_, or by opening an "
 "issue on our `GitHub repo <https://github.com/adap/flower>`_."
 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 "
+"<https://developers.google.com/protocol-buffers/docs/proto3>`_."
+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 ""
+
 #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:2
 msgid "Develop in VSCode Dev Containers"
 msgstr ""
@@ -320,10 +541,11 @@ 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 <vscode:extension"
-"/ms-vscode-remote.remote-containers>`_."
+"thing is you don't have to do it. Usually it should be enough to install "
+"`Docker <https://docs.docker.com/engine/install/>`_ on your system and "
+"ensure its available on your command line. Additionally, install the "
+"`VSCode Containers Extension <vscode:extension/ms-vscode-remote.remote-"
+"containers>`_."
 msgstr ""
 
 #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:19
@@ -408,13 +630,13 @@ msgstr ""
 
 #: ../../source/contributor-how-to-install-development-versions.rst:22
 msgid ""
-"``flwr = { path = \"../../dist/flwr-1.0.0-py3-none-any.whl\" }`` (without"
+"``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\" }`` (without"
 " extras)"
 msgstr ""
 
 #: ../../source/contributor-how-to-install-development-versions.rst:23
 msgid ""
-"``flwr = { path = \"../../dist/flwr-1.0.0-py3-none-any.whl\", extras = "
+"``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\", extras = "
 "[\"simulation\"] }`` (with extras)"
 msgstr ""
 
@@ -518,13 +740,13 @@ msgid ""
 msgstr ""
 
 #: ../../source/contributor-how-to-install-development-versions.rst:62
-msgid "Upload the whl (e.g., ``flwr-1.6.0-py3-none-any.whl``)"
+msgid "Upload the whl (e.g., ``flwr-1.8.0-py3-none-any.whl``)"
 msgstr ""
 
 #: ../../source/contributor-how-to-install-development-versions.rst:63
 msgid ""
 "Change ``!pip install -q 'flwr[simulation]' torch torchvision "
-"matplotlib`` to ``!pip install -q 'flwr-1.6.0-py3-none-"
+"matplotlib`` to ``!pip install -q 'flwr-1.8.0-py3-none-"
 "any.whl[simulation]' torch torchvision matplotlib``"
 msgstr ""
 
@@ -539,151 +761,136 @@ msgid ""
 msgstr ""
 
 #: ../../source/contributor-how-to-release-flower.rst:7
-msgid "Before the release"
+msgid "During the release"
 msgstr ""
 
 #: ../../source/contributor-how-to-release-flower.rst:9
 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 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 ""
 
 #: ../../source/contributor-how-to-release-flower.rst:11
 msgid ""
-"`GitHub: Compare v1.2.0...main "
-"<https://github.com/adap/flower/compare/v1.2.0...main>`_"
-msgstr ""
-
-#: ../../source/contributor-how-to-release-flower.rst:13
-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 ""
-
-#: ../../source/contributor-how-to-release-flower.rst:16
-msgid "During the release"
-msgstr ""
-
-#: ../../source/contributor-how-to-release-flower.rst:18
-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):"
+"Run ``python3 src/py/flwr_tool/update_changelog.py <YOUR_GH_TOKEN>`` 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 ""
 
-#: ../../source/contributor-how-to-release-flower.rst:20
+#: ../../source/contributor-how-to-release-flower.rst:12
 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."
+"Once the changelog has been updated with all the changes, run ``./dev"
+"/prepare-release-changelog.sh v<NEW_VERSION>``, where ``<NEW_VERSION>`` "
+"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 ""
 
-#: ../../source/contributor-how-to-release-flower.rst:21
+#: ../../source/contributor-how-to-release-flower.rst:13
 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."
+"Once the pull request is merged, tag the release commit with the version "
+"number as soon as the PR is merged: ``git tag v<NEW_VERSION>`` (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 ""
 
-#: ../../source/contributor-how-to-release-flower.rst:22
+#: ../../source/contributor-how-to-release-flower.rst:14
 msgid "Check the draft release on GitHub, and if everything is good, publish it."
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:25
+#: ../../source/contributor-how-to-release-flower.rst:17
 msgid "After the release"
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:27
+#: ../../source/contributor-how-to-release-flower.rst:19
 msgid "Create a pull request which contains the following changes:"
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:29
+#: ../../source/contributor-how-to-release-flower.rst:21
 msgid "Increase the minor version in ``pyproject.toml`` by one."
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:30
+#: ../../source/contributor-how-to-release-flower.rst:22
 msgid "Update all files which contain the current version number if necessary."
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:31
+#: ../../source/contributor-how-to-release-flower.rst:23
 msgid "Add a new ``Unreleased`` section in ``changelog.md``."
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:33
+#: ../../source/contributor-how-to-release-flower.rst:25
 msgid ""
-"Merge the pull request on the same day (i.e., before a new nighly release"
+"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:36
+#: ../../source/contributor-how-to-release-flower.rst:28
 msgid "Publishing a pre-release"
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:39
+#: ../../source/contributor-how-to-release-flower.rst:31
 msgid "Pre-release naming"
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:41
+#: ../../source/contributor-how-to-release-flower.rst:33
 msgid ""
-"PyPI supports pre-releases (alpha, beta, release candiate). Pre-releases "
+"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:43
+#: ../../source/contributor-how-to-release-flower.rst:35
 msgid "Alpha: ``MAJOR.MINOR.PATCHaN``"
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:44
+#: ../../source/contributor-how-to-release-flower.rst:36
 msgid "Beta: ``MAJOR.MINOR.PATCHbN``"
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:45
-msgid "Release candiate (RC): ``MAJOR.MINOR.PATCHrcN``"
+#: ../../source/contributor-how-to-release-flower.rst:37
+msgid "Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``"
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:47
+#: ../../source/contributor-how-to-release-flower.rst:39
 msgid "Examples include:"
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:49
+#: ../../source/contributor-how-to-release-flower.rst:41
 msgid "``1.0.0a0``"
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:50
+#: ../../source/contributor-how-to-release-flower.rst:42
 msgid "``1.0.0b0``"
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:51
+#: ../../source/contributor-how-to-release-flower.rst:43
 msgid "``1.0.0rc0``"
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:52
+#: ../../source/contributor-how-to-release-flower.rst:44
 msgid "``1.0.0rc1``"
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:54
+#: ../../source/contributor-how-to-release-flower.rst:46
 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:57
+#: ../../source/contributor-how-to-release-flower.rst:49
 msgid "`PEP-440 <https://peps.python.org/pep-0440/>`_"
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:58
+#: ../../source/contributor-how-to-release-flower.rst:50
 msgid ""
 "`PyPA Choosing a versioning scheme "
 "<https://packaging.python.org/en/latest/guides/distributing-packages-"
 "using-setuptools/#choosing-a-versioning-scheme>`_"
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:60
+#: ../../source/contributor-how-to-release-flower.rst:52
 msgid ""
 "Note that the approach defined by PyPA is not compatible with SemVer "
 "2.0.0 spec, for details consult the `Semantic Versioning Specification "
@@ -691,26 +898,26 @@ msgid ""
 "11 on precedence)."
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:63
+#: ../../source/contributor-how-to-release-flower.rst:55
 msgid "Pre-release classification"
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:65
+#: ../../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:67
+#: ../../source/contributor-how-to-release-flower.rst:59
 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:68
+#: ../../source/contributor-how-to-release-flower.rst:60
 msgid "Beta: feature complete, allowed to have known issues"
 msgstr ""
 
-#: ../../source/contributor-how-to-release-flower.rst:69
+#: ../../source/contributor-how-to-release-flower.rst:61
 msgid "Alpha: not feature complete, allowed to have known issues"
 msgstr ""
 
@@ -823,11 +1030,11 @@ msgid ""
 msgstr ""
 
 #: ../../source/contributor-how-to-write-documentation.rst:10
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:119
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:142
 msgid ""
 "Note that, in order to build the documentation locally (with ``poetry run"
 " make html``, like described below), `Pandoc "
-"<https://pandoc.org/installing.html>_` needs to be installed on the "
+"<https://pandoc.org/installing.html>`_ needs to be installed on the "
 "system."
 msgstr ""
 
@@ -907,7 +1114,7 @@ msgstr ""
 #: ../../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 <https://flower.dev/docs"
+"out our `contributing guide for baselines <https://flower.ai/docs"
 "/contributing-baselines.html>`_."
 msgstr ""
 
@@ -1002,20 +1209,19 @@ msgstr ""
 msgid ""
 "If you're familiar with how contributing on GitHub works, you can "
 "directly checkout our `getting started guide for contributors "
-"<https://flower.dev/docs/getting-started-for-contributors.html>`_ and "
-"examples of `good first contributions <https://flower.dev/docs/good-"
-"first-contributions.html>`_."
+"<https://flower.ai/docs/contributor-tutorial-get-started-"
+"as-a-contributor.html>`_."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:12
+#: ../../source/contributor-tutorial-contribute-on-github.rst:11
 msgid "Setting up the repository"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:23
+#: ../../source/contributor-tutorial-contribute-on-github.rst:22
 msgid "**Create a GitHub account and setup Git**"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:15
+#: ../../source/contributor-tutorial-contribute-on-github.rst:14
 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 "
@@ -1024,20 +1230,20 @@ msgid ""
 "/set-up-git>`_ to set it up."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:18
+#: ../../source/contributor-tutorial-contribute-on-github.rst:17
 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."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:20
+#: ../../source/contributor-tutorial-contribute-on-github.rst:19
 msgid ""
 "If you haven't already, you will need to create an account on `GitHub "
 "<https://github.com/signup>`_."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:22
+#: ../../source/contributor-tutorial-contribute-on-github.rst:21
 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 "
@@ -1045,11 +1251,11 @@ msgid ""
 "history back to GitHub."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:34
+#: ../../source/contributor-tutorial-contribute-on-github.rst:33
 msgid "**Forking the Flower repository**"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:26
+#: ../../source/contributor-tutorial-contribute-on-github.rst:25
 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 "
@@ -1057,7 +1263,7 @@ msgid ""
 "on the top right of the page."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:31
+#: ../../source/contributor-tutorial-contribute-on-github.rst:30
 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 "
@@ -1065,11 +1271,11 @@ msgid ""
 " the top left corner that you are looking at your own version of Flower."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:49
+#: ../../source/contributor-tutorial-contribute-on-github.rst:48
 msgid "**Cloning your forked repository**"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:37
+#: ../../source/contributor-tutorial-contribute-on-github.rst:36
 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 "
@@ -1077,27 +1283,27 @@ msgid ""
 "ability to copy the HTTPS link of the repository."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:43
+#: ../../source/contributor-tutorial-contribute-on-github.rst:42
 msgid ""
 "Once you copied the \\<URL\\>, you can open a terminal on your machine, "
 "navigate to the place you want to download the repository to and type:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:49
+#: ../../source/contributor-tutorial-contribute-on-github.rst:48
 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 ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:68
+#: ../../source/contributor-tutorial-contribute-on-github.rst:67
 msgid "**Add origin**"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:52
+#: ../../source/contributor-tutorial-contribute-on-github.rst:51
 msgid "You can then go into the repository folder:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:58
+#: ../../source/contributor-tutorial-contribute-on-github.rst:57
 msgid ""
 "And here we will need to add an origin to our repository. The origin is "
 "the \\<URL\\> of the remote fork repository. To obtain it, we can do as "
@@ -1105,27 +1311,27 @@ msgid ""
 "account and copying the link."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:63
+#: ../../source/contributor-tutorial-contribute-on-github.rst:62
 msgid ""
 "Once the \\<URL\\> is copied, we can type the following command in our "
 "terminal:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:92
+#: ../../source/contributor-tutorial-contribute-on-github.rst:91
 msgid "**Add upstream**"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:71
+#: ../../source/contributor-tutorial-contribute-on-github.rst:70
 msgid ""
 "Now we will add an upstream address to our repository. Still in the same "
 "directroy, we must run the following command:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:78
+#: ../../source/contributor-tutorial-contribute-on-github.rst:77
 msgid "The following diagram visually explains what we did in the previous steps:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:82
+#: ../../source/contributor-tutorial-contribute-on-github.rst:81
 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 "
@@ -1134,17 +1340,17 @@ msgid ""
 "in our own account."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:86
+#: ../../source/contributor-tutorial-contribute-on-github.rst:85
 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 ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:95
+#: ../../source/contributor-tutorial-contribute-on-github.rst:94
 msgid "Setting up the coding environment"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:97
+#: ../../source/contributor-tutorial-contribute-on-github.rst:96
 msgid ""
 "This can be achieved by following this `getting started guide for "
 "contributors`_ (note that you won't need to clone the repository). Once "
@@ -1152,151 +1358,151 @@ msgid ""
 "changes!"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:102
+#: ../../source/contributor-tutorial-contribute-on-github.rst:101
 msgid "Making changes"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:104
+#: ../../source/contributor-tutorial-contribute-on-github.rst:103
 msgid ""
 "Before making any changes make sure you are up-to-date with your "
 "repository:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:110
+#: ../../source/contributor-tutorial-contribute-on-github.rst:109
 msgid "And with Flower's repository:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:124
+#: ../../source/contributor-tutorial-contribute-on-github.rst:123
 msgid "**Create a new branch**"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:117
+#: ../../source/contributor-tutorial-contribute-on-github.rst:116
 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."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:120
+#: ../../source/contributor-tutorial-contribute-on-github.rst:119
 msgid ""
 "To do so, just run the following command inside the repository's "
 "directory:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:127
+#: ../../source/contributor-tutorial-contribute-on-github.rst:126
 msgid "**Make changes**"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:127
+#: ../../source/contributor-tutorial-contribute-on-github.rst:126
 msgid "Write great code and create wonderful changes using your favorite editor!"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:140
+#: ../../source/contributor-tutorial-contribute-on-github.rst:139
 msgid "**Test and format your code**"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:130
+#: ../../source/contributor-tutorial-contribute-on-github.rst:129
 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."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:133
+#: ../../source/contributor-tutorial-contribute-on-github.rst:132
 msgid "To do so, we have written a few scripts that you can execute:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:152
+#: ../../source/contributor-tutorial-contribute-on-github.rst:151
 msgid "**Stage changes**"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:143
+#: ../../source/contributor-tutorial-contribute-on-github.rst:142
 msgid ""
 "Before creating a commit that will update your history, you must specify "
 "to Git which files it needs to take into account."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:145
+#: ../../source/contributor-tutorial-contribute-on-github.rst:144
 msgid "This can be done with:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:151
+#: ../../source/contributor-tutorial-contribute-on-github.rst:150
 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."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:162
+#: ../../source/contributor-tutorial-contribute-on-github.rst:161
 msgid "**Commit changes**"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:155
+#: ../../source/contributor-tutorial-contribute-on-github.rst:154
 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 ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:161
+#: ../../source/contributor-tutorial-contribute-on-github.rst:160
 msgid ""
 "The \\<commit_message\\> 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 ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:173
+#: ../../source/contributor-tutorial-contribute-on-github.rst:172
 msgid "**Push the changes to the fork**"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:165
+#: ../../source/contributor-tutorial-contribute-on-github.rst:164
 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:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:172
+#: ../../source/contributor-tutorial-contribute-on-github.rst:171
 msgid ""
 "Once this is done, you will see on the GitHub that your forked repo was "
 "updated with the changes you have made."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:176
+#: ../../source/contributor-tutorial-contribute-on-github.rst:175
 msgid "Creating and merging a pull request (PR)"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:203
+#: ../../source/contributor-tutorial-contribute-on-github.rst:206
 msgid "**Create the PR**"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:179
+#: ../../source/contributor-tutorial-contribute-on-github.rst:178
 msgid ""
 "Once you have pushed changes, on the GitHub webpage of your repository "
 "you should see the following message:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:183
-msgid "Otherwise you can always find this option in the `Branches` page."
+#: ../../source/contributor-tutorial-contribute-on-github.rst:182
+msgid "Otherwise you can always find this option in the ``Branches`` page."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:185
+#: ../../source/contributor-tutorial-contribute-on-github.rst:184
 msgid ""
-"Once you click the `Compare & pull request` button, you should see "
+"Once you click the ``Compare & pull request`` button, you should see "
 "something similar to this:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:189
+#: ../../source/contributor-tutorial-contribute-on-github.rst:188
 msgid "At the top you have an explanation of which branch will be merged where:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:193
+#: ../../source/contributor-tutorial-contribute-on-github.rst:192
 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."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:195
+#: ../../source/contributor-tutorial-contribute-on-github.rst:194
 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 "
@@ -1304,163 +1510,172 @@ msgid ""
 "process."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:198
+#: ../../source/contributor-tutorial-contribute-on-github.rst:197
+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: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:201
+#: ../../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:206
+#: ../../source/contributor-tutorial-contribute-on-github.rst:209
 msgid "**Making new changes**"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:206
+#: ../../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:228
+#: ../../source/contributor-tutorial-contribute-on-github.rst:231
 msgid "**Review the PR**"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:209
+#: ../../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:213
+#: ../../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:215
+#: ../../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:219
+#: ../../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:223
+#: ../../source/contributor-tutorial-contribute-on-github.rst:226
 msgid "And resolve the conversation:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:227
+#: ../../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:248
+#: ../../source/contributor-tutorial-contribute-on-github.rst:251
 msgid "**Once the PR is merged**"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:231
+#: ../../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:235
+#: ../../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:242
+#: ../../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:251
+#: ../../source/contributor-tutorial-contribute-on-github.rst:254
 msgid "Example of first contribution"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:254
+#: ../../source/contributor-tutorial-contribute-on-github.rst:257
 msgid "Problem"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:256
+#: ../../source/contributor-tutorial-contribute-on-github.rst:259
 msgid ""
 "For our documentation, we’ve started to use the `Diàtaxis framework "
 "<https://diataxis.fr/>`_."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:258
+#: ../../source/contributor-tutorial-contribute-on-github.rst:261
 msgid ""
 "Our “How to” guides should have titles that continue the sencence “How to"
 " …”, for example, “How to upgrade to Flower 1.0”."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:260
+#: ../../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:262
+#: ../../source/contributor-tutorial-contribute-on-github.rst:265
 msgid ""
 "This issue is about changing the title of a doc from present continious "
 "to present simple."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:264
+#: ../../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:266
+#: ../../source/contributor-tutorial-contribute-on-github.rst:269
 msgid "Before: ”How to saving progress” ❌"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:268
+#: ../../source/contributor-tutorial-contribute-on-github.rst:271
 msgid "After: ”How to save progress” ✅"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:271
+#: ../../source/contributor-tutorial-contribute-on-github.rst:274
 msgid "Solution"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:273
+#: ../../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:275
-msgid "Find the source file in `doc/source`"
+#: ../../source/contributor-tutorial-contribute-on-github.rst:278
+msgid "Find the source file in ``doc/source``"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:276
+#: ../../source/contributor-tutorial-contribute-on-github.rst:279
 msgid ""
-"Make the change in the `.rst` file (beware, the dashes under the title "
+"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:277
+#: ../../source/contributor-tutorial-contribute-on-github.rst:280
 msgid ""
-"Build the docs and check the result: `<https://flower.dev/docs/writing-"
+"Build the docs and check the result: `<https://flower.ai/docs/writing-"
 "documentation.html#edit-an-existing-page>`_"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:280
+#: ../../source/contributor-tutorial-contribute-on-github.rst:283
 msgid "Rename file"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:282
+#: ../../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 "
@@ -1468,77 +1683,77 @@ msgid ""
 "engine ranking."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:285
+#: ../../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:287
-msgid "Change the file name to `save-progress.rst`"
+#: ../../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:288
-msgid "Add a redirect rule to `doc/source/conf.py`"
+#: ../../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:290
+#: ../../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 ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:293
+#: ../../source/contributor-tutorial-contribute-on-github.rst:296
 msgid "Apply changes in the index file"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:295
+#: ../../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 "
+"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:298
-msgid "Find and modify the file name in `index.rst`"
+#: ../../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:301
+#: ../../source/contributor-tutorial-contribute-on-github.rst:304
 msgid "Open PR"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:303
+#: ../../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:304
+#: ../../source/contributor-tutorial-contribute-on-github.rst:307
 msgid "Push the changes to your fork"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:305
+#: ../../source/contributor-tutorial-contribute-on-github.rst:308
 msgid "Open a PR (as shown above)"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:306
+#: ../../source/contributor-tutorial-contribute-on-github.rst:309
 msgid "Wait for it to be approved!"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:307
+#: ../../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:311
+#: ../../source/contributor-tutorial-contribute-on-github.rst:314
 msgid "How to write a good PR title"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:313
+#: ../../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:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:315
+#: ../../source/contributor-tutorial-contribute-on-github.rst:318
 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,\" "
@@ -1548,83 +1763,219 @@ msgid ""
 "Capitalization and Punctuation: Follow grammar rules for clarity."
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:321
+#: ../../source/contributor-tutorial-contribute-on-github.rst:324
 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:323
+#: ../../source/contributor-tutorial-contribute-on-github.rst:326
 msgid "Implement Algorithm"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:324
+#: ../../source/contributor-tutorial-contribute-on-github.rst:327
 msgid "Database"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:325
+#: ../../source/contributor-tutorial-contribute-on-github.rst:328
 msgid "Add my_new_file.py to codebase"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:326
+#: ../../source/contributor-tutorial-contribute-on-github.rst:329
 msgid "Improve code in module"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:327
+#: ../../source/contributor-tutorial-contribute-on-github.rst:330
 msgid "Change SomeModule"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:329
+#: ../../source/contributor-tutorial-contribute-on-github.rst:332
 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:331
+#: ../../source/contributor-tutorial-contribute-on-github.rst:334
 msgid "Update docs banner to mention Flower Summit 2023"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:332
+#: ../../source/contributor-tutorial-contribute-on-github.rst:335
 msgid "Remove unnecessary XGBoost dependency"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:333
+#: ../../source/contributor-tutorial-contribute-on-github.rst:336
 msgid "Remove redundant attributes in strategies subclassing FedAvg"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:334
-msgid "Add CI job to deploy the staging system when the `main` branch changes"
+#: ../../source/contributor-tutorial-contribute-on-github.rst:337
+msgid "Add CI job to deploy the staging system when the ``main`` branch changes"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:335
+#: ../../source/contributor-tutorial-contribute-on-github.rst:338
 msgid ""
 "Add new amazing library which will be used to improve the simulation "
 "engine"
 msgstr ""
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:339
+#: ../../source/contributor-tutorial-contribute-on-github.rst:342
 #: ../../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:747
+#: ../../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:341
+#: ../../source/contributor-tutorial-contribute-on-github.rst:344
 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:343
+#: ../../source/contributor-tutorial-contribute-on-github.rst:346
 msgid ""
-"`Good first contributions <https://flower.dev/docs/framework/contributor-"
+"`Good first contributions <https://flower.ai/docs/framework/contributor-"
 "ref-good-first-contributions.html>`_, where you should particularly look "
 "into the :code:`baselines` contributions."
 msgstr ""
 
+#: ../../source/contributor-tutorial-contribute-on-github.rst:350
+#: ../../source/fed/0000-20200102-fed-template.md:60
+msgid "Appendix"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:355
+msgid "Changelog entry"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:357
+msgid ""
+"When opening a new PR, inside its description, there should be a "
+"``Changelog entry`` header."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:359
+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:361
+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:364
+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 ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:367
+msgid ""
+"If the section contains some text other than tokens, it will use it to "
+"add a description to the change."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:369
+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:371
+msgid "<general> is for classifying a PR as a general improvement."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:373
+msgid "<skip> is to not add the PR to the changelog"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:375
+msgid "<baselines> is to add a general baselines change to the PR"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:377
+msgid "<examples> is to add a general examples change to the PR"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:379
+msgid "<sdk> is to add a general sdk change to the PR"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:381
+msgid "<simulations> is to add a general simulations change to the PR"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:383
+msgid "Note that only one token should be used."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:385
+msgid ""
+"Its content must have a specific format. We will break down what each "
+"possibility does:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:387
+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:391
+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:397
+msgid ""
+"If the ``### Changelog entry`` section contains ``<skip>``, nothing will "
+"change in the changelog."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:399
+msgid ""
+"If the ``### Changelog entry`` section contains ``<general>``, the "
+"following text will be added to the changelog::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:403
+msgid ""
+"If the ``### Changelog entry`` section contains ``<baselines>``, the "
+"following text will be added to the changelog::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:407
+msgid ""
+"If the ``### Changelog entry`` section contains ``<examples>``, the "
+"following text will be added to the changelog::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:411
+msgid ""
+"If the ``### Changelog entry`` section contains ``<sdk>``, the following "
+"text will be added to the changelog::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:415
+msgid ""
+"If the ``### Changelog entry`` section contains ``<simulations>``, the "
+"following text will be added to the changelog::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:419
+msgid ""
+"Note that only one token must be provided, otherwise, only the first "
+"action (in the order listed above), will be performed."
+msgstr ""
+
 #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:2
 msgid "Get started as a contributor"
 msgstr ""
@@ -1634,7 +1985,7 @@ msgid "Prerequisites"
 msgstr ""
 
 #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:7
-msgid "`Python 3.7 <https://docs.python.org/3.7/>`_ or above"
+msgid "`Python 3.8 <https://docs.python.org/3.8/>`_ or above"
 msgstr ""
 
 #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:8
@@ -1660,108 +2011,153 @@ msgstr ""
 msgid "Developer Machine Setup"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:20
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:21
+msgid "Preliminarities"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:22
+msgid "Some system-wide dependencies are needed."
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:25
+msgid "For macOS"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:27
+msgid ""
+"Install `homebrew <https://brew.sh/>`_. Don't forget the post-"
+"installation actions to add `brew` to your PATH."
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:28
+msgid ""
+"Install `xz` (to install different Python versions) and `pandoc` to build"
+" the docs::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:34
+msgid "For Ubuntu"
+msgstr ""
+
+#: ../../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::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:44
+msgid "Create Flower Dev Environment"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:46
+msgid ""
+"1. Clone the `Flower repository <https://github.com/adap/flower>`_ from "
+"GitHub::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:52
 msgid ""
-"First, clone the `Flower repository <https://github.com/adap/flower>`_ "
-"from GitHub::"
+"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 ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:26
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:54
 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:`<version>`)::"
+"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 ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:33
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:58
 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)::"
+"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)::"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:39
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:62
 msgid ""
-"Third, install the Flower package in development mode (think :code:`pip "
+"3. Install the Flower package in development mode (think :code:`pip "
 "install -e`) along with all necessary dependencies::"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:46
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:69
 msgid "Convenience Scripts"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:48
+#: ../../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 "
-"amonst the most important ones:"
+"amongst the most important ones:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:54
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:77
 msgid "Create/Delete Virtual Environment"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:62
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:85
 msgid "Compile ProtoBuf Definitions"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:69
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:92
 msgid "Auto-Format Code"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:76
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:99
 msgid "Run Linters and Tests"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:83
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:106
 msgid "Run Github Actions (CI) locally"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:85
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:108
 msgid ""
 "Developers could run the full set of Github Actions workflows under their"
-" local environment by using `Act <https://github.com/nektos/act>_`. "
+" local environment by using `Act <https://github.com/nektos/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:92
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:115
 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:97
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:120
 msgid "Build Release"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:99
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:122
 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:104
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:127
 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:109
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:132
 msgid "Build Documentation"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:111
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:134
 msgid ""
 "Flower's documentation uses `Sphinx <https://www.sphinx-doc.org/>`_. "
 "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:117
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:140
 msgid "This will generate HTML documentation in ``doc/build/html``."
 msgstr ""
 
@@ -1777,7 +2173,7 @@ msgid ""
 "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 <https://flower.dev/docs/examples"
+"PyTorch - From Centralized To Federated <https://flower.ai/docs/examples"
 "/pytorch-from-centralized-to-federated.html>`_."
 msgstr ""
 
@@ -1789,7 +2185,7 @@ msgstr ""
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:10
 msgid ""
 "All files are revised based on `Example: PyTorch - From Centralized To "
-"Federated <https://flower.dev/docs/examples/pytorch-from-centralized-to-"
+"Federated <https://flower.ai/docs/examples/pytorch-from-centralized-to-"
 "federated.html>`_. The only thing to do is modifying the file called "
 ":code:`cifar.py`, revised part is shown below:"
 msgstr ""
@@ -1821,11 +2217,11 @@ msgstr ""
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:53
 msgid ""
 "If you have read `Example: PyTorch - From Centralized To Federated "
-"<https://flower.dev/docs/examples/pytorch-from-centralized-to-"
+"<https://flower.ai/docs/examples/pytorch-from-centralized-to-"
 "federated.html>`_, 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 <https://flower.dev/docs/examples"
+"PyTorch - From Centralized To Federated <https://flower.ai/docs/examples"
 "/pytorch-from-centralized-to-federated.html>`_. first."
 msgstr ""
 
@@ -2073,7 +2469,7 @@ msgstr ""
 #: ../../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:169
+#: ../../source/tutorial-quickstart-mxnet.rst:171
 #: ../../source/tutorial-quickstart-pytorch.rst:155
 #: ../../source/tutorial-quickstart-scikitlearn.rst:108
 msgid ":code:`get_parameters`"
@@ -2092,7 +2488,7 @@ msgstr ""
 #: ../../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:175
+#: ../../source/tutorial-quickstart-mxnet.rst:177
 #: ../../source/tutorial-quickstart-pytorch.rst:161
 #: ../../source/tutorial-quickstart-scikitlearn.rst:115
 msgid ":code:`fit`"
@@ -2127,7 +2523,7 @@ msgstr ""
 #: ../../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:178
+#: ../../source/tutorial-quickstart-mxnet.rst:180
 #: ../../source/tutorial-quickstart-pytorch.rst:164
 #: ../../source/tutorial-quickstart-scikitlearn.rst:118
 msgid ":code:`evaluate`"
@@ -2334,7 +2730,7 @@ 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 slighly easier "
+"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 "
@@ -2496,7 +2892,7 @@ 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 paramters. The updated "
+"*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."
@@ -2507,7 +2903,7 @@ 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 paramters on our PyTorch model:"
+"update the parameters on our PyTorch model:"
 msgstr ""
 
 #: ../../source/example-pytorch-from-centralized-to-federated.rst:213
@@ -2515,7 +2911,7 @@ 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 slighly easier "
+"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. "
@@ -2544,8 +2940,8 @@ 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 adress we used in :code:`server.py`:"
+"with the function :code:`fl.client.start_client()` by pointing it at the "
+"same IP address we used in :code:`server.py`:"
 msgstr ""
 
 #: ../../source/example-pytorch-from-centralized-to-federated.rst:307
@@ -2577,7 +2973,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:6
-#: ../../source/tutorial-quickstart-mxnet.rst:14
+#: ../../source/tutorial-quickstart-mxnet.rst:16
 #: ../../source/tutorial-quickstart-pytorch.rst:17
 #: ../../source/tutorial-quickstart-scikitlearn.rst:14
 msgid ""
@@ -2646,10 +3042,11 @@ msgstr ""
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:69
 #: ../../source/tutorial-quickstart-ios.rst:129
-#: ../../source/tutorial-quickstart-mxnet.rst:224
+#: ../../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 ""
 
@@ -2671,10 +3068,11 @@ msgstr ""
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:89
 #: ../../source/tutorial-quickstart-ios.rst:34
-#: ../../source/tutorial-quickstart-mxnet.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 ""
 
@@ -2829,7 +3227,7 @@ msgstr ""
 
 #: ../../source/explanation-differential-privacy.rst:7
 msgid ""
-"Please note that these components are still experimental, the correct "
+"Please note that these components are still experimental; the correct "
 "configuration of DP for a specific task is still an unsolved problem."
 msgstr ""
 
@@ -2867,9 +3265,11 @@ msgstr ""
 #: ../../source/explanation-differential-privacy.rst:20
 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."
+"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 ""
 
 #: ../../source/explanation-differential-privacy.rst:23
@@ -2879,7 +3279,7 @@ msgstr ""
 #: ../../source/explanation-differential-privacy.rst:25
 msgid ""
 "We make (and attempt to enforce) a number of assumptions that must be "
-"satisfied to ensure that the training process actually realises the "
+"satisfied to ensure that the training process actually realizes the "
 ":math:`(\\epsilon, \\delta)` guarantees the user has in mind when "
 "configuring the setup."
 msgstr ""
@@ -2908,8 +3308,8 @@ msgstr ""
 #: ../../source/explanation-differential-privacy.rst:31
 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 "
+"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 ""
 
@@ -2969,7 +3369,7 @@ msgstr ""
 #: ../../source/explanation-differential-privacy.rst:51
 msgid ""
 "The first version of our solution was to define a decorator whose "
-"constructor accepted, among other things, a boolean valued variable "
+"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 "
@@ -2982,6 +3382,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/explanation-differential-privacy.rst:54
+#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst:2
 msgid "DPFedAvgFixed"
 msgstr ""
 
@@ -3016,7 +3417,7 @@ msgid ""
 ":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 "
+"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 ""
@@ -3042,6 +3443,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/explanation-differential-privacy.rst:67
+#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst:2
 msgid "DPFedAvgAdaptive"
 msgstr ""
 
@@ -3113,15 +3515,16 @@ msgstr ""
 
 #: ../../source/explanation-differential-privacy.rst:98
 msgid ""
-"McMahan, H. Brendan, et al. \"Learning differentially private recurrent "
-"language models.\" arXiv preprint arXiv:1710.06963 (2017)."
+"McMahan et al. \"Learning Differentially Private Recurrent Language "
+"Models.\" International Conference on Learning Representations (ICLR), "
+"2017."
 msgstr ""
 
 #: ../../source/explanation-differential-privacy.rst:100
 msgid ""
-"Andrew, Galen, et al. \"Differentially private learning with adaptive "
-"clipping.\" Advances in Neural Information Processing Systems 34 (2021): "
-"17455-17466."
+"Andrew, Galen, et al. \"Differentially Private Learning with Adaptive "
+"Clipping.\" Advances in Neural Information Processing Systems (NeurIPS), "
+"2021."
 msgstr ""
 
 #: ../../source/explanation-federated-evaluation.rst:2
@@ -3371,10 +3774,6 @@ msgstr ""
 msgid "\\[Alternative 2\\]"
 msgstr ""
 
-#: ../../source/fed/0000-20200102-fed-template.md:60
-msgid "Appendix"
-msgstr ""
-
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:10
 msgid "Flower Enhancement Doc"
 msgstr ""
@@ -3861,7 +4260,7 @@ msgstr ""
 msgid ""
 "This can be achieved by customizing an existing strategy or by "
 "`implementing a custom strategy from scratch "
-"<https://flower.dev/docs/framework/how-to-implement-strategies.html>`_. "
+"<https://flower.ai/docs/framework/how-to-implement-strategies.html>`_. "
 "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 "
@@ -3943,7 +4342,7 @@ msgid ""
 "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 whish to backup or analyze the logs "
+":code:`HTTPHandler` should you wish to backup or analyze the logs "
 "somewhere else."
 msgstr ""
 
@@ -4011,6 +4410,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/how-to-enable-ssl-connections.rst:41
+#: ../../source/ref-api/flwr.server.Server.rst:2
 msgid "Server"
 msgstr ""
 
@@ -4030,7 +4430,7 @@ msgstr ""
 
 #: ../../source/how-to-enable-ssl-connections.rst:65
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:37
-#: ../../source/ref-api-flwr.rst:15
+#: ../../source/ref-api/flwr.client.Client.rst:2
 msgid "Client"
 msgstr ""
 
@@ -4048,6 +4448,7 @@ msgid ""
 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"
 msgstr ""
@@ -4344,61 +4745,99 @@ msgstr ""
 msgid "Install stable release"
 msgstr ""
 
-#: ../../source/how-to-install-flower.rst:14
+#: ../../source/how-to-install-flower.rst:15
+msgid "Using pip"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:17
 msgid ""
 "Stable releases are available on `PyPI "
 "<https://pypi.org/project/flwr/>`_::"
 msgstr ""
 
-#: ../../source/how-to-install-flower.rst:18
+#: ../../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 ""
 
-#: ../../source/how-to-install-flower.rst:24
+#: ../../source/how-to-install-flower.rst:27
+msgid "Using conda (or mamba)"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:29
+msgid "Flower can also be installed from the ``conda-forge`` channel."
+msgstr ""
+
+#: ../../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 ""
+
+#: ../../source/how-to-install-flower.rst:36
+msgid ""
+"Once the ``conda-forge`` channel has been enabled, ``flwr`` can be "
+"installed with ``conda``::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:40
+msgid "or with ``mamba``::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:46
 msgid "Verify installation"
 msgstr ""
 
-#: ../../source/how-to-install-flower.rst:26
+#: ../../source/how-to-install-flower.rst:48
 msgid ""
-"The following command can be used to verfiy if Flower was successfully "
+"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::"
 msgstr ""
 
-#: ../../source/how-to-install-flower.rst:33
+#: ../../source/how-to-install-flower.rst:55
 msgid "Advanced installation options"
 msgstr ""
 
-#: ../../source/how-to-install-flower.rst:36
+#: ../../source/how-to-install-flower.rst:58
+msgid "Install via Docker"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:60
+msgid ""
+"`How to run Flower using Docker <https://flower.ai/docs/framework/how-"
+"to-run-flower-using-docker.html>`_"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:63
 msgid "Install pre-release"
 msgstr ""
 
-#: ../../source/how-to-install-flower.rst:38
+#: ../../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::"
 msgstr ""
 
-#: ../../source/how-to-install-flower.rst:42
+#: ../../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::"
 msgstr ""
 
-#: ../../source/how-to-install-flower.rst:47
+#: ../../source/how-to-install-flower.rst:74
 msgid "Install nightly release"
 msgstr ""
 
-#: ../../source/how-to-install-flower.rst:49
+#: ../../source/how-to-install-flower.rst:76
 msgid ""
 "The latest (potentially unstable) changes in Flower are available as "
 "nightly releases::"
 msgstr ""
 
-#: ../../source/how-to-install-flower.rst:53
+#: ../../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::"
@@ -4640,6 +5079,185 @@ msgid ""
 "metrics.html>`_"
 msgstr ""
 
+#: ../../source/how-to-run-flower-using-docker.rst:2
+msgid "Run Flower using Docker"
+msgstr ""
+
+#: ../../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 "
+"<https://hub.docker.com/r/flwr/server/tags>`_."
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:7
+msgid "Before you start, make sure that the Docker daemon is running:"
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:14
+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 <https://docs.docker.com/get-"
+"docker/>`_."
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:20
+msgid ""
+"On Linux, Docker commands require ``sudo`` privilege. If you want to "
+"avoid using ``sudo``, you can follow the `Post-installation steps "
+"<https://docs.docker.com/engine/install/linux-postinstall/>`_ on the "
+"official Docker website."
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:25
+msgid "Flower server"
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:28
+msgid "Quickstart"
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:30
+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
+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 ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:44
+msgid ""
+"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 ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:48
+msgid ""
+"The ``-p <host>:<container>`` 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 ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:55
+msgid ""
+"The ``--insecure`` flag enables insecure communication (using HTTP, not "
+"HTTPS) and should only be used for testing purposes. We strongly "
+"recommend enabling `SSL <https://flower.ai/docs/framework/how-to-run-"
+"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
+msgid ""
+"You can use ``--help`` to view all available flags that the server "
+"supports:"
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:67
+msgid "Mounting a volume to store the state on the host system"
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:69
+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 ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:82
+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."
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:87
+msgid "Enabling SSL for secure connections"
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:89
+msgid ""
+"To enable SSL, you will need a CA certificate, a server certificate and a"
+" server private key."
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:92
+msgid ""
+"For testing purposes, you can generate your own self-signed certificates."
+" The `Enable SSL connections <https://flower.ai/docs/framework/how-to-"
+"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
+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"
+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 <https://hub.docker.com/r/flwr/server/tags>`_."
+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:116
+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:121
+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:"
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:137
+msgid "Setting environment variables"
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:139
+msgid ""
+"To set a variable inside a Docker container, you can use the ``-e "
+"<name>=<value>`` flag."
+msgstr ""
+
 #: ../../source/how-to-run-simulations.rst:2
 msgid "Run simulations"
 msgstr ""
@@ -4664,12 +5282,12 @@ msgstr ""
 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 <ref-api-"
-"flwr.html#start-numpy-client>`_) in the sense that they can be configure "
-"by creating a class inheriting, for example, from "
-"`flwr.client.NumPyClient <ref-api-flwr.html#flwr.client.NumPyClient>`_ "
-"and therefore behave in an identical way. In addition to that, clients "
-"managed by the :code:`VirtualClientEngine` are:"
+"ones you launch via the command `flwr.client.start_client <ref-api-"
+"flwr.html#start-client>`_) in the sense that they can be configure by "
+"creating a class inheriting, for example, from `flwr.client.NumPyClient "
+"<ref-api-flwr.html#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
@@ -5088,6 +5706,12 @@ msgid ""
 " latest one:"
 msgstr ""
 
+#: ../../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``."
+msgstr ""
+
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:2
 msgid "Upgrade to Flower 1.0"
 msgstr ""
@@ -5330,7 +5954,7 @@ msgstr ""
 msgid ""
 "Remove \"placeholder\" methods from subclasses of ``Client`` or "
 "``NumPyClient``. If you, for example, use server-side evaluation, then "
-"empy placeholder implementations of ``evaluate`` are no longer necessary."
+"empty placeholder implementations of ``evaluate`` are no longer necessary."
 msgstr ""
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:85
@@ -5349,37 +5973,156 @@ msgid ""
 "Most official `Flower code examples "
 "<https://github.com/adap/flower/tree/main/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 questionsm, `join the Flower Slack "
-"<https://flower.dev/join-slack/>`_ and use the channgel ``#questions``."
+"API. If there are further questions, `join the Flower Slack "
+"<https://flower.ai/join-slack/>`_ and use the channel ``#questions``."
 msgstr ""
 
-#: ../../source/how-to-use-strategies.rst:2
-msgid "Use strategies"
+#: ../../source/how-to-use-built-in-mods.rst:2
+msgid "Use Built-in Mods"
 msgstr ""
 
-#: ../../source/how-to-use-strategies.rst:4
+#: ../../source/how-to-use-built-in-mods.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."
+"**Note: This tutorial covers experimental features. The functionality and"
+" interfaces may change in future versions.**"
 msgstr ""
 
-#: ../../source/how-to-use-strategies.rst:6
+#: ../../source/how-to-use-built-in-mods.rst:6
 msgid ""
-"There are three ways to customize the way Flower orchestrates the "
-"learning process on the server side:"
+"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/how-to-use-strategies.rst:8
-msgid "Use an existing strategy, for example, :code:`FedAvg`"
+#: ../../source/how-to-use-built-in-mods.rst:9
+msgid "What are Mods?"
 msgstr ""
 
-#: ../../source/how-to-use-strategies.rst:9
-#: ../../source/how-to-use-strategies.rst:40
-msgid "Customize an existing strategy with callback functions"
+#: ../../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/how-to-use-strategies.rst:10
+#: ../../source/how-to-use-built-in-mods.rst:18
+msgid "A typical mod function might look something like this:"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:31
+msgid "Using Mods"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:33
+msgid "To use mods in your ``ClientApp``, you can follow these steps:"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:36
+msgid "1. Import the required mods"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:38
+msgid "First, import the built-in mod you intend to use:"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:46
+msgid "2. Define your client function"
+msgstr ""
+
+#: ../../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/how-to-use-built-in-mods.rst:57
+msgid "3. Create the ``ClientApp`` with mods"
+msgstr ""
+
+#: ../../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/how-to-use-built-in-mods.rst:72
+msgid "Order of execution"
+msgstr ""
+
+#: ../../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/how-to-use-built-in-mods.rst:76
+msgid "``example_mod_1`` (outermost mod)"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:77
+msgid "``example_mod_2`` (next mod)"
+msgstr ""
+
+#: ../../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/how-to-use-built-in-mods.rst:79
+msgid "``example_mod_2`` (on the way back)"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:80
+msgid "``example_mod_1`` (outermost mod on the way back)"
+msgstr ""
+
+#: ../../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/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."
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:89
+msgid "Enjoy building more robust and flexible ``ClientApp``s with mods!"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:2
+msgid "Use strategies"
+msgstr ""
+
+#: ../../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/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/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"
+msgstr ""
+
+#: ../../source/how-to-use-strategies.rst:10
 #: ../../source/how-to-use-strategies.rst:87
 msgid "Implement a novel strategy"
 msgstr ""
@@ -5418,7 +6161,7 @@ msgid ""
 "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 arbitraty configuration values  :code:`client.fit`"
+"return a dictionary of arbitrary configuration values  :code:`client.fit`"
 " and :code:`client.evaluate` functions during each round of federated "
 "learning."
 msgstr ""
@@ -5468,35 +6211,35 @@ msgstr ""
 msgid "How-to guides"
 msgstr ""
 
-#: ../../source/index.rst:95
+#: ../../source/index.rst:97
 msgid "Legacy example guides"
 msgstr ""
 
-#: ../../source/index.rst:106 ../../source/index.rst:110
+#: ../../source/index.rst:108 ../../source/index.rst:112
 msgid "Explanations"
 msgstr ""
 
-#: ../../source/index.rst:122
+#: None:-1
 msgid "API reference"
 msgstr ""
 
-#: ../../source/index.rst:129
+#: ../../source/index.rst:137
 msgid "Reference docs"
 msgstr ""
 
-#: ../../source/index.rst:145
+#: ../../source/index.rst:153
 msgid "Contributor tutorials"
 msgstr ""
 
-#: ../../source/index.rst:152
+#: ../../source/index.rst:160
 msgid "Contributor how-to guides"
 msgstr ""
 
-#: ../../source/index.rst:164
+#: ../../source/index.rst:173
 msgid "Contributor explanations"
 msgstr ""
 
-#: ../../source/index.rst:170
+#: ../../source/index.rst:179
 msgid "Contributor references"
 msgstr ""
 
@@ -5512,7 +6255,7 @@ msgstr ""
 
 #: ../../source/index.rst:7
 msgid ""
-"Welcome to Flower's documentation. `Flower <https://flower.dev>`_ is a "
+"Welcome to Flower's documentation. `Flower <https://flower.ai>`_ is a "
 "friendly federated learning framework."
 msgstr ""
 
@@ -5580,25 +6323,33 @@ msgid ""
 "specific goal."
 msgstr ""
 
-#: ../../source/index.rst:108
+#: ../../source/index.rst:110
 msgid ""
 "Understanding-oriented concept guides explain and discuss key topics and "
 "underlying ideas behind Flower and collaborative AI."
 msgstr ""
 
-#: ../../source/index.rst:118
+#: ../../source/index.rst:120
 msgid "References"
 msgstr ""
 
-#: ../../source/index.rst:120
+#: ../../source/index.rst:122
 msgid "Information-oriented API reference and other reference material."
 msgstr ""
 
-#: ../../source/index.rst:140
+#: ../../source/index.rst:131:<autosummary>:1
+msgid ":py:obj:`flwr <flwr>`\\"
+msgstr ""
+
+#: ../../source/index.rst:131:<autosummary>:1 flwr:1 of
+msgid "Flower main package."
+msgstr ""
+
+#: ../../source/index.rst:148
 msgid "Contributor docs"
 msgstr ""
 
-#: ../../source/index.rst:142
+#: ../../source/index.rst:150
 msgid ""
 "The Flower community welcomes contributions. The following docs are "
 "intended to help along the way."
@@ -5609,7 +6360,7 @@ msgid "Flower CLI reference"
 msgstr ""
 
 #: ../../source/ref-api-cli.rst:7
-msgid "flower-server"
+msgid "flower-superlink"
 msgstr ""
 
 #: ../../source/ref-api-cli.rst:17
@@ -5620,363 +6371,4978 @@ msgstr ""
 msgid "flower-fleet-api"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:2
-msgid "flwr (Python API reference)"
+#: ../../source/ref-api/flwr.rst:2
+msgid "flwr"
+msgstr ""
+
+#: ../../source/ref-api/flwr.rst:25 ../../source/ref-api/flwr.server.rst:48
+msgid "Modules"
+msgstr ""
+
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1
+msgid ":py:obj:`flwr.client <flwr.client>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1 flwr.client:1 of
+msgid "Flower client."
+msgstr ""
+
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1
+msgid ":py:obj:`flwr.common <flwr.common>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1 flwr.common:1 of
+msgid "Common components shared between server and client."
+msgstr ""
+
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1
+msgid ":py:obj:`flwr.server <flwr.server>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1 flwr.server:1
+#: flwr.server.server.Server:1 of
+msgid "Flower server."
+msgstr ""
+
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1
+msgid ":py:obj:`flwr.simulation <flwr.simulation>`\\"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:8
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1 flwr.simulation:1 of
+msgid "Flower simulation."
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:2
 msgid "client"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:24
-msgid "start_client"
+#: ../../source/ref-api/flwr.client.rst:13
+#: ../../source/ref-api/flwr.common.rst:13
+#: ../../source/ref-api/flwr.server.driver.rst:13
+#: ../../source/ref-api/flwr.server.rst:13
+#: ../../source/ref-api/flwr.simulation.rst:13
+msgid "Functions"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:24:<autosummary>:1
+msgid ":py:obj:`run_client_app <flwr.client.run_client_app>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:24:<autosummary>:1
+#: flwr.client.app.run_client_app:1 of
+msgid "Run Flower client app."
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:24:<autosummary>:1
+msgid ""
+":py:obj:`start_client <flwr.client.start_client>`\\ \\(\\*\\, "
+"server\\_address\\[\\, client\\_fn\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:24:<autosummary>: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:<autosummary>:1
+msgid ""
+":py:obj:`start_numpy_client <flwr.client.start_numpy_client>`\\ \\(\\*\\,"
+" server\\_address\\, client\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:24:<autosummary>: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.common.rst:31
+#: ../../source/ref-api/flwr.server.driver.rst:24
+#: ../../source/ref-api/flwr.server.rst:28
+#: ../../source/ref-api/flwr.server.strategy.rst:17
+msgid "Classes"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
+msgid ":py:obj:`Client <flwr.client.Client>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
+#: flwr.client.client.Client:1 of
+msgid "Abstract base class for Flower clients."
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
+msgid ""
+":py:obj:`ClientApp <flwr.client.ClientApp>`\\ \\(client\\_fn\\[\\, "
+"mods\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
+#: flwr.client.client_app.ClientApp:1 of
+msgid "Flower ClientApp."
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
+msgid ":py:obj:`NumPyClient <flwr.client.NumPyClient>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
+#: flwr.client.numpy_client.NumPyClient:1 of
+msgid "Abstract base class for Flower clients using NumPy."
+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`"
+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.ClientMessage.rst:15
+#: ../../source/ref-api/flwr.common.DisconnectRes.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.Parameters.rst:15
+#: ../../source/ref-api/flwr.common.ReconnectIns.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.History.rst:15
+#: ../../source/ref-api/flwr.server.Server.rst:15
+#: ../../source/ref-api/flwr.server.ServerConfig.rst:15
+#: ../../source/ref-api/flwr.server.SimpleClientManager.rst:15
+#: ../../source/ref-api/flwr.server.driver.Driver.rst:15
+#: ../../source/ref-api/flwr.server.driver.GrpcDriver.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.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
+msgid "Methods"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`evaluate <flwr.client.Client.evaluate>`\\ \\(ins\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>: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.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`fit <flwr.client.Client.fit>`\\ \\(ins\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: flwr.client.client.Client.fit:1 of
+msgid "Refine the provided parameters using the locally held dataset."
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`get_context <flwr.client.Client.get_context>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>: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.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`get_parameters <flwr.client.Client.get_parameters>`\\ \\(ins\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>: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.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`get_properties <flwr.client.Client.get_properties>`\\ \\(ins\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: flwr.client.client.Client.get_properties:1 of
+msgid "Return set of client's properties."
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`set_context <flwr.client.Client.set_context>`\\ \\(context\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>: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.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`to_client <flwr.client.Client.to_client>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: flwr.client.client.Client.to_client:1 of
+msgid "Return client (itself)."
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:46
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:46
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:25
+#: ../../source/ref-api/flwr.common.Code.rst:19
+#: ../../source/ref-api/flwr.common.DisconnectRes.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.Parameters.rst:25
+#: ../../source/ref-api/flwr.common.ReconnectIns.rst:25
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:25
+#: ../../source/ref-api/flwr.common.Status.rst:25
+#: ../../source/ref-api/flwr.server.ServerConfig.rst:25
+msgid "Attributes"
+msgstr ""
+
+#: flwr.client.client.Client.evaluate:1:<autosummary>:1 of
+msgid ":py:obj:`context <flwr.client.Client.context>`\\"
+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.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.app.start_driver flwr.server.driver.driver.Driver
+#: flwr.server.strategy.bulyan.Bulyan
+#: 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.simulation.app.start_simulation of
+msgid "Parameters"
+msgstr ""
+
+#: 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 ""
+
+#: 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.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.app.start_driver
+#: 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 ""
+
+#: 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 ""
+
+#: 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.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.app.start_driver
+#: 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 ""
+
+#: 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 ""
+
+#: 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 ""
+
+#: flwr.client.client.Client.get_parameters:3 of
+msgid ""
+"The get parameters instructions received from the server containing a "
+"dictionary of configuration values."
+msgstr ""
+
+#: flwr.client.client.Client.get_parameters:7 of
+msgid "The current local model parameters."
+msgstr ""
+
+#: flwr.client.client.Client.get_properties:3 of
+msgid ""
+"The get properties instructions received from the server containing a "
+"dictionary of configuration values."
+msgstr ""
+
+#: flwr.client.client.Client.get_properties:7 of
+msgid "The current client properties."
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.ClientApp.rst:2
+msgid "ClientApp"
+msgstr ""
+
+#: flwr.client.client_app.ClientApp: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.app.ServerConfig:1
+#: flwr.server.driver.driver.Driver:1
+#: flwr.server.driver.grpc_driver.GrpcDriver:1 flwr.server.history.History:1
+#: flwr.server.server.Server:1 of
+msgid "Bases: :py:class:`object`"
+msgstr ""
+
+#: flwr.client.app.start_client:33 flwr.client.app.start_numpy_client:36
+#: flwr.client.client_app.ClientApp:4 flwr.server.app.start_server:41
+#: flwr.server.driver.app.start_driver:30 of
+msgid "Examples"
+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:"
+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:"
+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`."
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:32
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:2
 msgid "NumPyClient"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:41
-msgid "start_numpy_client"
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+msgid ""
+":py:obj:`evaluate <flwr.client.NumPyClient.evaluate>`\\ \\(parameters\\, "
+"config\\)"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:49
-msgid "start_simulation"
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+msgid ":py:obj:`fit <flwr.client.NumPyClient.fit>`\\ \\(parameters\\, config\\)"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:57
-msgid "server"
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+#: flwr.client.numpy_client.NumPyClient.fit:1 of
+msgid "Train the provided parameters using the locally held dataset."
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+msgid ":py:obj:`get_context <flwr.client.NumPyClient.get_context>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+msgid ""
+":py:obj:`get_parameters <flwr.client.NumPyClient.get_parameters>`\\ "
+"\\(config\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+msgid ""
+":py:obj:`get_properties <flwr.client.NumPyClient.get_properties>`\\ "
+"\\(config\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+#: flwr.client.numpy_client.NumPyClient.get_properties:1 of
+msgid "Return a client's set of properties."
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+msgid ""
+":py:obj:`set_context <flwr.client.NumPyClient.set_context>`\\ "
+"\\(context\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+msgid ":py:obj:`to_client <flwr.client.NumPyClient.to_client>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>: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:<autosummary>:1 of
+msgid ":py:obj:`context <flwr.client.NumPyClient.context>`\\"
+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."
+msgstr ""
+
+#: 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 ""
+
+#: 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 ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:11 of
+msgid ""
+"**loss** (*float*) -- The evaluation loss of the model on the local "
+"dataset."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.evaluate:12 of
+msgid "**num_examples** (*int*) -- The number of examples used for evaluation."
+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."
+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."
+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."
+msgstr ""
+
+#: 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 ""
+
+#: flwr.client.numpy_client.NumPyClient.fit:11 of
+msgid "**parameters** (*NDArrays*) -- The locally updated model parameters."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.fit:12 of
+msgid "**num_examples** (*int*) -- The number of examples used for training."
+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."
+msgstr ""
+
+#: flwr.client.numpy_client.NumPyClient.get_parameters:8 of
+msgid "**parameters** -- The local model parameters as a list of NumPy ndarrays."
+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."
+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."
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.run_client_app.rst:2
+msgid "run\\_client\\_app"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.start_client.rst:2
+msgid "start\\_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\"`."
+msgstr ""
+
+#: flwr.client.app.start_client:7 of
+msgid "A callable that instantiates a Client. (default: None)"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:65
-msgid "server.start_server"
+#: flwr.client.app.start_client:9 of
+msgid ""
+"An implementation of the abstract base class `flwr.client.Client` "
+"(default: None)"
+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."
+msgstr ""
+
+#: flwr.client.app.start_client:19 flwr.client.app.start_numpy_client:22
+#: flwr.server.driver.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."
+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."
+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)"
+msgstr ""
+
+#: flwr.client.app.start_client:34 flwr.client.app.start_numpy_client:37 of
+msgid "Starting a gRPC client with an insecure server connection:"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:73
-msgid "server.strategy"
+#: flwr.client.app.start_client:41 flwr.client.app.start_numpy_client:44 of
+msgid "Starting an SSL-enabled gRPC client using system certificates:"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:81
-msgid "server.strategy.Strategy"
+#: flwr.client.app.start_client:52 flwr.client.app.start_numpy_client:52 of
+msgid "Starting an SSL-enabled gRPC client using provided certificates:"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:90
-msgid "server.strategy.FedAvg"
+#: ../../source/ref-api/flwr.client.start_numpy_client.rst:2
+msgid "start\\_numpy\\_client"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:101
-msgid "server.strategy.FedAvgM"
+#: 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."
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:112
-msgid "server.strategy.FedMedian"
+#: 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.rst:2
+msgid "common"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+msgid ":py:obj:`bytes_to_ndarray <flwr.common.bytes_to_ndarray>`\\ \\(tensor\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.parameter.bytes_to_ndarray:1 of
+msgid "Deserialize NumPy ndarray from bytes."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+msgid ""
+":py:obj:`configure <flwr.common.configure>`\\ \\(identifier\\[\\, "
+"filename\\, host\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.logger.configure:1 of
+msgid "Configure logging to file and/or remote log server."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+msgid ""
+":py:obj:`event <flwr.common.event>`\\ \\(event\\_type\\[\\, "
+"event\\_details\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.telemetry.event:1 of
+msgid "Submit create_event to ThreadPoolExecutor to avoid blocking."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+msgid ""
+":py:obj:`log <flwr.common.log>`\\ \\(level\\, msg\\, \\*args\\, "
+"\\*\\*kwargs\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1 logging.Logger.log:1
+#: of
+msgid "Log 'msg % args' with the integer severity 'level'."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+msgid ":py:obj:`ndarray_to_bytes <flwr.common.ndarray_to_bytes>`\\ \\(ndarray\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.parameter.ndarray_to_bytes:1 of
+msgid "Serialize NumPy ndarray to bytes."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+msgid ":py:obj:`now <flwr.common.now>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>: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.rst:29:<autosummary>:1
+msgid ""
+":py:obj:`ndarrays_to_parameters <flwr.common.ndarrays_to_parameters>`\\ "
+"\\(ndarrays\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.parameter.ndarrays_to_parameters:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>: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.rst:29:<autosummary>:1
+msgid ""
+":py:obj:`parameters_to_ndarrays <flwr.common.parameters_to_ndarrays>`\\ "
+"\\(parameters\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.parameter.parameters_to_ndarrays:1 of
+msgid "Convert parameters object to NumPy ndarrays."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ""
+":py:obj:`ClientMessage <flwr.common.ClientMessage>`\\ "
+"\\(\\[get\\_properties\\_res\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.ClientMessage:1 of
+msgid "ClientMessage is a container used to hold one result message."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`Code <flwr.common.Code>`\\ \\(value\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.Code:1 of
+msgid "Client status codes."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`DisconnectRes <flwr.common.DisconnectRes>`\\ \\(reason\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.DisconnectRes:1 of
+msgid "DisconnectRes message from client to server."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ""
+":py:obj:`EvaluateIns <flwr.common.EvaluateIns>`\\ \\(parameters\\, "
+"config\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.EvaluateIns:1 of
+msgid "Evaluate instructions for a client."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ""
+":py:obj:`EvaluateRes <flwr.common.EvaluateRes>`\\ \\(status\\, loss\\, "
+"num\\_examples\\, metrics\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.EvaluateRes:1 of
+msgid "Evaluate response from a client."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`EventType <flwr.common.EventType>`\\ \\(value\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.telemetry.EventType:1 of
+msgid "Types of telemetry events."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`FitIns <flwr.common.FitIns>`\\ \\(parameters\\, config\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.FitIns:1 of
+msgid "Fit instructions for a client."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ""
+":py:obj:`FitRes <flwr.common.FitRes>`\\ \\(status\\, parameters\\, "
+"num\\_examples\\, metrics\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.FitRes:1 of
+msgid "Fit response from a client."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`GetParametersIns <flwr.common.GetParametersIns>`\\ \\(config\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.GetParametersIns:1 of
+msgid "Parameters request for a client."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ""
+":py:obj:`GetParametersRes <flwr.common.GetParametersRes>`\\ \\(status\\, "
+"parameters\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.GetParametersRes:1 of
+msgid "Response when asked to return parameters."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`GetPropertiesIns <flwr.common.GetPropertiesIns>`\\ \\(config\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.GetPropertiesIns:1 of
+msgid "Properties request for a client."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ""
+":py:obj:`GetPropertiesRes <flwr.common.GetPropertiesRes>`\\ \\(status\\, "
+"properties\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.GetPropertiesRes:1 of
+msgid "Properties response from a client."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`NDArray <flwr.common.NDArray>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ""
+"alias of :py:class:`~numpy.ndarray`\\ [:py:obj:`~typing.Any`, "
+":py:class:`~numpy.dtype`\\ [:py:obj:`~typing.Any`]]"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ""
+":py:obj:`Parameters <flwr.common.Parameters>`\\ \\(tensors\\, "
+"tensor\\_type\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.Parameters:1 of
+msgid "Model parameters."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`ReconnectIns <flwr.common.ReconnectIns>`\\ \\(seconds\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.ReconnectIns:1 of
+msgid "ReconnectIns message from server to client."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ""
+":py:obj:`ServerMessage <flwr.common.ServerMessage>`\\ "
+"\\(\\[get\\_properties\\_ins\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.ServerMessage:1 of
+msgid "ServerMessage is a container used to hold one instruction message."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`Status <flwr.common.Status>`\\ \\(code\\, message\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.Status:1 of
+msgid "Client status."
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:2
+msgid "ClientMessage"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:31:<autosummary>:1
+msgid ":py:obj:`evaluate_res <flwr.common.ClientMessage.evaluate_res>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:31:<autosummary>:1
+msgid ":py:obj:`fit_res <flwr.common.ClientMessage.fit_res>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:31:<autosummary>:1
+msgid ""
+":py:obj:`get_parameters_res "
+"<flwr.common.ClientMessage.get_parameters_res>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:31:<autosummary>:1
+msgid ""
+":py:obj:`get_properties_res "
+"<flwr.common.ClientMessage.get_properties_res>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Code.rst:2
+msgid "Code"
+msgstr ""
+
+#: flwr.common.typing.Code:1 of
+msgid "Bases: :py:class:`~enum.Enum`"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Code.rst:26:<autosummary>:1
+msgid ":py:obj:`OK <flwr.common.Code.OK>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Code.rst:26:<autosummary>:1
+msgid ""
+":py:obj:`GET_PROPERTIES_NOT_IMPLEMENTED "
+"<flwr.common.Code.GET_PROPERTIES_NOT_IMPLEMENTED>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Code.rst:26:<autosummary>:1
+msgid ""
+":py:obj:`GET_PARAMETERS_NOT_IMPLEMENTED "
+"<flwr.common.Code.GET_PARAMETERS_NOT_IMPLEMENTED>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Code.rst:26:<autosummary>:1
+msgid ":py:obj:`FIT_NOT_IMPLEMENTED <flwr.common.Code.FIT_NOT_IMPLEMENTED>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Code.rst:26:<autosummary>:1
+msgid ""
+":py:obj:`EVALUATE_NOT_IMPLEMENTED "
+"<flwr.common.Code.EVALUATE_NOT_IMPLEMENTED>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.DisconnectRes.rst:2
+msgid "DisconnectRes"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.DisconnectRes.rst:28:<autosummary>:1
+msgid ":py:obj:`reason <flwr.common.DisconnectRes.reason>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EvaluateIns.rst:2
+msgid "EvaluateIns"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EvaluateIns.rst:29:<autosummary>:1
+msgid ":py:obj:`parameters <flwr.common.EvaluateIns.parameters>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EvaluateIns.rst:29:<autosummary>:1
+msgid ":py:obj:`config <flwr.common.EvaluateIns.config>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EvaluateRes.rst:2
+msgid "EvaluateRes"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31:<autosummary>:1
+msgid ":py:obj:`status <flwr.common.EvaluateRes.status>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31:<autosummary>:1
+msgid ":py:obj:`loss <flwr.common.EvaluateRes.loss>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31:<autosummary>:1
+msgid ":py:obj:`num_examples <flwr.common.EvaluateRes.num_examples>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31:<autosummary>:1
+msgid ":py:obj:`metrics <flwr.common.EvaluateRes.metrics>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:2
+msgid "EventType"
+msgstr ""
+
+#: flwr.common.telemetry.EventType:1 of
+msgid "Bases: :py:class:`str`, :py:class:`~enum.Enum`"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`PING <flwr.common.EventType.PING>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_CLIENT_ENTER <flwr.common.EventType.START_CLIENT_ENTER>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_CLIENT_LEAVE <flwr.common.EventType.START_CLIENT_LEAVE>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_SERVER_ENTER <flwr.common.EventType.START_SERVER_ENTER>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_SERVER_LEAVE <flwr.common.EventType.START_SERVER_LEAVE>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_DRIVER_API_ENTER "
+"<flwr.common.EventType.RUN_DRIVER_API_ENTER>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_DRIVER_API_LEAVE "
+"<flwr.common.EventType.RUN_DRIVER_API_LEAVE>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_FLEET_API_ENTER "
+"<flwr.common.EventType.RUN_FLEET_API_ENTER>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_FLEET_API_LEAVE "
+"<flwr.common.EventType.RUN_FLEET_API_LEAVE>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_SUPERLINK_ENTER "
+"<flwr.common.EventType.RUN_SUPERLINK_ENTER>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_SUPERLINK_LEAVE "
+"<flwr.common.EventType.RUN_SUPERLINK_LEAVE>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`START_SIMULATION_ENTER "
+"<flwr.common.EventType.START_SIMULATION_ENTER>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`START_SIMULATION_LEAVE "
+"<flwr.common.EventType.START_SIMULATION_LEAVE>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`DRIVER_CONNECT <flwr.common.EventType.DRIVER_CONNECT>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`DRIVER_DISCONNECT <flwr.common.EventType.DRIVER_DISCONNECT>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_DRIVER_ENTER <flwr.common.EventType.START_DRIVER_ENTER>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_DRIVER_LEAVE <flwr.common.EventType.START_DRIVER_LEAVE>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_CLIENT_APP_ENTER "
+"<flwr.common.EventType.RUN_CLIENT_APP_ENTER>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_CLIENT_APP_LEAVE "
+"<flwr.common.EventType.RUN_CLIENT_APP_LEAVE>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_SERVER_APP_ENTER "
+"<flwr.common.EventType.RUN_SERVER_APP_ENTER>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_SERVER_APP_LEAVE "
+"<flwr.common.EventType.RUN_SERVER_APP_LEAVE>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.FitIns.rst:2
+msgid "FitIns"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.FitIns.rst:29:<autosummary>:1
+msgid ":py:obj:`parameters <flwr.common.FitIns.parameters>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.FitIns.rst:29:<autosummary>:1
+msgid ":py:obj:`config <flwr.common.FitIns.config>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.FitRes.rst:2
+msgid "FitRes"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.FitRes.rst:31:<autosummary>:1
+msgid ":py:obj:`status <flwr.common.FitRes.status>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.FitRes.rst:31:<autosummary>:1
+msgid ":py:obj:`parameters <flwr.common.FitRes.parameters>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.FitRes.rst:31:<autosummary>:1
+msgid ":py:obj:`num_examples <flwr.common.FitRes.num_examples>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.FitRes.rst:31:<autosummary>:1
+msgid ":py:obj:`metrics <flwr.common.FitRes.metrics>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.GetParametersIns.rst:2
+msgid "GetParametersIns"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.GetParametersIns.rst:28:<autosummary>:1
+msgid ":py:obj:`config <flwr.common.GetParametersIns.config>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.GetParametersRes.rst:2
+msgid "GetParametersRes"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29:<autosummary>:1
+msgid ":py:obj:`status <flwr.common.GetParametersRes.status>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29:<autosummary>:1
+msgid ":py:obj:`parameters <flwr.common.GetParametersRes.parameters>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:2
+msgid "GetPropertiesIns"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:28:<autosummary>:1
+msgid ":py:obj:`config <flwr.common.GetPropertiesIns.config>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:2
+msgid "GetPropertiesRes"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29:<autosummary>:1
+msgid ":py:obj:`status <flwr.common.GetPropertiesRes.status>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29:<autosummary>:1
+msgid ":py:obj:`properties <flwr.common.GetPropertiesRes.properties>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.NDArray.rst:2
+msgid "NDArray"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Parameters.rst:29:<autosummary>:1
+msgid ":py:obj:`tensors <flwr.common.Parameters.tensors>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Parameters.rst:29:<autosummary>:1
+msgid ":py:obj:`tensor_type <flwr.common.Parameters.tensor_type>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ReconnectIns.rst:2
+msgid "ReconnectIns"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ReconnectIns.rst:28:<autosummary>:1
+msgid ":py:obj:`seconds <flwr.common.ReconnectIns.seconds>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:2
+msgid "ServerMessage"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:31:<autosummary>:1
+msgid ":py:obj:`evaluate_ins <flwr.common.ServerMessage.evaluate_ins>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:31:<autosummary>:1
+msgid ":py:obj:`fit_ins <flwr.common.ServerMessage.fit_ins>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:31:<autosummary>:1
+msgid ""
+":py:obj:`get_parameters_ins "
+"<flwr.common.ServerMessage.get_parameters_ins>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:31:<autosummary>:1
+msgid ""
+":py:obj:`get_properties_ins "
+"<flwr.common.ServerMessage.get_properties_ins>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Status.rst:2
+msgid "Status"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Status.rst:29:<autosummary>:1
+msgid ":py:obj:`code <flwr.common.Status.code>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.Status.rst:29:<autosummary>:1
+msgid ":py:obj:`message <flwr.common.Status.message>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.bytes_to_ndarray.rst:2
+msgid "bytes\\_to\\_ndarray"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.configure.rst:2
+msgid "configure"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.event.rst:2
+msgid "event"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.log.rst:2
+msgid "log"
+msgstr ""
+
+#: logging.Logger.log:3 of
+msgid ""
+"To pass exception information, use the keyword argument exc_info with a "
+"true value, e.g."
+msgstr ""
+
+#: logging.Logger.log:6 of
+#, python-format
+msgid "logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=1)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ndarray_to_bytes.rst:2
+msgid "ndarray\\_to\\_bytes"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.ndarrays_to_parameters.rst:2
+msgid "ndarrays\\_to\\_parameters"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.now.rst:2
+msgid "now"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.parameters_to_ndarrays.rst:2
+msgid "parameters\\_to\\_ndarrays"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:2
+msgid "server"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+msgid ":py:obj:`run_driver_api <flwr.server.run_driver_api>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+#: flwr.server.app.run_driver_api:1 of
+msgid "Run Flower server (Driver API)."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+msgid ":py:obj:`run_fleet_api <flwr.server.run_fleet_api>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+#: flwr.server.app.run_fleet_api:1 of
+msgid "Run Flower server (Fleet API)."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+msgid ":py:obj:`run_server_app <flwr.server.run_server_app>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+#: flwr.server.app.run_server_app:1 of
+msgid "Run Flower server app."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+msgid ":py:obj:`run_superlink <flwr.server.run_superlink>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+#: flwr.server.app.run_superlink:1 of
+msgid "Run Flower server (Driver API and Fleet API)."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+msgid ""
+":py:obj:`start_server <flwr.server.start_server>`\\ \\(\\*\\[\\, "
+"server\\_address\\, server\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>: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:37:<autosummary>:1
+msgid ":py:obj:`ClientManager <flwr.server.ClientManager>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+#: flwr.server.client_manager.ClientManager:1 of
+msgid "Abstract base class for managing Flower clients."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+msgid ":py:obj:`History <flwr.server.History>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+#: flwr.server.history.History:1 of
+msgid "History class for training and/or evaluation metrics collection."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+msgid ""
+":py:obj:`Server <flwr.server.Server>`\\ \\(\\*\\, client\\_manager\\[\\, "
+"strategy\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+msgid ""
+":py:obj:`ServerConfig <flwr.server.ServerConfig>`\\ \\(\\[num\\_rounds\\,"
+" round\\_timeout\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+#: flwr.server.app.ServerConfig:1 of
+msgid "Flower server config."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+msgid ":py:obj:`SimpleClientManager <flwr.server.SimpleClientManager>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager:1 of
+msgid "Provides a pool of available clients."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:56:<autosummary>:1
+msgid ":py:obj:`flwr.server.driver <flwr.server.driver>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:56:<autosummary>:1 flwr.server.driver:1
+#: of
+msgid "Flower driver SDK."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:56:<autosummary>:1
+msgid ":py:obj:`flwr.server.strategy <flwr.server.strategy>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:56:<autosummary>:1
+#: flwr.server.strategy:1 of
+msgid "Contains the strategy abstraction and different implementations."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.ClientManager.rst:2
+msgid "ClientManager"
+msgstr ""
+
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
+msgid ":py:obj:`all <flwr.server.ClientManager.all>`\\ \\(\\)"
+msgstr ""
+
+#: flwr.server.client_manager.ClientManager.all:1
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.all:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
+msgid "Return all available clients."
+msgstr ""
+
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
+msgid ":py:obj:`num_available <flwr.server.ClientManager.num_available>`\\ \\(\\)"
+msgstr ""
+
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.ClientManager.num_available:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.num_available:1 of
+msgid "Return the number of available clients."
+msgstr ""
+
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
+msgid ":py:obj:`register <flwr.server.ClientManager.register>`\\ \\(client\\)"
+msgstr ""
+
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.ClientManager.register:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.register:1 of
+msgid "Register Flower ClientProxy instance."
+msgstr ""
+
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
+msgid ""
+":py:obj:`sample <flwr.server.ClientManager.sample>`\\ "
+"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)"
+msgstr ""
+
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.ClientManager.sample:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.sample:1 of
+msgid "Sample a number of Flower ClientProxy instances."
+msgstr ""
+
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
+msgid ":py:obj:`unregister <flwr.server.ClientManager.unregister>`\\ \\(client\\)"
+msgstr ""
+
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.ClientManager.unregister:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.unregister:1 of
+msgid "Unregister Flower ClientProxy instance."
+msgstr ""
+
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
+msgid ""
+":py:obj:`wait_for <flwr.server.ClientManager.wait_for>`\\ "
+"\\(num\\_clients\\, timeout\\)"
+msgstr ""
+
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.ClientManager.wait_for:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.wait_for:1 of
+msgid "Wait until at least `num_clients` are available."
+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."
+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."
+msgstr ""
+
+#: 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.History.rst:2
+msgid "History"
+msgstr ""
+
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1 of
+msgid ""
+":py:obj:`add_loss_centralized "
+"<flwr.server.History.add_loss_centralized>`\\ \\(server\\_round\\, "
+"loss\\)"
+msgstr ""
+
+#: flwr.server.history.History.add_loss_centralized:1
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1 of
+msgid "Add one loss entry (from centralized evaluation)."
+msgstr ""
+
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1 of
+msgid ""
+":py:obj:`add_loss_distributed "
+"<flwr.server.History.add_loss_distributed>`\\ \\(server\\_round\\, "
+"loss\\)"
+msgstr ""
+
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1
+#: flwr.server.history.History.add_loss_distributed:1 of
+msgid "Add one loss entry (from distributed evaluation)."
+msgstr ""
+
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1 of
+msgid ""
+":py:obj:`add_metrics_centralized "
+"<flwr.server.History.add_metrics_centralized>`\\ \\(server\\_round\\, "
+"metrics\\)"
+msgstr ""
+
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1
+#: flwr.server.history.History.add_metrics_centralized:1 of
+msgid "Add metrics entries (from centralized evaluation)."
+msgstr ""
+
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1 of
+msgid ""
+":py:obj:`add_metrics_distributed "
+"<flwr.server.History.add_metrics_distributed>`\\ \\(server\\_round\\, "
+"metrics\\)"
+msgstr ""
+
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1
+#: flwr.server.history.History.add_metrics_distributed:1 of
+msgid "Add metrics entries (from distributed evaluation)."
+msgstr ""
+
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1 of
+msgid ""
+":py:obj:`add_metrics_distributed_fit "
+"<flwr.server.History.add_metrics_distributed_fit>`\\ \\(server\\_round\\,"
+" ...\\)"
+msgstr ""
+
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1
+#: flwr.server.history.History.add_metrics_distributed_fit:1 of
+msgid "Add metrics entries (from distributed fit)."
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
+msgid ":py:obj:`client_manager <flwr.server.Server.client_manager>`\\ \\(\\)"
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
+msgid "Return ClientManager."
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
+msgid ""
+":py:obj:`disconnect_all_clients "
+"<flwr.server.Server.disconnect_all_clients>`\\ \\(timeout\\)"
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1
+#: flwr.server.server.Server.disconnect_all_clients:1 of
+msgid "Send shutdown signal to all clients."
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate_round <flwr.server.Server.evaluate_round>`\\ "
+"\\(server\\_round\\, timeout\\)"
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1
+#: flwr.server.server.Server.evaluate_round:1 of
+msgid "Validate current global model on a number of clients."
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
+msgid ":py:obj:`fit <flwr.server.Server.fit>`\\ \\(num\\_rounds\\, timeout\\)"
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1
+#: flwr.server.server.Server.fit:1 of
+msgid "Run federated averaging for a number of rounds."
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
+msgid ""
+":py:obj:`fit_round <flwr.server.Server.fit_round>`\\ \\(server\\_round\\,"
+" timeout\\)"
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1
+#: flwr.server.server.Server.fit_round:1 of
+msgid "Perform a single round of federated averaging."
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
+msgid ""
+":py:obj:`set_max_workers <flwr.server.Server.set_max_workers>`\\ "
+"\\(max\\_workers\\)"
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1
+#: flwr.server.server.Server.set_max_workers:1 of
+msgid "Set the max_workers used by ThreadPoolExecutor."
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
+msgid ":py:obj:`set_strategy <flwr.server.Server.set_strategy>`\\ \\(strategy\\)"
+msgstr ""
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1
+#: flwr.server.server.Server.set_strategy:1 of
+msgid "Replace server strategy."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.ServerConfig.rst:2
+msgid "ServerConfig"
+msgstr ""
+
+#: flwr.server.app.ServerConfig:3 of
+msgid ""
+"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:<autosummary>:1
+msgid ":py:obj:`num_rounds <flwr.server.ServerConfig.num_rounds>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.ServerConfig.rst:29:<autosummary>:1
+msgid ":py:obj:`round_timeout <flwr.server.ServerConfig.round_timeout>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.SimpleClientManager.rst:2
+msgid "SimpleClientManager"
+msgstr ""
+
+#: flwr.server.client_manager.SimpleClientManager:1 of
+msgid "Bases: :py:class:`~flwr.server.client_manager.ClientManager`"
+msgstr ""
+
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
+msgid ":py:obj:`all <flwr.server.SimpleClientManager.all>`\\ \\(\\)"
+msgstr ""
+
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_available <flwr.server.SimpleClientManager.num_available>`\\"
+" \\(\\)"
+msgstr ""
+
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
+msgid ""
+":py:obj:`register <flwr.server.SimpleClientManager.register>`\\ "
+"\\(client\\)"
+msgstr ""
+
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
+msgid ""
+":py:obj:`sample <flwr.server.SimpleClientManager.sample>`\\ "
+"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)"
+msgstr ""
+
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
+msgid ""
+":py:obj:`unregister <flwr.server.SimpleClientManager.unregister>`\\ "
+"\\(client\\)"
+msgstr ""
+
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
+msgid ""
+":py:obj:`wait_for <flwr.server.SimpleClientManager.wait_for>`\\ "
+"\\(num\\_clients\\[\\, timeout\\]\\)"
+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."
+msgstr ""
+
+#: flwr.server.client_manager.SimpleClientManager.wait_for:6 of
+msgid "The number of clients to wait for."
+msgstr ""
+
+#: flwr.server.client_manager.SimpleClientManager.wait_for:8 of
+msgid "The time in seconds to wait for, defaults to 86400 (24h)."
+msgstr ""
+
+#: flwr.server.client_manager.SimpleClientManager.wait_for:11 of
+msgid "**success**"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.driver.rst:2
+msgid "driver"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.driver.rst:22:<autosummary>:1
+msgid ""
+":py:obj:`start_driver <flwr.server.driver.start_driver>`\\ \\(\\*\\[\\, "
+"server\\_address\\, server\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.driver.rst:22:<autosummary>:1
+#: flwr.server.driver.app.start_driver:1 of
+msgid "Start a Flower Driver API server."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.driver.rst:30:<autosummary>:1
+msgid ""
+":py:obj:`Driver <flwr.server.driver.Driver>`\\ "
+"\\(\\[driver\\_service\\_address\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.driver.rst:30:<autosummary>:1
+#: flwr.server.driver.driver.Driver:1 of
+msgid "`Driver` class provides an interface to the Driver API."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.driver.rst:30:<autosummary>:1
+msgid ""
+":py:obj:`GrpcDriver <flwr.server.driver.GrpcDriver>`\\ "
+"\\(\\[driver\\_service\\_address\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.driver.rst:30:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver:1 of
+msgid "`GrpcDriver` provides access to the gRPC Driver API/service."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.driver.Driver.rst:2
+msgid "Driver"
+msgstr ""
+
+#: flwr.server.driver.driver.Driver:3 of
+msgid ""
+"The IPv4 or IPv6 address of the Driver API server. Defaults to "
+"`\"[::]:9091\"`."
+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."
+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:"
+msgstr ""
+
+#: flwr.server.app.start_server:32 flwr.server.driver.driver.Driver:10 of
+msgid "CA certificate."
+msgstr ""
+
+#: flwr.server.app.start_server:33 flwr.server.driver.driver.Driver:11 of
+msgid "server certificate."
+msgstr ""
+
+#: flwr.server.app.start_server:34 flwr.server.driver.driver.Driver:12 of
+msgid "server private key."
+msgstr ""
+
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1 of
+msgid ":py:obj:`get_nodes <flwr.server.driver.Driver.get_nodes>`\\ \\(\\)"
+msgstr ""
+
+#: flwr.server.driver.driver.Driver.get_nodes:1
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1 of
+msgid "Get node IDs."
+msgstr ""
+
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1 of
+msgid ""
+":py:obj:`pull_task_res <flwr.server.driver.Driver.pull_task_res>`\\ "
+"\\(task\\_ids\\)"
+msgstr ""
+
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1
+#: flwr.server.driver.driver.Driver.pull_task_res:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.pull_task_res:1 of
+msgid "Get task results."
+msgstr ""
+
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1 of
+msgid ""
+":py:obj:`push_task_ins <flwr.server.driver.Driver.push_task_ins>`\\ "
+"\\(task\\_ins\\_list\\)"
+msgstr ""
+
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1
+#: flwr.server.driver.driver.Driver.push_task_ins:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.push_task_ins:1 of
+msgid "Schedule tasks."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.driver.GrpcDriver.rst:2
+msgid "GrpcDriver"
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid ":py:obj:`connect <flwr.server.driver.GrpcDriver.connect>`\\ \\(\\)"
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid "Connect to the Driver API."
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid ""
+":py:obj:`create_run <flwr.server.driver.GrpcDriver.create_run>`\\ "
+"\\(req\\)"
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.create_run:1 of
+msgid "Request for run ID."
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid ":py:obj:`disconnect <flwr.server.driver.GrpcDriver.disconnect>`\\ \\(\\)"
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.disconnect:1 of
+msgid "Disconnect from the Driver API."
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid ":py:obj:`get_nodes <flwr.server.driver.GrpcDriver.get_nodes>`\\ \\(req\\)"
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.get_nodes:1 of
+msgid "Get client IDs."
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid ""
+":py:obj:`pull_task_res <flwr.server.driver.GrpcDriver.pull_task_res>`\\ "
+"\\(req\\)"
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid ""
+":py:obj:`push_task_ins <flwr.server.driver.GrpcDriver.push_task_ins>`\\ "
+"\\(req\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.driver.start_driver.rst:2
+msgid "start\\_driver"
+msgstr ""
+
+#: flwr.server.driver.app.start_driver:3 of
+msgid ""
+"The IPv4 or IPv6 address of the Driver API server. Defaults to "
+"`\"[::]:8080\"`."
+msgstr ""
+
+#: flwr.server.driver.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."
+msgstr ""
+
+#: flwr.server.app.start_server:9 flwr.server.driver.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 ""
+
+#: flwr.server.app.start_server:12 flwr.server.driver.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 ""
+
+#: flwr.server.driver.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`."
+msgstr ""
+
+#: flwr.server.app.start_server:37 flwr.server.driver.app.start_driver:26 of
+msgid "**hist** -- Object containing training and evaluation metrics."
+msgstr ""
+
+#: flwr.server.driver.app.start_driver:31 of
+msgid "Starting a driver that connects to an insecure server:"
+msgstr ""
+
+#: flwr.server.driver.app.start_driver:35 of
+msgid "Starting a driver that connects to an SSL-enabled server:"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.run_driver_api.rst:2
+msgid "run\\_driver\\_api"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.run_fleet_api.rst:2
+msgid "run\\_fleet\\_api"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.run_server_app.rst:2
+msgid "run\\_server\\_app"
+msgstr ""
+
+#: ../../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 ""
+"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: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`."
+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."
+msgstr ""
+
+#: flwr.server.app.start_server:42 of
+msgid "Starting an insecure server:"
+msgstr ""
+
+#: flwr.server.app.start_server:46 of
+msgid "Starting an SSL-enabled server:"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:2
+msgid "strategy"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FaultTolerantFedAvg "
+"<flwr.server.strategy.FaultTolerantFedAvg>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 of
+msgid "Configurable fault-tolerant FedAvg strategy implementation."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedAdagrad <flwr.server.strategy.FedAdagrad>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedadagrad.FedAdagrad:1 of
+msgid "FedAdagrad strategy - Adaptive Federated Optimization using Adagrad."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedAdam <flwr.server.strategy.FedAdam>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedadam.FedAdam:1 of
+msgid "FedAdam - Adaptive Federated Optimization using Adam."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedAvg <flwr.server.strategy.FedAvg>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid:1 of
+msgid "Federated Averaging strategy."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedXgbNnAvg <flwr.server.strategy.FedXgbNnAvg>`\\ \\(\\*args\\, "
+"\\*\\*kwargs\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 of
+msgid "Configurable FedXgbNnAvg strategy implementation."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedXgbBagging <flwr.server.strategy.FedXgbBagging>`\\ "
+"\\(\\[evaluate\\_function\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging:1 of
+msgid "Configurable FedXgbBagging strategy implementation."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedXgbCyclic <flwr.server.strategy.FedXgbCyclic>`\\ "
+"\\(\\*\\*kwargs\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic:1 of
+msgid "Configurable FedXgbCyclic strategy implementation."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedAvgAndroid <flwr.server.strategy.FedAvgAndroid>`\\ "
+"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedAvgM <flwr.server.strategy.FedAvgM>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedavgm.FedAvgM:1 of
+msgid "Federated Averaging with Momentum strategy."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedOpt <flwr.server.strategy.FedOpt>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedopt.FedOpt:1 of
+msgid "Federated Optim strategy."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedProx <flwr.server.strategy.FedProx>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedprox.FedProx:1 of
+msgid "Federated Optimization strategy."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedYogi <flwr.server.strategy.FedYogi>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedyogi.FedYogi:1 of
+msgid "FedYogi [Reddi et al., 2020] strategy."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`QFedAvg <flwr.server.strategy.QFedAvg>`\\ \\(\\*\\[\\, "
+"q\\_param\\, qffl\\_learning\\_rate\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg:1 of
+msgid "Configurable QFedAvg strategy implementation."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedMedian <flwr.server.strategy.FedMedian>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedmedian.FedMedian:1 of
+msgid "Configurable FedMedian strategy implementation."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedTrimmedAvg <flwr.server.strategy.FedTrimmedAvg>`\\ "
+"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1 of
+msgid "Federated Averaging with Trimmed Mean [Dong Yin, et al., 2021]."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`Krum <flwr.server.strategy.Krum>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.krum.Krum:1 of
+msgid "Krum [Blanchard et al., 2017] strategy."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`Bulyan <flwr.server.strategy.Bulyan>`\\ \\(\\*\\, "
+"fraction\\_fit\\, fraction\\_evaluate\\, ...\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.bulyan.Bulyan:1 of
+msgid "Bulyan strategy."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`DPFedAvgAdaptive <flwr.server.strategy.DPFedAvgAdaptive>`\\ "
+"\\(strategy\\, num\\_sampled\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of
+msgid "Wrapper for configuring a Strategy for DP with Adaptive Clipping."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`DPFedAvgFixed <flwr.server.strategy.DPFedAvgFixed>`\\ "
+"\\(strategy\\, num\\_sampled\\_clients\\, ...\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>: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.rst:41:<autosummary>:1
+msgid ":py:obj:`Strategy <flwr.server.strategy.Strategy>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.strategy.Strategy:1 of
+msgid "Abstract base class for server strategy implementations."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.Bulyan.rst:2
+msgid "Bulyan"
+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`"
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan:3 of
+msgid "Implementation based on https://arxiv.org/abs/1802.07927."
+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."
+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."
+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 ""
+
+#: 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.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.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.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.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.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.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.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.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.bulyan.Bulyan:29 of
+msgid "arguments to the first_aggregation rule"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.Bulyan.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:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid "Aggregate evaluation losses using weighted average."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.Bulyan.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.aggregate_fit:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid "Aggregate fit results using Bulyan."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.Bulyan.configure_evaluate>`\\ \\(server\\_round\\, "
+"parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.configure_evaluate:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.configure_evaluate:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_evaluate:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.configure_evaluate:1
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.strategy.Strategy.configure_evaluate:1 of
+msgid "Configure the next round of evaluation."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.Bulyan.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.configure_fit:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.configure_fit:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_fit:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.configure_fit:1
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.Bulyan.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.evaluate:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.evaluate:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.evaluate:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.evaluate:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid "Evaluate model parameters using an evaluation function."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.Bulyan.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.initialize_parameters:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid "Initialize global model parameters."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.Bulyan.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.num_evaluation_clients:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_evaluation_clients:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients <flwr.server.strategy.Bulyan.num_fit_clients>`\\"
+" \\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.num_fit_clients:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_fit_clients:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>: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.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."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.DPFedAvgAdaptive.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid "Aggregate evaluation losses using the given strategy."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.DPFedAvgAdaptive.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.aggregate_fit:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid "Aggregate training results as in DPFedAvgFixed and update clip norms."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.DPFedAvgAdaptive.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.DPFedAvgAdaptive.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.DPFedAvgAdaptive.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>: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.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.DPFedAvgAdaptive.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.initialize_parameters:1 of
+msgid "Initialize global model parameters using given strategy."
+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."
+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."
+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."
+msgstr ""
+
+#: 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.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.DPFedAvgFixed.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.DPFedAvgFixed.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.DPFedAvgFixed.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.DPFedAvgFixed.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:1 of
+msgid ""
+"Configure the next round of training incorporating Differential Privacy "
+"(DP)."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.DPFedAvgFixed.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.DPFedAvgFixed.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."
+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."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FaultTolerantFedAvg.rst:2
+msgid "FaultTolerantFedAvg"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FaultTolerantFedAvg.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FaultTolerantFedAvg.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_fit:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>: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:<autosummary>: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:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FaultTolerantFedAvg.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FaultTolerantFedAvg.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FaultTolerantFedAvg.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FaultTolerantFedAvg.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FaultTolerantFedAvg.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FaultTolerantFedAvg.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedAdagrad.rst:2
+#: ../../source/ref-changelog.md:839
+msgid "FedAdagrad"
+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`"
+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"
+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 ""
+
+#: 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:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedAdagrad.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedAdagrad.aggregate_fit>`\\"
+" \\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedAdagrad.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.FedAdagrad.configure_fit>`\\"
+" \\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedAdagrad.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedAdagrad.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedAdagrad.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedAdagrad.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../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:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedAdam.aggregate_evaluate>`\\ \\(server\\_round\\,"
+" results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedAdam.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedAdam.configure_evaluate>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.FedAdam.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedAdam.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedAdam.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedAdam.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedAdam.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedAvg.rst:2
+msgid "FedAvg"
+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 ""
+
+#: 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 ""
+
+#: 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.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedAvg.aggregate_evaluate>`\\ \\(server\\_round\\, "
+"results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedAvg.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedAvg.configure_evaluate>`\\ \\(server\\_round\\, "
+"parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.FedAvg.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedAvg.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedAvg.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedAvg.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients <flwr.server.strategy.FedAvg.num_fit_clients>`\\"
+" \\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedAvgAndroid.rst:2
+msgid "FedAvgAndroid"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedAvgAndroid.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FedAvgAndroid.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`bytes_to_ndarray "
+"<flwr.server.strategy.FedAvgAndroid.bytes_to_ndarray>`\\ \\(tensor\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.bytes_to_ndarray:1 of
+msgid "Deserialize NumPy array from bytes."
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedAvgAndroid.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FedAvgAndroid.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedAvgAndroid.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedAvgAndroid.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`ndarray_to_bytes "
+"<flwr.server.strategy.FedAvgAndroid.ndarray_to_bytes>`\\ \\(ndarray\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.ndarray_to_bytes:1 of
+msgid "Serialize NumPy array to bytes."
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`ndarrays_to_parameters "
+"<flwr.server.strategy.FedAvgAndroid.ndarrays_to_parameters>`\\ "
+"\\(ndarrays\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedAvgAndroid.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedAvgAndroid.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`parameters_to_ndarrays "
+"<flwr.server.strategy.FedAvgAndroid.parameters_to_ndarrays>`\\ "
+"\\(parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.parameters_to_ndarrays:1
+#: of
+msgid "Convert parameters object to NumPy weights."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedAvgM.rst:2
+msgid "FedAvgM"
+msgstr ""
+
+#: flwr.server.strategy.fedavgm.FedAvgM:3 of
+msgid "Implementation based on https://arxiv.org/abs/1909.06335"
+msgstr ""
+
+#: 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.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:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedAvgM.aggregate_evaluate>`\\ \\(server\\_round\\,"
+" results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedAvgM.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedAvgM.configure_evaluate>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.FedAvgM.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedAvgM.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedAvgM.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedAvgM.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedAvgM.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedMedian.rst:2
+msgid "FedMedian"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedMedian.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedMedian.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedmedian.FedMedian.aggregate_fit:1 of
+msgid "Aggregate fit results using median."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedMedian.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.FedMedian.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedMedian.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedMedian.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedMedian.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedMedian.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 ""
+
+#: flwr.server.strategy.fedopt.FedOpt:35 of
+msgid "Second moment parameter. Defaults to 0.0."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedOpt.aggregate_evaluate>`\\ \\(server\\_round\\, "
+"results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedOpt.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedOpt.configure_evaluate>`\\ \\(server\\_round\\, "
+"parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.FedOpt.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedOpt.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedOpt.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedOpt.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients <flwr.server.strategy.FedOpt.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 ""
+
+#: 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 ""
+
+#: flwr.server.strategy.fedprox.FedProx:9 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:"
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx:21 of
+msgid "To:"
+msgstr ""
+
+#: flwr.server.strategy.fedprox.FedProx:30 of
+msgid ""
+"With `global_params` being a copy of the parameters before the training "
+"takes place."
+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)."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedProx.aggregate_evaluate>`\\ \\(server\\_round\\,"
+" results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedProx.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedProx.configure_evaluate>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.FedProx.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedProx.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedProx.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedProx.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedProx.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"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedTrimmedAvg.rst:2
+msgid "FedTrimmedAvg"
+msgstr ""
+
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:3 of
+msgid "Implemented based on: https://arxiv.org/abs/1803.01498"
+msgstr ""
+
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:25 of
+msgid "Fraction to cut off of both tails of the distribution. Defaults to 0.2."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedTrimmedAvg.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FedTrimmedAvg.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.aggregate_fit:1 of
+msgid "Aggregate fit results using trimmed average."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedTrimmedAvg.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FedTrimmedAvg.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedTrimmedAvg.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedTrimmedAvg.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedTrimmedAvg.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedTrimmedAvg.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedXgbBagging.rst:2
+msgid "FedXgbBagging"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedXgbBagging.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid "Aggregate evaluation metrics using average."
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FedXgbBagging.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_fit:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedXgbBagging.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FedXgbBagging.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedXgbBagging.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedXgbBagging.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedXgbBagging.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedXgbBagging.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedXgbCyclic.rst:2
+msgid "FedXgbCyclic"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedXgbCyclic.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FedXgbCyclic.aggregate_fit>`\\ \\(server\\_round\\,"
+" results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedXgbCyclic.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FedXgbCyclic.configure_fit>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedXgbCyclic.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedXgbCyclic.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedXgbCyclic.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedXgbCyclic.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedXgbNnAvg.rst:2
+msgid "FedXgbNnAvg"
+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."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedXgbNnAvg.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FedXgbNnAvg.aggregate_fit>`\\ \\(server\\_round\\, "
+"results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedXgbNnAvg.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FedXgbNnAvg.configure_fit>`\\ \\(server\\_round\\, "
+"parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedXgbNnAvg.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedXgbNnAvg.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedXgbNnAvg.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedXgbNnAvg.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedYogi.rst:2
+msgid "FedYogi"
+msgstr ""
+
+#: flwr.server.strategy.fedyogi.FedYogi:32 of
+msgid "Server-side learning rate. Defaults to 1e-2."
+msgstr ""
+
+#: flwr.server.strategy.fedyogi.FedYogi:34 of
+msgid "Client-side learning rate. Defaults to 0.0316."
+msgstr ""
+
+#: flwr.server.strategy.fedyogi.FedYogi:40 of
+msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-3."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedYogi.aggregate_evaluate>`\\ \\(server\\_round\\,"
+" results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedYogi.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedYogi.configure_evaluate>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.FedYogi.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedYogi.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedYogi.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedYogi.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedYogi.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."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.Krum.aggregate_evaluate>`\\ \\(server\\_round\\, "
+"results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.Krum.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.krum.Krum.aggregate_fit:1 of
+msgid "Aggregate fit results using Krum."
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.Krum.configure_evaluate>`\\ \\(server\\_round\\, "
+"parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.Krum.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.Krum.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.Krum.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.Krum.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients <flwr.server.strategy.Krum.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.QFedAvg.rst:2
+msgid "QFedAvg"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.QFedAvg.aggregate_evaluate>`\\ \\(server\\_round\\,"
+" results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.QFedAvg.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.QFedAvg.configure_evaluate>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.QFedAvg.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.QFedAvg.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.QFedAvg.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.QFedAvg.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.QFedAvg.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.Strategy.rst:2
+msgid "Strategy"
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.Strategy.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid "Aggregate evaluation results."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.Strategy.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.strategy.Strategy.aggregate_fit:1 of
+msgid "Aggregate training results."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.Strategy.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.Strategy.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.Strategy.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.strategy.Strategy.evaluate:1 of
+msgid "Evaluate the current model parameters."
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.Strategy.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.strategy.Strategy.initialize_parameters:1 of
+msgid "Initialize the (global) model parameters."
+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`."
+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."
+msgstr ""
+
+#: 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 ""
+
+#: 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 ""
+
+#: 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."
+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."
+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)."
+msgstr ""
+
+#: 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 ""
+
+#: ../../source/ref-api/flwr.simulation.rst:2
+msgid "simulation"
+msgstr ""
+
+#: ../../source/ref-api/flwr.simulation.rst:17:<autosummary>:1
+msgid ""
+":py:obj:`start_simulation <flwr.simulation.start_simulation>`\\ \\(\\*\\,"
+" client\\_fn\\[\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.simulation.rst:17:<autosummary>:1
+#: flwr.simulation.app.start_simulation:1 of
+msgid "Start a Ray-based Flower simulation server."
+msgstr ""
+
+#: ../../source/ref-api/flwr.simulation.start_simulation.rst:2
+msgid "start\\_simulation"
+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, "
+"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 ""
+
+#: 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
+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."
+msgstr ""
+
+#: 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 ""
+
+#: 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`."
+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 ""
+
+#: 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:  { \"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 ""
+
+#: 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 ""
+
+#: flwr.simulation.app.start_simulation:43 of
+msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }"
+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."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:48 of
+msgid ""
+"Set to True to prevent `ray.shutdown()` in case "
+"`ray.is_initialized()=True`."
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:50 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 "
+"running the clients' jobs (i.e. their `fit()` method)."
+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."
+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"
+msgstr ""
+
+#: flwr.simulation.app.start_simulation:66 of
+msgid "**hist** -- Object containing metrics from training."
+msgstr ""
+
+#: ../../source/ref-changelog.md:1
+msgid "Changelog"
+msgstr ""
+
+#: ../../source/ref-changelog.md:3
+msgid "Unreleased"
+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?"
+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"
+msgstr ""
+
+#: ../../source/ref-changelog.md:9
+msgid "v1.7.0 (2024-02-05)"
+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"
+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
+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:15
+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` <!---TOKEN_v1.7.0-->"
+msgstr ""
+
+#: ../../source/ref-changelog.md:19
+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))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:21
+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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:23
+msgid ""
+"**Improve performance** "
+"([#2293](https://github.com/adap/flower/pull/2293))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:25
+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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:27
+msgid ""
+"**Support Federated Learning with Apple MLX and Flower** "
+"([#2693](https://github.com/adap/flower/pull/2693))"
+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."
+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))"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:122
-msgid "server.strategy.QFedAvg"
+#: ../../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 ""
 
-#: ../../source/ref-api-flwr.rst:133
-msgid "server.strategy.FaultTolerantFedAvg"
+#: ../../source/ref-changelog.md:35
+msgid ""
+"**Support Python 3.11** "
+"([#2394](https://github.com/adap/flower/pull/2394))"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:144
-msgid "server.strategy.FedOpt"
+#: ../../source/ref-changelog.md:37
+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."
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:155
-msgid "server.strategy.FedProx"
+#: ../../source/ref-changelog.md:39
+msgid ""
+"**Update gRPC and ProtoBuf dependencies** "
+"([#2814](https://github.com/adap/flower/pull/2814))"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:166
-msgid "server.strategy.FedAdagrad"
+#: ../../source/ref-changelog.md:41
+msgid ""
+"The `grpcio` and `protobuf` dependencies were updated to their latest "
+"versions for improved security and performance."
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:177
-msgid "server.strategy.FedAdam"
+#: ../../source/ref-changelog.md:43
+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 ""
 
-#: ../../source/ref-api-flwr.rst:188
-msgid "server.strategy.FedYogi"
+#: ../../source/ref-changelog.md:45
+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."
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:199
-msgid "server.strategy.FedTrimmedAvg"
+#: ../../source/ref-changelog.md:47
+msgid ""
+"**Introduce** `flower-via-docker-compose` **example** "
+"([#2626](https://github.com/adap/flower/pull/2626))"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:210
-msgid "server.strategy.Krum"
+#: ../../source/ref-changelog.md:49
+msgid ""
+"**Introduce** `quickstart-sklearn-tabular` **example** "
+"([#2719](https://github.com/adap/flower/pull/2719))"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:221
-msgid "server.strategy.FedXgbNnAvg"
+#: ../../source/ref-changelog.md:51
+msgid ""
+"**Introduce** `custom-metrics` **example** "
+"([#1958](https://github.com/adap/flower/pull/1958))"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:232
-msgid "server.strategy.DPFedAvgAdaptive"
+#: ../../source/ref-changelog.md:53
+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))"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:243
-msgid "server.strategy.DPFedAvgFixed"
+#: ../../source/ref-changelog.md:55
+msgid ""
+"Several code examples were updated to use [Flower "
+"Datasets](https://flower.ai/docs/datasets/)."
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:251
-msgid "common"
+#: ../../source/ref-changelog.md:57
+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 ""
 
-#: flwr.common:1 of
-msgid "Common components shared between server and client."
+#: ../../source/ref-changelog.md:59
+msgid "Many Flower code examples received substantial updates."
 msgstr ""
 
-#: flwr.common.typing.ClientMessage:1 of
-msgid "ClientMessage is a container used to hold one result message."
+#: ../../source/ref-changelog.md:61 ../../source/ref-changelog.md:154
+msgid "**Update Flower Baselines**"
 msgstr ""
 
-#: flwr.common.typing.Code:1 of
-msgid "Client status codes."
+#: ../../source/ref-changelog.md:63
+msgid ""
+"HFedXGBoost ([#2226](https://github.com/adap/flower/pull/2226), "
+"[#2771](https://github.com/adap/flower/pull/2771))"
 msgstr ""
 
-#: flwr.common.typing.DisconnectRes:1 of
-msgid "DisconnectRes message from client to server."
+#: ../../source/ref-changelog.md:64
+msgid "FedVSSL ([#2412](https://github.com/adap/flower/pull/2412))"
 msgstr ""
 
-#: flwr.common.typing.EvaluateIns:1 of
-msgid "Evaluate instructions for a client."
+#: ../../source/ref-changelog.md:65
+msgid "FedNova ([#2179](https://github.com/adap/flower/pull/2179))"
 msgstr ""
 
-#: flwr.common.typing.EvaluateRes:1 of
-msgid "Evaluate response from a client."
+#: ../../source/ref-changelog.md:66
+msgid "HeteroFL ([#2439](https://github.com/adap/flower/pull/2439))"
 msgstr ""
 
-#: flwr.common.telemetry.EventType:1 of
-msgid "Types of telemetry events."
+#: ../../source/ref-changelog.md:67
+msgid "FedAvgM ([#2246](https://github.com/adap/flower/pull/2246))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:68
+msgid "FedPara ([#2722](https://github.com/adap/flower/pull/2722))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:70
+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: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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:76
+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: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.common.typing.FitIns:1 of
-msgid "Fit instructions for a client."
+#: ../../source/ref-changelog.md:82
+msgid ""
+"**Deprecate** `start_numpy_client` "
+"([#2563](https://github.com/adap/flower/pull/2563), "
+"[#2718](https://github.com/adap/flower/pull/2718))"
 msgstr ""
 
-#: flwr.common.typing.FitRes:1 of
-msgid "Fit response from a client."
+#: ../../source/ref-changelog.md:84
+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 ""
 
-#: flwr.common.typing.GetParametersIns:1 of
-msgid "Parameters request for a client."
+#: ../../source/ref-changelog.md:86
+msgid ""
+"**Deprecate legacy DP wrappers** "
+"([#2749](https://github.com/adap/flower/pull/2749))"
 msgstr ""
 
-#: flwr.common.typing.GetParametersRes:1 of
-msgid "Response when asked to return parameters."
+#: ../../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."
 msgstr ""
 
-#: flwr.common.typing.GetPropertiesIns:1 of
-msgid "Properties request for a client."
+#: ../../source/ref-changelog.md:90
+msgid ""
+"**Make optional arg** `--callable` **in** `flower-client` **a required "
+"positional arg** ([#2673](https://github.com/adap/flower/pull/2673))"
 msgstr ""
 
-#: flwr.common.typing.GetPropertiesRes:1 of
-msgid "Properties response from a client."
+#: ../../source/ref-changelog.md:92
+msgid ""
+"**Rename** `certificates` **to** `root_certificates` **in** `Driver` "
+"([#2890](https://github.com/adap/flower/pull/2890))"
 msgstr ""
 
-#: flwr.common.typing.Parameters:1 of
-msgid "Model parameters."
+#: ../../source/ref-changelog.md:94
+msgid ""
+"**Drop experimental** `Task` **fields** "
+"([#2866](https://github.com/adap/flower/pull/2866), "
+"[#2865](https://github.com/adap/flower/pull/2865))"
 msgstr ""
 
-#: flwr.common.typing.ReconnectIns:1 of
-msgid "ReconnectIns message from server to client."
+#: ../../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 ""
 
-#: flwr.common.typing.ServerMessage:1 of
-msgid "ServerMessage is a container used to hold one instruction message."
+#: ../../source/ref-changelog.md:98
+msgid ""
+"**Retire MXNet examples** "
+"([#2724](https://github.com/adap/flower/pull/2724))"
 msgstr ""
 
-#: flwr.common.typing.Status:1 of
-msgid "Client status."
+#: ../../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."
 msgstr ""
 
-#: flwr.common.parameter.bytes_to_ndarray:1 of
-msgid "Deserialize NumPy ndarray from bytes."
+#: ../../source/ref-changelog.md:102
+msgid "v1.6.0 (2023-11-28)"
 msgstr ""
 
-#: flwr.common.logger.configure:1 of
-msgid "Configure logging to file and/or remote log server."
+#: ../../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` <!---TOKEN_v1.6.0-->"
 msgstr ""
 
-#: logging.Logger.log:1 of
-msgid "Log 'msg % args' with the integer severity 'level'."
+#: ../../source/ref-changelog.md:112
+msgid ""
+"**Add experimental support for Python 3.12** "
+"([#2565](https://github.com/adap/flower/pull/2565))"
 msgstr ""
 
-#: logging.Logger.log:3 of
+#: ../../source/ref-changelog.md:114
 msgid ""
-"To pass exception information, use the keyword argument exc_info with a "
-"true value, e.g."
+"**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 ""
 
-#: logging.Logger.log:6 of
-#, python-format
-msgid "logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=1)"
+#: ../../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.common.parameter.ndarray_to_bytes:1 of
-msgid "Serialize NumPy ndarray to bytes."
+#: ../../source/ref-changelog.md:118
+msgid ""
+"**Add Vertical FL example** "
+"([#2598](https://github.com/adap/flower/pull/2598))"
 msgstr ""
 
-#: flwr.common.parameter.ndarrays_to_parameters:1 of
-msgid "Convert NumPy ndarrays to parameters object."
+#: ../../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)."
 msgstr ""
 
-#: flwr.common.date.now:1 of
-msgid "Construct a datetime from time.time() with time zone set to UTC."
+#: ../../source/ref-changelog.md:122
+msgid ""
+"**Support custom** `ClientManager` **in** `start_driver()` "
+"([#2292](https://github.com/adap/flower/pull/2292))"
 msgstr ""
 
-#: flwr.common.parameter.parameters_to_ndarrays:1 of
-msgid "Convert parameters object to NumPy ndarrays."
+#: ../../source/ref-changelog.md:124
+msgid ""
+"**Update REST API to support create and delete nodes** "
+"([#2283](https://github.com/adap/flower/pull/2283))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:1
-msgid "Changelog"
+#: ../../source/ref-changelog.md:126
+msgid ""
+"**Update the Android SDK** "
+"([#2187](https://github.com/adap/flower/pull/2187))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:3
-msgid "Unreleased"
+#: ../../source/ref-changelog.md:128
+msgid "Add gRPC request-response capability to the Android SDK."
 msgstr ""
 
-#: ../../source/ref-changelog.md:5
+#: ../../source/ref-changelog.md:130
 msgid ""
-"**Support custom** `ClientManager` **in** `start_driver()` "
-"([#2292](https://github.com/adap/flower/pull/2292))"
+"**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:7
-msgid ""
-"**Update REST API to support create and delete nodes** "
-"([#2283](https://github.com/adap/flower/pull/2283))"
+#: ../../source/ref-changelog.md:132
+msgid "Add gRPC request-response capability to the C++ SDK."
 msgstr ""
 
-#: ../../source/ref-changelog.md:9 ../../source/ref-changelog.md:59
-#: ../../source/ref-changelog.md:143 ../../source/ref-changelog.md:207
-#: ../../source/ref-changelog.md:265 ../../source/ref-changelog.md:334
-#: ../../source/ref-changelog.md:463 ../../source/ref-changelog.md:505
-#: ../../source/ref-changelog.md:572 ../../source/ref-changelog.md:638
-#: ../../source/ref-changelog.md:683 ../../source/ref-changelog.md:722
-#: ../../source/ref-changelog.md:755 ../../source/ref-changelog.md:805
-msgid "What's new?"
+#: ../../source/ref-changelog.md:134
+msgid ""
+"**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:11
+#: ../../source/ref-changelog.md:136
 msgid ""
-"**Fix the incorrect return types of Strategy** "
-"([#2432](https://github.com/adap/flower/pull/2432/files))"
+"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:13
+#: ../../source/ref-changelog.md:138
 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."
+"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:15
+#: ../../source/ref-changelog.md:140
 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))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:17
+#: ../../source/ref-changelog.md:142
 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."
+" defined and instantiated. The `to_client()` function is introduced to "
+"convert a `NumPyClient` to a `Client`."
 msgstr ""
 
-#: ../../source/ref-changelog.md:19
-msgid "**Update Flower Baselines**"
+#: ../../source/ref-changelog.md:144
+msgid ""
+"**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:21
+#: ../../source/ref-changelog.md:146
+msgid ""
+"The new `Bulyan` strategy implements Bulyan by [El Mhamdi et al., "
+"2018](https://arxiv.org/abs/1802.07927)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:148
+msgid ""
+"**Add new** `XGB Bagging` **strategy** "
+"([#2611](https://github.com/adap/flower/pull/2611))"
+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))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:156
 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))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:23
+#: ../../source/ref-changelog.md:158
 msgid ""
 "Baselines Docs ([#2290](https://github.com/adap/flower/pull/2290), "
 "[#2400](https://github.com/adap/flower/pull/2400))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:25
+#: ../../source/ref-changelog.md:160
 msgid ""
 "FedMLB ([#2340](https://github.com/adap/flower/pull/2340), "
 "[#2507](https://github.com/adap/flower/pull/2507))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:27
+#: ../../source/ref-changelog.md:162
 msgid ""
 "TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), "
 "[#2508](https://github.com/adap/flower/pull/2508))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:29
+#: ../../source/ref-changelog.md:164
 msgid "FedMeta [#2438](https://github.com/adap/flower/pull/2438)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:31
+#: ../../source/ref-changelog.md:166
+msgid "FjORD [#2431](https://github.com/adap/flower/pull/2431)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:168
+msgid "MOON [#2421](https://github.com/adap/flower/pull/2421)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:170
+msgid "DepthFL [#2295](https://github.com/adap/flower/pull/2295)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:172
+msgid "FedPer [#2266](https://github.com/adap/flower/pull/2266)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:174
+msgid "FedWav2vec [#2551](https://github.com/adap/flower/pull/2551)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:176
+msgid "niid-Bench [#2428](https://github.com/adap/flower/pull/2428)"
+msgstr ""
+
+#: ../../source/ref-changelog.md:178
 msgid ""
-"**Update Flower Examples** "
-"([#2384](https://github.com/adap/flower/pull/2384)), "
-"([#2425](https://github.com/adap/flower/pull/2425))"
+"FedBN ([#2608](https://github.com/adap/flower/pull/2608), "
+"[#2615](https://github.com/adap/flower/pull/2615))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:33
+#: ../../source/ref-changelog.md:180
+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 ""
+
+#: ../../source/ref-changelog.md:182
 msgid ""
-"**General updates to baselines** "
+"**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))"
+"[#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:35
+#: ../../source/ref-changelog.md:184
 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))"
+"[#2448](https://github.com/adap/flower/pull/2448), "
+"[#2294](https://github.com/adap/flower/pull/2294))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:37
+#: ../../source/ref-changelog.md:186
+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))"
+msgstr ""
+
+#: ../../source/ref-changelog.md:188
 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), "
+"[#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), "
+"[#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 ""
-
-#: ../../source/ref-changelog.md:39 ../../source/ref-changelog.md:129
-#: ../../source/ref-changelog.md:193 ../../source/ref-changelog.md:247
-#: ../../source/ref-changelog.md:314
+"[#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: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 ""
 
-#: ../../source/ref-changelog.md:41 ../../source/ref-changelog.md:131
-#: ../../source/ref-changelog.md:195 ../../source/ref-changelog.md:253
-#: ../../source/ref-changelog.md:322 ../../source/ref-changelog.md:384
-#: ../../source/ref-changelog.md:403 ../../source/ref-changelog.md:559
-#: ../../source/ref-changelog.md:630 ../../source/ref-changelog.md:667
-#: ../../source/ref-changelog.md:710
-msgid "Incompatible changes"
-msgstr ""
-
-#: ../../source/ref-changelog.md:43
+#: ../../source/ref-changelog.md:194
 msgid ""
 "**Remove support for Python 3.7** "
 "([#2280](https://github.com/adap/flower/pull/2280), "
@@ -5987,43 +11353,30 @@ msgid ""
 "[#2356](https://github.com/adap/flower/pull/2356))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:45
+#: ../../source/ref-changelog.md:196
 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:47
+#: ../../source/ref-changelog.md:198
 msgid ""
 "**Remove experimental argument** `rest` **from** `start_client` "
 "([#2324](https://github.com/adap/flower/pull/2324))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:49
+#: ../../source/ref-changelog.md:200
 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:51
+#: ../../source/ref-changelog.md:202
 msgid "v1.5.0 (2023-08-31)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:53 ../../source/ref-changelog.md:137
-#: ../../source/ref-changelog.md:201 ../../source/ref-changelog.md:259
-#: ../../source/ref-changelog.md:328 ../../source/ref-changelog.md:397
-msgid "Thanks to our contributors"
-msgstr ""
-
-#: ../../source/ref-changelog.md:55 ../../source/ref-changelog.md:139
-#: ../../source/ref-changelog.md:203 ../../source/ref-changelog.md:261
-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:57
+#: ../../source/ref-changelog.md:208
 msgid ""
 "`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, "
 "`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo "
@@ -6032,7 +11385,7 @@ msgid ""
 "TOKEN_v1.5.0-->"
 msgstr ""
 
-#: ../../source/ref-changelog.md:61
+#: ../../source/ref-changelog.md:212
 msgid ""
 "**Introduce new simulation engine** "
 "([#1969](https://github.com/adap/flower/pull/1969), "
@@ -6040,7 +11393,7 @@ msgid ""
 "[#2248](https://github.com/adap/flower/pull/2248))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:63
+#: ../../source/ref-changelog.md:214
 msgid ""
 "The new simulation engine has been rewritten from the ground up, yet it "
 "remains fully backwards compatible. It offers much improved stability and"
@@ -6049,18 +11402,18 @@ msgid ""
 "only, CPU+GPU, multi-GPU, or multi-node multi-GPU environments."
 msgstr ""
 
-#: ../../source/ref-changelog.md:65
+#: ../../source/ref-changelog.md:216
 msgid ""
 "Comprehensive documentation includes a new [how-to run "
-"simulations](https://flower.dev/docs/framework/how-to-run-"
+"simulations](https://flower.ai/docs/framework/how-to-run-"
 "simulations.html) guide, new [simulation-"
-"pytorch](https://flower.dev/docs/examples/simulation-pytorch.html) and "
-"[simulation-tensorflow](https://flower.dev/docs/examples/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:67
+#: ../../source/ref-changelog.md:218
 msgid ""
 "**Restructure Flower Docs** "
 "([#1824](https://github.com/adap/flower/pull/1824), "
@@ -6092,42 +11445,42 @@ msgid ""
 "[#2227](https://github.com/adap/flower/pull/2227))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:69
+#: ../../source/ref-changelog.md:220
 msgid ""
 "Much effort went into a completely restructured Flower docs experience. "
-"The documentation on [flower.dev/docs](flower.dev/docs) is now divided "
+"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 ""
 
-#: ../../source/ref-changelog.md:71
+#: ../../source/ref-changelog.md:222
 msgid ""
 "**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:73
+#: ../../source/ref-changelog.md:224
 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 ""
 
-#: ../../source/ref-changelog.md:75
+#: ../../source/ref-changelog.md:226
 msgid ""
 "**Introduce Flower Android SDK** "
 "([#2131](https://github.com/adap/flower/pull/2131))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:77
+#: ../../source/ref-changelog.md:228
 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 ""
 
-#: ../../source/ref-changelog.md:79
+#: ../../source/ref-changelog.md:230
 msgid ""
 "**Introduce new end-to-end testing infrastructure** "
 "([#1842](https://github.com/adap/flower/pull/1842), "
@@ -6149,42 +11502,42 @@ msgid ""
 "[#2165](https://github.com/adap/flower/pull/2165))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:81
+#: ../../source/ref-changelog.md:232
 msgid ""
 "A new testing infrastructure ensures that new changes stay compatible "
 "with existing framework integrations or strategies."
 msgstr ""
 
-#: ../../source/ref-changelog.md:83
+#: ../../source/ref-changelog.md:234
 msgid "**Deprecate Python 3.7**"
 msgstr ""
 
-#: ../../source/ref-changelog.md:85
+#: ../../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 ""
 
-#: ../../source/ref-changelog.md:87
+#: ../../source/ref-changelog.md:238
 msgid ""
 "**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:89
+#: ../../source/ref-changelog.md:240
 msgid ""
 "The new `FedTrimmedAvg` strategy implements Trimmed Mean by [Dong Yin, "
 "2018](https://arxiv.org/abs/1803.01498)."
 msgstr ""
 
-#: ../../source/ref-changelog.md:91
+#: ../../source/ref-changelog.md:242
 msgid ""
 "**Introduce start_driver** "
 "([#1697](https://github.com/adap/flower/pull/1697))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:93
+#: ../../source/ref-changelog.md:244
 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` "
@@ -6193,13 +11546,13 @@ msgid ""
 "`start_driver`."
 msgstr ""
 
-#: ../../source/ref-changelog.md:95
+#: ../../source/ref-changelog.md:246
 msgid ""
 "**Add parameter aggregation to** `mt-pytorch` **code example** "
 "([#1785](https://github.com/adap/flower/pull/1785))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:97
+#: ../../source/ref-changelog.md:248
 msgid ""
 "The `mt-pytorch` example shows how to aggregate parameters when writing a"
 " driver script. The included `driver.py` and `server.py` have been "
@@ -6207,53 +11560,53 @@ msgid ""
 "building server-side logic."
 msgstr ""
 
-#: ../../source/ref-changelog.md:99
+#: ../../source/ref-changelog.md:250
 msgid ""
 "**Migrate experimental REST API to Starlette** "
 "([2171](https://github.com/adap/flower/pull/2171))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:101
+#: ../../source/ref-changelog.md:252
 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 ""
 
-#: ../../source/ref-changelog.md:103
+#: ../../source/ref-changelog.md:254
 msgid ""
 "Please note: The REST request-response API is still experimental and will"
 " likely change significantly over time."
 msgstr ""
 
-#: ../../source/ref-changelog.md:105
+#: ../../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))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:107
+#: ../../source/ref-changelog.md:258
 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-changelog.md:109
+#: ../../source/ref-changelog.md:260
 msgid ""
 "Please note: The gRPC request-response API is still experimental and will"
 " likely change significantly over time."
 msgstr ""
 
-#: ../../source/ref-changelog.md:111
+#: ../../source/ref-changelog.md:262
 msgid ""
 "**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-changelog.md:113
+#: ../../source/ref-changelog.md:264
 msgid ""
 "The (experimental) `start_client` argument `rest` was deprecated in "
 "favour of a new argument `transport`. `start_client(transport=\"rest\")` "
@@ -6262,30 +11615,30 @@ msgid ""
 "argument `rest` will be removed in a future release."
 msgstr ""
 
-#: ../../source/ref-changelog.md:115
+#: ../../source/ref-changelog.md:266
 msgid ""
 "**Add a new gRPC option** "
 "([#2197](https://github.com/adap/flower/pull/2197))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:117
+#: ../../source/ref-changelog.md:268
 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 ""
 
-#: ../../source/ref-changelog.md:119
+#: ../../source/ref-changelog.md:270
 msgid ""
 "**Improve example notebooks** "
 "([#2005](https://github.com/adap/flower/pull/2005))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:121
+#: ../../source/ref-changelog.md:272
 msgid "There's a new 30min Federated Learning PyTorch tutorial!"
 msgstr ""
 
-#: ../../source/ref-changelog.md:123
+#: ../../source/ref-changelog.md:274
 msgid ""
 "**Example updates** ([#1772](https://github.com/adap/flower/pull/1772), "
 "[#1873](https://github.com/adap/flower/pull/1873), "
@@ -6300,7 +11653,7 @@ msgid ""
 "[#2183](https://github.com/adap/flower/pull/2183))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:125
+#: ../../source/ref-changelog.md:276
 msgid ""
 "Many examples have received significant updates, including simplified "
 "advanced-tensorflow and advanced-pytorch examples, improved macOS "
@@ -6309,7 +11662,7 @@ msgid ""
 "(in addition to `pyproject.toml`)."
 msgstr ""
 
-#: ../../source/ref-changelog.md:127
+#: ../../source/ref-changelog.md:278
 msgid ""
 "**General improvements** "
 "([#1872](https://github.com/adap/flower/pull/1872), "
@@ -6320,17 +11673,17 @@ msgid ""
 "[#2171](https://github.com/adap/flower/pull/2171))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:133 ../../source/ref-changelog.md:197
-#: ../../source/ref-changelog.md:255 ../../source/ref-changelog.md:324
-#: ../../source/ref-changelog.md:386
+#: ../../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 ""
 
-#: ../../source/ref-changelog.md:135
+#: ../../source/ref-changelog.md:286
 msgid "v1.4.0 (2023-04-21)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:141
+#: ../../source/ref-changelog.md:292
 msgid ""
 "`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, "
 "`Chenyang Ma (Danny)`, `Daniel J. Beutel`, `Edoardo`, `Gautam Jajoo`, "
@@ -6340,7 +11693,7 @@ msgid ""
 "`Steve Laskaridis`, `Steven Hé (Sīchàng)`, `Taner Topal`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:145
+#: ../../source/ref-changelog.md:296
 msgid ""
 "**Introduce support for XGBoost (**`FedXgbNnAvg` **strategy and "
 "example)** ([#1694](https://github.com/adap/flower/pull/1694), "
@@ -6351,25 +11704,25 @@ msgid ""
 "[#1795](https://github.com/adap/flower/pull/1795))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:147
+#: ../../source/ref-changelog.md:298
 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/quickstart_xgboost_horizontal)"
+"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:149
+#: ../../source/ref-changelog.md:300
 msgid ""
 "**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:151
+#: ../../source/ref-changelog.md:302
 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 "
@@ -6380,23 +11733,23 @@ msgid ""
 "been updated!"
 msgstr ""
 
-#: ../../source/ref-changelog.md:153
+#: ../../source/ref-changelog.md:304
 msgid ""
 "**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:155
+#: ../../source/ref-changelog.md:306
 msgid ""
-"A new [entry-level tutorial](https://flower.dev/docs/framework/tutorial-"
+"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:157
+#: ../../source/ref-changelog.md:308
 msgid ""
 "**Introduce new Flower Baseline: FedProx MNIST** "
 "([#1513](https://github.com/adap/flower/pull/1513), "
@@ -6405,21 +11758,21 @@ msgid ""
 "[#1679](https://github.com/adap/flower/pull/1679))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:159
+#: ../../source/ref-changelog.md:310
 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 heterogenous settings."
+" which aims at making convergence more robust in heterogeneous settings."
 msgstr ""
 
-#: ../../source/ref-changelog.md:161
+#: ../../source/ref-changelog.md:312
 msgid ""
 "**Introduce new Flower Baseline: FedAvg FEMNIST** "
 "([#1655](https://github.com/adap/flower/pull/1655))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:163
+#: ../../source/ref-changelog.md:314
 msgid ""
 "This new baseline replicates an experiment evaluating the performance of "
 "the FedAvg algorithm on the FEMNIST dataset from the paper [LEAF: A "
@@ -6427,7 +11780,7 @@ msgid ""
 "2018)](https://arxiv.org/abs/1812.01097)."
 msgstr ""
 
-#: ../../source/ref-changelog.md:165
+#: ../../source/ref-changelog.md:316
 msgid ""
 "**Introduce (experimental) REST API** "
 "([#1594](https://github.com/adap/flower/pull/1594), "
@@ -6439,20 +11792,20 @@ msgid ""
 "[#1733](https://github.com/adap/flower/pull/1733))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:167
+#: ../../source/ref-changelog.md:318
 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:169
+#: ../../source/ref-changelog.md:320
 msgid ""
 "Please note: The REST API is still experimental and will likely change "
 "significantly over time."
 msgstr ""
 
-#: ../../source/ref-changelog.md:171
+#: ../../source/ref-changelog.md:322
 msgid ""
 "**Improve the (experimental) Driver API** "
 "([#1663](https://github.com/adap/flower/pull/1663), "
@@ -6466,7 +11819,7 @@ msgid ""
 "[#1794](https://github.com/adap/flower/pull/1794))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:173
+#: ../../source/ref-changelog.md:324
 msgid ""
 "The Driver API is still an experimental feature, but this release "
 "introduces some major upgrades. One of the main improvements is the "
@@ -6476,58 +11829,58 @@ msgid ""
 "improves the memory efficiency of a long-running Flower server."
 msgstr ""
 
-#: ../../source/ref-changelog.md:175
+#: ../../source/ref-changelog.md:326
 msgid ""
 "**Fix spilling issues related to Ray during simulations** "
 "([#1698](https://github.com/adap/flower/pull/1698))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:177
+#: ../../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 ""
 
-#: ../../source/ref-changelog.md:179
+#: ../../source/ref-changelog.md:330
 msgid ""
 "**Add new example using** `TabNet` **and Flower** "
 "([#1725](https://github.com/adap/flower/pull/1725))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:181
+#: ../../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: "
-"[https://github.com/adap/flower/tree/main/examples/tabnet](https://github.com/adap/flower/tree/main/examples/quickstart_tabnet)."
+"[quickstart-tabnet](https://github.com/adap/flower/tree/main/examples/quickstart-tabnet)."
 msgstr ""
 
-#: ../../source/ref-changelog.md:183
+#: ../../source/ref-changelog.md:334
 msgid ""
 "**Add new how-to guide for monitoring simulations** "
 "([#1649](https://github.com/adap/flower/pull/1649))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:185
+#: ../../source/ref-changelog.md:336
 msgid ""
 "We now have a documentation guide to help users monitor their performance"
 " during simulations."
 msgstr ""
 
-#: ../../source/ref-changelog.md:187
+#: ../../source/ref-changelog.md:338
 msgid ""
 "**Add training metrics to** `History` **object during simulations** "
 "([#1696](https://github.com/adap/flower/pull/1696))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:189
+#: ../../source/ref-changelog.md:340
 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!"
 msgstr ""
 
-#: ../../source/ref-changelog.md:191
+#: ../../source/ref-changelog.md:342
 msgid ""
 "**General improvements** "
 "([#1659](https://github.com/adap/flower/pull/1659), "
@@ -6581,23 +11934,23 @@ msgid ""
 "[#1805](https://github.com/adap/flower/pull/1805))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:199
+#: ../../source/ref-changelog.md:350
 msgid "v1.3.0 (2023-02-06)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:205
+#: ../../source/ref-changelog.md:356
 msgid ""
 "`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, "
 "`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:209
+#: ../../source/ref-changelog.md:360
 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:211
+#: ../../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"
@@ -6606,50 +11959,50 @@ msgid ""
 " to decide whether they want to handle a task or not."
 msgstr ""
 
-#: ../../source/ref-changelog.md:213
+#: ../../source/ref-changelog.md:364
 msgid ""
 "**Make Driver API and Fleet API address configurable** "
 "([#1637](https://github.com/adap/flower/pull/1637))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:215
+#: ../../source/ref-changelog.md:366
 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:"
 msgstr ""
 
-#: ../../source/ref-changelog.md:217
+#: ../../source/ref-changelog.md:368
 msgid ""
 "`flower-server --driver-api-address \"0.0.0.0:8081\" --fleet-api-address "
 "\"0.0.0.0:8086\"`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:219
+#: ../../source/ref-changelog.md:370
 msgid "Both IPv4 and IPv6 addresses are supported."
 msgstr ""
 
-#: ../../source/ref-changelog.md:221
+#: ../../source/ref-changelog.md:372
 msgid ""
 "**Add new example of Federated Learning using fastai and Flower** "
 "([#1598](https://github.com/adap/flower/pull/1598))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:223
+#: ../../source/ref-changelog.md:374
 msgid ""
-"A new code example (`quickstart_fastai`) demonstrates federated learning "
+"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:225
+#: ../../source/ref-changelog.md:376
 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 ""
 
-#: ../../source/ref-changelog.md:227
+#: ../../source/ref-changelog.md:378
 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 "
@@ -6657,13 +12010,13 @@ msgid ""
 "tooling."
 msgstr ""
 
-#: ../../source/ref-changelog.md:229
+#: ../../source/ref-changelog.md:380
 msgid ""
 "**Add new `FedProx` strategy** "
 "([#1619](https://github.com/adap/flower/pull/1619))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:231
+#: ../../source/ref-changelog.md:382
 msgid ""
 "This "
 "[strategy](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedprox.py)"
@@ -6675,25 +12028,25 @@ msgid ""
 "respect to the global models."
 msgstr ""
 
-#: ../../source/ref-changelog.md:233
+#: ../../source/ref-changelog.md:384
 msgid ""
 "**Add new metrics to telemetry events** "
 "([#1640](https://github.com/adap/flower/pull/1640))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:235
+#: ../../source/ref-changelog.md:386
 msgid ""
 "An updated event structure allows, for example, the clustering of events "
 "within the same workload."
 msgstr ""
 
-#: ../../source/ref-changelog.md:237
+#: ../../source/ref-changelog.md:388
 msgid ""
 "**Add new custom strategy tutorial section** "
 "[#1623](https://github.com/adap/flower/pull/1623)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:239
+#: ../../source/ref-changelog.md:390
 msgid ""
 "The Flower tutorial now has a new section that covers implementing a "
 "custom strategy from scratch: [Open in "
@@ -6701,13 +12054,13 @@ msgid ""
 "/tutorial-build-a-strategy-from-scratch-pytorch.ipynb)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:241
+#: ../../source/ref-changelog.md:392
 msgid ""
 "**Add new custom serialization tutorial section** "
 "([#1622](https://github.com/adap/flower/pull/1622))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:243
+#: ../../source/ref-changelog.md:394
 msgid ""
 "The Flower tutorial now has a new section that covers custom "
 "serialization: [Open in "
@@ -6715,7 +12068,7 @@ msgid ""
 "/tutorial-customize-the-client-pytorch.ipynb)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:245
+#: ../../source/ref-changelog.md:396
 msgid ""
 "**General improvements** "
 "([#1638](https://github.com/adap/flower/pull/1638), "
@@ -6753,7 +12106,7 @@ msgid ""
 "[#1586](https://github.com/adap/flower/pull/1586))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:249
+#: ../../source/ref-changelog.md:400
 msgid ""
 "**Updated documentation** "
 "([#1629](https://github.com/adap/flower/pull/1629), "
@@ -6765,48 +12118,48 @@ msgid ""
 "[#1614](https://github.com/adap/flower/pull/1614))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:251 ../../source/ref-changelog.md:318
+#: ../../source/ref-changelog.md:402 ../../source/ref-changelog.md:469
 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 ""
 
-#: ../../source/ref-changelog.md:257
+#: ../../source/ref-changelog.md:408
 msgid "v1.2.0 (2023-01-13)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:263
+#: ../../source/ref-changelog.md:414
 msgid ""
 "`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L."
 " Jiang`, `Ragy`, `Taner Topal`, `dannymcy`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:267
+#: ../../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))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:269
+#: ../../source/ref-changelog.md:420
 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.dev/blog/2023-01-12-fl-starter-"
+"this series. [Read more.](https://flower.ai/blog/2023-01-12-fl-starter-"
 "pack-fedavg-mnist-cnn/)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:271
+#: ../../source/ref-changelog.md:422
 msgid ""
 "**Improve GPU support in simulations** "
 "([#1555](https://github.com/adap/flower/pull/1555))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:273
+#: ../../source/ref-changelog.md:424
 msgid ""
 "The Ray-based Virtual Client Engine (`start_simulation`) has been updated"
 " to improve GPU support. The update includes some of the hard-earned "
@@ -6814,45 +12167,45 @@ msgid ""
 "defaults make running GPU-based simulations substantially more robust."
 msgstr ""
 
-#: ../../source/ref-changelog.md:275
+#: ../../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))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:277
+#: ../../source/ref-changelog.md:428
 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 ""
 
-#: ../../source/ref-changelog.md:279
+#: ../../source/ref-changelog.md:430
 msgid ""
-"[An Introduction to Federated Learning](https://flower.dev/docs/framework"
+"[An Introduction to Federated Learning](https://flower.ai/docs/framework"
 "/tutorial-get-started-with-flower-pytorch.html)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:280
+#: ../../source/ref-changelog.md:431
 msgid ""
-"[Strategies in Federated Learning](https://flower.dev/docs/framework"
+"[Strategies in Federated Learning](https://flower.ai/docs/framework"
 "/tutorial-use-a-federated-learning-strategy-pytorch.html)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:281
+#: ../../source/ref-changelog.md:432
 msgid ""
-"[Building a Strategy](https://flower.dev/docs/framework/tutorial-build-a"
+"[Building a Strategy](https://flower.ai/docs/framework/tutorial-build-a"
 "-strategy-from-scratch-pytorch.html)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:282
+#: ../../source/ref-changelog.md:433
 msgid ""
-"[Client and NumPyClient](https://flower.dev/docs/framework/tutorial-"
+"[Client and NumPyClient](https://flower.ai/docs/framework/tutorial-"
 "customize-the-client-pytorch.html)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:284
+#: ../../source/ref-changelog.md:435
 msgid ""
 "**Introduce optional telemetry** "
 "([#1533](https://github.com/adap/flower/pull/1533), "
@@ -6860,7 +12213,7 @@ msgid ""
 "[#1584](https://github.com/adap/flower/pull/1584))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:286
+#: ../../source/ref-changelog.md:437
 msgid ""
 "After a [request for "
 "feedback](https://github.com/adap/flower/issues/1534) from the community,"
@@ -6870,15 +12223,15 @@ msgid ""
 "used and what challenges users might face."
 msgstr ""
 
-#: ../../source/ref-changelog.md:288
+#: ../../source/ref-changelog.md:439
 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.dev/docs/telemetry.html)."
+"[Read more.](https://flower.ai/docs/telemetry.html)."
 msgstr ""
 
-#: ../../source/ref-changelog.md:290
+#: ../../source/ref-changelog.md:441
 msgid ""
 "**Introduce (experimental) Driver API** "
 "([#1520](https://github.com/adap/flower/pull/1520), "
@@ -6890,7 +12243,7 @@ msgid ""
 "[#1567](https://github.com/adap/flower/pull/1567))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:292
+#: ../../source/ref-changelog.md:443
 msgid ""
 "Flower now has a new (experimental) Driver API which will enable fully "
 "programmable, async, and multi-tenant Federated Learning and Federated "
@@ -6899,7 +12252,7 @@ msgid ""
 "and you can start building those things now, too."
 msgstr ""
 
-#: ../../source/ref-changelog.md:294
+#: ../../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 "
@@ -6907,58 +12260,58 @@ msgid ""
 "especially useful for users who want to deploy Flower in production."
 msgstr ""
 
-#: ../../source/ref-changelog.md:296
+#: ../../source/ref-changelog.md:447
 msgid ""
 "To learn more, check out the `mt-pytorch` code example. We look forward "
 "to you feedback!"
 msgstr ""
 
-#: ../../source/ref-changelog.md:298
+#: ../../source/ref-changelog.md:449
 msgid ""
 "Please note: *The Driver API is still experimental and will likely change"
 " significantly over time.*"
 msgstr ""
 
-#: ../../source/ref-changelog.md:300
+#: ../../source/ref-changelog.md:451
 msgid ""
 "**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:302
+#: ../../source/ref-changelog.md:453
 msgid ""
-"A new code example (`quickstart_pandas`) demonstrates federated analytics"
+"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)."
+"[quickstart-pandas](https://github.com/adap/flower/tree/main/examples/quickstart-pandas)."
 msgstr ""
 
-#: ../../source/ref-changelog.md:304
+#: ../../source/ref-changelog.md:455
 msgid ""
 "**Add new strategies: Krum and MultiKrum** "
 "([#1481](https://github.com/adap/flower/pull/1481))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:306
+#: ../../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 ""
 
-#: ../../source/ref-changelog.md:308
+#: ../../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 ""
 
-#: ../../source/ref-changelog.md:310
+#: ../../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 ""
 
-#: ../../source/ref-changelog.md:312
+#: ../../source/ref-changelog.md:463
 msgid ""
 "**General improvements** "
 "([#1491](https://github.com/adap/flower/pull/1491), "
@@ -6976,7 +12329,7 @@ msgid ""
 "[#1566](https://github.com/adap/flower/pull/1566))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:316
+#: ../../source/ref-changelog.md:467
 msgid ""
 "**Updated documentation** "
 "([#1494](https://github.com/adap/flower/pull/1494), "
@@ -6990,24 +12343,24 @@ msgid ""
 "[#1515](https://github.com/adap/flower/pull/1515))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:320
+#: ../../source/ref-changelog.md:471
 msgid ""
 "One highlight is the new [first time contributor "
-"guide](https://flower.dev/docs/first-time-contributors.html): if you've "
+"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:326
+#: ../../source/ref-changelog.md:477
 msgid "v1.1.0 (2022-10-31)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:330
+#: ../../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 ""
 
-#: ../../source/ref-changelog.md:332
+#: ../../source/ref-changelog.md:483
 msgid ""
 "`Akis Linardos`, `Christopher S`, `Daniel J. Beutel`, `George`, `Jan "
 "Schlicht`, `Mohammad Fares`, `Pedro Porto Buarque de Gusmão`, `Philipp "
@@ -7015,14 +12368,14 @@ msgid ""
 "`danielnugraha`, `edogab33`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:336
+#: ../../source/ref-changelog.md:487
 msgid ""
 "**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:338
+#: ../../source/ref-changelog.md:489
 msgid ""
 "The first (experimental) preview of pluggable Differential Privacy "
 "wrappers enables easy configuration and usage of differential privacy "
@@ -7031,13 +12384,13 @@ msgid ""
 "over to the Flower docs, a new explainer goes into more detail."
 msgstr ""
 
-#: ../../source/ref-changelog.md:340
+#: ../../source/ref-changelog.md:491
 msgid ""
 "**New iOS CoreML code example** "
 "([#1289](https://github.com/adap/flower/pull/1289))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:342
+#: ../../source/ref-changelog.md:493
 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 "
@@ -7045,39 +12398,39 @@ msgid ""
 "on CoreML."
 msgstr ""
 
-#: ../../source/ref-changelog.md:344
+#: ../../source/ref-changelog.md:495
 msgid ""
 "**New FedMedian strategy** "
 "([#1461](https://github.com/adap/flower/pull/1461))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:346
+#: ../../source/ref-changelog.md:497
 msgid ""
 "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:348
+#: ../../source/ref-changelog.md:499
 msgid ""
 "**Log** `Client` **exceptions in Virtual Client Engine** "
 "([#1493](https://github.com/adap/flower/pull/1493))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:350
+#: ../../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)."
 msgstr ""
 
-#: ../../source/ref-changelog.md:352
+#: ../../source/ref-changelog.md:503
 msgid ""
 "**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:354
+#: ../../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`"
@@ -7085,25 +12438,25 @@ msgid ""
 "allocated."
 msgstr ""
 
-#: ../../source/ref-changelog.md:356
+#: ../../source/ref-changelog.md:507
 msgid ""
 "**Support optional** `Client`**/**`NumPyClient` **methods in Virtual "
 "Client Engine**"
 msgstr ""
 
-#: ../../source/ref-changelog.md:358
+#: ../../source/ref-changelog.md:509
 msgid ""
 "The Virtual Client Engine now has full support for optional `Client` (and"
 " `NumPyClient`) methods."
 msgstr ""
 
-#: ../../source/ref-changelog.md:360
+#: ../../source/ref-changelog.md:511
 msgid ""
 "**Provide type information to packages using** `flwr` "
 "([#1377](https://github.com/adap/flower/pull/1377))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:362
+#: ../../source/ref-changelog.md:513
 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 "
@@ -7111,20 +12464,20 @@ msgid ""
 "static type checkers like `mypy`."
 msgstr ""
 
-#: ../../source/ref-changelog.md:364
+#: ../../source/ref-changelog.md:515
 msgid ""
 "**Updated code example** "
 "([#1344](https://github.com/adap/flower/pull/1344), "
 "[#1347](https://github.com/adap/flower/pull/1347))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:366
+#: ../../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."
 msgstr ""
 
-#: ../../source/ref-changelog.md:368
+#: ../../source/ref-changelog.md:519
 msgid ""
 "**Updated documentation** "
 "([#1355](https://github.com/adap/flower/pull/1355), "
@@ -7146,32 +12499,32 @@ msgid ""
 "[#1467](https://github.com/adap/flower/pull/1467))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:370
+#: ../../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 ""
 
-#: ../../source/ref-changelog.md:372
+#: ../../source/ref-changelog.md:523
 msgid ""
 "**Restructured documentation** "
 "([#1387](https://github.com/adap/flower/pull/1387))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:374
+#: ../../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!"
 msgstr ""
 
-#: ../../source/ref-changelog.md:376
+#: ../../source/ref-changelog.md:527
 msgid ""
 "**Open in Colab button** "
 "([#1389](https://github.com/adap/flower/pull/1389))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:378
+#: ../../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 "
@@ -7179,7 +12532,7 @@ msgid ""
 "only a single click away."
 msgstr ""
 
-#: ../../source/ref-changelog.md:380
+#: ../../source/ref-changelog.md:531
 msgid ""
 "**Improved tutorial** ([#1468](https://github.com/adap/flower/pull/1468),"
 " [#1470](https://github.com/adap/flower/pull/1470), "
@@ -7189,7 +12542,7 @@ msgid ""
 "[#1475](https://github.com/adap/flower/pull/1475))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:382
+#: ../../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 "
@@ -7197,40 +12550,40 @@ msgid ""
 "(many small changes and fixes)."
 msgstr ""
 
-#: ../../source/ref-changelog.md:388
+#: ../../source/ref-changelog.md:539
 msgid "v1.0.0 (2022-07-28)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:390
+#: ../../source/ref-changelog.md:541
 msgid "Highlights"
 msgstr ""
 
-#: ../../source/ref-changelog.md:392
+#: ../../source/ref-changelog.md:543
 msgid "Stable **Virtual Client Engine** (accessible via `start_simulation`)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:393
+#: ../../source/ref-changelog.md:544
 msgid "All `Client`/`NumPyClient` methods are now optional"
 msgstr ""
 
-#: ../../source/ref-changelog.md:394
+#: ../../source/ref-changelog.md:545
 msgid "Configurable `get_parameters`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:395
+#: ../../source/ref-changelog.md:546
 msgid ""
 "Tons of small API cleanups resulting in a more coherent developer "
 "experience"
 msgstr ""
 
-#: ../../source/ref-changelog.md:399
+#: ../../source/ref-changelog.md:550
 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:401
+#: ../../source/ref-changelog.md:552
 msgid ""
 "[@rtaiello](https://github.com/rtaiello), "
 "[@g-pichler](https://github.com/g-pichler), [@rob-"
@@ -7255,7 +12608,7 @@ msgid ""
 "[@mrinaald](https://github.com/mrinaald), "
 "[@zliel](https://github.com/zliel), "
 "[@MeiruiJiang](https://github.com/MeiruiJiang), "
-"[@sandracl72](https://github.com/sandracl72), "
+"[@sancarlim](https://github.com/sancarlim), "
 "[@gubertoli](https://github.com/gubertoli), "
 "[@Vingt100](https://github.com/Vingt100), "
 "[@MakGulati](https://github.com/MakGulati), "
@@ -7270,13 +12623,13 @@ msgid ""
 "[@danieljanes](https://github.com/danieljanes)."
 msgstr ""
 
-#: ../../source/ref-changelog.md:405
+#: ../../source/ref-changelog.md:556
 msgid ""
 "**All arguments must be passed as keyword arguments** "
 "([#1338](https://github.com/adap/flower/pull/1338))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:407
+#: ../../source/ref-changelog.md:558
 msgid ""
 "Pass all arguments as keyword arguments, positional arguments are not "
 "longer supported. Code that uses positional arguments (e.g., "
@@ -7286,14 +12639,14 @@ msgid ""
 "client=FlowerClient())`)."
 msgstr ""
 
-#: ../../source/ref-changelog.md:409
+#: ../../source/ref-changelog.md:560
 msgid ""
 "**Introduce configuration object** `ServerConfig` **in** `start_server` "
 "**and** `start_simulation` "
 "([#1317](https://github.com/adap/flower/pull/1317))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:411
+#: ../../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"
@@ -7302,37 +12655,37 @@ msgid ""
 "safe code easier and the default parameters values more transparent."
 msgstr ""
 
-#: ../../source/ref-changelog.md:413
+#: ../../source/ref-changelog.md:564
 msgid ""
 "**Rename built-in strategy parameters for clarity** "
 "([#1334](https://github.com/adap/flower/pull/1334))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:415
+#: ../../source/ref-changelog.md:566
 msgid ""
 "The following built-in strategy parameters were renamed to improve "
 "readability and consistency with other API's:"
 msgstr ""
 
-#: ../../source/ref-changelog.md:417
+#: ../../source/ref-changelog.md:568
 msgid "`fraction_eval` --> `fraction_evaluate`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:418
+#: ../../source/ref-changelog.md:569
 msgid "`min_eval_clients` --> `min_evaluate_clients`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:419
+#: ../../source/ref-changelog.md:570
 msgid "`eval_fn` --> `evaluate_fn`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:421
+#: ../../source/ref-changelog.md:572
 msgid ""
 "**Update default arguments of built-in strategies** "
 "([#1278](https://github.com/adap/flower/pull/1278))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:423
+#: ../../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 "
@@ -7341,29 +12694,29 @@ msgid ""
 "initializing the strategy in the following way:"
 msgstr ""
 
-#: ../../source/ref-changelog.md:425
+#: ../../source/ref-changelog.md:576
 msgid "`strategy = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:427
+#: ../../source/ref-changelog.md:578
 msgid ""
 "**Add** `server_round` **to** `Strategy.evaluate` "
 "([#1334](https://github.com/adap/flower/pull/1334))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:429
+#: ../../source/ref-changelog.md:580
 msgid ""
 "The `Strategy` method `evaluate` now receives the current round of "
 "federated learning/evaluation as the first parameter."
 msgstr ""
 
-#: ../../source/ref-changelog.md:431
+#: ../../source/ref-changelog.md:582
 msgid ""
 "**Add** `server_round` **and** `config` **parameters to** `evaluate_fn` "
 "([#1334](https://github.com/adap/flower/pull/1334))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:433
+#: ../../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 "
@@ -7371,13 +12724,13 @@ msgid ""
 "and (3) a config dictionary (`config`)."
 msgstr ""
 
-#: ../../source/ref-changelog.md:435
+#: ../../source/ref-changelog.md:586
 msgid ""
 "**Rename** `rnd` **to** `server_round` "
 "([#1321](https://github.com/adap/flower/pull/1321))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:437
+#: ../../source/ref-changelog.md:588
 msgid ""
 "Several Flower methods and functions (`evaluate_fn`, `configure_fit`, "
 "`aggregate_fit`, `configure_evaluate`, `aggregate_evaluate`) receive the "
@@ -7386,73 +12739,73 @@ msgid ""
 "has been renamed from `rnd` to `server_round`."
 msgstr ""
 
-#: ../../source/ref-changelog.md:439
+#: ../../source/ref-changelog.md:590
 msgid ""
 "**Move** `flwr.dataset` **to** `flwr_baselines` "
 "([#1273](https://github.com/adap/flower/pull/1273))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:441
+#: ../../source/ref-changelog.md:592
 msgid "The experimental package `flwr.dataset` was migrated to Flower Baselines."
 msgstr ""
 
-#: ../../source/ref-changelog.md:443
+#: ../../source/ref-changelog.md:594
 msgid ""
 "**Remove experimental strategies** "
 "([#1280](https://github.com/adap/flower/pull/1280))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:445
+#: ../../source/ref-changelog.md:596
 msgid ""
 "Remove unmaintained experimental strategies (`FastAndSlow`, `FedFSv0`, "
 "`FedFSv1`)."
 msgstr ""
 
-#: ../../source/ref-changelog.md:447
+#: ../../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 ""
 
-#: ../../source/ref-changelog.md:449
+#: ../../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 ""
 
-#: ../../source/ref-changelog.md:451
+#: ../../source/ref-changelog.md:602
 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))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:453
+#: ../../source/ref-changelog.md:604
 msgid ""
 "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:455
+#: ../../source/ref-changelog.md:606
 msgid ""
 "**Make** `get_parameters` **configurable** "
 "([#1242](https://github.com/adap/flower/pull/1242))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:457
+#: ../../source/ref-changelog.md:608
 msgid ""
 "The `get_parameters` method now accepts a configuration dictionary, just "
 "like `get_properties`, `fit`, and `evaluate`."
 msgstr ""
 
-#: ../../source/ref-changelog.md:459
+#: ../../source/ref-changelog.md:610
 msgid ""
 "**Replace** `num_rounds` **in** `start_simulation` **with new** `config` "
 "**parameter** ([#1281](https://github.com/adap/flower/pull/1281))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:461
+#: ../../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 "
@@ -7460,26 +12813,26 @@ msgid ""
 "transitioning between the two easier."
 msgstr ""
 
-#: ../../source/ref-changelog.md:465
+#: ../../source/ref-changelog.md:616
 msgid ""
 "**Support Python 3.10** "
 "([#1320](https://github.com/adap/flower/pull/1320))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:467
+#: ../../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 ""
 
-#: ../../source/ref-changelog.md:469
+#: ../../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 ""
 
-#: ../../source/ref-changelog.md:471
+#: ../../source/ref-changelog.md:622
 msgid ""
 "The `Client`/`NumPyClient` methods `get_properties`, `get_parameters`, "
 "`fit`, and `evaluate` are all optional. This enables writing clients that"
@@ -7487,13 +12840,13 @@ msgid ""
 "implement `evaluate` when using centralized evaluation!"
 msgstr ""
 
-#: ../../source/ref-changelog.md:473
+#: ../../source/ref-changelog.md:624
 msgid ""
 "**Enable passing a** `Server` **instance to** `start_simulation` "
 "([#1281](https://github.com/adap/flower/pull/1281))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:475
+#: ../../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 "
@@ -7501,7 +12854,7 @@ msgid ""
 " Virtual Client Engine."
 msgstr ""
 
-#: ../../source/ref-changelog.md:477
+#: ../../source/ref-changelog.md:628
 msgid ""
 "**Update code examples** "
 "([#1291](https://github.com/adap/flower/pull/1291), "
@@ -7509,50 +12862,50 @@ msgid ""
 "[#1282](https://github.com/adap/flower/pull/1282))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:479
+#: ../../source/ref-changelog.md:630
 msgid ""
 "Many code examples received small or even large maintenance updates, "
 "among them are"
 msgstr ""
 
-#: ../../source/ref-changelog.md:481
+#: ../../source/ref-changelog.md:632
 msgid "`scikit-learn`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:482
+#: ../../source/ref-changelog.md:633
 msgid "`simulation_pytorch`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:483
+#: ../../source/ref-changelog.md:634
 msgid "`quickstart_pytorch`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:484
+#: ../../source/ref-changelog.md:635
 msgid "`quickstart_simulation`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:485
+#: ../../source/ref-changelog.md:636
 msgid "`quickstart_tensorflow`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:486
+#: ../../source/ref-changelog.md:637
 msgid "`advanced_tensorflow`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:488
+#: ../../source/ref-changelog.md:639
 msgid ""
 "**Remove the obsolete simulation example** "
 "([#1328](https://github.com/adap/flower/pull/1328))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:490
+#: ../../source/ref-changelog.md:641
 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:492
+#: ../../source/ref-changelog.md:643
 msgid ""
 "**Update documentation** "
 "([#1223](https://github.com/adap/flower/pull/1223), "
@@ -7567,7 +12920,7 @@ msgid ""
 "[#1307](https://github.com/adap/flower/pull/1307))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:494
+#: ../../source/ref-changelog.md:645
 msgid ""
 "One substantial documentation update fixes multiple smaller rendering "
 "issues, makes titles more succinct to improve navigation, removes a "
@@ -7577,24 +12930,24 @@ msgid ""
 "fixes a number of smaller details!"
 msgstr ""
 
-#: ../../source/ref-changelog.md:496 ../../source/ref-changelog.md:551
-#: ../../source/ref-changelog.md:620 ../../source/ref-changelog.md:659
+#: ../../source/ref-changelog.md:647 ../../source/ref-changelog.md:702
+#: ../../source/ref-changelog.md:771 ../../source/ref-changelog.md:810
 msgid "**Minor updates**"
 msgstr ""
 
-#: ../../source/ref-changelog.md:498
+#: ../../source/ref-changelog.md:649
 msgid ""
 "Add round number to fit and evaluate log messages "
 "([#1266](https://github.com/adap/flower/pull/1266))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:499
+#: ../../source/ref-changelog.md:650
 msgid ""
 "Add secure gRPC connection to the `advanced_tensorflow` code example "
 "([#847](https://github.com/adap/flower/pull/847))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:500
+#: ../../source/ref-changelog.md:651
 msgid ""
 "Update developer tooling "
 "([#1231](https://github.com/adap/flower/pull/1231), "
@@ -7603,7 +12956,7 @@ msgid ""
 "[#1310](https://github.com/adap/flower/pull/1310))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:501
+#: ../../source/ref-changelog.md:652
 msgid ""
 "Rename ProtoBuf messages to improve consistency "
 "([#1214](https://github.com/adap/flower/pull/1214), "
@@ -7611,11 +12964,11 @@ msgid ""
 "[#1259](https://github.com/adap/flower/pull/1259))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:503
+#: ../../source/ref-changelog.md:654
 msgid "v0.19.0 (2022-05-18)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:507
+#: ../../source/ref-changelog.md:658
 msgid ""
 "**Flower Baselines (preview): FedOpt, FedBN, FedAvgM** "
 "([#919](https://github.com/adap/flower/pull/919), "
@@ -7623,50 +12976,50 @@ msgid ""
 "[#914](https://github.com/adap/flower/pull/914))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:509
+#: ../../source/ref-changelog.md:660
 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.dev/docs/using-baselines.html). "
+"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.dev/docs/contributing-"
+"[contribute their own baselines](https://flower.ai/docs/contributing-"
 "baselines.html)."
 msgstr ""
 
-#: ../../source/ref-changelog.md:511
+#: ../../source/ref-changelog.md:662
 msgid ""
 "**C++ client SDK (preview) and code example** "
 "([#1111](https://github.com/adap/flower/pull/1111))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:513
+#: ../../source/ref-changelog.md:664
 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/ref-changelog.md:515
+#: ../../source/ref-changelog.md:666
 msgid ""
 "**Add experimental support for Python 3.10 and Python 3.11** "
 "([#1135](https://github.com/adap/flower/pull/1135))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:517
+#: ../../source/ref-changelog.md:668
 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/ref-changelog.md:519
+#: ../../source/ref-changelog.md:670
 msgid ""
 "**Aggregate custom metrics through user-provided functions** "
 "([#1144](https://github.com/adap/flower/pull/1144))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:521
+#: ../../source/ref-changelog.md:672
 msgid ""
 "Custom metrics (e.g., `accuracy`) can now be aggregated without having to"
 " customize the strategy. Built-in strategies support two new arguments, "
@@ -7674,13 +13027,13 @@ msgid ""
 "allow passing custom metric aggregation functions."
 msgstr ""
 
-#: ../../source/ref-changelog.md:523
+#: ../../source/ref-changelog.md:674
 msgid ""
 "**User-configurable round timeout** "
 "([#1162](https://github.com/adap/flower/pull/1162))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:525
+#: ../../source/ref-changelog.md:676
 msgid ""
 "A new configuration value allows the round timeout to be set for "
 "`start_server` and `start_simulation`. If the `config` dictionary "
@@ -7689,14 +13042,14 @@ msgid ""
 "connection."
 msgstr ""
 
-#: ../../source/ref-changelog.md:527
+#: ../../source/ref-changelog.md:678
 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 ""
 
-#: ../../source/ref-changelog.md:529
+#: ../../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 "
@@ -7704,155 +13057,155 @@ msgid ""
 " `0.0`."
 msgstr ""
 
-#: ../../source/ref-changelog.md:531
+#: ../../source/ref-changelog.md:682
 msgid ""
 "**Two new Jupyter Notebook tutorials** "
 "([#1141](https://github.com/adap/flower/pull/1141))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:533
+#: ../../source/ref-changelog.md:684
 msgid ""
 "Two Jupyter Notebook tutorials (compatible with Google Colab) explain "
 "basic and intermediate Flower features:"
 msgstr ""
 
-#: ../../source/ref-changelog.md:535
+#: ../../source/ref-changelog.md:686
 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)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:537
+#: ../../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)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:539
+#: ../../source/ref-changelog.md:690
 msgid ""
 "**New FedAvgM strategy (Federated Averaging with Server Momentum)** "
 "([#1076](https://github.com/adap/flower/pull/1076))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:541
+#: ../../source/ref-changelog.md:692
 msgid ""
 "The new `FedAvgM` strategy implements Federated Averaging with Server "
 "Momentum \\[Hsu et al., 2019\\]."
 msgstr ""
 
-#: ../../source/ref-changelog.md:543
+#: ../../source/ref-changelog.md:694
 msgid ""
 "**New advanced PyTorch code example** "
 "([#1007](https://github.com/adap/flower/pull/1007))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:545
+#: ../../source/ref-changelog.md:696
 msgid ""
 "A new code example (`advanced_pytorch`) demonstrates advanced Flower "
 "concepts with PyTorch."
 msgstr ""
 
-#: ../../source/ref-changelog.md:547
+#: ../../source/ref-changelog.md:698
 msgid ""
 "**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:549
+#: ../../source/ref-changelog.md:700
 msgid ""
 "A new code example (`jax_from_centralized_to_federated`) shows federated "
 "learning with JAX and Flower."
 msgstr ""
 
-#: ../../source/ref-changelog.md:553
+#: ../../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 ""
 
-#: ../../source/ref-changelog.md:554
+#: ../../source/ref-changelog.md:705
 msgid ""
 "Add support for custom `ClientManager` as a `start_simulation` parameter "
 "([#1171](https://github.com/adap/flower/pull/1171))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:555
+#: ../../source/ref-changelog.md:706
 msgid ""
 "New documentation for [implementing "
-"strategies](https://flower.dev/docs/framework/how-to-implement-"
+"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:556
+#: ../../source/ref-changelog.md:707
 msgid ""
 "New mobile-friendly documentation theme "
 "([#1174](https://github.com/adap/flower/pull/1174))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:557
+#: ../../source/ref-changelog.md:708
 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/ref-changelog.md:561
+#: ../../source/ref-changelog.md:712
 msgid ""
 "**Remove deprecated support for Python 3.6** "
 "([#871](https://github.com/adap/flower/pull/871))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:562
+#: ../../source/ref-changelog.md:713
 msgid ""
 "**Remove deprecated KerasClient** "
 "([#857](https://github.com/adap/flower/pull/857))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:563
+#: ../../source/ref-changelog.md:714
 msgid ""
 "**Remove deprecated no-op extra installs** "
 "([#973](https://github.com/adap/flower/pull/973))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:564
+#: ../../source/ref-changelog.md:715
 msgid ""
 "**Remove deprecated proto fields from** `FitRes` **and** `EvaluateRes` "
 "([#869](https://github.com/adap/flower/pull/869))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:565
+#: ../../source/ref-changelog.md:716
 msgid ""
 "**Remove deprecated QffedAvg strategy (replaced by QFedAvg)** "
 "([#1107](https://github.com/adap/flower/pull/1107))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:566
+#: ../../source/ref-changelog.md:717
 msgid ""
 "**Remove deprecated DefaultStrategy strategy** "
 "([#1142](https://github.com/adap/flower/pull/1142))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:567
+#: ../../source/ref-changelog.md:718
 msgid ""
 "**Remove deprecated support for eval_fn accuracy return value** "
 "([#1142](https://github.com/adap/flower/pull/1142))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:568
+#: ../../source/ref-changelog.md:719
 msgid ""
 "**Remove deprecated support for passing initial parameters as NumPy "
 "ndarrays** ([#1142](https://github.com/adap/flower/pull/1142))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:570
+#: ../../source/ref-changelog.md:721
 msgid "v0.18.0 (2022-02-28)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:574
+#: ../../source/ref-changelog.md:725
 msgid ""
 "**Improved Virtual Client Engine compatibility with Jupyter Notebook / "
 "Google Colab** ([#866](https://github.com/adap/flower/pull/866), "
@@ -7861,7 +13214,7 @@ msgid ""
 "[#1036](https://github.com/adap/flower/pull/1036))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:576
+#: ../../source/ref-changelog.md:727
 msgid ""
 "Simulations (using the Virtual Client Engine through `start_simulation`) "
 "now work more smoothly on Jupyter Notebooks (incl. Google Colab) after "
@@ -7869,38 +13222,38 @@ msgid ""
 "flwr[simulation]`)."
 msgstr ""
 
-#: ../../source/ref-changelog.md:578
+#: ../../source/ref-changelog.md:729
 msgid ""
 "**New Jupyter Notebook code example** "
 "([#833](https://github.com/adap/flower/pull/833))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:580
+#: ../../source/ref-changelog.md:731
 msgid ""
 "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:582
+#: ../../source/ref-changelog.md:733
 msgid ""
 "**Client properties (feature preview)** "
 "([#795](https://github.com/adap/flower/pull/795))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:584
+#: ../../source/ref-changelog.md:735
 msgid ""
 "Clients can implement a new method `get_properties` to enable server-side"
 " strategies to query client properties."
 msgstr ""
 
-#: ../../source/ref-changelog.md:586
+#: ../../source/ref-changelog.md:737
 msgid ""
 "**Experimental Android support with TFLite** "
 "([#865](https://github.com/adap/flower/pull/865))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:588
+#: ../../source/ref-changelog.md:739
 msgid ""
 "Android support has finally arrived in `main`! Flower is both client-"
 "agnostic and framework-agnostic by design. One can integrate arbitrary "
@@ -7908,7 +13261,7 @@ msgid ""
 "become a lot easier."
 msgstr ""
 
-#: ../../source/ref-changelog.md:590
+#: ../../source/ref-changelog.md:741
 msgid ""
 "The example uses TFLite on the client side, along with a new "
 "`FedAvgAndroid` strategy. The Android client and `FedAvgAndroid` are "
@@ -7917,13 +13270,13 @@ msgid ""
 " functionality from `FedAvgAndroid`."
 msgstr ""
 
-#: ../../source/ref-changelog.md:592
+#: ../../source/ref-changelog.md:743
 msgid ""
 "**Make gRPC keepalive time user-configurable and decrease default "
 "keepalive time** ([#1069](https://github.com/adap/flower/pull/1069))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:594
+#: ../../source/ref-changelog.md:745
 msgid ""
 "The default gRPC keepalive time has been reduced to increase the "
 "compatibility of Flower with more cloud environments (for example, "
@@ -7931,31 +13284,31 @@ msgid ""
 " gRPC stack based on specific requirements."
 msgstr ""
 
-#: ../../source/ref-changelog.md:596
+#: ../../source/ref-changelog.md:747
 msgid ""
 "**New differential privacy example using Opacus and PyTorch** "
 "([#805](https://github.com/adap/flower/pull/805))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:598
+#: ../../source/ref-changelog.md:749
 msgid ""
 "A new code example (`opacus`) demonstrates differentially-private "
 "federated learning with Opacus, PyTorch, and Flower."
 msgstr ""
 
-#: ../../source/ref-changelog.md:600
+#: ../../source/ref-changelog.md:751
 msgid ""
 "**New Hugging Face Transformers code example** "
 "([#863](https://github.com/adap/flower/pull/863))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:602
+#: ../../source/ref-changelog.md:753
 msgid ""
 "A new code example (`quickstart_huggingface`) demonstrates usage of "
 "Hugging Face Transformers with Flower."
 msgstr ""
 
-#: ../../source/ref-changelog.md:604
+#: ../../source/ref-changelog.md:755
 msgid ""
 "**New MLCube code example** "
 "([#779](https://github.com/adap/flower/pull/779), "
@@ -7964,13 +13317,13 @@ msgid ""
 "[#1090](https://github.com/adap/flower/pull/1090))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:606
+#: ../../source/ref-changelog.md:757
 msgid ""
 "A new code example (`quickstart_mlcube`) demonstrates usage of MLCube "
 "with Flower."
 msgstr ""
 
-#: ../../source/ref-changelog.md:608
+#: ../../source/ref-changelog.md:759
 msgid ""
 "**SSL-enabled server and client** "
 "([#842](https://github.com/adap/flower/pull/842),  "
@@ -7981,33 +13334,33 @@ msgid ""
 "[#994](https://github.com/adap/flower/pull/994))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:610
+#: ../../source/ref-changelog.md:761
 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/ref-changelog.md:612
+#: ../../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))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:614
+#: ../../source/ref-changelog.md:765
 msgid ""
 "`FedAdam` and `FedAdam` match the latest version of the Adaptive "
 "Federated Optimization paper."
 msgstr ""
 
-#: ../../source/ref-changelog.md:616
+#: ../../source/ref-changelog.md:767
 msgid ""
 "**Initialize** `start_simulation` **with a list of client IDs** "
 "([#860](https://github.com/adap/flower/pull/860))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:618
+#: ../../source/ref-changelog.md:769
 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 "
@@ -8016,55 +13369,55 @@ msgid ""
 "identifiers."
 msgstr ""
 
-#: ../../source/ref-changelog.md:622
+#: ../../source/ref-changelog.md:773
 msgid ""
 "Update `num_examples` calculation in PyTorch code examples in "
 "([#909](https://github.com/adap/flower/pull/909))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:623
+#: ../../source/ref-changelog.md:774
 msgid ""
 "Expose Flower version through `flwr.__version__` "
 "([#952](https://github.com/adap/flower/pull/952))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:624
+#: ../../source/ref-changelog.md:775
 msgid ""
 "`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:625
+#: ../../source/ref-changelog.md:776
 msgid ""
 "Make `max_workers` (used by `ThreadPoolExecutor`) configurable "
 "([#978](https://github.com/adap/flower/pull/978))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:626
+#: ../../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 ""
 
-#: ../../source/ref-changelog.md:627
+#: ../../source/ref-changelog.md:778
 msgid ""
 "Added a new FAQ section to the documentation "
 "([#948](https://github.com/adap/flower/pull/948))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:628
+#: ../../source/ref-changelog.md:779
 msgid ""
 "And many more under-the-hood changes, library updates, documentation "
 "changes, and tooling improvements!"
 msgstr ""
 
-#: ../../source/ref-changelog.md:632
+#: ../../source/ref-changelog.md:783
 msgid ""
 "**Removed** `flwr_example` **and** `flwr_experimental` **from release "
 "build** ([#869](https://github.com/adap/flower/pull/869))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:634
+#: ../../source/ref-changelog.md:785
 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 "
@@ -8073,11 +13426,11 @@ msgid ""
 "an upcoming release."
 msgstr ""
 
-#: ../../source/ref-changelog.md:636
+#: ../../source/ref-changelog.md:787
 msgid "v0.17.0 (2021-09-24)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:640
+#: ../../source/ref-changelog.md:791
 msgid ""
 "**Experimental virtual client engine** "
 "([#781](https://github.com/adap/flower/pull/781) "
@@ -8085,7 +13438,7 @@ msgid ""
 "[#791](https://github.com/adap/flower/pull/791))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:642
+#: ../../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"
@@ -8095,7 +13448,7 @@ msgid ""
 "code examples called `quickstart_simulation` and `simulation_pytorch`."
 msgstr ""
 
-#: ../../source/ref-changelog.md:644
+#: ../../source/ref-changelog.md:795
 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 "
@@ -8103,86 +13456,86 @@ msgid ""
 "out and share their thoughts."
 msgstr ""
 
-#: ../../source/ref-changelog.md:646
+#: ../../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))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:648
+#: ../../source/ref-changelog.md:799
 msgid ""
 "FedYogi - Federated learning strategy using Yogi on server-side. "
 "Implementation based on https://arxiv.org/abs/2003.00295"
 msgstr ""
 
-#: ../../source/ref-changelog.md:649
+#: ../../source/ref-changelog.md:800
 msgid ""
 "FedAdam - Federated learning strategy using Adam on server-side. "
 "Implementation based on https://arxiv.org/abs/2003.00295"
 msgstr ""
 
-#: ../../source/ref-changelog.md:651
+#: ../../source/ref-changelog.md:802
 msgid ""
 "**New PyTorch Lightning code example** "
 "([#617](https://github.com/adap/flower/pull/617))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:653
+#: ../../source/ref-changelog.md:804
 msgid ""
 "**New Variational Auto-Encoder code example** "
 "([#752](https://github.com/adap/flower/pull/752))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:655
+#: ../../source/ref-changelog.md:806
 msgid ""
 "**New scikit-learn code example** "
 "([#748](https://github.com/adap/flower/pull/748))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:657
+#: ../../source/ref-changelog.md:808
 msgid ""
 "**New experimental TensorBoard strategy** "
 "([#789](https://github.com/adap/flower/pull/789))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:661
+#: ../../source/ref-changelog.md:812
 msgid ""
 "Improved advanced TensorFlow code example "
 "([#769](https://github.com/adap/flower/pull/769))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:662
+#: ../../source/ref-changelog.md:813
 msgid ""
 "Warning when `min_available_clients` is misconfigured "
 "([#830](https://github.com/adap/flower/pull/830))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:663
+#: ../../source/ref-changelog.md:814
 msgid ""
 "Improved gRPC server docs "
 "([#841](https://github.com/adap/flower/pull/841))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:664
+#: ../../source/ref-changelog.md:815
 msgid ""
 "Improved error message in `NumPyClient` "
 "([#851](https://github.com/adap/flower/pull/851))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:665
+#: ../../source/ref-changelog.md:816
 msgid ""
 "Improved PyTorch quickstart code example "
 "([#852](https://github.com/adap/flower/pull/852))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:669
+#: ../../source/ref-changelog.md:820
 msgid ""
 "**Disabled final distributed evaluation** "
 "([#800](https://github.com/adap/flower/pull/800))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:671
+#: ../../source/ref-changelog.md:822
 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 "
@@ -8190,29 +13543,29 @@ msgid ""
 "`force_final_distributed_eval=True` to `start_server`."
 msgstr ""
 
-#: ../../source/ref-changelog.md:673
+#: ../../source/ref-changelog.md:824
 msgid ""
 "**Renamed q-FedAvg strategy** "
 "([#802](https://github.com/adap/flower/pull/802))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:675
+#: ../../source/ref-changelog.md:826
 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 the original (now "
+"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:677
+#: ../../source/ref-changelog.md:828
 msgid ""
 "**Deprecated and renamed code example** `simulation_pytorch` **to** "
 "`simulation_pytorch_legacy` "
 "([#791](https://github.com/adap/flower/pull/791))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:679
+#: ../../source/ref-changelog.md:830
 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 "
@@ -8221,31 +13574,27 @@ msgid ""
 "removed in the future."
 msgstr ""
 
-#: ../../source/ref-changelog.md:681
+#: ../../source/ref-changelog.md:832
 msgid "v0.16.0 (2021-05-11)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:685
+#: ../../source/ref-changelog.md:836
 msgid ""
 "**New built-in strategies** "
 "([#549](https://github.com/adap/flower/pull/549))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:687
+#: ../../source/ref-changelog.md:838
 msgid "(abstract) FedOpt"
 msgstr ""
 
-#: ../../source/ref-changelog.md:688
-msgid "FedAdagrad"
-msgstr ""
-
-#: ../../source/ref-changelog.md:690
+#: ../../source/ref-changelog.md:841
 msgid ""
 "**Custom metrics for server and strategies** "
 "([#717](https://github.com/adap/flower/pull/717))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:692
+#: ../../source/ref-changelog.md:843
 msgid ""
 "The Flower server is now fully task-agnostic, all remaining instances of "
 "task-specific metrics (such as `accuracy`) have been replaced by custom "
@@ -8254,37 +13603,37 @@ msgid ""
 "release, custom metrics replace task-specific metrics on the server."
 msgstr ""
 
-#: ../../source/ref-changelog.md:694
+#: ../../source/ref-changelog.md:845
 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 build-in strategies (via "
+"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:696
+#: ../../source/ref-changelog.md:847
 msgid ""
-"Stratey implementations should migrate their `aggregate_fit` and "
+"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/ref-changelog.md:698
+#: ../../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 ""
 
-#: ../../source/ref-changelog.md:700
+#: ../../source/ref-changelog.md:851
 msgid ""
 "**Migration warnings for deprecated functionality** "
 "([#690](https://github.com/adap/flower/pull/690))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:702
+#: ../../source/ref-changelog.md:853
 msgid ""
 "Earlier versions of Flower were often migrated to new APIs, while "
 "maintaining compatibility with legacy APIs. This release introduces "
@@ -8293,7 +13642,7 @@ msgid ""
 "recent APIs, thus easing the transition from one release to another."
 msgstr ""
 
-#: ../../source/ref-changelog.md:704
+#: ../../source/ref-changelog.md:855
 msgid ""
 "Improved docs and docstrings "
 "([#691](https://github.com/adap/flower/pull/691) "
@@ -8301,11 +13650,11 @@ msgid ""
 "[#713](https://github.com/adap/flower/pull/713))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:706
+#: ../../source/ref-changelog.md:857
 msgid "MXNet example and documentation"
 msgstr ""
 
-#: ../../source/ref-changelog.md:708
+#: ../../source/ref-changelog.md:859
 msgid ""
 "FedBN implementation in example PyTorch: From Centralized To Federated "
 "([#696](https://github.com/adap/flower/pull/696) "
@@ -8313,13 +13662,13 @@ msgid ""
 "[#705](https://github.com/adap/flower/pull/705))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:712
+#: ../../source/ref-changelog.md:863
 msgid ""
 "**Serialization-agnostic server** "
 "([#721](https://github.com/adap/flower/pull/721))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:714
+#: ../../source/ref-changelog.md:865
 msgid ""
 "The Flower server is now fully serialization-agnostic. Prior usage of "
 "class `Weights` (which represents parameters as deserialized NumPy "
@@ -8330,41 +13679,41 @@ msgid ""
 "serialization/deserialization)."
 msgstr ""
 
-#: ../../source/ref-changelog.md:716
+#: ../../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 slighly changed Strategy method "
+"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/ref-changelog.md:718
+#: ../../source/ref-changelog.md:869
 msgid ""
 "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:720
+#: ../../source/ref-changelog.md:871
 msgid "v0.15.0 (2021-03-12)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:724
+#: ../../source/ref-changelog.md:875
 msgid ""
 "**Server-side parameter initialization** "
 "([#658](https://github.com/adap/flower/pull/658))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:726
+#: ../../source/ref-changelog.md:877
 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:728
+#: ../../source/ref-changelog.md:879
 msgid ""
 "Built-in strategies support a new constructor argument called "
 "`initial_parameters` to set the initial parameters. Built-in strategies "
@@ -8372,7 +13721,7 @@ msgid ""
 "delete them to free the memory afterwards."
 msgstr ""
 
-#: ../../source/ref-changelog.md:747
+#: ../../source/ref-changelog.md:898
 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 "
@@ -8380,21 +13729,21 @@ msgid ""
 "parameters)."
 msgstr ""
 
-#: ../../source/ref-changelog.md:749
+#: ../../source/ref-changelog.md:900
 msgid "Deprecations"
 msgstr ""
 
-#: ../../source/ref-changelog.md:751
+#: ../../source/ref-changelog.md:902
 msgid ""
 "Deprecate `flwr.server.strategy.DefaultStrategy` (migrate to "
 "`flwr.server.strategy.FedAvg`, which is equivalent)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:753
+#: ../../source/ref-changelog.md:904
 msgid "v0.14.0 (2021-02-18)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:757
+#: ../../source/ref-changelog.md:908
 msgid ""
 "**Generalized** `Client.fit` **and** `Client.evaluate` **return values** "
 "([#610](https://github.com/adap/flower/pull/610) "
@@ -8402,7 +13751,7 @@ msgid ""
 "[#633](https://github.com/adap/flower/pull/633))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:759
+#: ../../source/ref-changelog.md:910
 msgid ""
 "Clients can now return an additional dictionary mapping `str` keys to "
 "values of the following types: `bool`, `bytes`, `float`, `int`, `str`. "
@@ -8410,7 +13759,7 @@ msgid ""
 "and make use of them on the server side!"
 msgstr ""
 
-#: ../../source/ref-changelog.md:761
+#: ../../source/ref-changelog.md:912
 msgid ""
 "This improvement also allowed for more consistent return types between "
 "`fit` and `evaluate`: `evaluate` should now return a tuple `(float, int, "
@@ -8418,7 +13767,7 @@ msgid ""
 "holding arbitrary problem-specific values like accuracy."
 msgstr ""
 
-#: ../../source/ref-changelog.md:763
+#: ../../source/ref-changelog.md:914
 msgid ""
 "In case you wondered: this feature is compatible with existing projects, "
 "the additional dictionary return value is optional. New code should "
@@ -8428,19 +13777,19 @@ msgid ""
 "details."
 msgstr ""
 
-#: ../../source/ref-changelog.md:765
+#: ../../source/ref-changelog.md:916
 msgid ""
 "*Code example:* note the additional dictionary return values in both "
 "`FlwrClient.fit` and `FlwrClient.evaluate`:"
 msgstr ""
 
-#: ../../source/ref-changelog.md:780
+#: ../../source/ref-changelog.md:931
 msgid ""
 "**Generalized** `config` **argument in** `Client.fit` **and** "
 "`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:782
+#: ../../source/ref-changelog.md:933
 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 "
@@ -8448,58 +13797,58 @@ msgid ""
 "`bytes`, `float`, `int`, `str`."
 msgstr ""
 
-#: ../../source/ref-changelog.md:784
+#: ../../source/ref-changelog.md:935
 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!"
 msgstr ""
 
-#: ../../source/ref-changelog.md:786
+#: ../../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`:"
 msgstr ""
 
-#: ../../source/ref-changelog.md:803
+#: ../../source/ref-changelog.md:954
 msgid "v0.13.0 (2021-01-08)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:807
+#: ../../source/ref-changelog.md:958
 msgid ""
 "New example: PyTorch From Centralized To Federated "
 "([#549](https://github.com/adap/flower/pull/549))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:808
+#: ../../source/ref-changelog.md:959
 msgid "Improved documentation"
 msgstr ""
 
-#: ../../source/ref-changelog.md:809
+#: ../../source/ref-changelog.md:960
 msgid "New documentation theme ([#551](https://github.com/adap/flower/pull/551))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:810
+#: ../../source/ref-changelog.md:961
 msgid "New API reference ([#554](https://github.com/adap/flower/pull/554))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:811
+#: ../../source/ref-changelog.md:962
 msgid ""
 "Updated examples documentation "
 "([#549](https://github.com/adap/flower/pull/549))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:812
+#: ../../source/ref-changelog.md:963
 msgid ""
 "Removed obsolete documentation "
 "([#548](https://github.com/adap/flower/pull/548))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:814
+#: ../../source/ref-changelog.md:965
 msgid "Bugfix:"
 msgstr ""
 
-#: ../../source/ref-changelog.md:816
+#: ../../source/ref-changelog.md:967
 msgid ""
 "`Server.fit` does not disconnect clients when finished, disconnecting the"
 " clients is now handled in `flwr.server.start_server` "
@@ -8507,28 +13856,28 @@ msgid ""
 "[#540](https://github.com/adap/flower/issues/540))."
 msgstr ""
 
-#: ../../source/ref-changelog.md:818
+#: ../../source/ref-changelog.md:969
 msgid "v0.12.0 (2020-12-07)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:820 ../../source/ref-changelog.md:836
+#: ../../source/ref-changelog.md:971 ../../source/ref-changelog.md:987
 msgid "Important changes:"
 msgstr ""
 
-#: ../../source/ref-changelog.md:822
+#: ../../source/ref-changelog.md:973
 msgid ""
 "Added an example for embedded devices "
 "([#507](https://github.com/adap/flower/pull/507))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:823
+#: ../../source/ref-changelog.md:974
 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 ""
 
-#: ../../source/ref-changelog.md:824
+#: ../../source/ref-changelog.md:975
 msgid ""
 "Deprecated `flwr_example` package and started to migrate examples into "
 "the top-level `examples` directory "
@@ -8536,15 +13885,15 @@ msgid ""
 "[#512](https://github.com/adap/flower/pull/512))"
 msgstr ""
 
-#: ../../source/ref-changelog.md:826
+#: ../../source/ref-changelog.md:977
 msgid "v0.11.0 (2020-11-30)"
 msgstr ""
 
-#: ../../source/ref-changelog.md:828
+#: ../../source/ref-changelog.md:979
 msgid "Incompatible changes:"
 msgstr ""
 
-#: ../../source/ref-changelog.md:830
+#: ../../source/ref-changelog.md:981
 msgid ""
 "Renamed strategy methods "
 "([#486](https://github.com/adap/flower/pull/486)) to unify the naming of "
@@ -8554,48 +13903,48 @@ msgid ""
 "migrate rename the following `Strategy` methods accordingly:"
 msgstr ""
 
-#: ../../source/ref-changelog.md:831
+#: ../../source/ref-changelog.md:982
 msgid "`on_configure_evaluate` => `configure_evaluate`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:832
+#: ../../source/ref-changelog.md:983
 msgid "`on_aggregate_evaluate` => `aggregate_evaluate`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:833
+#: ../../source/ref-changelog.md:984
 msgid "`on_configure_fit` => `configure_fit`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:834
+#: ../../source/ref-changelog.md:985
 msgid "`on_aggregate_fit` => `aggregate_fit`"
 msgstr ""
 
-#: ../../source/ref-changelog.md:838
+#: ../../source/ref-changelog.md:989
 msgid ""
 "Deprecated `DefaultStrategy` "
 "([#479](https://github.com/adap/flower/pull/479)). To migrate use "
 "`FedAvg` instead."
 msgstr ""
 
-#: ../../source/ref-changelog.md:839
+#: ../../source/ref-changelog.md:990
 msgid ""
 "Simplified examples and baselines "
 "([#484](https://github.com/adap/flower/pull/484))."
 msgstr ""
 
-#: ../../source/ref-changelog.md:840
+#: ../../source/ref-changelog.md:991
 msgid ""
 "Removed presently unused `on_conclude_round` from strategy interface "
 "([#483](https://github.com/adap/flower/pull/483))."
 msgstr ""
 
-#: ../../source/ref-changelog.md:841
+#: ../../source/ref-changelog.md:992
 msgid ""
 "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:842
+#: ../../source/ref-changelog.md:993
 msgid ""
 "Improved `Strategy` docstrings "
 "([#470](https://github.com/adap/flower/pull/470))."
@@ -8645,13 +13994,13 @@ msgstr ""
 
 #: ../../source/ref-example-projects.rst:26
 msgid ""
-"`Quickstart TensorFlow (Tutorial) <https://flower.dev/docs/framework"
+"`Quickstart TensorFlow (Tutorial) <https://flower.ai/docs/framework"
 "/tutorial-quickstart-tensorflow.html>`_"
 msgstr ""
 
 #: ../../source/ref-example-projects.rst:27
 msgid ""
-"`Quickstart TensorFlow (Blog Post) <https://flower.dev/blog/2020-12-11"
+"`Quickstart TensorFlow (Blog Post) <https://flower.ai/blog/2020-12-11"
 "-federated-learning-in-less-than-20-lines-of-code>`_"
 msgstr ""
 
@@ -8674,7 +14023,7 @@ msgstr ""
 
 #: ../../source/ref-example-projects.rst:37
 msgid ""
-"`Quickstart PyTorch (Tutorial) <https://flower.dev/docs/framework"
+"`Quickstart PyTorch (Tutorial) <https://flower.ai/docs/framework"
 "/tutorial-quickstart-pytorch.html>`_"
 msgstr ""
 
@@ -8698,7 +14047,7 @@ msgstr ""
 #: ../../source/ref-example-projects.rst:46
 msgid ""
 "`PyTorch: From Centralized To Federated (Tutorial) "
-"<https://flower.dev/docs/framework/example-pytorch-from-centralized-to-"
+"<https://flower.ai/docs/framework/example-pytorch-from-centralized-to-"
 "federated.html>`_"
 msgstr ""
 
@@ -8721,7 +14070,7 @@ msgstr ""
 #: ../../source/ref-example-projects.rst:55
 msgid ""
 "`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
 msgstr ""
 
 #: ../../source/ref-example-projects.rst:60
@@ -8908,7 +14257,7 @@ msgstr ""
 #: ../../source/ref-faq.rst:15
 msgid ""
 "Find the `blog post about federated learning on embedded device here "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
 " and the corresponding `GitHub code example "
 "<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_."
 msgstr ""
@@ -8920,18 +14269,18 @@ msgstr ""
 #: ../../source/ref-faq.rst:19
 msgid ""
 "Yes, it does. Please take a look at our `blog post "
-"<https://flower.dev/blog/2021-12-15-federated-learning-on-android-"
+"<https://flower.ai/blog/2021-12-15-federated-learning-on-android-"
 "devices-with-flower>`_ or check out the code examples:"
 msgstr ""
 
 #: ../../source/ref-faq.rst:21
 msgid ""
-"`Android Kotlin example <https://flower.dev/docs/examples/android-"
+"`Android Kotlin example <https://flower.ai/docs/examples/android-"
 "kotlin.html>`_"
 msgstr ""
 
 #: ../../source/ref-faq.rst:22
-msgid "`Android Java example <https://flower.dev/docs/examples/android.html>`_"
+msgid "`Android Java example <https://flower.ai/docs/examples/android.html>`_"
 msgstr ""
 
 #: ../../source/ref-faq.rst
@@ -9117,7 +14466,7 @@ msgstr ""
 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.dev`. All events "
+"request mentioning the source ID to `telemetry@flower.ai`. All events "
 "related to that source ID will then be permanently deleted."
 msgstr ""
 
@@ -9165,8 +14514,8 @@ msgstr ""
 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.dev/join-slack/) (channel `#telemetry`) or email "
-"(`telemetry@flower.dev`)."
+"[Slack](https://flower.ai/join-slack/) (channel `#telemetry`) or email "
+"(`telemetry@flower.ai`)."
 msgstr ""
 
 #: ../../source/tutorial-quickstart-android.rst:-1
@@ -9388,7 +14737,7 @@ 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 "
-"<https://flower.dev/docs/recommended-env-setup.html>`_. For the Flower "
+"<https://flower.ai/docs/recommended-env-setup.html>`_. For the Flower "
 "client implementation in iOS, it is recommended to use Xcode as our IDE."
 msgstr ""
 
@@ -9486,7 +14835,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-quickstart-ios.rst:131
-#: ../../source/tutorial-quickstart-mxnet.rst:226
+#: ../../source/tutorial-quickstart-mxnet.rst:228
 #: ../../source/tutorial-quickstart-pytorch.rst:205
 #: ../../source/tutorial-quickstart-tensorflow.rst:100
 msgid ""
@@ -9496,7 +14845,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-quickstart-ios.rst:142
-#: ../../source/tutorial-quickstart-mxnet.rst:237
+#: ../../source/tutorial-quickstart-mxnet.rst:239
 #: ../../source/tutorial-quickstart-pytorch.rst:216
 #: ../../source/tutorial-quickstart-scikitlearn.rst:215
 #: ../../source/tutorial-quickstart-tensorflow.rst:112
@@ -9506,6 +14855,7 @@ 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
 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 "
@@ -9550,21 +14900,29 @@ msgstr ""
 msgid "Quickstart MXNet"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:10
+#: ../../source/tutorial-quickstart-mxnet.rst:7
+msgid ""
+"MXNet is no longer maintained and has been moved into `Attic "
+"<https://attic.apache.org/projects/mxnet.html>`_. 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 ""
+
+#: ../../source/tutorial-quickstart-mxnet.rst:12
 msgid ""
 "In this tutorial, we will learn how to train a :code:`Sequential` model "
 "on MNIST using Flower and MXNet."
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:12
+#: ../../source/tutorial-quickstart-mxnet.rst:14
 #: ../../source/tutorial-quickstart-scikitlearn.rst:12
 msgid ""
 "It is recommended to create a virtual environment and run everything "
-"within this `virtualenv <https://flower.dev/docs/recommended-env-"
+"within this `virtualenv <https://flower.ai/docs/recommended-env-"
 "setup.html>`_."
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:16
+#: ../../source/tutorial-quickstart-mxnet.rst:18
 #: ../../source/tutorial-quickstart-scikitlearn.rst:16
 msgid ""
 "*Clients* are responsible for generating individual model parameter "
@@ -9575,18 +14933,18 @@ msgid ""
 "called a *round*."
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:20
+#: ../../source/tutorial-quickstart-mxnet.rst:22
 #: ../../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-quickstart-mxnet.rst:26
+#: ../../source/tutorial-quickstart-mxnet.rst:28
 msgid "Since we want to use MXNet, let's go ahead and install it:"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:36
+#: ../../source/tutorial-quickstart-mxnet.rst:38
 msgid ""
 "Now that we have all our dependencies installed, let's run a simple "
 "distributed training with two clients and one server. Our training "
@@ -9595,53 +14953,53 @@ msgid ""
 "<https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html>`_."
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:38
+#: ../../source/tutorial-quickstart-mxnet.rst:40
 msgid ""
 "In a file called :code:`client.py`, import Flower and MXNet related "
 "packages:"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:53
+#: ../../source/tutorial-quickstart-mxnet.rst:55
 msgid "In addition, define the device allocation in MXNet with:"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:59
+#: ../../source/tutorial-quickstart-mxnet.rst:61
 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 ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:73
+#: ../../source/tutorial-quickstart-mxnet.rst:75
 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 ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:111
+#: ../../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."
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:135
+#: ../../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."
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:137
+#: ../../source/tutorial-quickstart-mxnet.rst:139
 msgid "Our Flower clients will use a simple :code:`Sequential` model:"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:156
+#: ../../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."
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:158
+#: ../../source/tutorial-quickstart-mxnet.rst:160
 #: ../../source/tutorial-quickstart-pytorch.rst:144
 #: ../../source/tutorial-quickstart-tensorflow.rst:54
 msgid ""
@@ -9652,7 +15010,7 @@ msgid ""
 "your code (i.e., to train the neural network we defined earlier)."
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:164
+#: ../../source/tutorial-quickstart-mxnet.rst:166
 msgid ""
 "Flower provides a convenience class called :code:`NumPyClient` which "
 "makes it easier to implement the :code:`Client` interface when your "
@@ -9661,19 +15019,19 @@ msgid ""
 "though):"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:170
+#: ../../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 ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:171
+#: ../../source/tutorial-quickstart-mxnet.rst:173
 #: ../../source/tutorial-quickstart-pytorch.rst:157
 #: ../../source/tutorial-quickstart-scikitlearn.rst:111
 msgid ":code:`set_parameters` (optional)"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:172
+#: ../../source/tutorial-quickstart-mxnet.rst:174
 #: ../../source/tutorial-quickstart-pytorch.rst:158
 #: ../../source/tutorial-quickstart-scikitlearn.rst:111
 msgid ""
@@ -9681,42 +15039,41 @@ msgid ""
 "server"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:174
+#: ../../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 ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:175
+#: ../../source/tutorial-quickstart-mxnet.rst:177
 #: ../../source/tutorial-quickstart-pytorch.rst:161
 #: ../../source/tutorial-quickstart-scikitlearn.rst:115
 msgid "train the local model"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:176
+#: ../../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 ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:178
+#: ../../source/tutorial-quickstart-mxnet.rst:180
 #: ../../source/tutorial-quickstart-pytorch.rst:164
 #: ../../source/tutorial-quickstart-scikitlearn.rst:118
 msgid "test the local model"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:180
+#: ../../source/tutorial-quickstart-mxnet.rst:182
 msgid "They can be implemented in the following way:"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:210
+#: ../../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:"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:217
-#: ../../source/tutorial-quickstart-scikitlearn.rst:150
+#: ../../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 "
@@ -9728,39 +15085,42 @@ msgid ""
 "that needs to change is the :code:`server_address` we pass to the client."
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:239
+#: ../../source/tutorial-quickstart-mxnet.rst:241
 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 ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:247
+#: ../../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
 msgid ""
 "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-mxnet.rst:254
+#: ../../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 ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:260
+#: ../../source/tutorial-quickstart-mxnet.rst:262
 #: ../../source/tutorial-quickstart-pytorch.rst:239
 #: ../../source/tutorial-quickstart-scikitlearn.rst:237
+#: ../../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):"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:292
+#: ../../source/tutorial-quickstart-mxnet.rst:294
 msgid ""
 "Congratulations! You've successfully built and run your first federated "
 "learning system. The full `source code "
@@ -9803,9 +15163,10 @@ msgid ""
 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 `virtualenv <https://flower.dev/docs/recommended-env-"
+"everything within a `virtualenv <https://flower.ai/docs/recommended-env-"
 "setup.html>`_."
 msgstr ""
 
@@ -9896,10 +15257,11 @@ msgstr ""
 #: ../../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()` or "
-":code:`fl.client.start_numpy_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:`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."
@@ -9976,7 +15338,7 @@ msgid ":code:`get_model_parameters()`"
 msgstr ""
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:46
-msgid "Returns the paramters of a :code:`sklearn` LogisticRegression model"
+msgid "Returns the parameters of a :code:`sklearn` LogisticRegression model"
 msgstr ""
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:47
@@ -10075,6 +15437,19 @@ msgid ""
 "one line to actually run this client:"
 msgstr ""
 
+#: ../../source/tutorial-quickstart-scikitlearn.rst:150
+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 ""
+
 #: ../../source/tutorial-quickstart-scikitlearn.rst:159
 msgid ""
 "The following Flower server is a little bit more advanced and returns an "
@@ -10202,17 +15577,496 @@ msgstr ""
 msgid "Quickstart XGBoost"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-xgboost.rst:10
+#: ../../source/tutorial-quickstart-xgboost.rst:14
+msgid "Federated XGBoost"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:16
 msgid ""
-"Let's build a horizontal federated learning system using XGBoost and "
-"Flower!"
+"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:12
+#: ../../source/tutorial-quickstart-xgboost.rst:20
 msgid ""
-"Please refer to the `full code example "
-"<https://github.com/adap/flower/tree/main/examples/quickstart-xgboost-"
-"horizontal>`_ to learn more."
+"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 "
+"<https://github.com/adap/flower/tree/main/examples/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 <https://github.com/adap/flower/tree/main/examples/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-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 <https://github.com/adap/flower/blob/main/examples"
+"/xgboost-quickstart/>`_ 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 "
+"<https://github.com/adap/flower/tree/main/examples/xgboost-"
+"comprehensive>`_), we provide more options to define various experimental"
+" setups, including aggregation strategies, data partitioning and "
+"centralised/distributed evaluation. We also support `Flower simulation "
+"<https://flower.ai/docs/framework/how-to-run-simulations.html>`_ 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 <https://github.com/adap/flower/blob/main/examples"
+"/xgboost-comprehensive/>`_ for this comprehensive example can be found in"
+" :code:`examples/xgboost-comprehensive`."
 msgstr ""
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:9
@@ -10223,10 +16077,10 @@ 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 <https://flower.dev/docs/framework/tutorial-"
+"PyTorch and Flower (`part 1 <https://flower.ai/docs/framework/tutorial-"
 "get-started-with-flower-pytorch.html>`__) and we learned how strategies "
 "can be used to customize the execution on both the server and the clients"
-" (`part 2 <https://flower.dev/docs/framework/tutorial-use-a-federated-"
+" (`part 2 <https://flower.ai/docs/framework/tutorial-use-a-federated-"
 "learning-strategy-pytorch.html>`__)."
 msgstr ""
 
@@ -10234,7 +16088,7 @@ msgstr ""
 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 <https://flower.dev/>`__ and `PyTorch "
+" using `Flower <https://flower.ai/>`__ and `PyTorch "
 "<https://pytorch.org/>`__)."
 msgstr ""
 
@@ -10245,7 +16099,7 @@ msgstr ""
 msgid ""
 "`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ and join "
 "the Flower community on Slack to connect, ask questions, and get help: "
-"`Join Slack <https://flower.dev/join-slack>`__ 🌼 We'd love to hear from "
+"`Join Slack <https://flower.ai/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 ""
@@ -10291,7 +16145,6 @@ msgstr ""
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:101
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:102
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:104
 #: ../../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 "
@@ -10390,17 +16243,17 @@ 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:749
+#: ../../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 <https://flower.dev/join-slack/>`__"
+"`Join Slack <https://flower.ai/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:751
+#: ../../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 ""
@@ -10411,7 +16264,7 @@ msgstr ""
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:554
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 4 "
-"<https://flower.dev/docs/framework/tutorial-customize-the-client-"
+"<https://flower.ai/docs/framework/tutorial-customize-the-client-"
 "pytorch.html>`__ introduces ``Client``, the flexible API underlying "
 "``NumPyClient``."
 msgstr ""
@@ -10424,12 +16277,12 @@ msgstr ""
 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 <https://flower.dev/docs/framework"
+"with PyTorch and Flower (`part 1 <https://flower.ai/docs/framework"
 "/tutorial-get-started-with-flower-pytorch.html>`__), we learned how "
 "strategies can be used to customize the execution on both the server and "
-"the clients (`part 2 <https://flower.dev/docs/framework/tutorial-use-a"
+"the clients (`part 2 <https://flower.ai/docs/framework/tutorial-use-a"
 "-federated-learning-strategy-pytorch.html>`__), and we built our own "
-"custom strategy from scratch (`part 3 <https://flower.dev/docs/framework"
+"custom strategy from scratch (`part 3 <https://flower.ai/docs/framework"
 "/tutorial-build-a-strategy-from-scratch-pytorch.html>`__)."
 msgstr ""
 
@@ -10635,7 +16488,7 @@ msgstr ""
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:615
 msgid ""
-"To be able to able to serialize our ``ndarray``\\ s into sparse "
+"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 ""
@@ -10712,7 +16565,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:954
-msgid "`Read Flower Docs <https://flower.dev/docs/>`__"
+msgid "`Read Flower Docs <https://flower.ai/docs/>`__"
 msgstr ""
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:955
@@ -10724,12 +16577,12 @@ msgstr ""
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:956
 msgid ""
 "`Use Flower Baselines for your research "
-"<https://flower.dev/docs/baselines/>`__"
+"<https://flower.ai/docs/baselines/>`__"
 msgstr ""
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:957
 msgid ""
-"`Watch Flower Summit 2023 videos <https://flower.dev/conf/flower-"
+"`Watch Flower Summit 2023 videos <https://flower.ai/conf/flower-"
 "summit-2023/>`__"
 msgstr ""
 
@@ -10744,10 +16597,11 @@ msgstr ""
 
 #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:13
 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."
+"In this notebook, we'll build a federated learning system using Flower, "
+"`Flower Datasets <https://flower.ai/docs/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
@@ -10763,23 +16617,38 @@ msgstr ""
 #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:45
 msgid ""
 "Next, we install the necessary packages for PyTorch (``torch`` and "
-"``torchvision``) 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``."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:117
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:118
 msgid "Loading the data"
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:119
+#: ../../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:"
+"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:150
+#: ../../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"
@@ -10790,22 +16659,22 @@ msgid ""
 "data is naturally partitioned)."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:152
+#: ../../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:"
+"clients connected to the federated learning server."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:172
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:144
 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:"
+"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:222
+#: ../../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 "
@@ -10817,13 +16686,13 @@ msgid ""
 "multiple partitions."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:225
+#: ../../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:264
+#: ../../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 "
@@ -10832,11 +16701,11 @@ msgid ""
 "batch of images."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:276
+#: ../../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:287
+#: ../../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 "
@@ -10846,26 +16715,26 @@ msgid ""
 "<https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html>`__."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:299
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:275
 msgid "Defining the model"
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:301
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:277
 msgid ""
 "We use the simple CNN described in the `PyTorch tutorial "
 "<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#define-a"
 "-convolutional-neural-network>`__:"
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:338
+#: ../../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:398
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:374
 msgid "Training the model"
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:400
+#: ../../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 "
@@ -10875,7 +16744,7 @@ msgid ""
 "models only on this internal data:"
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:430
+#: ../../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 "
@@ -10884,11 +16753,11 @@ msgid ""
 "sets the stage for what comes next - federated learning!"
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:442
+#: ../../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:444
+#: ../../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``)."
@@ -10897,11 +16766,11 @@ msgid ""
 "organizations using federated learning."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:456
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:432
 msgid "Updating model parameters"
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:458
+#: ../../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 "
@@ -10912,7 +16781,7 @@ msgid ""
 "parameters)."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:460
+#: ../../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"
@@ -10920,7 +16789,7 @@ msgid ""
 "two functions do just that for the PyTorch model above."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:462
+#: ../../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 "
@@ -10929,11 +16798,11 @@ msgid ""
 "ndarray's (which Flower knows how to serialize/deserialize):"
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:490
+#: ../../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:492
+#: ../../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 "
@@ -10943,39 +16812,39 @@ msgid ""
 "requires us to write less boilerplate."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:494
+#: ../../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:496
+#: ../../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:497
+#: ../../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:498
+#: ../../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:500
+#: ../../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:537
+#: ../../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"
@@ -10990,11 +16859,11 @@ msgid ""
 "evaluation)."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:541
+#: ../../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:543
+#: ../../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 "
@@ -11005,7 +16874,7 @@ msgid ""
 "clients participates in a single round of federated learning."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:545
+#: ../../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 "
@@ -11021,11 +16890,11 @@ msgid ""
 "different clients, as can be seen below:"
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:580
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:556
 msgid "Starting the training"
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:582
+#: ../../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 "
@@ -11034,7 +16903,7 @@ msgid ""
 "actual simulation using ``flwr.simulation.start_simulation``."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:584
+#: ../../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 "
@@ -11044,7 +16913,7 @@ msgid ""
 "*Federated Averaging* (FedAvg)."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:586
+#: ../../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"
@@ -11054,15 +16923,15 @@ msgid ""
 "starts the simulation:"
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:628
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:608
 msgid "Behind the scenes"
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:630
+#: ../../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:632
+#: ../../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 "
@@ -11072,7 +16941,7 @@ msgid ""
 "ahead and selects 10 random clients (i.e., 100% of 10)."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:634
+#: ../../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 "
@@ -11081,17 +16950,17 @@ msgid ""
 " then gets used in the next round of federated learning."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:646
+#: ../../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:648
+#: ../../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:650
+#: ../../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 "
@@ -11101,7 +16970,7 @@ msgid ""
 "handle these automatically."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:652
+#: ../../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 "
@@ -11111,19 +16980,19 @@ msgid ""
 "``evaluate_metrics_aggregation_fn``."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:654
+#: ../../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:680
+#: ../../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:717
+#: ../../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"
@@ -11131,7 +17000,7 @@ msgid ""
 "all clients on the server side."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:719
+#: ../../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 "
@@ -11139,12 +17008,12 @@ msgid ""
 "will cover centralized evaluation."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:731
+#: ../../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:733
+#: ../../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 "
@@ -11154,7 +17023,7 @@ msgid ""
 "Transformers or speech with SpeechBrain."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:735
+#: ../../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 "
@@ -11162,10 +17031,10 @@ msgid ""
 "all this and more in the next tutorial."
 msgstr ""
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:753
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:733
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 2 "
-"<https://flower.dev/docs/framework/tutorial-use-a-federated-learning-"
+"<https://flower.ai/docs/framework/tutorial-use-a-federated-learning-"
 "strategy-pytorch.html>`__ goes into more depth about strategies and all "
 "the advanced things you can build with them."
 msgstr ""
@@ -11178,7 +17047,7 @@ msgstr ""
 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 <https://flower.dev/docs/framework/tutorial-get-started-"
+" Flower (`part 1 <https://flower.ai/docs/framework/tutorial-get-started-"
 "with-flower-pytorch.html>`__)."
 msgstr ""
 
@@ -11186,7 +17055,7 @@ msgstr ""
 msgid ""
 "In this notebook, we'll begin to customize the federated learning system "
 "we built in the introductory notebook (again, using `Flower "
-"<https://flower.dev/>`__ and `PyTorch <https://pytorch.org/>`__)."
+"<https://flower.ai/>`__ and `PyTorch <https://pytorch.org/>`__)."
 msgstr ""
 
 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:17
@@ -11384,7 +17253,7 @@ msgstr ""
 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:719
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 3 "
-"<https://flower.dev/docs/framework/tutorial-build-a-strategy-from-"
+"<https://flower.ai/docs/framework/tutorial-build-a-strategy-from-"
 "scratch-pytorch.html>`__ shows how to build a fully custom ``Strategy`` "
 "from scratch."
 msgstr ""
@@ -11412,7 +17281,7 @@ msgstr ""
 msgid ""
 "`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ and join "
 "the open-source Flower community on Slack to connect, ask questions, and "
-"get help: `Join Slack <https://flower.dev/join-slack>`__ 🌼 We'd love to "
+"get help: `Join Slack <https://flower.ai/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 ""
@@ -11439,7 +17308,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:41
-msgid "|ed6498a023f2477a9ccd57ee4514bda4|"
+msgid "|31e4b1afa87c4b968327bbeafbf184d4|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:109
@@ -11454,7 +17323,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:53
-msgid "|5a4f742489ac4f819afefdd4dc9ab272|"
+msgid "|c9d935b4284e4c389a33d86b33e07c0a|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:111
@@ -11475,7 +17344,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:67
-msgid "|3331c80cd05045f6a56524d8e3e76d0c|"
+msgid "|00727b5faffb468f84dd1b03ded88638|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:113
@@ -11491,7 +17360,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:79
-msgid "|4987b26884ec4b2c8f06c1264bcebe60|"
+msgid "|daf0cf0ff4c24fd29439af78416cf47b|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:115
@@ -11507,7 +17376,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:91
-msgid "|ec8ae2d778aa493a986eb2fa29c220e5|"
+msgid "|9f093007080d471d94ca90d3e9fde9b6|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:117
@@ -11522,7 +17391,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:103
-msgid "|b8949d0669fe4f8eadc9a4932f4e9c57|"
+msgid "|46a26e6150e0479fbd3dfd655f36eb13|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:119
@@ -11542,7 +17411,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:138
-msgid "|94ff30bdcd09443e8488b5f29932a541|"
+msgid "|3daba297595c4c7fb845d90404a6179a|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:173
@@ -11557,7 +17426,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:150
-msgid "|48dccf1d6d0544bba8917d2783a47719|"
+msgid "|5769874fa9c4455b80b2efda850d39d7|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:175
@@ -11635,7 +17504,7 @@ msgid ""
 "The popularity of privacy-enhancing systems like the `Brave "
 "<https://brave.com/>`__ browser or the `Signal <https://signal.org/>`__ "
 "messenger shows that users care about privacy. In fact, they choose the "
-"privacy-enhancing version over other alternatives, if such an alernative "
+"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."
@@ -11697,7 +17566,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:210
-msgid "|0366618db96b4f329f0d4372d1150fde|"
+msgid "|ba47ffb421814b0f8f9fa5719093d839|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:307
@@ -11721,7 +17590,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:225
-msgid "|ac80eddc76e6478081b1ca35eed029c0|"
+msgid "|aeac5bf79cbf497082e979834717e01b|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:309
@@ -11745,7 +17614,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:240
-msgid "|1ac94140c317450e89678db133c7f3c2|"
+msgid "|ce27ed4bbe95459dba016afc42486ba2|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:311
@@ -11768,7 +17637,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:255
-msgid "|f8850c6e96fc4430b55e53bba237a7c0|"
+msgid "|ae94a7f71dda443cbec2385751427d41|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:313
@@ -11806,7 +17675,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:273
-msgid "|4a368fdd3fc34adabd20a46752a68582|"
+msgid "|e61fce4d43d243e7bb08bdde97d81ce6|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:315
@@ -11904,7 +17773,7 @@ msgid ""
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:334
-msgid "|40f69c17bb444652a7c8dfe577cd120e|"
+msgid "|08cb60859b07461588fe44e55810b050|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:340
@@ -11928,8 +17797,585 @@ msgstr ""
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:373
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 1 "
-"<https://flower.dev/docs/framework/tutorial-get-started-with-flower-"
+"<https://flower.ai/docs/framework/tutorial-get-started-with-flower-"
 "pytorch.html>`__ 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 <vscode:extension/ms-vscode-"
+#~ "remote.remote-containers>`_."
+#~ 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 "
+#~ "<https://github.com/adap/flower/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 <https://pandoc.org/installing.html>_` 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 <https://flower.ai/docs/getting-"
+#~ "started-for-contributors.html>`_ and examples "
+#~ "of `good first contributions "
+#~ "<https://flower.ai/docs/good-first-contributions.html>`_."
+#~ 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 <https://docs.python.org/3.7/>`_ or above"
+#~ msgstr ""
+
+#~ msgid ""
+#~ "First, clone the `Flower repository "
+#~ "<https://github.com/adap/flower>`_ 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:`<version>`)::"
+#~ 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 "
+#~ "<https://github.com/nektos/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 <https://github.com/adap/flower/tree/main/examples"
+#~ "/quickstart-xgboost-horizontal>`_ 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 ""
+
diff --git a/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po b/doc/locales/zh_Hans/LC_MESSAGES/framework-docs.po
index 87af28422d56..f22b74db8896 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: 2023-11-23 18:31+0100\n"
-"PO-Revision-Date: 2023-11-25 19:00+0000\n"
+"POT-Creation-Date: 2024-02-13 11:23+0100\n"
+"PO-Revision-Date: 2024-02-19 11:37+0000\n"
 "Last-Translator: Yan Gao <y.gaogy@gmail.com>\n"
 "Language-Team: Chinese (Simplified) <https://hosted.weblate.org/projects/"
 "flower-docs/framework/zh_Hans/>\n"
@@ -17,7 +17,7 @@ msgstr ""
 "Content-Type: text/plain; charset=utf-8\n"
 "Content-Transfer-Encoding: 8bit\n"
 "Plural-Forms: nplurals=1; plural=0;\n"
-"X-Generator: Weblate 5.2.1-rc\n"
+"X-Generator: Weblate 5.4\n"
 "Generated-By: Babel 2.13.1\n"
 
 #: ../../source/contributor-explanation-architecture.rst:2
@@ -30,9 +30,9 @@ msgstr "边缘客户端引擎"
 
 #: ../../source/contributor-explanation-architecture.rst:7
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with Edge "
+"`Flower <https://flower.ai>`_ core framework architecture with Edge "
 "Client Engine"
-msgstr "具有边缘客户端引擎的`Flower <https://flower.dev>`核心架构"
+msgstr "具有边缘客户端引擎的`Flower <https://flower.ai>`核心架构"
 
 #: ../../source/contributor-explanation-architecture.rst:13
 msgid "Virtual Client Engine"
@@ -40,9 +40,9 @@ msgstr "虚拟客户端引擎"
 
 #: ../../source/contributor-explanation-architecture.rst:15
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with Virtual "
+"`Flower <https://flower.ai>`_ core framework architecture with Virtual "
 "Client Engine"
-msgstr "具有虚拟客户端引擎的`Flower <https://flower.dev>`核心架构"
+msgstr "具有虚拟客户端引擎的`Flower <https://flower.ai>`核心架构"
 
 #: ../../source/contributor-explanation-architecture.rst:21
 msgid "Virtual Client Engine and Edge Client Engine in the same workload"
@@ -50,130 +50,251 @@ msgstr "可同步进行的虚拟客户端引擎和边缘客户端引擎"
 
 #: ../../source/contributor-explanation-architecture.rst:23
 msgid ""
-"`Flower <https://flower.dev>`_ core framework architecture with both "
+"`Flower <https://flower.ai>`_ core framework architecture with both "
 "Virtual Client Engine and Edge Client Engine"
-msgstr "具有虚拟客户端引擎和边缘客户端引擎的`Flower <https://flower.dev>`核心架构"
+msgstr "具有虚拟客户端引擎和边缘客户端引擎的`Flower <https://flower.ai>`核心架构"
 
-#: ../../source/contributor-how-create-new-messages.rst:2
-msgid "Creating New Messages"
-msgstr "创建新信息"
+#: ../../source/contributor-how-to-build-docker-images.rst:2
+msgid "How to build Docker Flower images locally"
+msgstr "如何在本地搭建Docker Flower images"
 
-#: ../../source/contributor-how-create-new-messages.rst:4
+#: ../../source/contributor-how-to-build-docker-images.rst:4
 msgid ""
-"This is a simple guide for creating a new type of message between the "
-"server and clients in Flower."
+"Flower provides pre-made docker images on `Docker Hub "
+"<https://hub.docker.com/r/flwr/server/tags>`_ 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-create-new-messages.rst:6
+#: ../../source/contributor-how-to-build-docker-images.rst:9
 msgid ""
-"Let's suppose we have the following example functions in "
-":code:`server.py` and :code:`numpy_client.py`..."
+"Before we can start, we need to meet a few prerequisites in our local "
+"development environment."
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:8
-msgid "Server's side:"
+#: ../../source/contributor-how-to-build-docker-images.rst:11
+#, fuzzy
+msgid "Clone the flower repository."
+msgstr "**叉花仓库**"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:17
+msgid "Verify the Docker daemon is running."
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:17
-msgid "Client's side:"
+#: ../../source/contributor-how-to-build-docker-images.rst:19
+msgid ""
+"Please follow the first section on `Run Flower using Docker "
+"<https://flower.ai/docs/framework/how-to-run-flower-using-docker>`_ "
+"which covers this step in more detail."
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:26
+#: ../../source/contributor-how-to-build-docker-images.rst:23
 msgid ""
-"Let's now see what we need to implement in order to get this simple "
-"function between the server and client to work!"
+"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-create-new-messages.rst:30
-msgid "Message Types for Protocol Buffers"
+#: ../../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 ""
 
-#: ../../source/contributor-how-create-new-messages.rst:32
+#: ../../source/contributor-how-to-build-docker-images.rst:31
 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://developers.google.com/protocol-buffers/docs/proto3>`_."
+"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 ""
 
-#: ../../source/contributor-how-create-new-messages.rst:35
-msgid "Within the :code:`ServerMessage` block:"
+#: ../../source/contributor-how-to-build-docker-images.rst:38
+#, 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
+#, 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
+#, fuzzy
+msgid "Description"
+msgstr "停用"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:46
+#: ../../source/contributor-how-to-build-docker-images.rst:88
+#, 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
+#, fuzzy
+msgid "Example"
+msgstr "实例"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:48
+#, fuzzy
+msgid "``PYTHON_VERSION``"
+msgstr "Python 版本"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:49
+msgid "Version of ``python`` to be installed."
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:52
-msgid "Within the ClientMessage block:"
+#: ../../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"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:51
+#, fuzzy
+msgid "``3.11``"
+msgstr "``1.0.0rc1``"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:52
+msgid "``PIP_VERSION``"
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:70
-msgid ""
-"Make sure to also add a field of the newly created message type in "
-":code:`oneof msg`."
+#: ../../source/contributor-how-to-build-docker-images.rst:53
+msgid "Version of ``pip`` to be installed."
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:72
-msgid "Once that is done, we will compile the file with:"
+#: ../../source/contributor-how-to-build-docker-images.rst:55
+#, fuzzy
+msgid "``23.0.1``"
+msgstr "``1.0.0rc1``"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:56
+msgid "``SETUPTOOLS_VERSION``"
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:78
-msgid "If it compiles succesfully, you should see the following message:"
+#: ../../source/contributor-how-to-build-docker-images.rst:57
+msgid "Version of ``setuptools`` to be installed."
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:87
-msgid "Serialization and Deserialization Functions"
+#: ../../source/contributor-how-to-build-docker-images.rst:59
+#, 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-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`."
+#: ../../source/contributor-how-to-build-docker-images.rst:61
+msgid "Version of the official Ubuntu Docker image."
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:91
-msgid "The four functions:"
+#: ../../source/contributor-how-to-build-docker-images.rst:62
+msgid "Defaults to ``22.04``."
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:112
-msgid "Sending the Message from the Server"
+#: ../../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-create-new-messages.rst:114
+#: ../../source/contributor-how-to-build-docker-images.rst:76
 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:"
+"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-create-new-messages.rst:128
-msgid "Receiving the Message by the Client"
+#: ../../source/contributor-how-to-build-docker-images.rst:80
+#, fuzzy
+msgid "Building the server image"
+msgstr "启动服务器"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:90
+msgid "``BASE_REPOSITORY``"
+msgstr ""
+
+#: ../../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: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``"
+msgstr ""
+
+#: ../../source/contributor-how-to-build-docker-images.rst:99
+msgid "Version of Flower to be installed."
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:130
+#: ../../source/contributor-how-to-build-docker-images.rst:101
+#, fuzzy
+msgid "``1.7.0``"
+msgstr "``1.0.0b0``"
+
+#: ../../source/contributor-how-to-build-docker-images.rst:103
 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!"
+"The following example creates a server image with the official Flower "
+"base image py3.11-ubuntu22.04 and Flower 1.7.0:"
 msgstr ""
 
-#: ../../source/contributor-how-create-new-messages.rst:132
-msgid "Within the handle function:"
+#: ../../source/contributor-how-to-build-docker-images.rst:114
+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 ""
 
-#: ../../source/contributor-how-create-new-messages.rst:139
-msgid "And add a new function:"
+#: ../../source/contributor-how-to-build-docker-images.rst:117
+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 ""
 
-#: ../../source/contributor-how-create-new-messages.rst:149
-msgid "Hopefully, when you run your program you will get the intended result!"
+#: ../../source/contributor-how-to-build-docker-images.rst:131
+msgid "After creating the image, we can test whether the image is working:"
 msgstr ""
 
 #: ../../source/contributor-how-to-contribute-translations.rst:2
 msgid "Contribute translations"
-msgstr ""
+msgstr "贡献译文"
 
 #: ../../source/contributor-how-to-contribute-translations.rst:4
 msgid ""
-"Since `Flower 1.5 <https://flower.dev/docs/framework/ref-"
+"Since `Flower 1.5 <https://flower.ai/docs/framework/ref-"
 "changelog.html#v1-5-0-2023-08-31>`_ 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 "
@@ -182,6 +303,10 @@ msgid ""
 "also be a great opportunity for those wanting to become open source "
 "contributors with little prerequistes."
 msgstr ""
+"从 `Flower 1.5 <https://flower.ai/docs/framework/ref-"
+"changelog.html#v1-5-0-2023-08-31>`_ "
+"开始，我们在文档页面中引入了翻译，但正如你可能已经注意到的，这些翻译往往并不完美。如果您会说英语以外的语言，也许您可以帮助我们翻译这些文档，让更多的人了解"
+" Federated Learning！对于那些想成为开源贡献者的人来说，这也是一个很好的机会。"
 
 #: ../../source/contributor-how-to-contribute-translations.rst:13
 msgid ""
@@ -189,10 +314,12 @@ msgid ""
 "<https://hosted.weblate.org/projects/flower-docs/framework/>`_, this "
 "where most of the work will happen."
 msgstr ""
+"我们的翻译项目已在 \"Weblate <https://hosted.weblate.org/projects/flower-"
+"docs/framework/>`_\"上公开，大部分工作都将在这里进行。"
 
 #: ../../source/contributor-how-to-contribute-translations.rst:18
 msgid "Contribute to existing languages"
-msgstr ""
+msgstr "为现有语言作出贡献"
 
 #: ../../source/contributor-how-to-contribute-translations.rst:23
 msgid ""
@@ -202,6 +329,8 @@ msgid ""
 " profile settings can be found `here "
 "<https://docs.weblate.org/en/latest/user/profile.html>`_."
 msgstr ""
+"您需要做的第一件事就是在本`网页<https://hosted.weblate.org/accounts/register/>`_上创建一个免费的Weblate帐户。有关个人资料设置的更多信息，请参阅`这里"
+" <https://docs.weblate.org/en/latest/user/profile.html>`_。"
 
 #: ../../source/contributor-how-to-contribute-translations.rst:29
 msgid ""
@@ -210,12 +339,15 @@ msgid ""
 "docs/framework/>`_. Here, you should see the different existing languages"
 " that can be found on the website."
 msgstr ""
+"登录到Weblate后，您可以导航到 \"Flower Framework "
+"\"项目<https://hosted.weblate.org/projects/flower-"
+"docs/framework/>`_。在这里，您可以看到网站上现有的各种语言。"
 
 #: ../../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 ""
+msgstr "选择您要贡献的语言后，您应该会看到与此类似的界面："
 
 #: ../../source/contributor-how-to-contribute-translations.rst:39
 msgid ""
@@ -223,11 +355,11 @@ msgid ""
 "button on the top right (in the ``Translation status`` section). This "
 "will automatically bring you to the translation interface for "
 "untranslated strings."
-msgstr ""
+msgstr "最简单的方法是点击右上角（\"翻译状态 \"部分）的 \"翻译 \"按钮。这将自动带您进入未翻译字符串的翻译界面。"
 
 #: ../../source/contributor-how-to-contribute-translations.rst:43
 msgid "This is what the interface looks like:"
-msgstr ""
+msgstr "这就是界面的样子："
 
 #: ../../source/contributor-how-to-contribute-translations.rst:47
 msgid ""
@@ -238,6 +370,9 @@ msgid ""
 "your translation to suggestions for other users to view), or ``Skip`` (to"
 " go to the next untranslated string without saving anything)."
 msgstr ""
+"您可以在顶部的文本框中输入翻译内容，满意后按 "
+"\"保存并继续\"（保存翻译内容并转到下一个未翻译的字符串）、\"保存并停留\"（保存翻译内容并停留在同一页面）、\"建议\"（将您的翻译添加到建议中供其他用户查看）或"
+" \"跳过\"（转到下一个未翻译的字符串而不保存任何内容）。"
 
 #: ../../source/contributor-how-to-contribute-translations.rst:54
 msgid ""
@@ -247,13 +382,15 @@ msgid ""
 "translations in ``Other languages``, and the ``History`` of translations "
 "for this string."
 msgstr ""
+"为了帮助翻译，您可以在底部看到 \"邻近字符串\"、\"评论\"（来自其他贡献者）、\"自动建议\"（来自机器翻译引擎）、\"其他语言 "
+"\"中的翻译以及该字符串的 \"历史翻译\"。"
 
 #: ../../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."
-msgstr ""
+msgstr "在右侧的 \"字符串信息 \"部分，您还可以单击 \"源字符串位置 \"下的链接，以查看包含字符串的 doc 文件的源文件。"
 
 #: ../../source/contributor-how-to-contribute-translations.rst:63
 msgid ""
@@ -261,21 +398,145 @@ msgid ""
 "this `in-depth guide "
 "<https://docs.weblate.org/en/latest/user/translating.html>`_."
 msgstr ""
+"有关使用 Weblate 进行翻译的更多信息，您可以查看本 \"深入指南 "
+"<https://docs.weblate.org/en/latest/user/translating.html>`_\"。"
 
 #: ../../source/contributor-how-to-contribute-translations.rst:67
 msgid "Add new languages"
-msgstr ""
+msgstr "添加新语言"
 
 #: ../../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 <https://flower.dev/join-slack>`_, or by opening an "
+"either on `Slack <https://flower.ai/join-slack>`_, or by opening an "
 "issue on our `GitHub repo <https://github.com/adap/flower>`_."
 msgstr ""
+"如果您想添加新语言，请先联系我们，可以在 `Slack <https://flower.ai/join-slack>`_ 上联系，也可以在我们的"
+" `GitHub repo <https://github.com/adap/flower>`_ 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
+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://developers.google.com/protocol-buffers/docs/proto3>`_."
+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 "
+"<https://developers.google.com/protocol-buffers/docs/proto3>`_。"
+
+#: ../../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 ""
+msgstr "使用 VSCode Dev Containers 进行开发"
 
 #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:4
 msgid ""
@@ -284,6 +545,8 @@ msgid ""
 "tests. For this purpose we are using the VSCode Remote Containers "
 "extension. What is it? Read the following quote:"
 msgstr ""
+"在开发 Flower 框架时，我们希望确保所有贡献者使用相同的开发环境来格式化代码或运行测试。为此，我们使用了 VSCode "
+"远程容器扩展。这是什么？请阅读下面这段话："
 
 #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:7
 msgid ""
@@ -297,6 +560,10 @@ msgid ""
 "separate tools, libraries, or runtimes needed for working with a "
 "codebase."
 msgstr ""
+"Visual Studio Code Remote - "
+"Containers扩展可让你将Docker容器用作功能齐全的开发环境。它允许你打开容器内（或挂载到容器内）的任何文件夹，并利用 Visual "
+"Studio Code 的全部功能集。项目中的 :code:`devcontainer.json` 文件会告诉 VS Code "
+"如何访问（或创建）一个带有定义明确的工具和运行时栈的开发容器。该容器可用于运行应用程序，也可用于分离处理代码库所需的工具、库或运行时。"
 
 #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:9
 msgid ""
@@ -305,27 +572,32 @@ msgid ""
 "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 ""
+msgstr "工作区文件从本地文件系统加载，或复制或克隆到容器中。扩展在容器内安装和运行，在容器内它们可以完全访问工具、平台和文件系统。这意味着，只需连接到不同的容器，就能无缝切换整个开发环境。"
 
 #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:11
 msgid ""
 "Source: `Official VSCode documentation "
 "<https://code.visualstudio.com/docs/remote/containers>`_"
-msgstr ""
+msgstr "来源：`VSCode 官方文档 <https://code.visualstudio.com/docs/remote/containers>`_"
 
 #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:15
 msgid "Getting started"
-msgstr ""
+msgstr "开始"
 
 #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:17
+#, fuzzy
 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 <vscode:extension"
-"/ms-vscode-remote.remote-containers>`_."
+"thing is you don't have to do it. Usually it should be enough to install "
+"`Docker <https://docs.docker.com/engine/install/>`_ on your system and "
+"ensure its available on your command line. Additionally, install the "
+"`VSCode Containers Extension <vscode:extension/ms-vscode-remote.remote-"
+"containers>`_."
 msgstr ""
+"配置和设置 :code:`Dockerfile` 以及 devcontainer 的配置可能比较复杂。好在你想做就得做。通常只需在系统中安装 "
+"Docker 并确保其在命令行中可用即可。此外，请安装 `VSCode Containers Extension "
+"<vscode:extension/ms-vscode-remote.remote-containers>`_。"
 
 #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:19
 msgid ""
@@ -336,12 +608,14 @@ msgid ""
 "area in the bottom left corner of your VSCode window and select the "
 "option *(Re)Open Folder in Container*."
 msgstr ""
+"现在你应该可以开始了。启动 VSCode 时，它会要求你在容器环境中运行，如果你确认，它会自动构建容器并使用它。要手动指示 VSCode 使用 "
+"devcontainer，可以在安装扩展后，点击 VSCode 窗口左下角的绿色区域，然后选择 \"*（重新）在容器中打开文件夹*\"选项。"
 
 #: ../../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:"
-msgstr ""
+msgstr "在某些情况下，您的设置可能更复杂。有关这些情况，请参考以下资料："
 
 #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:23
 msgid ""
@@ -349,24 +623,26 @@ msgid ""
 "<https://code.visualstudio.com/docs/remote/containers#_system-"
 "requirements>`_"
 msgstr ""
+"在容器内开发 <https://code.visualstudio.com/docs/remote/containers#_system-"
+"requirements>`_"
 
 #: ../../source/contributor-how-to-develop-in-vscode-dev-containers.rst:24
 msgid ""
 "`Remote development in Containers "
 "<https://code.visualstudio.com/docs/remote/containers-tutorial>`_"
-msgstr ""
+msgstr "容器中的远程开发 <https://code.visualstudio.com/docs/remote/containers-tutorial>`_"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:2
 msgid "Install development versions"
-msgstr ""
+msgstr "安装开发版本"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:5
 msgid "Install development versions of Flower"
-msgstr ""
+msgstr "安装 Flower 的开发版本"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:8
 msgid "Using Poetry (recommended)"
-msgstr ""
+msgstr "使用诗歌（推荐）"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:10
 msgid ""
@@ -374,50 +650,61 @@ msgid ""
 "in ``pyproject.toml`` and then reinstall (don't forget to delete "
 "``poetry.lock`` (``rm poetry.lock``) before running ``poetry install``)."
 msgstr ""
+"安装来自 PyPI 的 ``flwr`` 预发布版本：更新 ``pyproject.toml`` 中的 ``flwr`` "
+"依赖关系，然后重新安装（运行 ``poetry install` 前，别忘了删除 ``poetry.lock` (``rm "
+"poetry.lock`))。"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:12
 msgid ""
 "``flwr = { version = \"1.0.0a0\", allow-prereleases = true }`` (without "
 "extras)"
-msgstr ""
+msgstr "``flwr = { version = \"1.0.0a0\", allow-prereleases = true }`` （不含额外内容）"
 
 #: ../../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\"] }`` (包含额外内容)"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:15
 msgid ""
 "Install ``flwr`` from a local copy of the Flower source code via "
 "``pyproject.toml``:"
-msgstr ""
+msgstr "通过 ``pyproject.toml`` 从 Flower 源代码的本地副本安装 ``flwr``："
 
 #: ../../source/contributor-how-to-install-development-versions.rst:17
 msgid "``flwr = { path = \"../../\", develop = true }`` (without extras)"
-msgstr ""
+msgstr "``flwr = { path = \"../../\", develop = true }`` （不含额外内容）"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:18
 msgid ""
 "``flwr = { path = \"../../\", develop = true, extras = [\"simulation\"] "
 "}`` (with extras)"
 msgstr ""
+"``flwr = { path = \"../../\", develop = true, extras = [\"simulation\"] "
+"}`` (包含额外内容)"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:20
 msgid "Install ``flwr`` from a local wheel file via ``pyproject.toml``:"
-msgstr ""
+msgstr "通过 ``pyproject.toml`` 从本地轮子文件安装 ``flwr``："
 
 #: ../../source/contributor-how-to-install-development-versions.rst:22
+#, fuzzy
 msgid ""
-"``flwr = { path = \"../../dist/flwr-1.0.0-py3-none-any.whl\" }`` (without"
+"``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\" }`` (without"
 " extras)"
-msgstr ""
+msgstr "``flwr = { path = \"../../dist/flwr-1.0.0-py3-none-any.whl\" }``（无额外内容）"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:23
+#, fuzzy
 msgid ""
-"``flwr = { path = \"../../dist/flwr-1.0.0-py3-none-any.whl\", extras = "
+"``flwr = { path = \"../../dist/flwr-1.8.0-py3-none-any.whl\", extras = "
 "[\"simulation\"] }`` (with extras)"
 msgstr ""
+"``flwr = { path = \"../../dist/flwr-1.0.0-py3-none-any.whl\", extras = "
+"[\"simulation\"] }`` (包含额外内容)"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:25
 msgid ""
@@ -425,299 +712,309 @@ msgid ""
 "Dependency Specification <https://python-poetry.org/docs/dependency-"
 "specification/>`_"
 msgstr ""
+"有关详细信息，请参阅 Poetry 文档： 诗歌依赖性规范 <https://python-poetry.org/docs/dependency-"
+"specification/>`_"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:28
 msgid "Using pip (recommended on Colab)"
-msgstr ""
+msgstr "使用 pip（建议在 Colab 上使用）"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:30
 msgid "Install a ``flwr`` pre-release from PyPI:"
-msgstr ""
+msgstr "从 PyPI 安装 ``flwr`` 预发行版："
 
 #: ../../source/contributor-how-to-install-development-versions.rst:32
 msgid "``pip install -U --pre flwr`` (without extras)"
-msgstr ""
+msgstr "`pip install -U -pre flwr``（不含额外功能）"
 
 #: ../../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]``（包含额外功能）"
 
 #: ../../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 ""
+msgstr "Python 软件包可以从 git 仓库安装。使用以下命令之一直接从 GitHub 安装 Flower。"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:37
 msgid "Install ``flwr`` from the default GitHub branch (``main``):"
-msgstr ""
+msgstr "从 GitHub 的默认分支 (``main`) 安装 ``flwr``："
 
 #: ../../source/contributor-how-to-install-development-versions.rst:39
 msgid ""
 "``pip install flwr@git+https://github.com/adap/flower.git`` (without "
 "extras)"
-msgstr ""
+msgstr "`pip install flwr@git+https://github.com/adap/flower.git`` （不含额外功能）"
 
 #: ../../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``（带附加功能）"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:42
 msgid "Install ``flwr`` from a specific GitHub branch (``branch-name``):"
-msgstr ""
+msgstr "从特定的 GitHub 分支 (`分支名`) 安装 ``flwr``："
 
 #: ../../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`` "
+"（不含附加功能）"
 
 #: ../../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)"
-msgstr ""
+msgstr "`pip安装flwr[模拟]@git+https://github.com/adap/flower.git@分支名``（带附加功能）"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:49
 msgid "Open Jupyter Notebooks on Google Colab"
-msgstr ""
+msgstr "在谷歌 Colab 上打开 Jupyter 笔记本"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:51
 msgid ""
 "Open the notebook ``doc/source/tutorial-get-started-with-flower-"
 "pytorch.ipynb``:"
-msgstr ""
+msgstr "打开笔记本 ``doc/source/tutorial-get-started-with-flower-pytorch.ipynb``："
 
 #: ../../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"
 msgstr ""
+"https://colab.research.google.com/github/adap/flower/blob/main/doc/source"
+"/tutorial-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``):"
 msgstr ""
+"将 ``main`` 改为 ``branch-name``（紧跟在 ``blob``之后），从分支 `branch-name` "
+"打开同一笔记本的开发版本："
 
 #: ../../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"
 msgstr ""
+"https://colab.research.google.com/github/adap/flower/blob/branch-"
+"name/doc/source/tutorial-get-started-with-flower-pytorch.ipynb"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:59
 msgid "Install a `whl` on Google Colab:"
-msgstr ""
+msgstr "在 Google Colab 上安装 `whl`："
 
 #: ../../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``"
-msgstr ""
+msgstr "在左侧的垂直图标网格中，选择 \"文件\">\"上传到会话存储\""
 
 #: ../../source/contributor-how-to-install-development-versions.rst:62
-msgid "Upload the whl (e.g., ``flwr-1.6.0-py3-none-any.whl``)"
-msgstr ""
+#, fuzzy
+msgid "Upload the whl (e.g., ``flwr-1.8.0-py3-none-any.whl``)"
+msgstr "更新 whl (e.g., ``flwr-1.7.0-py3-none-any.whl``)"
 
 #: ../../source/contributor-how-to-install-development-versions.rst:63
+#, fuzzy
 msgid ""
 "Change ``!pip install -q 'flwr[simulation]' torch torchvision "
-"matplotlib`` to ``!pip install -q 'flwr-1.6.0-py3-none-"
+"matplotlib`` to ``!pip install -q 'flwr-1.8.0-py3-none-"
 "any.whl[simulation]' torch torchvision matplotlib``"
 msgstr ""
+"把``!pip install -q 'flwr[simulation]' torch torchvision "
+"matplotlib``变为``!pip install -q 'flwr-1.7.0-py3-none-any.whl[simulation]'"
+" torch torchvision matplotlib``"
 
 #: ../../source/contributor-how-to-release-flower.rst:2
 msgid "Release Flower"
-msgstr ""
+msgstr "发布 Flower"
 
 #: ../../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 ""
+msgstr "本文件描述了当前的发布流程。今后可能会有变化，也可能不会有变化。"
 
 #: ../../source/contributor-how-to-release-flower.rst:7
-msgid "Before the release"
-msgstr ""
-
-#: ../../source/contributor-how-to-release-flower.rst:9
-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 ""
-
-#: ../../source/contributor-how-to-release-flower.rst:11
-msgid ""
-"`GitHub: Compare v1.2.0...main "
-"<https://github.com/adap/flower/compare/v1.2.0...main>`_"
-msgstr ""
-
-#: ../../source/contributor-how-to-release-flower.rst:13
-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 ""
-
-#: ../../source/contributor-how-to-release-flower.rst:16
 msgid "During the release"
-msgstr ""
+msgstr "在发布期间"
 
-#: ../../source/contributor-how-to-release-flower.rst:18
+#: ../../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):"
+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
+msgid ""
+"Run ``python3 src/py/flwr_tool/update_changelog.py <YOUR_GH_TOKEN>`` 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 ""
 
-#: ../../source/contributor-how-to-release-flower.rst:20
+#: ../../source/contributor-how-to-release-flower.rst:12
 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."
+"Once the changelog has been updated with all the changes, run ``./dev"
+"/prepare-release-changelog.sh v<NEW_VERSION>``, where ``<NEW_VERSION>`` "
+"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 ""
 
-#: ../../source/contributor-how-to-release-flower.rst:21
+#: ../../source/contributor-how-to-release-flower.rst:13
+#, fuzzy
 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."
+"Once the pull request is merged, tag the release commit with the version "
+"number as soon as the PR is merged: ``git tag v<NEW_VERSION>`` (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 ""
+"在 PR 合并后立即用版本号标记发布提交：``git tag v0.12.3``，然后``git push --tags``。这将在 GitHub"
+" 上创建一个包含正确工件和更新日志相关部分的发布草案。"
 
-#: ../../source/contributor-how-to-release-flower.rst:22
+#: ../../source/contributor-how-to-release-flower.rst:14
 msgid "Check the draft release on GitHub, and if everything is good, publish it."
-msgstr ""
+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:25
+#: ../../source/contributor-how-to-release-flower.rst:17
 msgid "After the release"
-msgstr ""
+msgstr "发布后"
 
-#: ../../source/contributor-how-to-release-flower.rst:27
+#: ../../source/contributor-how-to-release-flower.rst:19
 msgid "Create a pull request which contains the following changes:"
-msgstr ""
+msgstr "创建包含以下更改的拉取请求："
 
-#: ../../source/contributor-how-to-release-flower.rst:29
+#: ../../source/contributor-how-to-release-flower.rst:21
 msgid "Increase the minor version in ``pyproject.toml`` by one."
-msgstr ""
+msgstr "将 ``pyproject.toml`` 中的次要版本增加一个。"
 
-#: ../../source/contributor-how-to-release-flower.rst:30
+#: ../../source/contributor-how-to-release-flower.rst:22
 msgid "Update all files which contain the current version number if necessary."
-msgstr ""
+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:31
+#: ../../source/contributor-how-to-release-flower.rst:23
 msgid "Add a new ``Unreleased`` section in ``changelog.md``."
-msgstr ""
+msgstr "在 ``changelog.md`` 中添加新的 ``Unreleased`` 部分。"
 
-#: ../../source/contributor-how-to-release-flower.rst:33
+#: ../../source/contributor-how-to-release-flower.rst:25
 msgid ""
-"Merge the pull request on the same day (i.e., before a new nighly release"
+"Merge the pull request on the same day (i.e., before a new nightly release"
 " gets published to PyPI)."
-msgstr ""
+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:36
+#: ../../source/contributor-how-to-release-flower.rst:28
 msgid "Publishing a pre-release"
-msgstr ""
+msgstr "发布预发布版本"
 
-#: ../../source/contributor-how-to-release-flower.rst:39
+#: ../../source/contributor-how-to-release-flower.rst:31
 msgid "Pre-release naming"
-msgstr ""
+msgstr "释放前命名"
 
-#: ../../source/contributor-how-to-release-flower.rst:41
+#: ../../source/contributor-how-to-release-flower.rst:33
 msgid ""
-"PyPI supports pre-releases (alpha, beta, release candiate). Pre-releases "
+"PyPI supports pre-releases (alpha, beta, release candidate). Pre-releases "
 "MUST use one of the following naming patterns:"
-msgstr ""
+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:43
+#: ../../source/contributor-how-to-release-flower.rst:35
 msgid "Alpha: ``MAJOR.MINOR.PATCHaN``"
-msgstr ""
+msgstr "阿尔法 ``MAJOR.MINOR.PATCHaN``"
 
-#: ../../source/contributor-how-to-release-flower.rst:44
+#: ../../source/contributor-how-to-release-flower.rst:36
 msgid "Beta: ``MAJOR.MINOR.PATCHbN``"
-msgstr ""
+msgstr "贝塔： ``MAJOR.MINOR.PATCHbN``"
 
-#: ../../source/contributor-how-to-release-flower.rst:45
-msgid "Release candiate (RC): ``MAJOR.MINOR.PATCHrcN``"
-msgstr ""
+#: ../../source/contributor-how-to-release-flower.rst:37
+msgid "Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``"
+msgstr "版本代号 (RC)： ``MAJOR.MINOR.PATCHrcN``"
 
-#: ../../source/contributor-how-to-release-flower.rst:47
+#: ../../source/contributor-how-to-release-flower.rst:39
 msgid "Examples include:"
-msgstr ""
+msgstr "例子包括："
 
-#: ../../source/contributor-how-to-release-flower.rst:49
+#: ../../source/contributor-how-to-release-flower.rst:41
 msgid "``1.0.0a0``"
-msgstr ""
+msgstr "``1.0.0a0``"
 
-#: ../../source/contributor-how-to-release-flower.rst:50
+#: ../../source/contributor-how-to-release-flower.rst:42
 msgid "``1.0.0b0``"
-msgstr ""
+msgstr "``1.0.0b0``"
 
-#: ../../source/contributor-how-to-release-flower.rst:51
+#: ../../source/contributor-how-to-release-flower.rst:43
 msgid "``1.0.0rc0``"
-msgstr ""
+msgstr "``1.0.0rc0``"
 
-#: ../../source/contributor-how-to-release-flower.rst:52
+#: ../../source/contributor-how-to-release-flower.rst:44
 msgid "``1.0.0rc1``"
-msgstr ""
+msgstr "``1.0.0rc1``"
 
-#: ../../source/contributor-how-to-release-flower.rst:54
+#: ../../source/contributor-how-to-release-flower.rst:46
 msgid ""
 "This is in line with PEP-440 and the recommendations from the Python "
 "Packaging Authority (PyPA):"
-msgstr ""
+msgstr "这符合 PEP-440 和 Python 包装管理局 (PyPA) 的建议："
 
-#: ../../source/contributor-how-to-release-flower.rst:57
+#: ../../source/contributor-how-to-release-flower.rst:49
 msgid "`PEP-440 <https://peps.python.org/pep-0440/>`_"
-msgstr ""
+msgstr "`PEP-440 <https://peps.python.org/pep-0440/>`_"
 
-#: ../../source/contributor-how-to-release-flower.rst:58
+#: ../../source/contributor-how-to-release-flower.rst:50
 msgid ""
 "`PyPA Choosing a versioning scheme "
 "<https://packaging.python.org/en/latest/guides/distributing-packages-"
 "using-setuptools/#choosing-a-versioning-scheme>`_"
 msgstr ""
+"`PyPA 选择版本控制方案 <https://packaging.python.org/en/latest/guides"
+"/distributing-packages-using-setuptools/#choosing-a-versioning-scheme>`_"
 
-#: ../../source/contributor-how-to-release-flower.rst:60
+#: ../../source/contributor-how-to-release-flower.rst:52
 msgid ""
 "Note that the approach defined by PyPA is not compatible with SemVer "
 "2.0.0 spec, for details consult the `Semantic Versioning Specification "
 "<https://semver.org/spec/v2.0.0.html#spec-item-11>`_ (specifically item "
 "11 on precedence)."
 msgstr ""
+"请注意，PyPA 所定义的方法与 SemVer 2.0.0 "
+"规范不兼容，详情请查阅《语义版本规范》<https://semver.org/spec/v2.0.0.html#spec-"
+"item-11>`_（特别是关于优先级的第 11 项）。"
 
-#: ../../source/contributor-how-to-release-flower.rst:63
+#: ../../source/contributor-how-to-release-flower.rst:55
 msgid "Pre-release classification"
-msgstr ""
+msgstr "发布前分类"
 
-#: ../../source/contributor-how-to-release-flower.rst:65
+#: ../../source/contributor-how-to-release-flower.rst:57
 msgid "Should the next pre-release be called alpha, beta, or release candidate?"
-msgstr ""
+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:67
+#: ../../source/contributor-how-to-release-flower.rst:59
 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 ""
+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:68
+#: ../../source/contributor-how-to-release-flower.rst:60
 msgid "Beta: feature complete, allowed to have known issues"
-msgstr ""
+msgstr "贝塔版：功能完整，允许存在已知问题"
 
-#: ../../source/contributor-how-to-release-flower.rst:69
+#: ../../source/contributor-how-to-release-flower.rst:61
 msgid "Alpha: not feature complete, allowed to have known issues"
-msgstr ""
+msgstr "阿尔法版：功能不完整，允许存在已知问题"
 
 #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:2
 msgid "Set up a virtual env"
-msgstr ""
+msgstr "建立虚拟环境"
 
 #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:4
 msgid ""
@@ -726,10 +1023,12 @@ msgid ""
 "environment with pyenv virtualenv, poetry, or Anaconda. You can follow "
 "the instructions or choose your preferred setup."
 msgstr ""
+"建议在虚拟环境中运行 Python 设置。本指南展示了如何使用 pyenv virtualenv、poes 或 Anaconda "
+"创建虚拟环境的三个不同示例。您可以按照说明或选择您喜欢的设置。"
 
 #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:9
 msgid "Python Version"
-msgstr ""
+msgstr "Python 版本"
 
 #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:11
 #: ../../source/how-to-install-flower.rst:8
@@ -738,10 +1037,12 @@ msgid ""
 "but `Python 3.10 <https://docs.python.org/3.10/>`_ or above is "
 "recommended."
 msgstr ""
+"Flower 至少需要 `Python 3.8 <https://docs.python.org/3.8/>`_，但建议使用 `Python "
+"3.10 <https://docs.python.org/3.10/>`_或更高版本。"
 
 #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:14
 msgid "Virutualenv with Pyenv/Virtualenv"
-msgstr ""
+msgstr "Virutualenv 和 Pyenv/Virtualenv"
 
 #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:16
 msgid ""
@@ -750,24 +1051,27 @@ msgid ""
 "/pyenv-virtualenv>`_. Please see `Flower examples "
 "<https://github.com/adap/flower/tree/main/examples/>`_ for details."
 msgstr ""
+"其中一个推荐的虚拟环境是 `pyenv <https://github.com/pyenv/pyenv>`_/`virtualenv "
+"<https://github.com/pyenv/pyenv-virtualenv>`_。详情请参见 `Flower 示例 "
+"<https://github.com/adap/flower/tree/main/examples/>`_。"
 
 #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:18
 msgid ""
 "Once Pyenv is set up, you can use it to install `Python Version 3.10 "
 "<https://docs.python.org/3.10/>`_ or above:"
-msgstr ""
+msgstr "一旦设置好 Pyenv，就可以用它来安装 `Python 3.10 <https://docs.python.org/3.10/>`_ 或更高版本："
 
 #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:24
 msgid "Create the virtualenv with:"
-msgstr ""
+msgstr "创建虚拟环境："
 
 #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:31
 msgid "Activate the virtualenv by running the following command:"
-msgstr ""
+msgstr "运行以下命令激活 virtualenv："
 
 #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:39
 msgid "Virtualenv with Poetry"
-msgstr ""
+msgstr "有诗意的 Virtualenv"
 
 #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:41
 msgid ""
@@ -775,16 +1079,18 @@ msgid ""
 "poetry.org/docs/>`_ to manage dependencies. After installing Poetry you "
 "simply create a virtual environment with:"
 msgstr ""
+"Flower 示例基于 `Poetry <https://python-poetry.org/docs/>`_ 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
 msgid ""
 "If you open a new terminal you can activate the previously created "
 "virtual environment with the following command:"
-msgstr ""
+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
 msgid "Virtualenv with Anaconda"
-msgstr ""
+msgstr "使用 Anaconda 的 Virtualenv"
 
 #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:57
 msgid ""
@@ -793,28 +1099,33 @@ msgid ""
 "/user-guide/install/index.html>`_  package. After setting it up you can "
 "create a virtual environment with:"
 msgstr ""
+"如果你更喜欢在虚拟环境中使用 Anaconda，那么请安装并设置 `conda "
+"<https://docs.conda.io/projects/conda/en/latest/user-"
+"guide/install/index.html>`_ 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
 msgid "and activate the virtual environment with:"
-msgstr ""
+msgstr "并激活虚拟环境："
 
 #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:71
 msgid "And then?"
-msgstr ""
+msgstr "然后呢？"
 
 #: ../../source/contributor-how-to-set-up-a-virtual-env.rst:73
 msgid ""
 "As soon as you created your virtual environment you clone one of the "
 "`Flower examples <https://github.com/adap/flower/tree/main/examples/>`_."
 msgstr ""
+"创建虚拟环境后，您可以克隆一个 `Flower 示例 "
+"<https://github.com/adap/flower/tree/main/examples/>`_。"
 
 #: ../../source/contributor-how-to-write-documentation.rst:2
 msgid "Write documentation"
-msgstr ""
+msgstr "编写文件"
 
 #: ../../source/contributor-how-to-write-documentation.rst:6
 msgid "Project layout"
-msgstr ""
+msgstr "项目布局"
 
 #: ../../source/contributor-how-to-write-documentation.rst:8
 msgid ""
@@ -822,53 +1133,58 @@ msgid ""
 " documentation system supports both reStructuredText (``.rst`` files) and"
 " Markdown (``.md`` files)."
 msgstr ""
+"Flower 文档位于 ``doc`` 目录中。基于 Sphinx 的文档系统支持 reStructuredText（``.rst`` 文件）和 "
+"Markdown（``.md`` 文件）。"
 
 #: ../../source/contributor-how-to-write-documentation.rst:10
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:119
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:142
+#, fuzzy
 msgid ""
 "Note that, in order to build the documentation locally (with ``poetry run"
 " make html``, like described below), `Pandoc "
-"<https://pandoc.org/installing.html>_` needs to be installed on the "
+"<https://pandoc.org/installing.html>`_ needs to be installed on the "
 "system."
 msgstr ""
+"请注意，要在本地构建文档（使用 ``poetry run make html``，如下所述），系统上必须安装 ``Pandoc "
+"<https://pandoc.org/installing.html>_`。"
 
 #: ../../source/contributor-how-to-write-documentation.rst:14
 msgid "Edit an existing page"
-msgstr ""
+msgstr "编辑现有页面"
 
 #: ../../source/contributor-how-to-write-documentation.rst:16
 msgid "Edit an existing ``.rst`` (or ``.md``) file under ``doc/source/``"
-msgstr ""
+msgstr "编辑 ``doc/source/`` 下现有的 ``.rst`` (或 ``.md``) 文件"
 
 #: ../../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 ""
+msgstr "编译文档： cd doc``，然后 ``poetry run make html``"
 
 #: ../../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 "在浏览器中打开 ``doc/build/html/index.html`` 查看结果"
 
 #: ../../source/contributor-how-to-write-documentation.rst:22
 msgid "Create a new page"
-msgstr ""
+msgstr "创建新页面"
 
 #: ../../source/contributor-how-to-write-documentation.rst:24
 msgid "Add new ``.rst`` file under ``doc/source/``"
-msgstr ""
+msgstr "在 ``doc/source/`` 下添加新的 ``.rst`` 文件"
 
 #: ../../source/contributor-how-to-write-documentation.rst:25
 msgid "Add content to the new ``.rst`` file"
-msgstr ""
+msgstr "为新的 ``.rst`` 文件添加内容"
 
 #: ../../source/contributor-how-to-write-documentation.rst:26
 msgid "Link to the new rst from ``index.rst``"
-msgstr ""
+msgstr "从 ``index.rst`` 链接到新的 rst"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:2
 msgid "Good first contributions"
-msgstr ""
+msgstr "首次代码贡献"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:4
 msgid ""
@@ -877,40 +1193,44 @@ msgid ""
 "where to start to increase your chances of getting your PR accepted into "
 "the Flower codebase."
 msgstr ""
+"我们欢迎为Flower做出代码贡献！然而，要知道从哪里开始并非易事。因此，我们提出"
+"了一些建议，告诉您从哪里开始，以增加您的 PR 被 Flower 代码库接受的机会。"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:11
 msgid "Where to start"
-msgstr ""
+msgstr "从哪里开始"
 
 #: ../../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:"
-msgstr ""
+msgstr "在 Flower 核心库成熟之前，如果 PR 只涉及代码库中的非核心区域，则会更容易被接受。可以从以下方面入手："
 
 #: ../../source/contributor-ref-good-first-contributions.rst:17
 msgid "Documentation: What's missing? What could be expressed more clearly?"
-msgstr ""
+msgstr "文档： 缺少什么？哪些内容可以表达得更清楚？"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:18
 msgid "Baselines: See below."
-msgstr ""
+msgstr "Baselines： 见下文。"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:19
 msgid "Examples: See below."
-msgstr ""
+msgstr "示例： 见下文。"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:23
 msgid "Request for Flower Baselines"
-msgstr ""
+msgstr "Flower Baselines的申请"
 
 #: ../../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 <https://flower.dev/docs"
+"out our `contributing guide for baselines <https://flower.ai/docs"
 "/contributing-baselines.html>`_."
 msgstr ""
+"如果您对 Flower Baselines 还不熟悉，也许可以看看我们的 `Baselines贡献指南 "
+"<https://flower.ai/docs/baselines/how-to-contribute-baselines.html>`_。"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:27
 msgid ""
@@ -920,39 +1240,45 @@ msgid ""
 " and that has no assignes, feel free to assign it to yourself and start "
 "working on it!"
 msgstr ""
+"然后查看开放的 `issues <https://github.com/adap/flower/"
+"issues?q=is%3Aopen+is%3Aissue+label%3A%22new+baseline%22>`_ baseline请求。如"
+"果您发现了自己想做的baseline，而它还没有被分配，请随时把它分配给自己，然后开"
+"始工作！"
 
 #: ../../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!"
-msgstr ""
+msgstr "如果您没有找到想要做的baseline，请务必使用baseline请求模板打开一个新问题（"
+"GitHub issue）！"
 
 #: ../../source/contributor-ref-good-first-contributions.rst:34
 msgid "Request for examples"
-msgstr ""
+msgstr "示例请求"
 
 #: ../../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:"
-msgstr ""
+msgstr "我们希望有更多的时间来撰写使用示例，因为我们相信这些示例可以帮助用户开始构建"
+"他们想要的东西。以下是我们乐意接受 PR 的几个想法："
 
 #: ../../source/contributor-ref-good-first-contributions.rst:40
 msgid "Llama 2 fine-tuning, with Hugging Face Transformers and PyTorch"
-msgstr ""
+msgstr "微调 Llama 2，使用 Hugging Face Transformers 和 PyTorch"
 
 #: ../../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 "安卓 ONNX 设备上训练"
 
 #: ../../source/contributor-ref-secure-aggregation-protocols.rst:2
 msgid "Secure Aggregation Protocols"
-msgstr ""
+msgstr "安全聚合协议"
 
 #: ../../source/contributor-ref-secure-aggregation-protocols.rst:4
 msgid ""
@@ -961,10 +1287,12 @@ msgid ""
 " not be accurate in practice. The SecAgg protocol can be considered as a "
 "special case of the SecAgg+ protocol."
 msgstr ""
+"包括 SecAgg、SecAgg+ 和 LightSecAgg 协议。LightSecAgg "
+"协议尚未实施，因此其图表和抽象在实践中可能并不准确。SecAgg 协议可视为 SecAgg+ 协议的特例。"
 
 #: ../../source/contributor-ref-secure-aggregation-protocols.rst:8
 msgid "The :code:`SecAgg+` abstraction"
-msgstr ""
+msgstr "代码：`SecAgg+` 抽象"
 
 #: ../../source/contributor-ref-secure-aggregation-protocols.rst:10
 #: ../../source/contributor-ref-secure-aggregation-protocols.rst:161
@@ -973,50 +1301,55 @@ msgid ""
 "(int) for secure aggregation, and thus many python dictionaries used have"
 " keys of int type rather than ClientProxy type."
 msgstr ""
+"在此实现中，将为每个客户端分配一个唯一索引（int），以确保聚合的安全性，因此使用的许多 python 字典的键都是 int 类型，而不是 "
+"ClientProxy 类型。"
 
 #: ../../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 ""
+msgstr "Flower 服务器将按以下顺序执行和处理收到的结果："
 
 #: ../../source/contributor-ref-secure-aggregation-protocols.rst:159
 msgid "The :code:`LightSecAgg` abstraction"
-msgstr ""
+msgstr "代码：`LightSecAgg` 抽象"
 
 #: ../../source/contributor-ref-secure-aggregation-protocols.rst:271
 msgid "Types"
-msgstr ""
+msgstr "类型"
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:2
 msgid "Contribute on GitHub"
-msgstr ""
+msgstr "在 GitHub 上投稿"
 
 #: ../../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."
-msgstr ""
+msgstr "本指南适用于想参与 Flower，但不习惯为 GitHub 项目贡献的人。"
 
 #: ../../source/contributor-tutorial-contribute-on-github.rst:6
 msgid ""
 "If you're familiar with how contributing on GitHub works, you can "
 "directly checkout our `getting started guide for contributors "
-"<https://flower.dev/docs/getting-started-for-contributors.html>`_ and "
-"examples of `good first contributions <https://flower.dev/docs/good-"
-"first-contributions.html>`_."
+"<https://flower.ai/docs/contributor-tutorial-get-started-"
+"as-a-contributor.html>`_."
 msgstr ""
+"如果您熟悉如何在 GitHub 上贡献，可以直接查看我们的 \"贡献者入门指南\" "
+"<https://flower.ai/docs/getting-started-for-contributors.html>`_ 和 "
+"\"优秀的首次贡献示例\" <https://flower.ai/docs/good-first-contributions."
+"html>`_。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:12
+#: ../../source/contributor-tutorial-contribute-on-github.rst:11
 msgid "Setting up the repository"
-msgstr ""
+msgstr "建立资源库"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:23
+#: ../../source/contributor-tutorial-contribute-on-github.rst:22
 msgid "**Create a GitHub account and setup Git**"
-msgstr ""
+msgstr "**创建 GitHub 账户并设置 Git**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:15
+#: ../../source/contributor-tutorial-contribute-on-github.rst:14
 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 "
@@ -1024,109 +1357,122 @@ msgid ""
 "follow this `guide <https://docs.github.com/en/get-started/quickstart"
 "/set-up-git>`_ to set it up."
 msgstr ""
+"Git 是一种分布式版本控制工具。它可以将整个代码库的历史记录保存在每个开发人员的机器上。您需要在本地计算机上安装该软件，可以按照本指南 "
+"<https://docs.github.com/en/get-started/quickstart/set-up-git>`_ 进行设置。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:18
+#: ../../source/contributor-tutorial-contribute-on-github.rst:17
 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."
-msgstr ""
+msgstr "GitHub 本身是一个用于版本控制和协作的代码托管平台。它允许每个人在任何地方对远程仓库进行协作和工作。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:20
+#: ../../source/contributor-tutorial-contribute-on-github.rst:19
 msgid ""
 "If you haven't already, you will need to create an account on `GitHub "
 "<https://github.com/signup>`_."
-msgstr ""
+msgstr "如果还没有，您需要在 `GitHub <https://github.com/signup>`_ 上创建一个账户。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:22
+#: ../../source/contributor-tutorial-contribute-on-github.rst:21
 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."
 msgstr ""
+"通用的 Git 和 GitHub 工作流程背后的理念可以归结为：从 GitHub 上的远程仓库下载代码，在本地进行修改并使用 Git "
+"进行跟踪，然后将新的历史记录上传回 GitHub。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:34
+#: ../../source/contributor-tutorial-contribute-on-github.rst:33
 msgid "**Forking the Flower repository**"
-msgstr ""
+msgstr "**叉花仓库**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:26
+#: ../../source/contributor-tutorial-contribute-on-github.rst:25
 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 ""
+"fork 是 GitHub 仓库的个人副本。要为 Flower 创建一个 fork，您必须导航到 "
+"https://github.com/adap/flower（同时连接到您的 GitHub 账户），然后点击页面右上方的 ``Fork`` 按钮。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:31
+#: ../../source/contributor-tutorial-contribute-on-github.rst:30
 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."
 msgstr ""
+"您可以更改名称，但没有必要，因为这个版本的 Flower "
+"将是您自己的，并位于您自己的账户中（即，在您自己的版本库列表中）。创建完成后，您会在左上角看到自己的 Flower 版本。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:49
+#: ../../source/contributor-tutorial-contribute-on-github.rst:48
 msgid "**Cloning your forked repository**"
-msgstr ""
+msgstr "**克隆你的分叉仓库**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:37
+#: ../../source/contributor-tutorial-contribute-on-github.rst:36
 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."
 msgstr ""
+"下一步是在你的机器上下载分叉版本库，以便对其进行修改。在分叉版本库页面上，首先点击右侧的 \"代码 \"按钮，这样就能复制版本库的 HTTPS "
+"链接。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:43
+#: ../../source/contributor-tutorial-contribute-on-github.rst:42
 msgid ""
 "Once you copied the \\<URL\\>, you can open a terminal on your machine, "
 "navigate to the place you want to download the repository to and type:"
-msgstr ""
+msgstr "一旦复制了 （<URL\\>），你就可以在你的机器上打开一个终端，导航到你想下载软件源的地方，然后键入："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:49
+#: ../../source/contributor-tutorial-contribute-on-github.rst:48
+#, fuzzy
 msgid ""
-"This will create a `flower/` (or the name of your fork if you renamed it)"
-" folder in the current working directory."
-msgstr ""
+"This will create a ``flower/`` (or the name of your fork if you renamed "
+"it) folder in the current working directory."
+msgstr "这将在当前工作目录下创建一个 `flower/`（如果重命名了，则使用 fork 的名称）文件夹。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:68
+#: ../../source/contributor-tutorial-contribute-on-github.rst:67
 msgid "**Add origin**"
-msgstr ""
+msgstr "**添加原产地**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:52
+#: ../../source/contributor-tutorial-contribute-on-github.rst:51
 msgid "You can then go into the repository folder:"
-msgstr ""
+msgstr "然后，您就可以进入存储库文件夹："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:58
+#: ../../source/contributor-tutorial-contribute-on-github.rst:57
 msgid ""
 "And here we will need to add an origin to our repository. The origin is "
 "the \\<URL\\> 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 ""
+"在这里，我们需要为我们的版本库添加一个 origin。origin 是远程 fork 仓库的 "
+"\\<URL/>。要获得它，我们可以像前面提到的那样，访问 GitHub 账户上的分叉仓库并复制链接。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:63
+#: ../../source/contributor-tutorial-contribute-on-github.rst:62
 msgid ""
 "Once the \\<URL\\> is copied, we can type the following command in our "
 "terminal:"
-msgstr ""
+msgstr "一旦复制了 \\<URL\\> ，我们就可以在终端中键入以下命令："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:92
+#: ../../source/contributor-tutorial-contribute-on-github.rst:91
 msgid "**Add upstream**"
-msgstr ""
+msgstr "**增加上游**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:71
+#: ../../source/contributor-tutorial-contribute-on-github.rst:70
 msgid ""
 "Now we will add an upstream address to our repository. Still in the same "
 "directroy, we must run the following command:"
-msgstr ""
+msgstr "现在，我们要为版本库添加一个上游地址。还是在同一目录下，我们必须运行以下命令："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:78
+#: ../../source/contributor-tutorial-contribute-on-github.rst:77
 msgid "The following diagram visually explains what we did in the previous steps:"
-msgstr ""
+msgstr "下图直观地解释了我们在前面步骤中的操作："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:82
+#: ../../source/contributor-tutorial-contribute-on-github.rst:81
 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 "
@@ -1134,412 +1480,438 @@ msgid ""
 "remote address of the forked repository we created, i.e. the copy (fork) "
 "in our own account."
 msgstr ""
+"上游是父版本库（这里是 Flower）的 GitHub 远程地址，即我们最终要贡献的版本库，因此需要最新的历史记录。origin "
+"只是我们创建的分叉仓库的 GitHub 远程地址，即我们自己账户中的副本（分叉）。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:86
+#: ../../source/contributor-tutorial-contribute-on-github.rst:85
 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 ""
+msgstr "为了确保本地版本的分叉程序与 Flower 代码库的最新更改保持一致，我们可以执行以下命令："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:95
+#: ../../source/contributor-tutorial-contribute-on-github.rst:94
 msgid "Setting up the coding environment"
-msgstr ""
+msgstr "设置编码环境"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:97
+#: ../../source/contributor-tutorial-contribute-on-github.rst:96
 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 ""
+msgstr "您可以按照这份 \"贡献者入门指南\"__（注意，您不需要克隆版本库）来实现这一点。一旦您能够编写代码并进行测试，您就可以开始修改了！"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:102
+#: ../../source/contributor-tutorial-contribute-on-github.rst:101
 msgid "Making changes"
-msgstr ""
+msgstr "做出改变"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:104
+#: ../../source/contributor-tutorial-contribute-on-github.rst:103
 msgid ""
 "Before making any changes make sure you are up-to-date with your "
 "repository:"
-msgstr ""
+msgstr "在进行任何更改之前，请确保您的版本库是最新的："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:110
+#: ../../source/contributor-tutorial-contribute-on-github.rst:109
 msgid "And with Flower's repository:"
-msgstr ""
+msgstr "还有Flower的存储库："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:124
+#: ../../source/contributor-tutorial-contribute-on-github.rst:123
 msgid "**Create a new branch**"
-msgstr ""
+msgstr "**创建一个新分支**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:117
+#: ../../source/contributor-tutorial-contribute-on-github.rst:116
 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."
-msgstr ""
+msgstr "为了使历史记录更简洁、更易于操作，为每个需要实现的功能/项目创建一个新分支是个不错的做法。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:120
+#: ../../source/contributor-tutorial-contribute-on-github.rst:119
 msgid ""
 "To do so, just run the following command inside the repository's "
 "directory:"
-msgstr ""
+msgstr "为此，只需在版本库目录下运行以下命令即可："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:127
+#: ../../source/contributor-tutorial-contribute-on-github.rst:126
 msgid "**Make changes**"
-msgstr ""
+msgstr "**进行修改**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:127
+#: ../../source/contributor-tutorial-contribute-on-github.rst:126
 msgid "Write great code and create wonderful changes using your favorite editor!"
-msgstr ""
+msgstr "使用您最喜欢的编辑器编写优秀的代码并创建精彩的更改！"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:140
+#: ../../source/contributor-tutorial-contribute-on-github.rst:139
 msgid "**Test and format your code**"
-msgstr ""
+msgstr "**测试并格式化您的代码**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:130
+#: ../../source/contributor-tutorial-contribute-on-github.rst:129
 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."
-msgstr ""
+msgstr "不要忘记测试和格式化您的代码！否则您的代码将无法并入 Flower 代码库。这样做是为了使代码库保持一致并易于理解。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:133
+#: ../../source/contributor-tutorial-contribute-on-github.rst:132
 msgid "To do so, we have written a few scripts that you can execute:"
-msgstr ""
+msgstr "为此，我们编写了一些脚本供您执行："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:152
+#: ../../source/contributor-tutorial-contribute-on-github.rst:151
 msgid "**Stage changes**"
-msgstr ""
+msgstr "**舞台变化**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:143
+#: ../../source/contributor-tutorial-contribute-on-github.rst:142
 msgid ""
 "Before creating a commit that will update your history, you must specify "
 "to Git which files it needs to take into account."
-msgstr ""
+msgstr "在创建更新历史记录的提交之前，必须向 Git 说明需要考虑哪些文件。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:145
+#: ../../source/contributor-tutorial-contribute-on-github.rst:144
 msgid "This can be done with:"
-msgstr ""
+msgstr "这可以通过："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:151
+#: ../../source/contributor-tutorial-contribute-on-github.rst:150
 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."
-msgstr ""
+msgstr "要查看与上一版本（上次提交）相比哪些文件已被修改，以及哪些文件处于提交阶段，可以使用 :code:`git status` 命令。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:162
+#: ../../source/contributor-tutorial-contribute-on-github.rst:161
 msgid "**Commit changes**"
-msgstr ""
+msgstr "**提交更改**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:155
+#: ../../source/contributor-tutorial-contribute-on-github.rst:154
 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 ""
+msgstr "使用 :code:`git add` 添加完所有要提交的文件后，就可以使用此命令创建提交了："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:161
+#: ../../source/contributor-tutorial-contribute-on-github.rst:160
 msgid ""
 "The \\<commit_message\\> 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 ""
+"<commit_message\\> 用于向他人解释提交的作用。它应该以命令式风格书写，并且简明扼要。例如 :code:`git commit "
+"-m \"Add images to README\"`。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:173
+#: ../../source/contributor-tutorial-contribute-on-github.rst:172
 msgid "**Push the changes to the fork**"
-msgstr ""
+msgstr "**将更改推送到分叉**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:165
+#: ../../source/contributor-tutorial-contribute-on-github.rst:164
 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:"
-msgstr ""
+msgstr "一旦提交了修改，我们就有效地更新了本地历史记录，但除非我们将修改推送到原点的远程地址，否则 GitHub 无法得知："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:172
+#: ../../source/contributor-tutorial-contribute-on-github.rst:171
 msgid ""
 "Once this is done, you will see on the GitHub that your forked repo was "
 "updated with the changes you have made."
-msgstr ""
+msgstr "完成此操作后，您将在 GitHub 上看到您的分叉仓库已根据您所做的更改进行了更新。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:176
+#: ../../source/contributor-tutorial-contribute-on-github.rst:175
 msgid "Creating and merging a pull request (PR)"
-msgstr ""
+msgstr "创建和合并拉取请求 (PR)"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:203
+#: ../../source/contributor-tutorial-contribute-on-github.rst:206
 msgid "**Create the PR**"
-msgstr ""
+msgstr "**创建 PR**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:179
+#: ../../source/contributor-tutorial-contribute-on-github.rst:178
 msgid ""
 "Once you have pushed changes, on the GitHub webpage of your repository "
 "you should see the following message:"
-msgstr ""
+msgstr "推送更改后，在仓库的 GitHub 网页上应该会看到以下信息："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:183
-msgid "Otherwise you can always find this option in the `Branches` page."
-msgstr ""
+#: ../../source/contributor-tutorial-contribute-on-github.rst:182
+#, fuzzy
+msgid "Otherwise you can always find this option in the ``Branches`` page."
+msgstr "否则，您可以在 \"分支 \"页面找到该选项。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:185
+#: ../../source/contributor-tutorial-contribute-on-github.rst:184
+#, fuzzy
 msgid ""
-"Once you click the `Compare & pull request` button, you should see "
+"Once you click the ``Compare & pull request`` button, you should see "
 "something similar to this:"
-msgstr ""
+msgstr "点击 \"比较和拉取请求 \"按钮后，您应该会看到类似下面的内容："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:189
+#: ../../source/contributor-tutorial-contribute-on-github.rst:188
 msgid "At the top you have an explanation of which branch will be merged where:"
-msgstr ""
+msgstr "在顶部，你可以看到关于哪个分支将被合并的说明："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:193
+#: ../../source/contributor-tutorial-contribute-on-github.rst:192
 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."
-msgstr ""
+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:195
+#: ../../source/contributor-tutorial-contribute-on-github.rst:194
 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 "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:197
+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:198
+#: ../../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 ""
+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:201
+#: ../../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 ""
+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:206
+#: ../../source/contributor-tutorial-contribute-on-github.rst:209
 msgid "**Making new changes**"
-msgstr ""
+msgstr "**作出新的改变**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:206
+#: ../../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 ""
+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:228
+#: ../../source/contributor-tutorial-contribute-on-github.rst:231
 msgid "**Review the PR**"
-msgstr ""
+msgstr "**审查 PR**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:209
+#: ../../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 ""
+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:213
+#: ../../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 ""
+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:215
+#: ../../source/contributor-tutorial-contribute-on-github.rst:218
 msgid "Merging will be blocked if there are ongoing requested changes."
-msgstr ""
+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:219
+#: ../../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 ""
+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:223
+#: ../../source/contributor-tutorial-contribute-on-github.rst:226
 msgid "And resolve the conversation:"
-msgstr ""
+msgstr "并解决对话："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:227
+#: ../../source/contributor-tutorial-contribute-on-github.rst:230
 msgid ""
 "Once all the conversations have been resolved, you can re-request a "
 "review."
-msgstr ""
+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:248
+#: ../../source/contributor-tutorial-contribute-on-github.rst:251
 msgid "**Once the PR is merged**"
-msgstr ""
+msgstr "**一旦 PR 被合并**"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:231
+#: ../../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 ""
+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:235
+#: ../../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 ""
+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:242
+#: ../../source/contributor-tutorial-contribute-on-github.rst:245
 msgid "Then you should update your forked repository by doing:"
-msgstr ""
+msgstr "然后，你应该更新你的分叉仓库："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:251
+#: ../../source/contributor-tutorial-contribute-on-github.rst:254
 msgid "Example of first contribution"
-msgstr ""
+msgstr "首次捐款实例"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:254
+#: ../../source/contributor-tutorial-contribute-on-github.rst:257
 msgid "Problem"
-msgstr ""
+msgstr "问题"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:256
+#: ../../source/contributor-tutorial-contribute-on-github.rst:259
 msgid ""
 "For our documentation, we’ve started to use the `Diàtaxis framework "
 "<https://diataxis.fr/>`_."
-msgstr ""
+msgstr "对于我们的文档，我们已经开始使用 \"Diàtaxis 框架 <https://diataxis.fr/>`_\"。"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:258
+#: ../../source/contributor-tutorial-contribute-on-github.rst:261
 msgid ""
 "Our “How to” guides should have titles that continue the sencence “How to"
 " …”, for example, “How to upgrade to Flower 1.0”."
-msgstr ""
+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:260
+#: ../../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 ""
+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:262
+#: ../../source/contributor-tutorial-contribute-on-github.rst:265
 msgid ""
 "This issue is about changing the title of a doc from present continious "
 "to present simple."
-msgstr ""
+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:264
+#: ../../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 ""
+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:266
+#: ../../source/contributor-tutorial-contribute-on-github.rst:269
 msgid "Before: ”How to saving progress” ❌"
-msgstr ""
+msgstr "之前： \"如何保存进度\" ❌"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:268
+#: ../../source/contributor-tutorial-contribute-on-github.rst:271
 msgid "After: ”How to save progress” ✅"
-msgstr ""
+msgstr "之后： \"如何保存进度\"✅"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:271
+#: ../../source/contributor-tutorial-contribute-on-github.rst:274
 msgid "Solution"
-msgstr ""
+msgstr "解决方案"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:273
+#: ../../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 ""
+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:275
-msgid "Find the source file in `doc/source`"
-msgstr ""
+#: ../../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:276
+#: ../../source/contributor-tutorial-contribute-on-github.rst:279
+#, fuzzy
 msgid ""
-"Make the change in the `.rst` file (beware, the dashes under the title "
+"Make the change in the ``.rst`` file (beware, the dashes under the title "
 "should be the same length as the title itself)"
-msgstr ""
+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:277
+#: ../../source/contributor-tutorial-contribute-on-github.rst:280
 msgid ""
-"Build the docs and check the result: `<https://flower.dev/docs/writing-"
+"Build the docs and check the result: `<https://flower.ai/docs/writing-"
 "documentation.html#edit-an-existing-page>`_"
 msgstr ""
+"构建文档并检查结果： `<https://flower.ai/docs/writing-documentation.html#edit-an-"
+"existing-page>`_"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:280
+#: ../../source/contributor-tutorial-contribute-on-github.rst:283
 msgid "Rename file"
-msgstr ""
+msgstr "重命名文件"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:282
+#: ../../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."
 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:285
+#: ../../source/contributor-tutorial-contribute-on-github.rst:288
 msgid "Here’s how to change the file name:"
-msgstr ""
+msgstr "下面是更改文件名的方法："
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:287
-msgid "Change the file name to `save-progress.rst`"
-msgstr ""
+#: ../../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:288
-msgid "Add a redirect rule to `doc/source/conf.py`"
-msgstr ""
+#: ../../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:290
+#: ../../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 ""
+"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:293
+#: ../../source/contributor-tutorial-contribute-on-github.rst:296
 msgid "Apply changes in the index file"
-msgstr ""
+msgstr "应用索引文件中的更改"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:295
+#: ../../source/contributor-tutorial-contribute-on-github.rst:298
+#, fuzzy
 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 "
+"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:298
-msgid "Find and modify the file name in `index.rst`"
-msgstr ""
+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: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:304
 msgid "Open PR"
-msgstr ""
+msgstr "开放式 PR"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:303
+#: ../../source/contributor-tutorial-contribute-on-github.rst:306
 msgid ""
 "Commit the changes (commit messages are always imperative: “Do "
 "something”, in this case “Change …”)"
-msgstr ""
+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:304
+#: ../../source/contributor-tutorial-contribute-on-github.rst:307
 msgid "Push the changes to your fork"
-msgstr ""
+msgstr "将更改推送到分叉"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:305
+#: ../../source/contributor-tutorial-contribute-on-github.rst:308
 msgid "Open a PR (as shown above)"
-msgstr ""
+msgstr "打开 PR（如上图所示）"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:306
+#: ../../source/contributor-tutorial-contribute-on-github.rst:309
 msgid "Wait for it to be approved!"
-msgstr ""
+msgstr "等待审批！"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:307
+#: ../../source/contributor-tutorial-contribute-on-github.rst:310
 msgid "Congrats! 🥳 You're now officially a Flower contributor!"
-msgstr ""
+msgstr "祝贺你 🥳 您现在正式成为 \"Flower \"贡献者！"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:311
+#: ../../source/contributor-tutorial-contribute-on-github.rst:314
 msgid "How to write a good PR title"
-msgstr ""
+msgstr "如何撰写好的公关标题"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:313
+#: ../../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:"
-msgstr ""
+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:315
+#: ../../source/contributor-tutorial-contribute-on-github.rst:318
 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,\" "
@@ -1548,227 +1920,432 @@ msgid ""
 "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:321
+#: ../../source/contributor-tutorial-contribute-on-github.rst:324
 msgid ""
 "Let's start with a few examples for titles that should be avoided because"
 " they do not provide meaningful information:"
-msgstr ""
+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:323
+#: ../../source/contributor-tutorial-contribute-on-github.rst:326
 msgid "Implement Algorithm"
-msgstr ""
+msgstr "执行算法"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:324
+#: ../../source/contributor-tutorial-contribute-on-github.rst:327
 msgid "Database"
-msgstr ""
+msgstr "数据库"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:325
+#: ../../source/contributor-tutorial-contribute-on-github.rst:328
 msgid "Add my_new_file.py to codebase"
-msgstr ""
+msgstr "在代码库中添加 my_new_file.py"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:326
+#: ../../source/contributor-tutorial-contribute-on-github.rst:329
 msgid "Improve code in module"
-msgstr ""
+msgstr "改进模块中的代码"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:327
+#: ../../source/contributor-tutorial-contribute-on-github.rst:330
 msgid "Change SomeModule"
-msgstr ""
+msgstr "更改 SomeModule"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:329
+#: ../../source/contributor-tutorial-contribute-on-github.rst:332
 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 ""
+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:331
+#: ../../source/contributor-tutorial-contribute-on-github.rst:334
 msgid "Update docs banner to mention Flower Summit 2023"
-msgstr ""
+msgstr "更新文件横幅，提及 2023 年 Flower 峰会"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:332
+#: ../../source/contributor-tutorial-contribute-on-github.rst:335
 msgid "Remove unnecessary XGBoost dependency"
-msgstr ""
+msgstr "移除不必要的 XGBoost 依赖性"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:333
+#: ../../source/contributor-tutorial-contribute-on-github.rst:336
 msgid "Remove redundant attributes in strategies subclassing FedAvg"
-msgstr ""
+msgstr "删除 FedAvg 子类化策略中的多余属性"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:334
-msgid "Add CI job to deploy the staging system when the `main` branch changes"
-msgstr ""
+#: ../../source/contributor-tutorial-contribute-on-github.rst:337
+#, 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:335
+#: ../../source/contributor-tutorial-contribute-on-github.rst:338
 msgid ""
 "Add new amazing library which will be used to improve the simulation "
 "engine"
-msgstr ""
+msgstr "添加新的惊人库，用于改进模拟引擎"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:339
+#: ../../source/contributor-tutorial-contribute-on-github.rst:342
 #: ../../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:747
+#: ../../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 ""
+msgstr "接下来的步骤"
 
-#: ../../source/contributor-tutorial-contribute-on-github.rst:341
+#: ../../source/contributor-tutorial-contribute-on-github.rst:344
 msgid ""
 "Once you have made your first PR, and want to contribute more, be sure to"
 " check out the following :"
-msgstr ""
+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:343
+#: ../../source/contributor-tutorial-contribute-on-github.rst:346
 msgid ""
-"`Good first contributions <https://flower.dev/docs/framework/contributor-"
+"`Good first contributions <https://flower.ai/docs/framework/contributor-"
 "ref-good-first-contributions.html>`_, where you should particularly look "
 "into the :code:`baselines` contributions."
 msgstr ""
+"`优秀的首次贡献 <https://flower.ai/docs/framework/contributor-ref-good-first-"
+"contributions.html>`_，在这里你应该特别看看 :code:`baselines` 的贡献。"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:2
-msgid "Get started as a contributor"
-msgstr ""
-
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:5
-msgid "Prerequisites"
-msgstr ""
-
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:7
-msgid "`Python 3.7 <https://docs.python.org/3.7/>`_ or above"
-msgstr ""
-
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:8
-msgid "`Poetry 1.3 <https://python-poetry.org/>`_ or above"
-msgstr ""
-
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:9
-msgid "(Optional) `pyenv <https://github.com/pyenv/pyenv>`_"
-msgstr ""
+#: ../../source/contributor-tutorial-contribute-on-github.rst:350
+#: ../../source/fed/0000-20200102-fed-template.md:60
+msgid "Appendix"
+msgstr "附录"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:10
-msgid "(Optional) `pyenv-virtualenv <https://github.com/pyenv/pyenv-virtualenv>`_"
-msgstr ""
+#: ../../source/contributor-tutorial-contribute-on-github.rst:355
+#, fuzzy
+msgid "Changelog entry"
+msgstr "更新日志"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:12
+#: ../../source/contributor-tutorial-contribute-on-github.rst:357
 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 <https://www.python.org/dev/peps/pep-0517/>`_."
+"When opening a new PR, inside its description, there should be a "
+"``Changelog entry`` header."
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:18
-msgid "Developer Machine Setup"
+#: ../../source/contributor-tutorial-contribute-on-github.rst:359
+msgid ""
+"Above this header you should see the following comment that explains how "
+"to write your changelog entry:"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:20
+#: ../../source/contributor-tutorial-contribute-on-github.rst:361
 msgid ""
-"First, clone the `Flower repository <https://github.com/adap/flower>`_ "
-"from GitHub::"
+"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-get-started-as-a-contributor.rst:26
+#: ../../source/contributor-tutorial-contribute-on-github.rst:364
 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:`<version>`)::"
+"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 ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:33
+#: ../../source/contributor-tutorial-contribute-on-github.rst:367
 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)::"
+"If the section contains some text other than tokens, it will use it to "
+"add a description to the change."
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:39
+#: ../../source/contributor-tutorial-contribute-on-github.rst:369
 msgid ""
-"Third, install the Flower package in development mode (think :code:`pip "
-"install -e`) along with all necessary dependencies::"
+"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-get-started-as-a-contributor.rst:46
-msgid "Convenience Scripts"
+#: ../../source/contributor-tutorial-contribute-on-github.rst:371
+msgid "<general> is for classifying a PR as a general improvement."
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:48
-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 "
-"amonst the most important ones:"
+#: ../../source/contributor-tutorial-contribute-on-github.rst:373
+msgid "<skip> is to not add the PR to the changelog"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:54
-msgid "Create/Delete Virtual Environment"
+#: ../../source/contributor-tutorial-contribute-on-github.rst:375
+msgid "<baselines> is to add a general baselines change to the PR"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:62
-msgid "Compile ProtoBuf Definitions"
+#: ../../source/contributor-tutorial-contribute-on-github.rst:377
+msgid "<examples> is to add a general examples change to the PR"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:69
-msgid "Auto-Format Code"
+#: ../../source/contributor-tutorial-contribute-on-github.rst:379
+msgid "<sdk> is to add a general sdk change to the PR"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:76
-msgid "Run Linters and Tests"
+#: ../../source/contributor-tutorial-contribute-on-github.rst:381
+msgid "<simulations> is to add a general simulations change to the PR"
 msgstr ""
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:83
-msgid "Run Github Actions (CI) locally"
+#: ../../source/contributor-tutorial-contribute-on-github.rst:383
+msgid "Note that only one token should be used."
 msgstr ""
 
+#: ../../source/contributor-tutorial-contribute-on-github.rst:385
+msgid ""
+"Its content must have a specific format. We will break down what each "
+"possibility does:"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:387
+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:391
+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:397
+msgid ""
+"If the ``### Changelog entry`` section contains ``<skip>``, nothing will "
+"change in the changelog."
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:399
+msgid ""
+"If the ``### Changelog entry`` section contains ``<general>``, the "
+"following text will be added to the changelog::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:403
+msgid ""
+"If the ``### Changelog entry`` section contains ``<baselines>``, the "
+"following text will be added to the changelog::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:407
+msgid ""
+"If the ``### Changelog entry`` section contains ``<examples>``, the "
+"following text will be added to the changelog::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:411
+msgid ""
+"If the ``### Changelog entry`` section contains ``<sdk>``, the following "
+"text will be added to the changelog::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:415
+msgid ""
+"If the ``### Changelog entry`` section contains ``<simulations>``, the "
+"following text will be added to the changelog::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-contribute-on-github.rst:419
+msgid ""
+"Note that only one token must be provided, otherwise, only the first "
+"action (in the order listed above), will be performed."
+msgstr ""
+
+#: ../../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
+msgid "Prerequisites"
+msgstr "先决条件"
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:7
+#, fuzzy
+msgid "`Python 3.8 <https://docs.python.org/3.8/>`_ or above"
+msgstr "Python 3.7 <https://docs.python.org/3.7/>`_ 或更高版本"
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:8
+msgid "`Poetry 1.3 <https://python-poetry.org/>`_ or above"
+msgstr "`Poetry 1.3 <https://python-poetry.org/>`_ 或更高版本"
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:9
+msgid "(Optional) `pyenv <https://github.com/pyenv/pyenv>`_"
+msgstr "(可选） `pyenv <https://github.com/pyenv/pyenv>`_"
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:10
+msgid "(Optional) `pyenv-virtualenv <https://github.com/pyenv/pyenv-virtualenv>`_"
+msgstr "(可选） `pyenv-virtualenv <https://github.com/pyenv/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 <https://www.python.org/dev/peps/pep-0517/>`_."
+msgstr ""
+"Flower 使用 :code:`pyproject.toml` 来管理依赖关系和配置开发工具（支持它的）。Poetry 是一种支持 `PEP "
+"517 <https://www.python.org/dev/peps/pep-0517/>`_ 的构建工具。"
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:18
+msgid "Developer Machine Setup"
+msgstr "开发者机器设置"
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:21
+msgid "Preliminarities"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:22
+msgid "Some system-wide dependencies are needed."
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:25
+msgid "For macOS"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:27
+msgid ""
+"Install `homebrew <https://brew.sh/>`_. Don't forget the post-"
+"installation actions to add `brew` to your PATH."
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:28
+msgid ""
+"Install `xz` (to install different Python versions) and `pandoc` to build"
+" the docs::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:34
+msgid "For Ubuntu"
+msgstr ""
+
+#: ../../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::"
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:44
+#, fuzzy
+msgid "Create Flower Dev Environment"
+msgstr "创建/删除虚拟环境"
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:46
+#, fuzzy
+msgid ""
+"1. Clone the `Flower repository <https://github.com/adap/flower>`_ from "
+"GitHub::"
+msgstr "首先，从 GitHub 克隆 \"Flower 存储库 <https://github.com/adap/flower>`_\"："
+
+#: ../../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 "
+"environment, activate and skip to the last point where all packages are "
+"installed."
+msgstr ""
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:54
+#, fuzzy
+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)::"
+msgstr ""
+"如果没有安装 :code:`pyenv`，可以使用以下脚本安装 pyenv、设置并创建虚拟环境（默认使用 "
+":code:`Python3.8.17）：："
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:58
+#, fuzzy
+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)::"
+msgstr ""
+"如果没有安装 :code:`pyenv`，可以使用以下脚本安装 pyenv、设置并创建虚拟环境（默认使用 "
+":code:`Python3.8.17）：："
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:62
+#, fuzzy
+msgid ""
+"3. Install the Flower package in development mode (think :code:`pip "
+"install -e`) along with all necessary dependencies::"
+msgstr "第三，在开发模式下安装 Flower 软件包（想想 :code:`pip install -e`）以及所有必要的依赖项：："
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:69
+msgid "Convenience Scripts"
+msgstr "便捷脚本"
+
+#: ../../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:"
+msgstr "Flower 软件仓库包含大量便捷脚本，可使重复性开发任务更轻松、更不易出错。完整列表请参见 :code:`/dev` 子目录。以下是最重要的脚本："
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:77
+msgid "Create/Delete Virtual Environment"
+msgstr "创建/删除虚拟环境"
+
 #: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:85
+msgid "Compile ProtoBuf Definitions"
+msgstr "编译 ProtoBuf 定义"
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:92
+msgid "Auto-Format Code"
+msgstr "自动格式化代码"
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:99
+msgid "Run Linters and Tests"
+msgstr "运行分类器和测试"
+
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:106
+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
+#, fuzzy
 msgid ""
 "Developers could run the full set of Github Actions workflows under their"
-" local environment by using `Act <https://github.com/nektos/act>_`. "
+" local environment by using `Act <https://github.com/nektos/act>`_. "
 "Please refer to the installation instructions under the linked repository"
 " and run the next command under Flower main cloned repository folder::"
 msgstr ""
+"开发人员可以使用 `Act <https://github.com/nektos/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:92
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:115
 msgid ""
 "The Flower default workflow would run by setting up the required Docker "
 "machines underneath."
-msgstr ""
+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:97
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:120
 msgid "Build Release"
-msgstr ""
+msgstr "版本发布"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:99
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:122
 msgid ""
 "Flower uses Poetry to build releases. The necessary command is wrapped in"
 " a simple script::"
-msgstr ""
+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:104
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:127
 msgid ""
 "The resulting :code:`.whl` and :code:`.tar.gz` releases will be stored in"
 " the :code:`/dist` subdirectory."
-msgstr ""
+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:109
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:132
 msgid "Build Documentation"
-msgstr ""
+msgstr "构建文档"
 
-#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:111
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:134
 msgid ""
 "Flower's documentation uses `Sphinx <https://www.sphinx-doc.org/>`_. "
 "There's no convenience script to re-build the documentation yet, but it's"
 " pretty easy::"
 msgstr ""
+"Flower 的文档使用 `Sphinx <https://www.sphinx-"
+"doc.org/>`_。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:117
+#: ../../source/contributor-tutorial-get-started-as-a-contributor.rst:140
 msgid "This will generate HTML documentation in ``doc/build/html``."
-msgstr ""
+msgstr "这将在 ``doc/build/html`` 中生成 HTML 文档。"
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:2
 msgid "Example: FedBN in PyTorch - From Centralized To Federated"
-msgstr ""
+msgstr "示例： PyTorch 中的 FedBN - 从集中式到联邦式"
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:4
 msgid ""
@@ -1778,33 +2355,41 @@ msgid ""
 "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 <https://flower.dev/docs/examples"
+"PyTorch - From Centralized To Federated <https://flower.ai/docs/examples"
 "/pytorch-from-centralized-to-federated.html>`_."
 msgstr ""
+"本教程将向您展示如何使用 Flower 为现有的机器学习框架构建一个联邦学习的版本，并使用 \"FedBN <https://github.com"
+"/med-air/FedBN>`_\"（一种针对非 iid 数据设计的联邦训练策略）。我们使用 PyTorch 在 CIFAR-10 "
+"数据集上训练一个卷积神经网络（带有Batch Normalization层）。在应用 FedBN 时，只需对 `示例: PyTorch - "
+"从集中式到联邦式 <https://flower.ai/docs/examples/pytorch-from-centralized-to-"
+"federated.html>`_ 做少量改动。"
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:9
 #: ../../source/example-pytorch-from-centralized-to-federated.rst:10
 msgid "Centralized Training"
-msgstr ""
+msgstr "集中式训练"
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:10
 msgid ""
 "All files are revised based on `Example: PyTorch - From Centralized To "
-"Federated <https://flower.dev/docs/examples/pytorch-from-centralized-to-"
+"Federated <https://flower.ai/docs/examples/pytorch-from-centralized-to-"
 "federated.html>`_. The only thing to do is modifying the file called "
 ":code:`cifar.py`, revised part is shown below:"
 msgstr ""
+"所有文件均根据 `示例： PyTorch -从集中式到联邦式 <https://flower.ai/docs/examples/pytorch-"
+"from-centralized-to-federated.html>`_。唯一要做的就是修改名为 :code:`cifar.py` "
+"的文件，修改部分如下所示："
 
 #: ../../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 ""
+msgstr "类 Net() 中定义的模型架构会相应添加Batch Normalization层。"
 
 #: ../../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 "现在，您可以运行您的机器学习工作了："
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:47
 msgid ""
@@ -1813,28 +2398,35 @@ msgid ""
 "federated learning system within FedBN, the sytstem consists of one "
 "server and two clients."
 msgstr ""
+"到目前为止，如果您以前使用过 PyTorch，这一切看起来应该相当熟悉。让我们进行下一步，使用我们所构建的内容在 FedBN "
+"中创建一个联邦学习系统，该系统由一个服务器和两个客户端组成。"
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:51
 #: ../../source/example-pytorch-from-centralized-to-federated.rst:167
 msgid "Federated Training"
-msgstr ""
+msgstr "联邦培训"
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:53
 msgid ""
 "If you have read `Example: PyTorch - From Centralized To Federated "
-"<https://flower.dev/docs/examples/pytorch-from-centralized-to-"
+"<https://flower.ai/docs/examples/pytorch-from-centralized-to-"
 "federated.html>`_, 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 <https://flower.dev/docs/examples"
+"PyTorch - From Centralized To Federated <https://flower.ai/docs/examples"
 "/pytorch-from-centralized-to-federated.html>`_. first."
 msgstr ""
+"如果你读过 `示例： PyTorch - 从集中式到联邦式 <https://flower.ai/docs/examples/pytorch-"
+"from-centralized-to-federated.html>`_，下面的部分就很容易理解了，只需要修改 "
+":code:`get_parameters` 和 :code:`set_parameters` 中的 :code:`client.py` "
+"函数。如果没有，请阅读 `示例： PyTorch - 从集中式到联邦式 <https://flower.ai/docs/examples"
+"/pytorch-from-centralized-to-federated.html>`_。"
 
 #: ../../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."
-msgstr ""
+msgstr "我们的示例包括一个*服务器*和两个*客户端*。在 FedBN 中，:code:`server.py` 保持不变，我们可以直接启动服务器。"
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:62
 msgid ""
@@ -1843,17 +2435,20 @@ msgid ""
 "we will exclude batch normalization parameters from model parameter list "
 "when sending to or receiving from the server."
 msgstr ""
+"最后，我们将修改 *client* 的逻辑，修改 :code:`client.py` 中的 :code:`get_parameters` 和 "
+":code:`set_parameters`，在向服务器发送或从服务器接收时，我们将从模型参数列表中排除batch "
+"normalization层的参数。"
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:85
 msgid "Now, you can now open two additional terminal windows and run"
-msgstr ""
+msgstr "现在，您可以打开另外两个终端窗口并运行程序"
 
 #: ../../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!"
-msgstr ""
+msgstr "确保服务器仍在运行后，然后您就能看到您的 PyTorch 项目（之前是集中式的）通过 FedBN 策略在两个客户端上运行联合学习。祝贺！"
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:94
 #: ../../source/example-jax-from-centralized-to-federated.rst:277
@@ -1861,7 +2456,7 @@ msgstr ""
 #: ../../source/example-pytorch-from-centralized-to-federated.rst:310
 #: ../../source/tutorial-quickstart-jax.rst:283
 msgid "Next Steps"
-msgstr ""
+msgstr "下一步工作"
 
 #: ../../source/example-fedbn-pytorch-from-centralized-to-federated.rst:96
 msgid ""
@@ -1873,10 +2468,14 @@ msgid ""
 "using different subsets of CIFAR-10 on each client? How about adding more"
 " clients?"
 msgstr ""
+"本示例的完整源代码可在 <https://github.com/adap/flower/blob/main/examples/pytorch-"
+"from-centralized-to-federated>`_ "
+"找到。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。让我们准备好进一步探讨这一主题。如在每个客户端使用不同的 "
+"CIFAR-10 子集，或者增加客户端的数量。"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:2
 msgid "Example: JAX - Run JAX Federated"
-msgstr ""
+msgstr "示例： JAX - 运行联邦式 JAX"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:4
 #: ../../source/tutorial-quickstart-jax.rst:10
@@ -1892,6 +2491,12 @@ msgid ""
 " 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 JAX 的联邦学习版本。我们将使用 JAX 在 scikit-learn "
+"数据集上训练线性回归模型。我们将采用与 `PyTorch - 从集中式到联邦式 "
+"<https://github.com/adap/flower/blob/main/examples/pytorch-from-"
+"centralized-to-federated>`_ 教程中类似的示例结构。首先，我们根据 `JAX 的线性回归 "
+"<https://coax.readthedocs.io/en/latest/examples/linear_regression/jax.html>`_"
+" 教程构建集中式训练方法。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。"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:10
 #: ../../source/tutorial-quickstart-jax.rst:16
@@ -1899,11 +2504,13 @@ msgid ""
 "Before we start building our JAX example, we need install the packages "
 ":code:`jax`, :code:`jaxlib`, :code:`scikit-learn`, and :code:`flwr`:"
 msgstr ""
+"在开始构建 JAX 示例之前，我们需要安装软件包 :code:`jax`、:code:`jaxlib`、:code:`scikit-learn` "
+"和 :code:`flwr`："
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:18
 #: ../../source/tutorial-quickstart-jax.rst:24
 msgid "Linear Regression with JAX"
-msgstr ""
+msgstr "使用 JAX 进行线性回归"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:20
 #: ../../source/tutorial-quickstart-jax.rst:26
@@ -1913,6 +2520,8 @@ msgid ""
 "explanation of what's going on then have a look at the official `JAX "
 "documentation <https://jax.readthedocs.io/>`_."
 msgstr ""
+"首先，我们将简要介绍基于 :code:`Linear Regression` 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 "
+"`JAX 文档 <https://jax.readthedocs.io/>`_。"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:23
 #: ../../source/tutorial-quickstart-jax.rst:29
@@ -1926,20 +2535,24 @@ msgid ""
 "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:`jax_training.py` 的新文件，其中包含传统（集中式）线性回归训练所需的所有组件。首先，需要导入 "
+"JAX 包 :code:`jax` 和 :code:`jaxlib`。此外，我们还需要导入 :code:`sklearn`，因为我们使用 "
+":code:`make_regression` 创建数据集，并使用 :code:`train_test_split` "
+"将数据集拆分成训练集和测试集。您可以看到，我们还没有导入用于联邦学习的 :code:`flwr` 软件包，这将在稍后完成。"
 
 #: ../../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."
-msgstr ""
+msgstr ":code:`load_data()` 函数会加载上述训练集和测试集。"
 
 #: ../../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()`."
-msgstr ""
+msgstr "模型结构（一个非常简单的 :code:`Linear Regression` 线性回归模型）在 :code:`load_model()` 中定义。"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:59
 #: ../../source/tutorial-quickstart-jax.rst:65
@@ -1950,6 +2563,9 @@ msgid ""
 " is separate since JAX takes derivatives with a :code:`grad()` function "
 "(defined in the :code:`main()` function and called in :code:`train()`)."
 msgstr ""
+"现在，我们需要定义训练函数（ :code:`train()`）。它循环遍历训练集，并计算每批训练数据的损失值（函数 "
+":code:`loss_fn()`）。由于 JAX 使用 :code:`grad()` 函数提取导数（在 :code:`main()` "
+"函数中定义，并在 :code:`train()` 中调用），因此损失函数是独立的。"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:77
 #: ../../source/tutorial-quickstart-jax.rst:83
@@ -1957,7 +2573,7 @@ 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."
-msgstr ""
+msgstr "模型的评估在函数 :code:`evaluation()` 中定义。该函数获取所有测试数据，并计算线性回归模型的损失值。"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:88
 #: ../../source/tutorial-quickstart-jax.rst:94
@@ -1967,11 +2583,14 @@ msgid ""
 "As already mentioned, the :code:`jax.grad()` function is defined in "
 ":code:`main()` and passed to :code:`train()`."
 msgstr ""
+"在定义了数据加载、模型架构、训练和评估之后，我们就可以把这些放在一起，使用 JAX "
+"训练我们的模型了。如前所述，:code:`jax.grad()` 函数在 :code:`main()` 中定义，并传递给 "
+":code:`train()`。"
 
 #: ../../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 "现在您可以运行（集中式）JAX 线性回归工作了："
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:111
 #: ../../source/tutorial-quickstart-jax.rst:117
@@ -1979,12 +2598,12 @@ 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."
-msgstr ""
+msgstr "到目前为止，如果你以前使用过 JAX，就会对这一切感到很熟悉。下一步，让我们利用已构建的代码创建一个简单的联邦学习系统（一个服务器和两个客户端）。"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:115
 #: ../../source/tutorial-quickstart-jax.rst:121
 msgid "JAX meets Flower"
-msgstr ""
+msgstr "JAX 结合 Flower"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:117
 #: ../../source/tutorial-quickstart-jax.rst:123
@@ -1998,6 +2617,8 @@ msgid ""
 "parameter updates. This describes one round of the federated learning "
 "process, and we repeat this for multiple rounds."
 msgstr ""
+"把现有工作联邦化的概念始终是相同的，也很容易理解。我们要启动一个*服务器*，然后对连接到*服务器*的*客户端*运行 "
+":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
@@ -2009,13 +2630,15 @@ msgid ""
 ":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 ""
+"我们的示例包括一个*服务器*和两个*客户端*。让我们先设置 :code:`server.py`。*服务器*需要导入 Flower 软件包 "
+":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*:"
-msgstr ""
+msgstr "我们已经可以启动*服务器*了："
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:139
 #: ../../source/tutorial-quickstart-jax.rst:145
@@ -2025,6 +2648,9 @@ msgid ""
 " *client* needs to import :code:`flwr`, but also :code:`jax` and "
 ":code:`jaxlib` to update the parameters on our JAX 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:`jax_training.py` 中定义的 JAX 训练为基础。我们的 *client* 需要导入 "
+":code:`flwr`，还需要导入 :code:`jax` 和 :code:`jaxlib` 以更新 JAX 模型的参数："
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:154
 #: ../../source/tutorial-quickstart-jax.rst:160
@@ -2040,12 +2666,18 @@ msgid ""
 "parameters, one method for training the model, and one method for testing"
 " the model:"
 msgstr ""
+"实现一个 Flower *client*基本上意味着去实现一个 :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:`FlowerClient`。如果使用具有良好 "
+"NumPy 互操作性的框架（如 JAX），: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:`FlowerClient` "
+"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 ""
+msgstr ":code:`set_parameters (可选)`"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:160
 #: ../../source/example-mxnet-walk-through.rst:241
@@ -2054,12 +2686,12 @@ msgstr ""
 msgid ""
 "set the model parameters on the local model that are received from the "
 "server"
-msgstr ""
+msgstr "在本地模型上设置从服务器接收的模型参数"
 
 #: ../../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 "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
@@ -2068,17 +2700,17 @@ msgstr ""
 msgid ""
 "loop over the list of model parameters received as NumPy "
 ":code:`ndarray`'s (think list of neural network layers)"
-msgstr ""
+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:169
+#: ../../source/tutorial-quickstart-mxnet.rst:171
 #: ../../source/tutorial-quickstart-pytorch.rst:155
 #: ../../source/tutorial-quickstart-scikitlearn.rst:108
 msgid ":code:`get_parameters`"
-msgstr ""
+msgstr ":code:`get_parameters`"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:164
 #: ../../source/example-mxnet-walk-through.rst:245
@@ -2088,16 +2720,18 @@ msgid ""
 "get the model parameters and return them as a list of NumPy "
 ":code:`ndarray`'s (which is what :code:`flwr.client.NumPyClient` expects)"
 msgstr ""
+"获取模型参数，并以 NumPy :code:`ndarray`的列表形式返回（这正是 "
+":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:175
+#: ../../source/tutorial-quickstart-mxnet.rst:177
 #: ../../source/tutorial-quickstart-pytorch.rst:161
 #: ../../source/tutorial-quickstart-scikitlearn.rst:115
 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
@@ -2110,41 +2744,41 @@ msgstr ""
 msgid ""
 "update the parameters of the local model with the parameters received "
 "from the server"
-msgstr ""
+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"
-msgstr ""
+msgstr "在本地训练集上训练模型"
 
 #: ../../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 "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:178
+#: ../../source/tutorial-quickstart-mxnet.rst:180
 #: ../../source/tutorial-quickstart-pytorch.rst:164
 #: ../../source/tutorial-quickstart-scikitlearn.rst:118
 msgid ":code:`evaluate`"
-msgstr ""
+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"
-msgstr ""
+msgstr "在本地测试集上评估更新后的模型"
 
 #: ../../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 "向服务器返回本地损失值"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:174
 #: ../../source/tutorial-quickstart-jax.rst:180
@@ -2153,6 +2787,8 @@ msgid ""
 ":code:`DeviceArray` to :code:`NumPy ndarray` to make them compatible with"
 " `NumPyClient`."
 msgstr ""
+"具有挑战性的部分是将 JAX 模型参数从 :code:`DeviceArray` 转换为 :code:`NumPy ndarray`，使其与 "
+"`NumPyClient` 兼容。"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:176
 #: ../../source/tutorial-quickstart-jax.rst:182
@@ -2165,18 +2801,22 @@ msgid ""
 "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:`jax_training.py` 中定义的函数 :code:`train()` 和 "
+":code:`evaluate()`。因此，我们在这里要做的就是通过 :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-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 "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"
-msgstr ""
+msgstr "就是这样，现在你可以打开另外两个终端窗口，然后运行"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:274
 #: ../../source/tutorial-quickstart-jax.rst:280
@@ -2184,7 +2824,7 @@ 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. "
 "Congratulations!"
-msgstr ""
+msgstr "确保服务器仍在运行，然后在每个客户端窗口就能看到你的 JAX 项目在两个客户端上运行联邦学习了。祝贺!"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:279
 #: ../../source/tutorial-quickstart-jax.rst:285
@@ -2194,6 +2834,9 @@ msgid ""
 "/quickstart-jax>`_. Our example is somewhat over-simplified because both "
 "clients load the same dataset."
 msgstr ""
+"此示例的源代码经过长期改进，可在此处找到： `Quickstart JAX "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"jax>`_。我们的示例有些过于简单，因为两个客户端都加载了相同的数据集。"
 
 #: ../../source/example-jax-from-centralized-to-federated.rst:282
 #: ../../source/tutorial-quickstart-jax.rst:288
@@ -2201,11 +2844,11 @@ 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 "
 "clients?"
-msgstr ""
+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 ""
+msgstr "示例： MXNet - 运行联邦式 MXNet"
 
 #: ../../source/example-mxnet-walk-through.rst:4
 msgid ""
@@ -2223,16 +2866,25 @@ msgid ""
 " 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 "
+"<https://github.com/adap/flower/blob/main/examples/pytorch-from-"
+"centralized-to-federated>`_ 教程类似的示例结构。MXNet 和 PyTorch 非常相似，参考 `此处 "
+"<https://mxnet.apache.org/versions/1.7.0/api/python/docs/tutorials"
+"/getting-started/to-mxnet/pytorch.html>`_对 MXNet 和 PyTorch "
+"进行了详细的比较。首先，我们根据 `手写数字识别 "
+"<https://mxnet.apache.org/versions/1.7.0/api/python/docs/tutorials/packages/gluon/image/mnist.html>`"
+" 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。"
 
 #: ../../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 ""
+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 ""
+msgstr "使用 MXNet 进行 MNIST 训练"
 
 #: ../../source/example-mxnet-walk-through.rst:21
 msgid ""
@@ -2241,6 +2893,8 @@ msgid ""
 " what's going on then have a look at the official `MXNet tutorial "
 "<https://mxnet.apache.org/versions/1.7.0/api/python/docs/tutorials/>`_."
 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 "
+"<https://mxnet.apache.org/versions/1.7.0/api/python/docs/tutorials/>`_。"
 
 #: ../../source/example-mxnet-walk-through.rst:24
 msgid ""
@@ -2250,10 +2904,12 @@ msgid ""
 "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。"
 
 #: ../../source/example-mxnet-walk-through.rst:42
 msgid "The :code:`load_data()` function loads the MNIST training and test sets."
-msgstr ""
+msgstr ":code:`load_data()` 函数加载 MNIST 训练集和测试集。"
 
 #: ../../source/example-mxnet-walk-through.rst:57
 msgid ""
@@ -2261,20 +2917,22 @@ msgid ""
 "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。"
 
 #: ../../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 ""
+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。"
 
 #: ../../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 ""
+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。"
 
 #: ../../source/example-mxnet-walk-through.rst:158
 msgid ""
@@ -2283,10 +2941,12 @@ msgid ""
 "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 ""
+msgstr "现在，您可以运行（集中式）MXNet 机器学习工作："
 
 #: ../../source/example-mxnet-walk-through.rst:190
 msgid ""
@@ -2295,10 +2955,12 @@ msgid ""
 "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）。"
 
 #: ../../source/example-mxnet-walk-through.rst:194
 msgid "MXNet meets Flower"
-msgstr ""
+msgstr "MXNet 结合 Flower"
 
 #: ../../source/example-mxnet-walk-through.rst:196
 msgid ""
@@ -2309,6 +2971,8 @@ msgid ""
 "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。"
 
 #: ../../source/example-mxnet-walk-through.rst:198
 msgid ""
@@ -2321,6 +2985,8 @@ msgid ""
 "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。"
 
 #: ../../source/example-mxnet-walk-through.rst:220
 msgid ""
@@ -2329,13 +2995,16 @@ msgid ""
 "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 slighly easier "
+"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 "
@@ -2343,26 +3012,34 @@ msgid ""
 "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 ""
+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 ""
+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 ""
+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 ""
@@ -2374,6 +3051,10 @@ msgid ""
 "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 ""
@@ -2381,13 +3062,15 @@ msgid ""
 " 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 ""
+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 ""
@@ -2399,10 +3082,15 @@ msgid ""
 " 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) "
+"<https://github.com/adap/flower/blob/main/examples/mxnet-from-"
+"centralized-to-"
+"federated>`_。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 ""
+msgstr "实例： PyTorch - 从集中式到联邦式"
 
 #: ../../source/example-pytorch-from-centralized-to-federated.rst:4
 msgid ""
@@ -2415,6 +3103,9 @@ msgid ""
 "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 "
+"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_\"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 ""
@@ -2423,6 +3114,8 @@ msgid ""
 "look at the official `PyTorch tutorial "
 "<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_."
 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 "
+"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_。"
 
 #: ../../source/example-pytorch-from-centralized-to-federated.rst:15
 msgid ""
@@ -2433,6 +3126,9 @@ msgid ""
 "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 ""
@@ -2440,32 +3136,36 @@ msgid ""
 "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 ""
+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 ""
+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 ""
+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 ""
@@ -2474,6 +3174,8 @@ msgid ""
 "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 ""
@@ -2484,39 +3186,44 @@ msgid ""
 "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 ""
+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 ""
+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 paramters. The updated "
+"*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 paramters on our PyTorch model:"
+"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 "
+"PyTorch 模型的参数："
 
 #: ../../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 slighly easier "
+"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. "
@@ -2524,10 +3231,16 @@ msgid ""
 "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:`CifarClient`。如果使用具有良好 NumPy"
+" 互操作性的框架（如 PyTorch 或 TensorFlow/Keras），:code:`NumPyClient`的实现比 "
+":code:`Client`略微容易一些，因为它避免了一些不必要的操作。:code:`CifarClient` "
+"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-pytorch-from-centralized-to-federated.rst:219
 msgid ":code:`set_parameters`"
-msgstr ""
+msgstr ":code:`set_parameters`"
 
 #: ../../source/example-pytorch-from-centralized-to-federated.rst:232
 msgid ""
@@ -2539,22 +3252,26 @@ msgid ""
 "annotations to give you a better understanding of the data types that get"
 " passed around."
 msgstr ""
+"这两个 :code:`NumPyClient` 中的方法 :code:`fit` 和 :code:`evaluate` 使用了之前在 "
+":code:`cifar.py` 中定义的函数 :code:`train()` 和 :code:`test()`。因此，我们在这里要做的就是通过 "
+":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，以便让你更好地理解传递的数据类型。"
 
 #: ../../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_numpy_client()` by pointing it "
-"at the same IP adress we used in :code:`server.py`:"
-msgstr ""
+"with the function :code:`fl.client.start_client()` by pointing it at the "
+"same IP address we used in :code:`server.py`:"
+msgstr "剩下的就是定义模型和数据加载函数了。创建一个:code:`CifarClient`类，并运行这个客服端。您将通过:code:`cifar.py`加载数据和模型。另外，通过:code:`fl.client.start_client()`函数来运行客户端:code:`CifarClient`，需要保证IP地址和:code:`server.py`中所使用的一致："
 
 #: ../../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!"
-msgstr ""
+msgstr "确保服务器正在运行后，您就能看到您的 PyTorch 项目（之前是集中式的）在两个客户端上运行联邦学习了。祝贺！"
 
 #: ../../source/example-pytorch-from-centralized-to-federated.rst:312
 msgid ""
@@ -2566,25 +3283,30 @@ msgid ""
 " further. How about using different subsets of CIFAR-10 on each client? "
 "How about adding more clients?"
 msgstr ""
+"本示例的完整源代码为：`PyTorch： 从集中式到联合式 "
+"<https://github.com/adap/flower/blob/main/examples/pytorch-from-"
+"centralized-to-"
+"federated>`_。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这一主题了。比如在每个客户端使用不同的"
+" CIFAR-10 子集会如何？增加更多客户端会如何？"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:2
 msgid "Example: Walk-Through PyTorch & MNIST"
-msgstr ""
+msgstr "实例： PyTorch 和 MNIST 的演练"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:4
 msgid ""
 "In this tutorial we will learn, how to train a Convolutional Neural "
 "Network on MNIST using Flower and PyTorch."
-msgstr ""
+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。"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:6
-#: ../../source/tutorial-quickstart-mxnet.rst:14
+#: ../../source/tutorial-quickstart-mxnet.rst:16
 #: ../../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 ""
+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/example-walkthrough-pytorch-mnist.rst:8
 #: ../../source/tutorial-quickstart-pytorch.rst:19
@@ -2594,24 +3316,24 @@ msgid ""
 "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 ""
+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/example-walkthrough-pytorch-mnist.rst:12
 #: ../../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 ""
+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/example-walkthrough-pytorch-mnist.rst:18
 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 ""
+msgstr "我们想用 PyTorch 来做计算机视觉任务，需要先安装 PyTorch 和 **torchvision** 库："
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:26
 msgid "Ready... Set... Train!"
-msgstr ""
+msgstr "准备...设置...训练!"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:28
 msgid ""
@@ -2624,42 +3346,47 @@ msgid ""
 "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 "
+"<https://github.com/pytorch/examples/tree/master/mnist>`_。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 =)。"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:31
 msgid ""
 "Go ahead and launch on a terminal the *run-server.sh* script first as "
 "follows:"
-msgstr ""
+msgstr "首先在终端上启动 *run-server.sh* 脚本，如下所示："
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:38
 msgid "Now that the server is up and running, go ahead and launch the clients."
-msgstr ""
+msgstr "现在服务器已经启动并运行，请继续启动客户端。"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:45
 msgid ""
 "Et voilà! You should be seeing the training procedure and, after a few "
 "iterations, the test accuracy for each client."
-msgstr ""
+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。"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:66
 msgid "Now, let's see what is really happening inside."
-msgstr ""
+msgstr "现在，让我们看看里面到底发生了什么。"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:69
 #: ../../source/tutorial-quickstart-ios.rst:129
-#: ../../source/tutorial-quickstart-mxnet.rst:224
+#: ../../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:306
+#: ../../source/tutorial-quickstart-xgboost.rst:309
 msgid "Flower Server"
-msgstr ""
+msgstr "Flower 服务器"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:71
 msgid ""
 "Inside the server helper script *run-server.sh* you will find the "
 "following code that basically runs the :code:`server.py`"
-msgstr ""
+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"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:78
 msgid ""
@@ -2670,32 +3397,35 @@ msgid ""
 "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 <flwr-server-start_server-"
+"apiref>`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。"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:89
 #: ../../source/tutorial-quickstart-ios.rst:34
-#: ../../source/tutorial-quickstart-mxnet.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:52
+#: ../../source/tutorial-quickstart-xgboost.rst:55
 msgid "Flower Client"
-msgstr ""
+msgstr "Flower 客户端"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:91
 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 ""
+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。"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:100
 msgid ""
 "**cid**: is the client ID. It is an integer that uniquely identifies "
 "client identifier."
-msgstr ""
+msgstr "**cid**：是客户 ID。它是一个整数，可唯一标识客户标识符。"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:101
 msgid "**sever_address**: String that identifies IP and port of the server."
-msgstr ""
+msgstr "**sever_address**： 标识服务器 IP 和端口的字符串。"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:102
 msgid ""
@@ -2704,6 +3434,8 @@ msgid ""
 "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/example-walkthrough-pytorch-mnist.rst:104
 msgid ""
@@ -2717,6 +3449,12 @@ msgid ""
 "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/example-walkthrough-pytorch-mnist.rst:119
 msgid ""
@@ -2724,22 +3462,26 @@ msgid ""
 ":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/example-walkthrough-pytorch-mnist.rst:123
 msgid "A Closer Look"
-msgstr ""
+msgstr "仔细看一下"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:125
 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："
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:226
 msgid ""
 "The first thing to notice is that :code:`PytorchMNISTClient` instantiates"
 " a CNN model inside its constructor"
-msgstr ""
+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"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:244
 msgid ""
@@ -2747,6 +3489,8 @@ msgid ""
 "and it is reproduced below. It is the same network found in `Basic MNIST "
 "Example <https://github.com/pytorch/examples/tree/master/mnist>`_."
 msgstr ""
+"CNN 的代码可在 :code:`quickstart-pytorch.mnist` 下找到，现复制如下。它与 `Basic MNIST "
+"Example <https://github.com/pytorch/examples/tree/master/mnist>`_中的网络相同。"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:290
 msgid ""
@@ -2754,6 +3498,8 @@ msgid ""
 "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："
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:315
 msgid ""
@@ -2762,12 +3508,14 @@ msgid ""
 ":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。"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:317
 msgid ""
 "These functions can both be found inside the same :code:`quickstart-"
 "pytorch.mnist` module:"
-msgstr ""
+msgstr "这些函数都可以在同一个 :code:`quickstart-pytorch.mnist` 模块中找到："
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:437
 msgid ""
@@ -2778,10 +3526,12 @@ msgid ""
 "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？"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:444
 msgid "Give It a Try"
-msgstr ""
+msgstr "试试看"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:445
 msgid ""
@@ -2791,36 +3541,39 @@ msgid ""
 "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："
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:448
 msgid ""
 "Try and change :code:`PytorchMNISTClient` so it can accept different "
 "architectures."
-msgstr ""
+msgstr "尝试修改 :code:`PytorchMNISTClient`，使其可以接受不同的架构。"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:449
 msgid "Modify the :code:`train` function so that it accepts different optimizers"
-msgstr ""
+msgstr "修改 :code:`train` 函数，使其接受不同的优化器"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:450
 msgid ""
 "Modify the :code:`test` function so that it proves not only the top-1 "
 "(regular accuracy) but also the top-5 accuracy?"
-msgstr ""
+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？"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:451
 msgid ""
 "Go larger! Try to adapt the code to larger images and datasets. Why not "
 "try training on ImageNet with a ResNet-50?"
-msgstr ""
+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？"
 
 #: ../../source/example-walkthrough-pytorch-mnist.rst:453
 msgid "You are ready now. Enjoy learning in a federated way!"
-msgstr ""
+msgstr "您现在已经准备就绪。尽情享受联邦学习的乐趣吧！"
 
 #: ../../source/explanation-differential-privacy.rst:2
 msgid "Differential privacy"
-msgstr ""
+msgstr "差别隐私"
 
 #: ../../source/explanation-differential-privacy.rst:4
 msgid ""
@@ -2829,69 +3582,78 @@ msgid ""
 "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。"
 
 #: ../../source/explanation-differential-privacy.rst:7
+#, fuzzy
 msgid ""
-"Please note that these components are still experimental, the correct "
+"Please note that these components are still experimental; the correct "
 "configuration of DP for a specific task is still an unsolved problem."
-msgstr ""
+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。"
 
 #: ../../source/explanation-differential-privacy.rst:10
 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 ""
+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。"
 
 #: ../../source/explanation-differential-privacy.rst:13
 msgid "DP-FedAvg"
-msgstr ""
+msgstr "DP-FedAvg"
 
 #: ../../source/explanation-differential-privacy.rst:15
 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 ""
+msgstr "DP-FedAvg 最初由McMahan等人提出，并由Andrew等人加以扩展。"
 
 #: ../../source/explanation-differential-privacy.rst:17
 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 ""
+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。"
 
 #: ../../source/explanation-differential-privacy.rst:18
 msgid ""
 "**Noising** :  Gaussian noise, calibrated to the clipping threshold, is "
 "added to the average computed at the server."
-msgstr ""
+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。"
 
 #: ../../source/explanation-differential-privacy.rst:20
+#, fuzzy
 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 ""
+"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。"
 
 #: ../../source/explanation-differential-privacy.rst:23
 msgid "Simplifying Assumptions"
-msgstr ""
+msgstr "简化假设"
 
 #: ../../source/explanation-differential-privacy.rst:25
+#, fuzzy
 msgid ""
 "We make (and attempt to enforce) a number of assumptions that must be "
-"satisfied to ensure that the training process actually realises the "
+"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)`"
+" 。"
 
 #: ../../source/explanation-differential-privacy.rst:27
 msgid ""
 "**Fixed-size subsampling** :Fixed-size subsamples of the clients must be "
 "taken at each round, as opposed to variable-sized Poisson subsamples."
-msgstr ""
+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。"
 
 #: ../../source/explanation-differential-privacy.rst:28
 msgid ""
@@ -2899,32 +3661,33 @@ msgid ""
 "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 ""
+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。"
 
 #: ../../source/explanation-differential-privacy.rst:29
 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 ""
+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。"
 
 #: ../../source/explanation-differential-privacy.rst:31
+#, fuzzy
 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 "
+"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 ""
+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。"
 
 #: ../../source/explanation-differential-privacy.rst:34
 msgid ""
 "These restrictions are in line with constraints imposed by Andrew et al. "
 "[andrew]_."
-msgstr ""
+msgstr "这些限制与 Andrew 等人所施加的限制一致。"
 
 #: ../../source/explanation-differential-privacy.rst:37
 msgid "Customizable Responsibility for Noise injection"
-msgstr ""
+msgstr "可定制的噪声注入"
 
 #: ../../source/explanation-differential-privacy.rst:38
 msgid ""
@@ -2935,7 +3698,7 @@ msgid ""
 "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 ""
+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。"
 
 #: ../../source/explanation-differential-privacy.rst:41
 msgid ""
@@ -2945,10 +3708,13 @@ msgid ""
 "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。"
 
 #: ../../source/explanation-differential-privacy.rst:44
 msgid "Wrapper-based approach"
-msgstr ""
+msgstr "基于封装的方法"
 
 #: ../../source/explanation-differential-privacy.rst:46
 msgid ""
@@ -2964,15 +3730,21 @@ msgid ""
 "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。"
 
 #: ../../source/explanation-differential-privacy.rst:49
 msgid "Server-side logic"
-msgstr ""
+msgstr "服务器端逻辑"
 
 #: ../../source/explanation-differential-privacy.rst:51
+#, fuzzy
 msgid ""
 "The first version of our solution was to define a decorator whose "
-"constructor accepted, among other things, a boolean valued variable "
+"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 "
@@ -2983,10 +3755,17 @@ msgid ""
 "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。"
 
 #: ../../source/explanation-differential-privacy.rst:54
+#: ../../source/ref-api/flwr.server.strategy.DPFedAvgFixed.rst:2
 msgid "DPFedAvgFixed"
-msgstr ""
+msgstr "DPFedAvgFixed"
 
 #: ../../source/explanation-differential-privacy.rst:56
 msgid ""
@@ -2995,6 +3774,8 @@ msgid ""
 "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。"
 
 #: ../../source/explanation-differential-privacy.rst:58
 msgid ""
@@ -3007,8 +3788,13 @@ msgid ""
 "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。"
 
 #: ../../source/explanation-differential-privacy.rst:59
+#, fuzzy
 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 "
@@ -3019,17 +3805,22 @@ msgid ""
 ":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 "
+"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。"
 
 #: ../../source/explanation-differential-privacy.rst:62
 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 ""
+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。"
 
 #: ../../source/explanation-differential-privacy.rst:64
 msgid ""
@@ -3043,10 +3834,13 @@ msgid ""
 "required to calculate the amount of noise that must be added to each "
 "individual update, either by the server or the clients."
 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。"
 
 #: ../../source/explanation-differential-privacy.rst:67
+#: ../../source/ref-api/flwr.server.strategy.DPFedAvgAdaptive.rst:2
 msgid "DPFedAvgAdaptive"
-msgstr ""
+msgstr "DPFedAvgAdaptive"
 
 #: ../../source/explanation-differential-privacy.rst:69
 msgid ""
@@ -3055,6 +3849,8 @@ msgid ""
 ":code:`DPFedAvgFixed`. It overrides the above-mentioned methods to do the"
 " following."
 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。"
 
 #: ../../source/explanation-differential-privacy.rst:71
 msgid ""
@@ -3064,6 +3860,10 @@ msgid ""
 "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()`：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。"
 
 #: ../../source/explanation-differential-privacy.rst:73
 msgid ""
@@ -3071,11 +3871,11 @@ msgid ""
 ":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 ""
+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。"
 
 #: ../../source/explanation-differential-privacy.rst:77
 msgid "Client-side logic"
-msgstr ""
+msgstr "客户端逻辑"
 
 #: ../../source/explanation-differential-privacy.rst:79
 msgid ""
@@ -3088,12 +3888,14 @@ msgid ""
 " work if either (or both) of the following keys are also present in the "
 "dict."
 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。"
 
 #: ../../source/explanation-differential-privacy.rst:81
 msgid ""
 ":code:`dpfedavg_noise_stddev` : Generate and add the specified amount of "
 "noise to the clipped update."
-msgstr ""
+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。"
 
 #: ../../source/explanation-differential-privacy.rst:82
 msgid ""
@@ -3101,10 +3903,12 @@ msgid ""
 ":code:`FitRes` object being returned to the server with an indicator bit,"
 " calculated as described earlier."
 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。"
 
 #: ../../source/explanation-differential-privacy.rst:86
 msgid "Performing the :math:`(\\epsilon, \\delta)` analysis"
-msgstr ""
+msgstr "进行 :math:`(epsilon, \\delta)` 分析"
 
 #: ../../source/explanation-differential-privacy.rst:88
 msgid ""
@@ -3113,39 +3917,49 @@ msgid ""
 ":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。"
 
 #: ../../source/explanation-differential-privacy.rst:98
+#, fuzzy
 msgid ""
-"McMahan, H. Brendan, et al. \"Learning differentially private recurrent "
-"language models.\" arXiv preprint arXiv:1710.06963 (2017)."
+"McMahan et al. \"Learning Differentially Private Recurrent Language "
+"Models.\" International Conference on Learning Representations (ICLR), "
+"2017."
 msgstr ""
+"McMahan, H. Brendan等. \"Learning differentially private recurrent "
+"language models.\" arXiv preprint arXiv:1710.06963 (2017)."
 
 #: ../../source/explanation-differential-privacy.rst:100
+#, fuzzy
 msgid ""
-"Andrew, Galen, et al. \"Differentially private learning with adaptive "
+"Andrew, Galen, et al. \"Differentially Private Learning with Adaptive "
+"Clipping.\" Advances in Neural Information Processing Systems (NeurIPS), "
+"2021."
+msgstr ""
+"Andrew, Galen等. \"Differentially private learning with adaptive "
 "clipping.\" Advances in Neural Information Processing Systems 34 (2021): "
 "17455-17466."
-msgstr ""
 
 #: ../../source/explanation-federated-evaluation.rst:2
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:292
 msgid "Federated evaluation"
-msgstr ""
+msgstr "联邦学习评估"
 
 #: ../../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."
-msgstr ""
+msgstr "评估联合学习系统中的模型主要有两种方法：集中（或服务器端）评估和联邦（或客户端）评估。"
 
 #: ../../source/explanation-federated-evaluation.rst:8
 msgid "Centralized Evaluation"
-msgstr ""
+msgstr "集中评估"
 
 #: ../../source/explanation-federated-evaluation.rst:11
 msgid "Built-In Strategies"
-msgstr ""
+msgstr "内置策略"
 
 #: ../../source/explanation-federated-evaluation.rst:13
 msgid ""
@@ -3153,11 +3967,11 @@ msgid ""
 "evaluation function during initialization. An evaluation function is any "
 "function that can take the current global model parameters as input and "
 "return evaluation results:"
-msgstr ""
+msgstr "所有内置策略都通过在初始化过程中提供一个评估函数来支持集中评估。评估函数是任何可以将当前全局模型参数作为输入并返回评估结果的函数："
 
 #: ../../source/explanation-federated-evaluation.rst:58
 msgid "Custom Strategies"
-msgstr ""
+msgstr "定制策略"
 
 #: ../../source/explanation-federated-evaluation.rst:60
 msgid ""
@@ -3167,30 +3981,32 @@ msgid ""
 ":code:`evaluate` after parameter aggregation and before federated "
 "evaluation (see next paragraph)."
 msgstr ""
+":code:`Strategy` 抽象提供了一个名为 :code:`evaluate` "
+"的方法，可直接用于评估当前的全局模型参数。服务器会在参数聚合后和联邦评估前调用 :code:`evaluate`（见下段）。"
 
 #: ../../source/explanation-federated-evaluation.rst:65
 msgid "Federated Evaluation"
-msgstr ""
+msgstr "联邦评估"
 
 #: ../../source/explanation-federated-evaluation.rst:68
 msgid "Implementing Federated Evaluation"
-msgstr ""
+msgstr "实现联邦评估"
 
 #: ../../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."
-msgstr ""
+msgstr "客户端评估在 :code:`Client.evaluate` 方法中进行，并可从服务器端进行配置。"
 
 #: ../../source/explanation-federated-evaluation.rst:101
 msgid "Configuring Federated Evaluation"
-msgstr ""
+msgstr "配置联邦评估"
 
 #: ../../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 "联邦评估可从服务器端进行配置。内置策略支持以下参数："
 
 #: ../../source/explanation-federated-evaluation.rst:105
 msgid ""
@@ -3201,6 +4017,10 @@ msgid ""
 "for evaluation. If :code:`fraction_evaluate` is set to :code:`0.0`, "
 "federated evaluation will be disabled."
 msgstr ""
+":code:`fraction_evaluate`： :code:`float`，定义了被选中进行评估的客户端的比例。如果 "
+":code:`fraction_evaluate` 设置为 :code:`0.1`，并且 :code:`100` 个客户端连接到服务器，那么 "
+":code:`10` 个客户端将被随机选中进行评估。如果 :code:`fraction_evaluate` 设置为 "
+":code:`0.0`，联邦评估将被禁用。"
 
 #: ../../source/explanation-federated-evaluation.rst:106
 msgid ""
@@ -3210,6 +4030,9 @@ msgid ""
 ":code:`100` clients are connected to the server, then :code:`20` clients "
 "will be selected for evaluation."
 msgstr ""
+":code:`min_evaluate_clients`：一个 :code:`int`，需要评估的客户的最小数量。如果 "
+":code:`fraction_evaluate` 设置为 :code:`0.1`，:code:`min_evaluate_clients` "
+"设置为 20，并且有 :code:`100` 个客户端已连接到服务器，那么 :code:`20` 个客户端将被选中进行评估。"
 
 #: ../../source/explanation-federated-evaluation.rst:107
 msgid ""
@@ -3220,6 +4043,9 @@ msgid ""
 "will wait until more clients are connected before it continues to sample "
 "clients for evaluation."
 msgstr ""
+":code:`min_available_clients`： "
+":code:`int`，定义了在一轮联邦评估开始之前，需要连接到服务器的最小客户端数量。如果连接到服务器的客户端数量少于 "
+":code:`min_available_clients`，服务器将等待更多客户端连接后，才继续采样客户端进行评估。"
 
 #: ../../source/explanation-federated-evaluation.rst:108
 msgid ""
@@ -3228,22 +4054,22 @@ msgid ""
 "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 ""
+msgstr "code:`on_evaluate_config_fn`：返回配置字典的函数，该字典将发送给选定的客户端。该函数将在每一轮中被调用，并提供了一种方便的方法来从服务器端自定义客户端评估，例如，配置执行的验证步骤数。"
 
 #: ../../source/explanation-federated-evaluation.rst:135
 msgid "Evaluating Local Model Updates During Training"
-msgstr ""
+msgstr "评估训练期间的本地模型更新"
 
 #: ../../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:"
-msgstr ""
+msgstr "模型参数也可在训练过程中进行评估。 :code:`Client.fit`可以字典形式返回任意评估结果："
 
 #: ../../source/explanation-federated-evaluation.rst:177
 msgid "Full Code Example"
-msgstr ""
+msgstr "完整代码示例"
 
 #: ../../source/explanation-federated-evaluation.rst:179
 msgid ""
@@ -3252,79 +4078,82 @@ msgid ""
 "be applied to workloads implemented in any other framework): "
 "https://github.com/adap/flower/tree/main/examples/advanced-tensorflow"
 msgstr ""
+"有关同时使用集中评估和联邦评估的完整代码示例，请参阅 *Advanced TensorFlow "
+"Example*（同样的方法也可应用于任何其他框架中）： "
+"https://github.com/adap/flower/tree/main/examples/advanced-tensorflow"
 
 #: ../../source/fed/0000-20200102-fed-template.md:10
 msgid "FED Template"
-msgstr ""
+msgstr "FED 模板"
 
 #: ../../source/fed/0000-20200102-fed-template.md:12
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:12
 msgid "Table of Contents"
-msgstr ""
+msgstr "目录"
 
 #: ../../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 "[目录]（#table-of-contents）"
 
 #: ../../source/fed/0000-20200102-fed-template.md:15
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:15
 msgid "[Summary](#summary)"
-msgstr ""
+msgstr "[总结](#summary)"
 
 #: ../../source/fed/0000-20200102-fed-template.md:16
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:16
 msgid "[Motivation](#motivation)"
-msgstr ""
+msgstr "[动机](#motivation)"
 
 #: ../../source/fed/0000-20200102-fed-template.md:17
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:17
 msgid "[Goals](#goals)"
-msgstr ""
+msgstr "[目标](#goals)"
 
 #: ../../source/fed/0000-20200102-fed-template.md:18
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:18
 msgid "[Non-Goals](#non-goals)"
-msgstr ""
+msgstr "[非目标](#non-goals)"
 
 #: ../../source/fed/0000-20200102-fed-template.md:19
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:19
 msgid "[Proposal](#proposal)"
-msgstr ""
+msgstr "[计划](#proposal)"
 
 #: ../../source/fed/0000-20200102-fed-template.md:20
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:23
 msgid "[Drawbacks](#drawbacks)"
-msgstr ""
+msgstr "[缺点](#drawbacks)"
 
 #: ../../source/fed/0000-20200102-fed-template.md:21
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:24
 msgid "[Alternatives Considered](#alternatives-considered)"
-msgstr ""
+msgstr "[备选方案](#alternatives-considered)"
 
 #: ../../source/fed/0000-20200102-fed-template.md:22
 msgid "[Appendix](#appendix)"
-msgstr ""
+msgstr "[附录](#appendix)"
 
 #: ../../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 "总结"
 
 #: ../../source/fed/0000-20200102-fed-template.md:26
 msgid "\\[TODO - sentence 1: summary of the problem\\]"
-msgstr ""
+msgstr "\\[TODO - 句子 1: 问题概括\\]"
 
 #: ../../source/fed/0000-20200102-fed-template.md:28
 msgid "\\[TODO - sentence 2: summary of the solution\\]"
-msgstr ""
+msgstr "\\[TODO - 句子 2: 解决方案概括\\]"
 
 #: ../../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 "动机"
 
 #: ../../source/fed/0000-20200102-fed-template.md:32
 #: ../../source/fed/0000-20200102-fed-template.md:36
@@ -3334,126 +4163,122 @@ 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 "目标"
 
 #: ../../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 "非目标"
 
 #: ../../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 "提案"
 
 #: ../../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 "缺点"
 
 #: ../../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 "备选方案"
 
 #: ../../source/fed/0000-20200102-fed-template.md:52
 msgid "\\[Alternative 1\\]"
-msgstr ""
+msgstr "\\[备选 1\\]"
 
 #: ../../source/fed/0000-20200102-fed-template.md:56
 msgid "\\[Alternative 2\\]"
-msgstr ""
-
-#: ../../source/fed/0000-20200102-fed-template.md:60
-msgid "Appendix"
-msgstr ""
+msgstr "\\[备选 2\\]"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:10
 msgid "Flower Enhancement Doc"
-msgstr ""
+msgstr "Flower 改善文档"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:20
 msgid "[Enhancement Doc Template](#enhancement-doc-template)"
-msgstr ""
+msgstr "[增强文档模版](#enhancement-doc-template)"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:21
 msgid "[Metadata](#metadata)"
-msgstr ""
+msgstr "[描述数据](#metadata)"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:22
 msgid "[Workflow](#workflow)"
-msgstr ""
+msgstr "[工作流程](#workflow)"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:25
 msgid "[GitHub Issues](#github-issues)"
-msgstr ""
+msgstr "[GitHub 问题](#github-issues)"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:26
 msgid "[Google Docs](#google-docs)"
-msgstr ""
+msgstr "[谷歌文档](#google-docs)"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:30
 msgid "A Flower Enhancement is a standardized development process to"
-msgstr ""
+msgstr "改善 Flower 功能是一个标准化的开发流程，目的是"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:32
 msgid "provide a common structure for proposing larger changes"
-msgstr ""
+msgstr "为提出更大规模的改动提供一个共同的结构"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:33
 msgid "ensure that the motivation for a change is clear"
-msgstr ""
+msgstr "确保改动的动机明确"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:34
 msgid "persist project information in a version control system"
-msgstr ""
+msgstr "将项目信息保存在版本控制系统中"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:35
 msgid "document the motivation for impactful user-facing changes"
-msgstr ""
+msgstr "记录面向用户的具有影响力的改动的动机"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:36
 msgid "reserve GitHub issues for tracking work in flight"
-msgstr ""
+msgstr "保留 GitHub 问题，用于跟踪进行中的工作"
 
 #: ../../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"
-msgstr ""
+msgstr "确保社区参与者能够成功推动改动，完成一个或多个版本，同时利益相关者在整个过程中得到充分展现"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:39
 msgid "Hence, an Enhancement Doc combines aspects of"
-msgstr ""
+msgstr "因此，\"增强文件\"将以下方面结合起来"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:41
 msgid "a feature, and effort-tracking document"
-msgstr ""
+msgstr "一个功能和效力跟踪文档"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:42
 msgid "a product requirements document"
-msgstr ""
+msgstr "一个产品需要文档"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:43
 msgid "a design document"
-msgstr ""
+msgstr "一个设计文档"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:45
 msgid ""
 "into one file, which is created incrementally in collaboration with the "
 "community."
-msgstr ""
+msgstr "该文件是与社区合作逐步创建的。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:49
 msgid ""
@@ -3461,6 +4286,8 @@ msgid ""
 "beyond a single GitHub issue or pull request is required to understand "
 "and communicate upcoming changes to the project."
 msgstr ""
+"对于向 Flower 提出的远期变更或功能，需要一个超越单个 GitHub 问题或拉取请求（pull "
+"request）的抽象概念，以了解和沟通项目即将发生的变更。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:51
 msgid ""
@@ -3469,6 +4296,8 @@ msgid ""
 "video calls, and hallway conversations into a well-tracked artifact, this"
 " process aims to enhance communication and discoverability."
 msgstr ""
+"这一流程的目的是减少我们社区中 \"部落知识 \"的数量。通过将决策从 Slack "
+"线程、视频通话和走廊对话转移到一个跟踪良好的工作环境中，该流程旨在加强沟通和可发现性。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:55
 msgid ""
@@ -3476,7 +4305,7 @@ msgid ""
 " 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 ""
+msgstr "任何较大的、面向用户的增强都应遵循增强流程。如果要以书面或口头形式向作者或开发人员以外的任何人描述增强功能，则应考虑创建改善文档。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:57
 msgid ""
@@ -3484,7 +4313,7 @@ msgid ""
 " 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 ""
+msgstr "同样，任何会对开发社区的大部分人产生影响的技术工作（重构、重大架构变更）也应广泛传播。即使对典型用户或操作员的影响为零，改进流程也适用于这种情况。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:61
 msgid ""
@@ -3493,118 +4322,120 @@ msgid ""
 "adding new Federated Learning algorithms, as these only add features "
 "without changing how Flower works or is used."
 msgstr ""
+"对于小的改动和添加，通过 \"改善\"程序既耗时又没有必要。例如，这包括添加新的联邦学习算法，因为这只会增加功能，而不会改变 \"Flower "
+"\"的工作或使用方式。"
 
 #: ../../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."
-msgstr ""
+msgstr "增强功能与功能请求不同，因为它们已经提供了实施路径，并得到了社区成员的支持。"
 
 #: ../../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."
-msgstr ""
+msgstr "增强功能被记录在一个 Markdown 文件中，该文件遵循已定义的模板和工作流程，用于审查和存储增强功能文档（即增强功能文档）以供参考。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:69
 msgid "Enhancement Doc Template"
-msgstr ""
+msgstr "增强文档模板"
 
 #: ../../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 "每个增强文档都以 Markdown 文件的形式提供，其结构如下"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:73
 msgid "Metadata (as [described below](#metadata) in form of a YAML preamble)"
-msgstr ""
+msgstr "描述数据（[如下所述](#metadata) 以 YAML 前言的形式出现）"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:74
 msgid "Title (same as in metadata)"
-msgstr ""
+msgstr "标题（与描述数据中的标题相同）"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:75
 msgid "Table of Contents (if needed)"
-msgstr ""
+msgstr "目录（如有需要）"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:81
 msgid "Notes/Constraints/Caveats (optional)"
-msgstr ""
+msgstr "注意事项/限制/警告（可选）"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:82
 msgid "Design Details (optional)"
-msgstr ""
+msgstr "设计细节（可选）"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:83
 msgid "Graduation Criteria"
-msgstr ""
+msgstr "毕业标准"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:84
 msgid "Upgrade/Downgrade Strategy (if applicable)"
-msgstr ""
+msgstr "升级/降级策略（如适用）"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:88
 msgid "As a reference, this document follows the above structure."
-msgstr ""
+msgstr "作为参考，本文件采用上述结构。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:90
 msgid "Metadata"
-msgstr ""
+msgstr "描述数据"
 
 #: ../../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."
-msgstr ""
+msgstr "**fed-number**（必填）上一个Flower增强文件的 \"fed-number \"+1。有了这个编号，就很容易参考其他提案。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:94
 msgid "**title** (Required) The title of the proposal in plain language."
-msgstr ""
+msgstr "**标题** （必填）用简明语言写出提案的标题。"
 
 #: ../../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."
-msgstr ""
+msgstr "**status** （必填）提案的当前状态。有关可能的状态，请参阅 [工作流程](#workflow)。"
 
 #: ../../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 ""
+msgstr "**作者**（必填） 提案的作者列表。这只是 GitHub ID。"
 
 #: ../../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 ""
+msgstr "**创建日期**（必填） 建议书在 PR 中首次提交的日期。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:102
 msgid ""
 "**last-updated** (Optional) The date that the proposal was last changed "
 "significantly."
-msgstr ""
+msgstr "**最后更新** （可选）提案最后一次重大修改的日期。"
 
 #: ../../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 ""
+msgstr "**另见** （可选）与本提案相关的其他提案清单。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:106
 msgid "**replaces** (Optional) A list of proposals that this one replaces."
-msgstr ""
+msgstr "**取代**（可选） 这份提案所取代的提案列表。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:108
 msgid "**superseded-by** (Optional) A list of proposals that this one supersedes."
-msgstr ""
+msgstr "**被取代者** （可选） 此提案取代的提案列表。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:111
 msgid "Workflow"
-msgstr ""
+msgstr "工作流程"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:113
 msgid ""
@@ -3612,7 +4443,7 @@ msgid ""
 "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 ""
+msgstr "形成增强功能的想法应该已经在社区中讨论过或提出过。因此，它需要一个支持者（通常是作者）来引导增强。这个人还必须找到愿意审核提案的提交者。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:115
 msgid ""
@@ -3622,6 +4453,9 @@ msgid ""
 "state as part of a pull request. Discussions are done as part of the pull"
 " request review."
 msgstr ""
+"新的增强功能以 `NNNN-YYYYMMDD-enhancement-title.md` 的文件名签入，其中 `NNNN` "
+"是花朵增强文档的编号，并将其转入 `enhancements`。作为拉取请求（pull request）的一部分，所有增强功能都从 "
+"`provisional` 状态开始。讨论是作为拉取请求审查的一部分进行的。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:117
 msgid ""
@@ -3631,65 +4465,67 @@ msgid ""
 "enhancement as part of their description. After the implementation is "
 "done, the proposal status is changed to `implemented`."
 msgstr ""
+"一旦增强功能通过审核和批准，其状态就会变为 "
+"`可实施`。实际的实施工作将在单独的拉取请求中完成。这些拉取请求应在其描述中提及相应的增强功能。实施完成后，提案状态将更改为 `已实施`。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:119
 msgid ""
 "Under certain conditions, other states are possible. An Enhancement has "
 "the following states:"
-msgstr ""
+msgstr "在某些条件下，还可能出现其他状态。增强提案具有以下状态："
 
 #: ../../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."
-msgstr ""
+msgstr "`暂定`： 已提出改进建议并正在积极定义。这是在提案得到充实、积极定义和讨论时的起始状态。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:122
 msgid "`implementable`: The enhancement has been reviewed and approved."
-msgstr ""
+msgstr "`可实施`： 增强功能已审核通过。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:123
 msgid ""
 "`implemented`: The enhancement has been implemented and is no longer "
 "actively changed."
-msgstr ""
+msgstr "`已实施`： 增强功能已实施，不再主动更改。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:124
 msgid "`deferred`: The enhancement is proposed but not actively being worked on."
-msgstr ""
+msgstr "`推迟`： 已提出改进建议，但尚未积极开展工作。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:125
 msgid ""
 "`rejected`: The authors and reviewers have decided that this enhancement "
 "is not moving forward."
-msgstr ""
+msgstr "`拒绝`： 作者和审稿人已决定不再推进该增强功能。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:126
 msgid "`withdrawn`: The authors have withdrawn the enhancement."
-msgstr ""
+msgstr "`撤回`： 作者已撤回增强功能。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:127
 msgid "`replaced`: The enhancement has been replaced by a new enhancement."
-msgstr ""
+msgstr "`已替换`： 增强功能已被新的增强功能取代。"
 
 #: ../../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."
-msgstr ""
+msgstr "在 GitHub 已提供的流程（问题和拉取请求）之外再增加一个流程，会增加复杂性，并可能成为潜在首次贡献者的障碍。"
 
 #: ../../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."
-msgstr ""
+msgstr "对于英语非母语者来说，将提案模板扩展到目前要求的单句描述之外可能是一个沉重的负担。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:137
 msgid "GitHub Issues"
-msgstr ""
+msgstr "GitHub 问题"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:139
 msgid ""
@@ -3701,10 +4537,13 @@ msgid ""
 "parts of the doc. Managing these multiple discussions can be confusing "
 "when using GitHub Issues."
 msgstr ""
+"使用 GitHub Issues 进行此类改进是可行的。例如，我们可以使用标签来区分和过滤这些问题。主要的问题在于讨论和审查增强功能： "
+"GitHub 问题只有一个评论线程。而增强功能通常会同时有多个讨论线程，针对文档的不同部分。在使用 GitHub "
+"问题时，管理这些多重讨论会很混乱。"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:141
 msgid "Google Docs"
-msgstr ""
+msgstr "谷歌文档"
 
 #: ../../source/fed/0001-20220311-flower-enhancement-doc.md:143
 msgid ""
@@ -3715,41 +4554,43 @@ msgid ""
 "proposals as part of Flower's repository, the potential for missing links"
 " is much higher."
 msgstr ""
+"谷歌文档允许多线程讨论。但是，由于谷歌文档是在项目之外托管的，因此需要注意它们是否能被社区发现。我们必须管理所有提案的链接列表，并提供给社区使用。与作为"
+" Flower 资源库一部分的提案相比，丢失链接的可能性要大得多。"
 
 #: ../../source/fed/index.md:1
 msgid "FED - Flower Enhancement Doc"
-msgstr ""
+msgstr "FED - Flower 增强文件"
 
 #: ../../source/how-to-aggregate-evaluation-results.rst:2
 msgid "Aggregate evaluation results"
-msgstr ""
+msgstr "整合评估结果"
 
 #: ../../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."
-msgstr ""
+msgstr "Flower 服务器没有规定整合评估结果的方法，但用户可以完全自定义如何整合。"
 
 #: ../../source/how-to-aggregate-evaluation-results.rst:8
 msgid "Aggregate Custom Evaluation Results"
-msgstr ""
+msgstr "自定义整合评估结果"
 
 #: ../../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:"
-msgstr ""
+msgstr "同样的 :code:`Strategy` 定制方法也可用于汇总来自单个客户端的自定义评估结果。客户端可以通过返回字典的方式向服务器返回自定义指标："
 
 #: ../../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 ""
+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 ""
+msgstr "配置客户端"
 
 #: ../../source/how-to-configure-clients.rst:4
 msgid ""
@@ -3757,11 +4598,11 @@ msgid ""
 "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 ""
+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 ""
+msgstr "配置值"
 
 #: ../../source/how-to-configure-clients.rst:9
 msgid ""
@@ -3770,6 +4611,8 @@ msgid ""
 "float), ``int``, or ``str`` (or equivalent types in different languages)."
 " Here is an example of a configuration dictionary in Python:"
 msgstr ""
+"配置值以字典的形式表示，字典的键为 ``str``，值的类型为 ``bool``、``bytes``、``double``（64 "
+"位精度浮点型）、``int``或 ``str`（或不同语言中的等效类型）。下面是一个 Python 配置字典的示例："
 
 #: ../../source/how-to-configure-clients.rst:20
 msgid ""
@@ -3777,6 +4620,8 @@ msgid ""
 "short) to their ProtoBuf representation, transports them to the client "
 "using gRPC, and then deserializes them back to Python dictionaries."
 msgstr ""
+"Flower 将这些配置字典（简称 *config dict*）序列化为 ProtoBuf 表示形式，使用 gRPC "
+"将其传输到客户端，然后再反序列化为 Python 字典。"
 
 #: ../../source/how-to-configure-clients.rst:24
 msgid ""
@@ -3786,6 +4631,8 @@ msgid ""
 " by converting them to one of the supported value types (and converting "
 "them back on the client-side)."
 msgstr ""
+"目前，还不支持在配置字典中直接发送作为值的集合类型（例如，`Set``, `List`, "
+"`Map``）。有几种变通方法可将集合转换为支持的值类型之一（并在客户端将其转换回），从而将集合作为值发送。"
 
 #: ../../source/how-to-configure-clients.rst:26
 msgid ""
@@ -3793,11 +4640,11 @@ msgid ""
 "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 ""
+msgstr "例如，可以将浮点数列表转换为 JSON 字符串，然后使用配置字典发送 JSON 字符串，再在客户端将 JSON 字符串转换回浮点数列表。"
 
 #: ../../source/how-to-configure-clients.rst:30
 msgid "Configuration through built-in strategies"
-msgstr ""
+msgstr "通过内置策略进行配置"
 
 #: ../../source/how-to-configure-clients.rst:32
 msgid ""
@@ -3808,6 +4655,8 @@ msgid ""
 "the current round. It then forwards the configuration dictionary to all "
 "the clients selected during that round."
 msgstr ""
+"向客户端发送配置值的最简单方法是使用内置策略，如 "
+":code:`FedAvg`。内置策略支持所谓的配置函数。配置函数是内置策略调用的函数，用于获取当前轮的配置字典。然后，它会将配置字典转发给该轮中选择的所有客户端。"
 
 #: ../../source/how-to-configure-clients.rst:34
 msgid ""
@@ -3815,18 +4664,18 @@ msgid ""
 "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 ""
+msgstr "让我们从一个简单的例子开始。想象一下，我们想要发送给客户端（a）应该使用的批次大小，（b）当前联邦学习的全局轮次，以及（c）客户端训练的遍历数。我们的配置函数可以是这样的："
 
 #: ../../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`:"
-msgstr ""
+msgstr "为了让内置策略使用这个函数，我们可以在初始化时使用参数 :code:`on_fit_config_fn` 将它传递给 ``FedAvg`` ："
 
 #: ../../source/how-to-configure-clients.rst:56
 msgid "One the client side, we receive the configuration dictionary in ``fit``:"
-msgstr ""
+msgstr "在客户端，我们在 ``fit`` 中接收配置字典："
 
 #: ../../source/how-to-configure-clients.rst:67
 msgid ""
@@ -3835,6 +4684,8 @@ msgid ""
 " send different configuration values to `evaluate` (for example, to use a"
 " different batch size)."
 msgstr ""
+"还有一个 `on_evaluate_config_fn` 用于配置评估，其工作方式相同。它们是不同的函数，因为可能需要向 `evaluate` "
+"发送不同的配置值（例如，使用不同的批量大小）。"
 
 #: ../../source/how-to-configure-clients.rst:69
 msgid ""
@@ -3845,43 +4696,51 @@ msgid ""
 "hyperparameter schedule, for example, to increase the number of local "
 "epochs during later rounds, we could do the following:"
 msgstr ""
+"内置策略每轮都会调用此函数（即每次运行 `Strategy.configure_fit` 或 "
+"`Strategy.configure_evaluate` 时）。每轮调用 `on_evaluate_config_fn` "
+"允许我们在连续几轮中改变配置指令。例如，如果我们想实现一个超参数时间表，以增加后几轮的本地遍历次数，我们可以这样做："
 
 #: ../../source/how-to-configure-clients.rst:82
 msgid "The :code:`FedAvg` strategy will call this function *every round*."
-msgstr ""
+msgstr "代码:`FedAvg`策略*每轮*都会调用该函数。"
 
 #: ../../source/how-to-configure-clients.rst:85
 msgid "Configuring individual clients"
-msgstr ""
+msgstr "配置个别客户端"
 
 #: ../../source/how-to-configure-clients.rst:87
 msgid ""
 "In some cases, it is necessary to send different configuration values to "
 "different clients."
-msgstr ""
+msgstr "在某些情况下，有必要向不同的客户端发送不同的配置值。"
 
 #: ../../source/how-to-configure-clients.rst:89
 msgid ""
 "This can be achieved by customizing an existing strategy or by "
 "`implementing a custom strategy from scratch "
-"<https://flower.dev/docs/framework/how-to-implement-strategies.html>`_. "
+"<https://flower.ai/docs/framework/how-to-implement-strategies.html>`_. "
 "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 ""
+"这可以通过定制现有策略或 `从头开始实施一个定制策略 <https://flower.ai/docs/framework/how-to-"
+"implement-"
+"strategies.html>`_来实现。下面是一个无厘头的例子，`FedAvg`通过在*单个客户端*的配置指令（config "
+"dict）中添加自定义的``\"hello\"： \"world\"``配置键/值对添加到此的配置 dict "
+"中（仅列表中的第一个客户端，本轮中的其他客户端不会收到此 \"特殊 \"配置值）："
 
 #: ../../source/how-to-configure-logging.rst:2
 msgid "Configure logging"
-msgstr ""
+msgstr "配置日志记录"
 
 #: ../../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:"
-msgstr ""
+msgstr "Flower 日志记录器会跟踪联邦学习工作负载中发生的所有核心事件。它默认按照标准信息格式提供信息："
 
 #: ../../source/how-to-configure-logging.rst:13
 msgid ""
@@ -3890,10 +4749,12 @@ msgid ""
 "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 ""
+"相关信息包括：日志信息级别（例如 "
+":code:`INFO`、:code:`DEBUG`）、时间戳、日志记录的行以及日志信息本身。这样，日志记录器通常会在终端上显示如下信息："
 
 #: ../../source/how-to-configure-logging.rst:34
 msgid "Saving log to file"
-msgstr ""
+msgstr "将日志保存到文件"
 
 #: ../../source/how-to-configure-logging.rst:36
 msgid ""
@@ -3907,6 +4768,12 @@ msgid ""
 "<https://github.com/adap/flower/blob/main/src/py/flwr/common/logger.py>`_"
 " function. For example:"
 msgstr ""
+"默认情况下，Flower 日志会输出到启动联邦学习工作负载的终端。这既适用于基于 gRPC 的联邦学习（即执行 "
+":code:`fl.server.start_server` 时），也适用于使用 :code:`VirtualClientEngine` "
+"时（即执行 :code:`fl.simulation.start_simulation` "
+"时）。在某些情况下，您可能希望将此日志保存到磁盘。为此，您可以调用 `fl.common.logger.configure() "
+"<https://github.com/adap/flower/blob/main/src/py/flwr/common/logger.py>`_"
+" 函数。例如："
 
 #: ../../source/how-to-configure-logging.rst:53
 msgid ""
@@ -3915,27 +4782,30 @@ msgid ""
 "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 ""
+"通过上述操作，Flower 会将您在终端上看到的日志记录到 "
+":code:`log.txt`。该文件将创建在运行代码的同一目录下。如果我们检查一下，就会发现上面的日志也被记录了下来，但每一行都以 "
+":code:`identifier` 作为前缀："
 
 #: ../../source/how-to-configure-logging.rst:74
 msgid "Log your own messages"
-msgstr ""
+msgstr "记录自己的信息"
 
 #: ../../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."
-msgstr ""
+msgstr "您可以通过添加更多与应用程序相关的信息来扩展 Flower 日志记录器默认显示的信息。您可以通过以下方法轻松实现这一目标。"
 
 #: ../../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."
-msgstr ""
+msgstr "这样，除默认信息外，您的日志记录器还将显示由客户引入的信息，如上文所述。"
 
 #: ../../source/how-to-configure-logging.rst:128
 msgid "Log to a remote service"
-msgstr ""
+msgstr "登录远程服务"
 
 #: ../../source/how-to-configure-logging.rst:130
 msgid ""
@@ -3946,19 +4816,23 @@ msgid ""
 "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 whish to backup or analyze the logs "
+":code:`HTTPHandler` should you wish to backup or analyze the logs "
 "somewhere else."
 msgstr ""
+"此外，:code:`fl.common.logger.configure`函数还允许指定主机，通过本地 Python "
+":code:`logging.handler.HTTPHandler`，向该主机推送日志（通过 :code:`POST`）。在基于 "
+":code:`gRPC` 的联邦学习工作负载中，这是一个特别有用的功能，否则从所有实体（即服务器和客户端）收集日志可能会很麻烦。请注意，在 "
+"Flower 模拟器中，服务器会自动显示所有日志。如果希望在其他地方备份或分析日志，仍可指定 :code:`HTTPHandler`。"
 
 #: ../../source/how-to-enable-ssl-connections.rst:2
 msgid "Enable SSL connections"
-msgstr ""
+msgstr "启用 SSL 连接"
 
 #: ../../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."
-msgstr ""
+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
 msgid ""
@@ -3966,6 +4840,8 @@ msgid ""
 "`here <https://github.com/adap/flower/tree/main/examples/advanced-"
 "tensorflow>`_."
 msgstr ""
+"有关安全连接的完整代码示例，请参见 <https://github.com/adap/flower/tree/main/examples"
+"/advanced-tensorflow>`_ 。"
 
 #: ../../source/how-to-enable-ssl-connections.rst:10
 msgid ""
@@ -3973,11 +4849,11 @@ msgid ""
 "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 ""
+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
 msgid "Certificates"
-msgstr ""
+msgstr "证书"
 
 #: ../../source/how-to-enable-ssl-connections.rst:18
 msgid ""
@@ -3987,16 +4863,18 @@ msgid ""
 "to ask you to run the script in :code:`examples/advanced-"
 "tensorflow/certificates/generate.sh`"
 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 ""
+msgstr "使用以下命令序列："
 
 #: ../../source/how-to-enable-ssl-connections.rst:30
 msgid ""
 "This will generate the certificates in :code:`examples/advanced-"
 "tensorflow/.cache/certificates`."
-msgstr ""
+msgstr "这将在 :code:`examples/advanced-tensorflow/.cache/certificates` 中生成证书。"
 
 #: ../../source/how-to-enable-ssl-connections.rst:32
 msgid ""
@@ -4005,23 +4883,24 @@ msgid ""
 "complete for production environments. Please refer to other sources "
 "regarding the issue of correctly generating certificates for production "
 "environments."
-msgstr ""
+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 ""
+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:41
+#: ../../source/ref-api/flwr.server.Server.rst:2
 msgid "Server"
-msgstr ""
+msgstr "服务器"
 
 #: ../../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."
-msgstr ""
+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。"
 
 #: ../../source/how-to-enable-ssl-connections.rst:61
 msgid ""
@@ -4030,18 +4909,20 @@ msgid ""
 "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。"
 
 #: ../../source/how-to-enable-ssl-connections.rst:65
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:37
-#: ../../source/ref-api-flwr.rst:15
+#: ../../source/ref-api/flwr.client.Client.rst:2
 msgid "Client"
-msgstr ""
+msgstr "客户端"
 
 #: ../../source/how-to-enable-ssl-connections.rst:67
 msgid ""
 "We are now going to show how to write a client which uses the previously "
 "generated scripts:"
-msgstr ""
+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
 msgid ""
@@ -4049,40 +4930,43 @@ msgid ""
 "encoded root certificates as a byte string. We are again using "
 ":code:`Path` to simplify reading those as byte strings."
 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 ""
+msgstr "总结"
 
 #: ../../source/how-to-enable-ssl-connections.rst:91
 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 "
 "establish a secure connection to it."
-msgstr ""
+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
 msgid "Additional resources"
-msgstr ""
+msgstr "补充资源"
 
 #: ../../source/how-to-enable-ssl-connections.rst:98
 msgid ""
 "These additional sources might be relevant if you would like to dive "
 "deeper into the topic of certificates:"
-msgstr ""
+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
 msgid "`Let's Encrypt <https://letsencrypt.org/docs/>`_"
-msgstr ""
+msgstr "`让我们加密 <https://letsencrypt.org/docs/>`_"
 
 #: ../../source/how-to-enable-ssl-connections.rst:101
 msgid "`certbot <https://certbot.eff.org/>`_"
-msgstr ""
+msgstr "`certbot <https://certbot.eff.org/>`_"
 
 #: ../../source/how-to-implement-strategies.rst:2
 msgid "Implement strategies"
-msgstr ""
+msgstr "实施策略"
 
 #: ../../source/how-to-implement-strategies.rst:4
 msgid ""
@@ -4093,10 +4977,12 @@ msgid ""
 "evaluate models. Flower provides a few built-in strategies which are "
 "based on the same API described below."
 msgstr ""
+"策略抽象类可以实现完全定制的策略。策略基本上就是在服务器上运行的联邦学习算法。策略决定如何对客户端进行采样、如何配置客户端进行训练、如何聚合参数更新以及如何评估模型。Flower"
+" 提供了一些内置策略，这些策略基于下文所述的相同 API。"
 
 #: ../../source/how-to-implement-strategies.rst:11
 msgid "The :code:`Strategy` abstraction"
-msgstr ""
+msgstr ":code:`策略 ` 抽象类"
 
 #: ../../source/how-to-implement-strategies.rst:13
 msgid ""
@@ -4106,31 +4992,33 @@ msgid ""
 "implementations have the exact same capabilities at their disposal as "
 "built-in ones."
 msgstr ""
+"所有策略实现均源自抽象基类 "
+":code:`flwr.server.strategy.Strategy`，包括内置实现和第三方实现。这意味着自定义策略实现与内置实现具有完全相同的功能。"
 
 #: ../../source/how-to-implement-strategies.rst:18
 msgid ""
 "The strategy abstraction defines a few abstract methods that need to be "
 "implemented:"
-msgstr ""
+msgstr "策略抽象定义了一些需要实现的抽象方法："
 
 #: ../../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:"
-msgstr ""
+msgstr "创建一个新策略意味着要实现一个新的 :code:`class`（从抽象基类 :code:`Strategy` 派生），该类要实现前面显示的抽象方法："
 
 #: ../../source/how-to-implement-strategies.rst:100
 msgid "The Flower server calls these methods in the following order:"
-msgstr ""
+msgstr "Flower 服务器按以下顺序调用这些方法："
 
 #: ../../source/how-to-implement-strategies.rst:177
 msgid "The following sections describe each of those methods in more detail."
-msgstr ""
+msgstr "下文将详细介绍每种方法。"
 
 #: ../../source/how-to-implement-strategies.rst:180
 msgid "The :code:`initialize_parameters` method"
-msgstr ""
+msgstr ":code:`初始化参数` 方法"
 
 #: ../../source/how-to-implement-strategies.rst:182
 msgid ""
@@ -4138,13 +5026,15 @@ msgid ""
 "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` 只调用一次，即在执行开始时。它负责以序列化形式（即 "
+":code:`Parameters` 对象）提供初始全局模型参数。"
 
 #: ../../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`:"
-msgstr ""
+msgstr "内置策略会返回用户提供的初始参数。下面的示例展示了如何将初始参数传递给 :code:`FedAvg`："
 
 #: ../../source/how-to-implement-strategies.rst:209
 msgid ""
@@ -4157,6 +5047,10 @@ msgid ""
 "useful for prototyping. In practice, it is recommended to always use "
 "server-side parameter initialization."
 msgstr ""
+"Flower 服务器将调用 :code:`initialize_parameters`，返回传给 "
+":code:`initial_parameters` 的参数或 :code:`None`。如果 "
+":code:`initialize_parameters` 没有返回任何参数（即 "
+":code:`None`），服务器将随机选择一个客户端并要求其提供参数。这只是一个便捷的功能，在实际应用中并不推荐使用，但在原型开发中可能很有用。在实践中，建议始终使用服务器端参数初始化。"
 
 #: ../../source/how-to-implement-strategies.rst:213
 msgid ""
@@ -4165,11 +5059,11 @@ msgid ""
 " It is also the fundamental capability needed to implement hybrid "
 "approaches, for example, to fine-tune a pre-trained model using federated"
 " learning."
-msgstr ""
+msgstr "服务器端参数初始化是一种强大的机制。例如，它可以用来从先前保存的检查点恢复训练。它也是实现混合方法所需的基本能力，例如，使用联邦学习对预先训练好的模型进行微调。"
 
 #: ../../source/how-to-implement-strategies.rst:216
 msgid "The :code:`configure_fit` method"
-msgstr ""
+msgstr ":code:`configure_fit`方法"
 
 #: ../../source/how-to-implement-strategies.rst:218
 msgid ""
@@ -4178,13 +5072,16 @@ msgid ""
 "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` "
+"负责配置即将开始的一轮训练。*配置*在这里是什么意思？配置一轮训练意味着选择客户并决定向这些客户发送什么指令。:code:`configure_fit`"
+" 说明了这一点："
 
 #: ../../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`:"
-msgstr ""
+msgstr "返回值是一个元组列表，每个元组代表将发送到特定客户端的指令。策略实现通常在 :code:`configure_fit` 中执行以下步骤："
 
 #: ../../source/how-to-implement-strategies.rst:233
 #: ../../source/how-to-implement-strategies.rst:280
@@ -4192,12 +5089,16 @@ msgid ""
 "Use the :code:`client_manager` to randomly sample all (or a subset of) "
 "available clients (each represented as a :code:`ClientProxy` object)"
 msgstr ""
+"使用 :code:`client_manager` 随机抽样所有（或部分）可用客户端（每个客户端都表示为 :code:`ClientProxy` "
+"对象）"
 
 #: ../../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 ""
+"将每个 :code:`ClientProxy` 与持有当前全局模型 :code:`parameters` 和 :code:`config` "
+"dict 的 :code:`FitIns` 配对"
 
 #: ../../source/how-to-implement-strategies.rst:236
 msgid ""
@@ -4206,6 +5107,8 @@ msgid ""
 "in a round if the corresponding :code:`ClientProxy` is included in the "
 "the list returned from :code:`configure_fit`."
 msgstr ""
+"更复杂的实现可以使用 :code:`configure_fit` 来实现自定义的客户端选择逻辑。只有当相应的 "
+":code:`ClientProxy` 包含在 :code:`configure_fit` 返回的列表中时，客户端才会参与进来。"
 
 #: ../../source/how-to-implement-strategies.rst:240
 msgid ""
@@ -4216,17 +5119,19 @@ msgid ""
 "different hyperparameters on different clients (via the :code:`config` "
 "dict)."
 msgstr ""
+"该返回值的结构为用户提供了很大的灵活性。由于指令是按客户端定义的，因此可以向每个客户端发送不同的指令。这使得自定义策略成为可能，例如在不同的客户端上训练不同的模型，或在不同的客户端上使用不同的超参数（通过"
+" :code:`config` dict）。"
 
 #: ../../source/how-to-implement-strategies.rst:243
 msgid "The :code:`aggregate_fit` method"
-msgstr ""
+msgstr ":code:`aggregate_fit` 方法"
 
 #: ../../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`."
-msgstr ""
+msgstr ":code:`aggregate_fit` 负责汇总在 :code:`configure_fit` 中选择并要求训练的客户端所返回的结果。"
 
 #: ../../source/how-to-implement-strategies.rst:258
 msgid ""
@@ -4235,6 +5140,8 @@ msgid ""
 ":code:`configure_fit`). :code:`aggregate_fit` therefore receives a list "
 "of :code:`results`, but also a list of :code:`failures`."
 msgstr ""
+"当然，失败是有可能发生的，因此无法保证服务器会从它发送指令（通过 :code:`configure_fit`）的所有客户端获得结果。因此 "
+":code:`aggregate_fit` 会收到 :code:`results` 的列表，但也会收到 :code:`failures` 的列表。"
 
 #: ../../source/how-to-implement-strategies.rst:260
 msgid ""
@@ -4243,10 +5150,13 @@ msgid ""
 " 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` 返回一个可选的 :code:`Parameters` "
+"对象和一个聚合度量的字典。:code:`Parameters` 返回值是可选的，因为 :code:`aggregate_fit` "
+"可能会认为所提供的结果不足以进行聚合（例如，失败次数过多）。"
 
 #: ../../source/how-to-implement-strategies.rst:263
 msgid "The :code:`configure_evaluate` method"
-msgstr ""
+msgstr ":code:`configure_evaluate`方法"
 
 #: ../../source/how-to-implement-strategies.rst:265
 msgid ""
@@ -4256,19 +5166,24 @@ msgid ""
 "instructions to send to these clients. The signature of "
 ":code:`configure_evaluate` makes this clear:"
 msgstr ""
+":code:`configure_evaluate` "
+"负责配置下一轮评估。*配置*在这里是什么意思？配置一轮评估意味着选择客户端并决定向这些客户端发送什么指令。:code:`configure_evaluate`"
+" 说明了这一点："
 
 #: ../../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`:"
-msgstr ""
+msgstr "返回值是一个元组列表，每个元组代表将发送到特定客户端的指令。策略实现通常在 :code:`configure_evaluate` 中执行以下步骤："
 
 #: ../../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"
 msgstr ""
+"将每个 :code:`ClientProxy` 与持有当前全局模型 :code:`parameters` 和 :code:`config` "
+"dict 的 :code:`EvaluateIns` 配对"
 
 #: ../../source/how-to-implement-strategies.rst:283
 msgid ""
@@ -4277,6 +5192,8 @@ msgid ""
 "in a round if the corresponding :code:`ClientProxy` is included in the "
 "the list returned from :code:`configure_evaluate`."
 msgstr ""
+"更复杂的实现可以使用 :code:`configure_evaluate` 来实现自定义的客户端选择逻辑。只有当相应的 "
+":code:`ClientProxy` 包含在 :code:`configure_evaluate` 返回的列表中时，客户端才会参与进来。"
 
 #: ../../source/how-to-implement-strategies.rst:287
 msgid ""
@@ -4287,10 +5204,12 @@ msgid ""
 "different hyperparameters on different clients (via the :code:`config` "
 "dict)."
 msgstr ""
+"该返回值的结构为用户提供了很大的灵活性。由于指令是按客户端定义的，因此可以向每个客户端发送不同的指令。这使得自定义策略可以在不同客户端上评估不同的模型，或在不同客户端上使用不同的超参数（通过"
+" :code:`config` dict）。"
 
 #: ../../source/how-to-implement-strategies.rst:291
 msgid "The :code:`aggregate_evaluate` method"
-msgstr ""
+msgstr ":code:`aggregate_evaluate` 方法"
 
 #: ../../source/how-to-implement-strategies.rst:293
 msgid ""
@@ -4298,6 +5217,8 @@ msgid ""
 "returned by the clients that were selected and asked to evaluate in "
 ":code:`configure_evaluate`."
 msgstr ""
+":code:`aggregate_evaluate` 负责汇总在 :code:`configure_evaluate` "
+"中选择并要求评估的客户端返回的结果。"
 
 #: ../../source/how-to-implement-strategies.rst:306
 msgid ""
@@ -4306,6 +5227,9 @@ msgid ""
 ":code:`configure_evaluate`). :code:`aggregate_evaluate` therefore "
 "receives a list of :code:`results`, but also a list of :code:`failures`."
 msgstr ""
+"当然，失败是有可能发生的，因此无法保证服务器会从它发送指令（通过 "
+":code:`configure_evaluate`）的所有客户端获得结果。因此， :code:`aggregate_evaluate` 会接收 "
+":code:`results` 的列表，但也会接收 :code:`failures` 的列表。"
 
 #: ../../source/how-to-implement-strategies.rst:308
 msgid ""
@@ -4314,10 +5238,13 @@ msgid ""
 "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` 返回一个可选的 "
+":code:`float`（损失值）和一个聚合指标字典。:code:`float` 返回值是可选的，因为 "
+":code:`aggregate_evaluate` 可能会认为所提供的结果不足以进行聚合（例如，失败次数过多）。"
 
 #: ../../source/how-to-implement-strategies.rst:311
 msgid "The :code:`evaluate` method"
-msgstr ""
+msgstr ":code:`evaluate`方法"
 
 #: ../../source/how-to-implement-strategies.rst:313
 msgid ""
@@ -4326,6 +5253,9 @@ msgid ""
 ":code:`configure_evaluate`/:code:`aggregate_evaluate` enables strategies "
 "to perform both servers-side and client-side (federated) evaluation."
 msgstr ""
+":code:`evaluate` 负责在服务器端评估模型参数。除了 "
+":code:`configure_evaluate`/:code:`aggregate_evaluate` 之外，:code:`evaluate`"
+" 可以使策略同时执行服务器端和客户端（联邦）评估。"
 
 #: ../../source/how-to-implement-strategies.rst:323
 msgid ""
@@ -4334,82 +5264,127 @@ msgid ""
 ":code:`evaluate` method might not complete successfully (e.g., it might "
 "fail to load the server-side evaluation data)."
 msgstr ""
+"返回值也是可选的，因为策略可能不需要执行服务器端评估，或者因为用户定义的 :code:`evaluate` "
+"方法可能无法成功完成（例如，它可能无法加载服务器端评估数据）。"
 
 #: ../../source/how-to-install-flower.rst:2
 msgid "Install Flower"
-msgstr ""
+msgstr "安装Flower"
 
 #: ../../source/how-to-install-flower.rst:6
 msgid "Python version"
-msgstr ""
+msgstr "Python 版本"
 
 #: ../../source/how-to-install-flower.rst:12
 msgid "Install stable release"
+msgstr "安装稳定版"
+
+#: ../../source/how-to-install-flower.rst:15
+msgid "Using pip"
 msgstr ""
 
-#: ../../source/how-to-install-flower.rst:14
+#: ../../source/how-to-install-flower.rst:17
 msgid ""
 "Stable releases are available on `PyPI "
 "<https://pypi.org/project/flwr/>`_::"
-msgstr ""
+msgstr "稳定版本可在 `PyPI <https://pypi.org/project/flwr/>`_:："
 
-#: ../../source/how-to-install-flower.rst:18
+#: ../../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 "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
+msgid "Using conda (or mamba)"
 msgstr ""
 
-#: ../../source/how-to-install-flower.rst:24
-msgid "Verify installation"
+#: ../../source/how-to-install-flower.rst:29
+msgid "Flower can also be installed from the ``conda-forge`` channel."
+msgstr ""
+
+#: ../../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 ""
+
+#: ../../source/how-to-install-flower.rst:36
+msgid ""
+"Once the ``conda-forge`` channel has been enabled, ``flwr`` can be "
+"installed with ``conda``::"
+msgstr ""
+
+#: ../../source/how-to-install-flower.rst:40
+msgid "or with ``mamba``::"
 msgstr ""
 
-#: ../../source/how-to-install-flower.rst:26
+#: ../../source/how-to-install-flower.rst:46
+msgid "Verify installation"
+msgstr "验证安装"
+
+#: ../../source/how-to-install-flower.rst:48
+#, fuzzy
 msgid ""
-"The following command can be used to verfiy if Flower was successfully "
+"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::"
-msgstr ""
+msgstr "可以使用以下命令来验证 Flower 是否安装成功。如果一切正常，它将在命令行中打印 Flower 的版本：："
 
-#: ../../source/how-to-install-flower.rst:33
+#: ../../source/how-to-install-flower.rst:55
 msgid "Advanced installation options"
+msgstr "高级安装选项"
+
+#: ../../source/how-to-install-flower.rst:58
+#, fuzzy
+msgid "Install via Docker"
+msgstr "安装Flower"
+
+#: ../../source/how-to-install-flower.rst:60
+#, fuzzy
+msgid ""
+"`How to run Flower using Docker <https://flower.ai/docs/framework/how-"
+"to-run-flower-using-docker.html>`_"
 msgstr ""
+"`TensorFlow快速入门 (教程) <https://flower.ai/docs/framework/tutorial-"
+"quickstart-tensorflow.html>`_"
 
-#: ../../source/how-to-install-flower.rst:36
+#: ../../source/how-to-install-flower.rst:63
 msgid "Install pre-release"
-msgstr ""
+msgstr "安装预发布版本"
 
-#: ../../source/how-to-install-flower.rst:38
+#: ../../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::"
-msgstr ""
+msgstr "在稳定版发布之前，Flower 的新版本（可能是不稳定版）有时会作为预发布版本（alpha、beta、候选发布版本）提供：："
 
-#: ../../source/how-to-install-flower.rst:42
+#: ../../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::"
-msgstr ""
+msgstr "对于使用虚拟客户端引擎的模拟，`flwr``预发行版应与`simulation``一起安装："
 
-#: ../../source/how-to-install-flower.rst:47
+#: ../../source/how-to-install-flower.rst:74
 msgid "Install nightly release"
-msgstr ""
+msgstr "安装隔夜版本"
 
-#: ../../source/how-to-install-flower.rst:49
+#: ../../source/how-to-install-flower.rst:76
 msgid ""
 "The latest (potentially unstable) changes in Flower are available as "
 "nightly releases::"
-msgstr ""
+msgstr "Flower 中最新（可能不稳定）的更改以隔夜发布的形式提供：："
 
-#: ../../source/how-to-install-flower.rst:53
+#: ../../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::"
-msgstr ""
+msgstr "对于使用虚拟客户端引擎的模拟，`flwr-nightly`应与`simulation`一起安装："
 
 #: ../../source/how-to-monitor-simulation.rst:2
 msgid "Monitor simulation"
-msgstr ""
+msgstr "监控模拟"
 
 #: ../../source/how-to-monitor-simulation.rst:4
 msgid ""
@@ -4419,16 +5394,18 @@ msgid ""
 "constrain the total usage. Insights from resource consumption can help "
 "you make smarter decisions and speed up the execution time."
 msgstr ""
+"Flower 允许您在运行模拟时监控系统资源。此外，Flower "
+"仿真引擎功能强大，能让您决定如何按客户端方式分配资源并限制总使用量。从资源消耗中获得的观察可以帮助您做出更明智的决策，并加快执行时间。"
 
 #: ../../source/how-to-monitor-simulation.rst:6
 msgid ""
 "The specific instructions assume you are using macOS and have the "
 "`Homebrew <https://brew.sh/>`_ package manager installed."
-msgstr ""
+msgstr "具体说明假定你使用的是 macOS，并且安装了 `Homebrew <https://brew.sh/>`_ 软件包管理器。"
 
 #: ../../source/how-to-monitor-simulation.rst:10
 msgid "Downloads"
-msgstr ""
+msgstr "下载"
 
 #: ../../source/how-to-monitor-simulation.rst:16
 msgid ""
@@ -4437,88 +5414,91 @@ msgid ""
 "collected data. They are both well integrated with `Ray "
 "<https://www.ray.io/>`_ which Flower uses under the hood."
 msgstr ""
+"`Prometheus <https://prometheus.io/>`_ 用于收集数据，而 `Grafana "
+"<https://grafana.com/>`_ 则能让你将收集到的数据可视化。它们都与 Flower 在引擎下使用的 `Ray "
+"<https://www.ray.io/>`_ 紧密集成。"
 
 #: ../../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 "重写配置文件（根据设备的不同，可能安装在不同的路径上）。"
 
 #: ../../source/how-to-monitor-simulation.rst:20
 msgid "If you are on an M1 Mac, it should be:"
-msgstr ""
+msgstr "如果你使用的是 M1 Mac，应该是这样："
 
 #: ../../source/how-to-monitor-simulation.rst:27
 msgid "On the previous generation Intel Mac devices, it should be:"
-msgstr ""
+msgstr "在上一代英特尔 Mac 设备上，应该是这样："
 
 #: ../../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:"
-msgstr ""
+msgstr "打开相应的配置文件并修改它们。根据设备情况，使用以下两个命令之一："
 
 #: ../../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:"
-msgstr ""
+msgstr "然后删除文件中的所有文本，粘贴一个新的 Prometheus 配置文件，如下所示。您可以根据需要调整时间间隔："
 
 #: ../../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:"
-msgstr ""
+msgstr "编辑完 Prometheus 配置后，请对 Grafana 配置文件执行同样的操作。与之前一样，使用以下命令之一打开这些文件："
 
 #: ../../source/how-to-monitor-simulation.rst:69
 msgid ""
 "Your terminal editor should open and allow you to apply the following "
 "configuration as before."
-msgstr ""
+msgstr "您的终端编辑器应该会打开，并允许您像之前一样应用以下配置。"
 
 #: ../../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."
-msgstr ""
+msgstr "恭喜您，您刚刚下载了指标跟踪所需的所有软件。现在，让我们开始吧。"
 
 #: ../../source/how-to-monitor-simulation.rst:88
 msgid "Tracking metrics"
-msgstr ""
+msgstr "跟踪指标"
 
 #: ../../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 ""
+msgstr "在运行 Flower 模拟之前，您必须启动刚刚安装和配置的监控工具。"
 
 #: ../../source/how-to-monitor-simulation.rst:97
 msgid ""
 "Please include the following argument in your Python code when starting a"
 " simulation."
-msgstr ""
+msgstr "开始模拟时，请在 Python 代码中加入以下参数。"
 
 #: ../../source/how-to-monitor-simulation.rst:108
 msgid "Now, you are ready to start your workload."
-msgstr ""
+msgstr "现在，您可以开始工作了。"
 
 #: ../../source/how-to-monitor-simulation.rst:110
 msgid ""
 "Shortly after the simulation starts, you should see the following logs in"
 " your terminal:"
-msgstr ""
+msgstr "模拟启动后不久，您就会在终端中看到以下日志："
 
 #: ../../source/how-to-monitor-simulation.rst:117
 msgid "You can look at everything at `<http://127.0.0.1:8265>`_ ."
-msgstr ""
+msgstr "您可以在 `<http://127.0.0.1:8265>`_ 查看所有内容。"
 
 #: ../../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)."
-msgstr ""
+msgstr "这是一个 Ray Dashboard。您可以导航到 \"度量标准\"（左侧面板，最低选项）。"
 
 #: ../../source/how-to-monitor-simulation.rst:121
 msgid ""
@@ -4528,23 +5508,26 @@ msgid ""
 "can only use Grafana to explore the metrics. You can start Grafana by "
 "going to ``http://localhost:3000/``."
 msgstr ""
+"或者，您也可以点击右上角的 \"在 Grafana 中查看\"，在 Grafana 中查看它们。请注意，Ray "
+"仪表盘只能在模拟期间访问。模拟结束后，您只能使用 Grafana 浏览指标。您可以访问 ``http://localhost:3000/``启动 "
+"Grafana。"
 
 #: ../../source/how-to-monitor-simulation.rst:123
 msgid ""
 "After you finish the visualization, stop Prometheus and Grafana. This is "
 "important as they will otherwise block, for example port :code:`3000` on "
 "your machine as long as they are running."
-msgstr ""
+msgstr "完成可视化后，请停止 Prometheus 和 Grafana。这一点很重要，否则只要它们在运行，就会阻塞机器上的端口 :code:`3000`。"
 
 #: ../../source/how-to-monitor-simulation.rst:132
 msgid "Resource allocation"
-msgstr ""
+msgstr "资源分配"
 
 #: ../../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."
-msgstr ""
+msgstr "您必须了解 Ray 库是如何工作的，才能有效地为自己的仿真客户端分配系统资源。"
 
 #: ../../source/how-to-monitor-simulation.rst:136
 msgid ""
@@ -4555,27 +5538,29 @@ msgid ""
 "You will learn more about that in the later part of this blog. You can "
 "check the system resources by running the following:"
 msgstr ""
+"最初，模拟（由 Ray "
+"在引擎下处理）默认使用系统上的所有可用资源启动，并在客户端之间共享。但这并不意味着它会将资源平均分配给所有客户端，也不意味着模型训练会在所有客户端同时进行。您将在本博客的后半部分了解到更多相关信息。您可以运行以下命令检查系统资源："
 
 #: ../../source/how-to-monitor-simulation.rst:143
 msgid "In Google Colab, the result you see might be similar to this:"
-msgstr ""
+msgstr "在 Google Colab 中，您看到的结果可能与此类似："
 
 #: ../../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):"
-msgstr ""
+msgstr "不过，您可以覆盖默认值。开始模拟时，请执行以下操作（不必全部覆盖）："
 
 #: ../../source/how-to-monitor-simulation.rst:175
 msgid "Let’s also specify the resource for a single client."
-msgstr ""
+msgstr "我们还可以为单个客户指定资源。"
 
 #: ../../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."
-msgstr ""
+msgstr "现在到了关键部分。只有在资源允许的情况下，Ray 才会在拥有所有所需资源（如并行运行）时启动新客户端。"
 
 #: ../../source/how-to-monitor-simulation.rst:207
 msgid ""
@@ -4586,132 +5571,337 @@ msgid ""
 ":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 ""
+"在上面的示例中，将只运行一个客户端，因此您的客户端不会并发运行。设置 :code:`client_num_gpus = 0.5` "
+"将允许运行两个客户端，从而使它们能够并发运行。请注意，所需的资源不要超过可用资源。如果您指定 :code:`client_num_gpus = "
+"2`，模拟将无法启动（即使您有 2 个 GPU，但决定在 :code:`ray_init_args` 中设置为 1）。"
 
 #: ../../source/how-to-monitor-simulation.rst:212 ../../source/ref-faq.rst:2
 msgid "FAQ"
-msgstr ""
+msgstr "常见问题"
 
 #: ../../source/how-to-monitor-simulation.rst:214
 msgid "Q: I don't see any metrics logged."
-msgstr ""
+msgstr "问：我没有看到任何指标记录。"
 
 #: ../../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."
-msgstr ""
+msgstr "答：时间范围可能没有正确设置。设置在右上角（默认为 \"最后 30 分钟\"）。请更改时间框架，以反映模拟运行的时间段。"
 
 #: ../../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."
-msgstr ""
+msgstr "问：我看到 \"未检测到 Grafana 服务器。请确保 Grafana 服务器正在运行并刷新此页面\"。"
 
 #: ../../source/how-to-monitor-simulation.rst:220
 msgid ""
 "A: You probably don't have Grafana running. Please check the running "
 "services"
-msgstr ""
+msgstr "答：您可能没有运行 Grafana。请检查正在运行的服务"
 
 #: ../../source/how-to-monitor-simulation.rst:226
 msgid ""
 "Q: I see \"This site can't be reached\" when going to "
 "`<http://127.0.0.1:8265>`_."
-msgstr ""
+msgstr "问：在访问 `<http://127.0.0.1:8265>`_时，我看到 \"无法访问该网站\"。"
 
 #: ../../source/how-to-monitor-simulation.rst:228
 msgid ""
 "A: Either the simulation has already finished, or you still need to start"
 " Prometheus."
-msgstr ""
+msgstr "答：要么模拟已经完成，要么您还需要启动Prometheus。"
 
 #: ../../source/how-to-monitor-simulation.rst:232
 msgid "Resources"
-msgstr ""
+msgstr "资源"
 
 #: ../../source/how-to-monitor-simulation.rst:234
 msgid ""
 "Ray Dashboard: `<https://docs.ray.io/en/latest/ray-core/ray-"
 "dashboard.html>`_"
-msgstr ""
+msgstr "Ray 仪表盘: `<https://docs.ray.io/en/latest/ray-core/ray-dashboard.html>`_"
 
 #: ../../source/how-to-monitor-simulation.rst:236
 msgid ""
 "Ray Metrics: `<https://docs.ray.io/en/latest/ray-observability/ray-"
 "metrics.html>`_"
 msgstr ""
+"Ray 指标: `<https://docs.ray.io/en/latest/ray-observability/ray-"
+"metrics.html>`_"
 
-#: ../../source/how-to-run-simulations.rst:2
-msgid "Run simulations"
+#: ../../source/how-to-run-flower-using-docker.rst:2
+msgid "Run Flower using Docker"
 msgstr ""
 
-#: ../../source/how-to-run-simulations.rst:8
+#: ../../source/how-to-run-flower-using-docker.rst:4
 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 "
-"<contributor-explanation-architecture.html#virtual-client-engine>`_ or "
-"VCE."
+"The simplest way to get started with Flower is by using the pre-made "
+"Docker images, which you can find on `Docker Hub "
+"<https://hub.docker.com/r/flwr/server/tags>`_."
 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_numpy_client <ref-api-"
-"flwr.html#start-numpy-client>`_) in the sense that they can be configure "
-"by creating a class inheriting, for example, from "
-"`flwr.client.NumPyClient <ref-api-flwr.html#flwr.client.NumPyClient>`_ "
-"and therefore behave in an identical way. In addition to that, clients "
-"managed by the :code:`VirtualClientEngine` are:"
+#: ../../source/how-to-run-flower-using-docker.rst:7
+msgid "Before you start, make sure that the Docker daemon is running:"
 msgstr ""
 
-#: ../../source/how-to-run-simulations.rst:12
+#: ../../source/how-to-run-flower-using-docker.rst:14
 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."
+"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 <https://docs.docker.com/get-"
+"docker/>`_."
 msgstr ""
 
-#: ../../source/how-to-run-simulations.rst:13
+#: ../../source/how-to-run-flower-using-docker.rst:20
 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."
+"On Linux, Docker commands require ``sudo`` privilege. If you want to "
+"avoid using ``sudo``, you can follow the `Post-installation steps "
+"<https://docs.docker.com/engine/install/linux-postinstall/>`_ on the "
+"official Docker website."
 msgstr ""
 
-#: ../../source/how-to-run-simulations.rst:14
+#: ../../source/how-to-run-flower-using-docker.rst:25
+#, fuzzy
+msgid "Flower server"
+msgstr "Flower 服务器"
+
+#: ../../source/how-to-run-flower-using-docker.rst:28
+#, fuzzy
+msgid "Quickstart"
+msgstr "快速入门 JAX"
+
+#: ../../source/how-to-run-flower-using-docker.rst:30
+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
 msgid ""
-"ephemeral: this means that a client is only materialized when it is "
-"required in the FL process (e.g. to do `fit() <ref-api-"
-"flwr.html#flwr.client.Client.fit>`_). The object is destroyed afterwards,"
-" releasing the resources it was assigned and allowing in this way other "
-"clients to participate."
+"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 ""
 
-#: ../../source/how-to-run-simulations.rst:16
+#: ../../source/how-to-run-flower-using-docker.rst:44
 msgid ""
-"The :code:`VirtualClientEngine` implements `virtual` clients using `Ray "
-"<https://www.ray.io/>`_, an open-source framework for scalable Python "
-"workloads. In particular, Flower's :code:`VirtualClientEngine` makes use "
-"of `Actors <https://docs.ray.io/en/latest/ray-core/actors.html>`_ to "
-"spawn `virtual` clients and run their workload."
+"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 ""
 
+#: ../../source/how-to-run-flower-using-docker.rst:48
+msgid ""
+"The ``-p <host>:<container>`` 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 ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:55
+msgid ""
+"The ``--insecure`` flag enables insecure communication (using HTTP, not "
+"HTTPS) and should only be used for testing purposes. We strongly "
+"recommend enabling `SSL <https://flower.ai/docs/framework/how-to-run-"
+"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
+msgid ""
+"You can use ``--help`` to view all available flags that the server "
+"supports:"
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:67
+msgid "Mounting a volume to store the state on the host system"
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:69
+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 ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:82
+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."
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:87
+#, fuzzy
+msgid "Enabling SSL for secure connections"
+msgstr "启用 SSL 连接"
+
+#: ../../source/how-to-run-flower-using-docker.rst:89
+msgid ""
+"To enable SSL, you will need a CA certificate, a server certificate and a"
+" server private key."
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:92
+msgid ""
+"For testing purposes, you can generate your own self-signed certificates."
+" The `Enable SSL connections <https://flower.ai/docs/framework/how-to-"
+"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
+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"
+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 <https://hub.docker.com/r/flwr/server/tags>`_."
+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:116
+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:121
+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:"
+msgstr ""
+
+#: ../../source/how-to-run-flower-using-docker.rst:137
+#, fuzzy
+msgid "Setting environment variables"
+msgstr "设置编码环境"
+
+#: ../../source/how-to-run-flower-using-docker.rst:139
+msgid ""
+"To set a variable inside a Docker container, you can use the ``-e "
+"<name>=<value>`` flag."
+msgstr ""
+
+#: ../../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 "
+"<contributor-explanation-architecture.html#virtual-client-engine>`_ 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）<contributor-"
+"explanation-architecture.html#virtual-client-engine>_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 <ref-api-"
+"flwr.html#start-client>`_) in the sense that they can be configure by "
+"creating a class inheriting, for example, from `flwr.client.NumPyClient "
+"<ref-api-flwr.html#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"
+" <ref-api-flwr.html#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 <ref-api-flwr.html#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() <ref-api-"
+"flwr.html#flwr.client.Client.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() <ref-api-"
+"flwr.html#flwr.client.Client.fit>`_ "
+"）。之后该对象将被销毁，释放分配给它的资源，并允许其他客户端以这种方式参与。"
+
+#: ../../source/how-to-run-simulations.rst:16
+msgid ""
+"The :code:`VirtualClientEngine` implements `virtual` clients using `Ray "
+"<https://www.ray.io/>`_, an open-source framework for scalable Python "
+"workloads. In particular, Flower's :code:`VirtualClientEngine` makes use "
+"of `Actors <https://docs.ray.io/en/latest/ray-core/actors.html>`_ to "
+"spawn `virtual` clients and run their workload."
+msgstr ""
+":code:`VirtualClientEngine`使用`Ray "
+"<https://www.ray.io/>`_来实现`虚拟`客户端，这是一个用于可扩展 Python 工作负载的开源框架。特别地，Flower 的"
+" :code:`VirtualClientEngine` 使用 `Actors <https://docs.ray.io/en/latest"
+"/ray-core/actors.html>`_ 来生成 `virtual` 客户端并运行它们的工作负载。"
+
 #: ../../source/how-to-run-simulations.rst:20
 msgid "Launch your Flower simulation"
-msgstr ""
+msgstr "启动 Flower 模拟"
 
 #: ../../source/how-to-run-simulations.rst:22
 msgid ""
@@ -4722,10 +5912,13 @@ msgid ""
 "flwr.html#flwr.simulation.start_simulation>`_ and a minimal example looks"
 " as follows:"
 msgstr ""
+"运行 Flower 模拟器仍然需要定义客户端类、策略以及下载和加载（可能还需要分割）数据集的实用程序。在完成这些工作后，就可以使用 "
+"\"start_simulation <ref-api-"
+"flwr.html#flwr.simulation.start_simulation>`_\" 来启动模拟了，一个最简单的示例如下："
 
 #: ../../source/how-to-run-simulations.rst:44
 msgid "VirtualClientEngine resources"
-msgstr ""
+msgstr "虚拟客户端引擎资源"
 
 #: ../../source/how-to-run-simulations.rst:45
 msgid ""
@@ -4740,10 +5933,16 @@ msgid ""
 " documentation. Do not set :code:`ray_init_args` if you want the VCE to "
 "use all your system's CPUs and GPUs."
 msgstr ""
+"默认情况下，VCE 可以访问所有系统资源（即所有 CPU、所有 GPU 等），因为这也是启动 Ray "
+"时的默认行为。不过，在某些设置中，您可能希望限制有多少系统资源用于模拟。您可以通过 :code:`ray_init_args` 输入到 "
+":code:`start_simulation` 的参数来做到这一点，VCE 会在内部将该参数传递给 Ray 的 :code:`ray.init`"
+" 命令。有关您可以配置的设置的完整列表，请查看 `ray.init <https://docs.ray.io/en/latest/ray-"
+"core/api/doc/ray.init.html#ray-init>`_ 文档。如果希望 VCE 使用系统中所有的 CPU 和 "
+"GPU，请不要设置 :code:`ray_init_args`。"
 
 #: ../../source/how-to-run-simulations.rst:62
 msgid "Assigning client resources"
-msgstr ""
+msgstr "分配客户端资源"
 
 #: ../../source/how-to-run-simulations.rst:63
 msgid ""
@@ -4751,6 +5950,8 @@ msgid ""
 " nothing else) to each virtual client. This means that if your system has"
 " 10 cores, that many virtual clients can be concurrently running."
 msgstr ""
+"默认情况下，:code:`VirtualClientEngine` 会为每个虚拟客户端分配一个 CPU "
+"内核（不分配其他任何内核）。这意味着，如果系统有 10 个内核，那么可以同时运行这么多虚拟客户端。"
 
 #: ../../source/how-to-run-simulations.rst:65
 msgid ""
@@ -4762,20 +5963,24 @@ msgid ""
 " Two keys are internally used by Ray to schedule and spawn workloads (in "
 "our case Flower clients):"
 msgstr ""
+"通常情况下，您可能希望根据 FL 工作负载的复杂性（即计算和内存占用）来调整分配给客户端的资源。您可以在启动模拟时将参数 "
+"`client_resources` 设置为 `start_simulation <ref-api-"
+"flwr.html#flwr.simulation.start_simulation>`_ 。Ray "
+"内部使用两个键来调度和生成工作负载（在我们的例子中是 Flower 客户端）："
 
 #: ../../source/how-to-run-simulations.rst:67
 msgid ":code:`num_cpus` indicates the number of CPU cores a client would get."
-msgstr ""
+msgstr ":code:`num_cpus` 表示客户端将获得的 CPU 内核数量。"
 
 #: ../../source/how-to-run-simulations.rst:68
 msgid ""
 ":code:`num_gpus` indicates the **ratio** of GPU memory a client gets "
 "assigned."
-msgstr ""
+msgstr ":code:`num_gpus` 表示分配给客户端的 GPU 内存的**比例**。"
 
 #: ../../source/how-to-run-simulations.rst:70
 msgid "Let's see a few examples:"
-msgstr ""
+msgstr "让我们来看几个例子："
 
 #: ../../source/how-to-run-simulations.rst:89
 msgid ""
@@ -4789,6 +5994,11 @@ msgid ""
 "simulating a client sampled by the strategy) and then will execute them "
 "in a resource-aware manner in batches of 8."
 msgstr ""
+"虽然 :code:`client_resources` 可用来控制 FL "
+"模拟的并发程度，但这并不能阻止您在同一轮模拟中运行几十、几百甚至上千个客户端，并拥有数量级更多的 "
+"\"休眠\"（即不参与一轮模拟）客户端。比方说，您希望每轮有 100 个客户端，但您的系统只能同时容纳 8 "
+"个客户端。:code:`VirtualClientEngine` 将安排运行 100 "
+"个工作（每个工作模拟策略采样的一个客户端），然后以资源感知的方式分批执行。"
 
 #: ../../source/how-to-run-simulations.rst:91
 msgid ""
@@ -4797,10 +6007,12 @@ msgid ""
 "look at the `Ray documentation <https://docs.ray.io/en/latest/ray-"
 "core/scheduling/resources.html>`_."
 msgstr ""
+"要了解资源如何用于调度 FL 客户端以及如何定义自定义资源的所有复杂细节，请查看 `Ray 文档 "
+"<https://docs.ray.io/en/latest/ray-core/scheduling/resources.html>`_。"
 
 #: ../../source/how-to-run-simulations.rst:94
 msgid "Simulation examples"
-msgstr ""
+msgstr "模拟示例"
 
 #: ../../source/how-to-run-simulations.rst:96
 msgid ""
@@ -4808,6 +6020,8 @@ msgid ""
 "Tensorflow/Keras and PyTorch are provided in the `Flower repository "
 "<https://github.com/adap/flower>`_. You can run them on Google Colab too:"
 msgstr ""
+"在 Tensorflow/Keras 和 PyTorch 中进行 Flower 模拟的几个可随时运行的完整示例已在 `Flower 库 "
+"<https://github.com/adap/flower>`_ 中提供。您也可以在 Google Colab 上运行它们："
 
 #: ../../source/how-to-run-simulations.rst:98
 msgid ""
@@ -4815,6 +6029,8 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples/simulation-"
 "tensorflow>`_: 100 clients collaboratively train a MLP model on MNIST."
 msgstr ""
+"Tensorflow/Keras模拟 <https://github.com/adap/flower/tree/main/examples"
+"/simulation-tensorflow>`_：100个客户端在MNIST上协作训练一个MLP模型。"
 
 #: ../../source/how-to-run-simulations.rst:99
 msgid ""
@@ -4822,31 +6038,33 @@ msgid ""
 "/simulation-pytorch>`_: 100 clients collaboratively train a CNN model on "
 "MNIST."
 msgstr ""
+"PyTorch 模拟 <https://github.com/adap/flower/tree/main/examples/simulation-"
+"pytorch>`_：100 个客户端在 MNIST 上协作训练一个 CNN 模型。"
 
 #: ../../source/how-to-run-simulations.rst:104
 msgid "Multi-node Flower simulations"
-msgstr ""
+msgstr "多节点 Flower 模拟"
 
 #: ../../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:"
-msgstr ""
+msgstr "Flower 的 :code:`VirtualClientEngine` 允许您在多个计算节点上运行 FL 模拟。在开始多节点模拟之前，请确保："
 
 #: ../../source/how-to-run-simulations.rst:108
 msgid "Have the same Python environment in all nodes."
-msgstr ""
+msgstr "所有节点都有相同的 Python 环境。"
 
 #: ../../source/how-to-run-simulations.rst:109
 msgid "Have a copy of your code (e.g. your entire repo) in all nodes."
-msgstr ""
+msgstr "在所有节点上都有一份代码副本（例如整个软件包）。"
 
 #: ../../source/how-to-run-simulations.rst:110
 msgid ""
 "Have a copy of your dataset in all nodes (more about this in "
 ":ref:`simulation considerations <considerations-for-simulations>`)"
-msgstr ""
+msgstr "在所有节点中都有一份数据集副本（更多相关信息请参阅 :ref:`模拟注意事项<considerations-for-simulations>`）"
 
 #: ../../source/how-to-run-simulations.rst:111
 msgid ""
@@ -4854,6 +6072,9 @@ msgid ""
 "<ref-api-flwr.html#flwr.simulation.start_simulation>`_ so the "
 ":code:`VirtualClientEngine` attaches to a running Ray instance."
 msgstr ""
+"将 :code:`ray_init_args={\"address\"=\"auto\"}`传递给 `start_simulation <ref-"
+"api-flwr.html#flwr.simulation.start_simulation>`_ ，这样 "
+":code:`VirtualClientEngine`就会连接到正在运行的 Ray 实例。"
 
 #: ../../source/how-to-run-simulations.rst:112
 msgid ""
@@ -4861,6 +6082,8 @@ msgid ""
 "--head`. This command will print a few lines, one of which indicates how "
 "to attach other nodes to the head node."
 msgstr ""
+"在头部节点上启动 Ray：在终端上输入 :code:`raystart--"
+"head`。该命令将打印几行输出，其中一行说明如何将其他节点连接到头部节点。"
 
 #: ../../source/how-to-run-simulations.rst:113
 msgid ""
@@ -4868,29 +6091,31 @@ msgid ""
 "starting the head and execute it on terminal of a new node: for example "
 ":code:`ray start --address='192.168.1.132:6379'`"
 msgstr ""
+"将其他节点附加到头部节点：复制启动头部后显示的命令，并在新节点的终端上执行：例如 :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 ""
+msgstr "完成上述所有操作后，您就可以在头部节点上运行代码了，就像在单个节点上运行模拟一样。"
 
 #: ../../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 ""
+msgstr "模拟结束后，如果要拆除集群，只需在每个节点（包括头部节点）的终端运行 :code:`ray stop` 命令即可。"
 
 #: ../../source/how-to-run-simulations.rst:120
 msgid "Multi-node simulation good-to-know"
-msgstr ""
+msgstr "了解多节点模拟"
 
 #: ../../source/how-to-run-simulations.rst:122
 msgid ""
 "Here we list a few interesting functionality when running multi-node FL "
 "simulations:"
-msgstr ""
+msgstr "在此，我们列举了运行多节点 FL 模拟时的一些有趣功能："
 
 #: ../../source/how-to-run-simulations.rst:124
 msgid ""
@@ -4898,6 +6123,8 @@ msgid ""
 " well as the total resources available to the "
 ":code:`VirtualClientEngine`."
 msgstr ""
+"使用 :code:`ray status` 查看连接到头部节点的所有节点，以及 :code:`VirtualClientEngine` "
+"可用的总资源。"
 
 #: ../../source/how-to-run-simulations.rst:126
 msgid ""
@@ -4910,16 +6137,20 @@ msgid ""
 "gpus=<NUM_GPUS_FROM_NODE>` in any :code:`ray start` command (including "
 "when starting the head)"
 msgstr ""
+"将新节点附加到头部节点时，头部节点将可见其所有资源（即所有 CPU 和 GPU）。这意味着 :code:`VirtualClientEngine`"
+" 可以调度尽可能多的 \"虚拟 \"客户端来运行该节点。在某些设置中，您可能希望将某些资源排除在模拟之外。为此，您可以在任何 :code:`ray"
+" start` 命令（包括启动头部时）中添加 `--num-cpus=<NUM_CPUS_FROM_NODE>`和/或 `--num-"
+"gpus=<NUM_GPUS_FROM_NODE>`"
 
 #: ../../source/how-to-run-simulations.rst:132
 msgid "Considerations for simulations"
-msgstr ""
+msgstr "模拟的注意事项"
 
 #: ../../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 ""
+msgstr "我们正在积极开展这些方面的工作，以便使 FL 工作负载与 Flower 模拟的运行变得轻而易举。"
 
 #: ../../source/how-to-run-simulations.rst:138
 msgid ""
@@ -4931,10 +6162,13 @@ msgid ""
 " mind when designing your FL pipeline with Flower. We also highlight a "
 "couple of current limitations in our implementation."
 msgstr ""
+"当前的 VCE 允许您在模拟模式下运行联邦学习工作负载，无论您是在个人笔记本电脑上建立简单的场景原型，还是要在多个高性能 GPU 节点上训练复杂的"
+" FL情景。虽然我们为 VCE 增加了更多的功能，但以下几点强调了在使用 Flower 设计 FL "
+"时需要注意的一些事项。我们还强调了我们的实现中目前存在的一些局限性。"
 
 #: ../../source/how-to-run-simulations.rst:141
 msgid "GPU resources"
-msgstr ""
+msgstr "GPU 资源"
 
 #: ../../source/how-to-run-simulations.rst:143
 msgid ""
@@ -4942,6 +6176,8 @@ msgid ""
 ":code:`num_gpus` in :code:`client_resources`. This being said, Ray (used "
 "internally by the VCE) is by default:"
 msgstr ""
+"VCE 会为指定 :code:`client_resources` 中 :code:`num_gpus` 关键字的客户端分配 GPU "
+"内存份额。也就是说，Ray（VCE 内部使用）是默认的："
 
 #: ../../source/how-to-run-simulations.rst:146
 msgid ""
@@ -4950,12 +6186,14 @@ msgid ""
 "different (e.g. 32GB and 8GB) VRAM amounts, they both would run 2 clients"
 " concurrently."
 msgstr ""
+"不知道 GPU 上可用的总 VRAM。这意味着，如果您设置 :code:`num_gpus=0.5`，而系统中有两个不同（如 32GB 和 "
+"8GB）VRAM 的 GPU，它们都将同时运行 2 个客户端。"
 
 #: ../../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 ""
+msgstr "不知道 GPU 上正在运行其他无关（即不是由 VCE 创建）的工作负载。从中可以得到以下两点启示："
 
 #: ../../source/how-to-run-simulations.rst:149
 msgid ""
@@ -4963,6 +6201,8 @@ msgid ""
 "aggregation (by instance when making use of the `evaluate method <how-to-"
 "implement-strategies.html#the-evaluate-method>`_)"
 msgstr ""
+"您的 Flower 服务器可能需要 GPU 来评估聚合后的 \"全局模型\"（例如在使用 \"评估方法\"<how-to-implement-"
+"strategies.html#the-evaluate-method>`_时）"
 
 #: ../../source/how-to-run-simulations.rst:150
 msgid ""
@@ -4971,6 +6211,8 @@ msgid ""
 ":code:`CUDA_VISIBLE_DEVICES=\"<GPU_IDs>\"` when launching your "
 "experiment."
 msgstr ""
+"如果您想在同一台机器上运行多个独立的 Flower 模拟，则需要在启动实验时使用 "
+":code:`CUDA_VISIBLE_DEVICES=\"<GPU_IDs>\"` 屏蔽 GPU。"
 
 #: ../../source/how-to-run-simulations.rst:153
 msgid ""
@@ -4979,10 +6221,12 @@ msgid ""
 "situation of client using more VRAM than the ratio specified when "
 "starting the simulation."
 msgstr ""
+"此外，传递给 :code:`client_resources` 的 GPU 资源限制并不是 \"强制 \"的（即可以超出），这可能导致客户端使用的"
+" VRAM 超过启动模拟时指定的比例。"
 
 #: ../../source/how-to-run-simulations.rst:156
 msgid "TensorFlow with GPUs"
-msgstr ""
+msgstr "使用 GPU 的 TensorFlow"
 
 #: ../../source/how-to-run-simulations.rst:158
 msgid ""
@@ -4995,6 +6239,10 @@ msgid ""
 "default behavior by `enabling memory growth "
 "<https://www.tensorflow.org/guide/gpu#limiting_gpu_memory_growth>`_."
 msgstr ""
+"在 TensorFlow <https://www.tensorflow.org/guide/gpu>`_ 中使用 GPU 时，几乎所有进程可见的"
+" GPU 内存都将被映射。TensorFlow 这样做是出于优化目的。然而，在 FL 模拟等设置中，我们希望将 GPU 分割成多个 \"虚拟 "
+"\"客户端，这并不是一个理想的机制。幸运的是，我们可以通过 `启用内存增长 "
+"<https://www.tensorflow.org/guide/gpu#limiting_gpu_memory_growth>`_来禁用这一默认行为。"
 
 #: ../../source/how-to-run-simulations.rst:160
 msgid ""
@@ -5005,6 +6253,9 @@ msgid ""
 "In this case, to enable GPU growth for TF workloads. It would look as "
 "follows:"
 msgstr ""
+"这需要在主进程（也就是服务器运行的地方）和 VCE 创建的每个角色中完成。通过 "
+":code:`actor_kwargs`，我们可以传递保留关键字`\"on_actor_init_fn\"`，以指定在角色初始化时执行的函数。在本例中，为了使"
+" TF 工作负载的 GPU 增长，它看起来如下："
 
 #: ../../source/how-to-run-simulations.rst:179
 msgid ""
@@ -5012,10 +6263,13 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples/simulation-"
 "tensorflow>`_ example."
 msgstr ""
+"这正是 \"Tensorflow/Keras 模拟 "
+"<https://github.com/adap/flower/tree/main/examples/simulation-"
+"tensorflow>`_\"示例中使用的机制。"
 
 #: ../../source/how-to-run-simulations.rst:183
 msgid "Multi-node setups"
-msgstr ""
+msgstr "多节点设置"
 
 #: ../../source/how-to-run-simulations.rst:185
 msgid ""
@@ -5029,6 +6283,11 @@ msgid ""
 "nodes or a dataset serving mechanism (e.g. using nfs, a database) to "
 "circumvent data duplication."
 msgstr ""
+"VCE 目前不提供控制特定 \"虚拟 "
+"\"客户端在哪个节点上执行的方法。换句话说，如果不止一个节点拥有客户端运行所需的资源，那么这些节点中的任何一个都可能被调度到客户端工作负载上。在 "
+"FL "
+"进程的稍后阶段（即在另一轮中），同一客户端可以由不同的节点执行。根据客户访问数据集的方式，这可能需要在所有节点上复制所有数据集分区，或采用数据集服务机制（如使用"
+" nfs 或数据库）来避免数据重复。"
 
 #: ../../source/how-to-run-simulations.rst:187
 msgid ""
@@ -5040,21 +6299,24 @@ msgid ""
 " above also since, in some way, the client's dataset could be seen as a "
 "type of `state`."
 msgstr ""
+"根据定义，虚拟客户端是 \"无状态 \"的，因为它们具有即时性。客户机状态可以作为 Flower "
+"客户机类的一部分来实现，但用户需要确保将其保存到持久存储（如数据库、磁盘）中，而且无论客户机在哪个节点上运行，都能在以后检索到。这也与上述观点有关，因为在某种程度上，客户端的数据集可以被视为一种"
+" \"状态\"。"
 
 #: ../../source/how-to-save-and-load-model-checkpoints.rst:2
 msgid "Save and load model checkpoints"
-msgstr ""
+msgstr "保存和加载模型检查点"
 
 #: ../../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."
-msgstr ""
+msgstr "Flower 不会在服务器端自动保存模型更新。本指南将介绍在 Flower 中保存（和加载）模型检查点的步骤。"
 
 #: ../../source/how-to-save-and-load-model-checkpoints.rst:8
 msgid "Model checkpointing"
-msgstr ""
+msgstr "模型检查点"
 
 #: ../../source/how-to-save-and-load-model-checkpoints.rst:10
 msgid ""
@@ -5069,10 +6331,15 @@ msgid ""
 " before it returns those aggregated weights to the caller (i.e., the "
 "server):"
 msgstr ""
+"模型更新可通过自定义 :code:`Strategy` "
+"方法在服务器端持久化。实现自定义策略始终是一种选择，但在许多情况下，简单地自定义现有策略可能更方便。下面的代码示例定义了一个新的 "
+":code:`SaveModelStrategy`，它自定义了现有的内置 :code:`FedAvg` "
+"策略。特别是，它通过调用基类（:code:`FedAvg`）中的 :code:`aggregate_fit` 来定制 "
+":code:`aggregate_fit`。然后继续保存返回的（聚合）参数，然后再将这些聚合参数返回给调用者（即服务器）："
 
 #: ../../source/how-to-save-and-load-model-checkpoints.rst:47
 msgid "Save and load PyTorch checkpoints"
-msgstr ""
+msgstr "保存和加载 PyTorch 检查点"
 
 #: ../../source/how-to-save-and-load-model-checkpoints.rst:49
 msgid ""
@@ -5083,17 +6350,26 @@ msgid ""
 "transformed into the PyTorch ``state_dict`` following the ``OrderedDict``"
 " class structure."
 msgstr ""
+"与前面的例子类似，但多了几个步骤，我们将展示如何存储一个 PyTorch 检查点，我们将使用 ``torch.save`` "
+"函数。首先，``aggregate_fit`` 返回一个 ``Parameters`` 对象，它必须被转换成一个 NumPy "
+"``ndarray`` 的列表，然后这些对象按照 ``OrderedDict`` 类结构被转换成 PyTorch `state_dict` 对象。"
 
 #: ../../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 "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
+msgid ""
+"Return/use this object of type ``Parameters`` wherever necessary, such as"
+" in the ``initial_parameters`` when defining a ``Strategy``."
 msgstr ""
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:2
 msgid "Upgrade to Flower 1.0"
-msgstr ""
+msgstr "升级至 Flower 1.0"
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:4
 msgid ""
@@ -5102,32 +6378,34 @@ msgid ""
 "series releases), there are a few breaking changes that make it necessary"
 " to change the code of existing 0.x-series projects."
 msgstr ""
+"Flower 1.0 正式发布。除了新功能，Flower 1.0 还为未来的发展奠定了稳定的基础。与 Flower 0.19（以及其他 0.x "
+"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
 msgid "Install update"
-msgstr ""
+msgstr "安装更新"
 
 #: ../../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:"
-msgstr ""
+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-1.0.rst:12
 msgid "pip: add ``-U`` when installing."
-msgstr ""
+msgstr "pip: 安装时添加 ``-U``."
 
 #: ../../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 ""
+msgstr "`python -m pip install -U flwr``（当使用`start_server`和`start_client`时）"
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:15
 msgid ""
 "``python -m pip install -U flwr[simulation]`` (when using "
 "``start_simulation``)"
-msgstr ""
+msgstr "`python -m pip install -U flwr[simulation]``（当使用`start_simulation``时）"
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:17
 msgid ""
@@ -5135,40 +6413,44 @@ msgid ""
 "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-1.0.rst:19
 msgid "``flwr = \"^1.0.0\"`` (when using ``start_server`` and ``start_client``)"
-msgstr ""
+msgstr "``flwr = \"^1.0.0\"`` （当使用 ``start_server` 和 ``start_client` 时）"
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:20
 msgid ""
 "``flwr = { version = \"^1.0.0\", extras = [\"simulation\"] }`` (when "
 "using ``start_simulation``)"
 msgstr ""
+"``flwr = { version = \"^1.0.0\", extras = [\"simulation\"] "
+"}``（当使用``start_simulation``时）"
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:24
 msgid "Required changes"
-msgstr ""
+msgstr "所需变更"
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:26
 msgid "The following breaking changes require manual updates."
-msgstr ""
+msgstr "以下更改需要手动更新。"
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:29
 msgid "General"
-msgstr ""
+msgstr "一般情况"
 
 #: ../../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 "将所有参数作为关键字参数传递（而不是位置参数）。下面是一个例子："
 
 #: ../../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 ""
+msgstr "Flower 0.19 (位置参数)： ``start_client(\"127.0.0.1:8080\", FlowerClient())``"
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:34
 msgid ""
@@ -5176,34 +6458,44 @@ msgid ""
 "``start_client(server_address=\"127.0.0.1:8080\", "
 "client=FlowerClient())``"
 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:39
 msgid ""
 "Subclasses of ``NumPyClient``: change ``def get_parameters(self):``` to "
 "``def get_parameters(self, config):``"
 msgstr ""
+"NumPyClient的子类：将``def get_parameters(self):```改为``def "
+"get_parameters(self,config):``"
 
 #: ../../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 ""
+"客户端 \"的子类：将 \"get_parameters(self): \"改为 \"get_parameters(self, ins: "
+"GetParametersIns):\""
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:43
 msgid "Strategies / ``start_server`` / ``start_simulation``"
-msgstr ""
+msgstr "策略 / ``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 ""
+"向 ``start_server`` 和 ``start_simulation` 传递 ``ServerConfig``（而不是 "
+"dictionary）。下面是一个例子："
 
 #: ../../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 ""
@@ -5211,12 +6503,15 @@ msgid ""
 "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 ""
+msgstr "将`start_simulation``中的`num_rounds=1``替换为新的`config=ServerConfig(...)`（参见前一项）"
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:51
 msgid ""
@@ -5225,18 +6520,20 @@ msgid ""
 " configuring the strategy to sample all clients for evaluation after the "
 "last round of training."
 msgstr ""
+"删除调用 ``start_server`` 时的 ``force_final_distributed_eval` "
+"参数。可以通过配置策略，在最后一轮训练后对所有客户端进行抽样评估，从而启用对所有客户端的分布式评估。"
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:52
 msgid "Rename parameter/ndarray conversion functions:"
-msgstr ""
+msgstr "重命名参数/数组转换函数："
 
 #: ../../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 ""
@@ -5247,22 +6544,26 @@ msgid ""
 "without passing a strategy instance) should now manually initialize "
 "FedAvg with ``fraction_fit`` and ``fraction_evaluate`` set to ``0.1``."
 msgstr ""
+"策略初始化：如果策略依赖于 ``fraction_fit`` 和 ``fraction_evaluate`` 的默认值，请手动将 "
+"``fraction_fit`` 和 ``fraction_evaluate`` 设置为 ``0.1``。未手动创建策略的项目（调用 "
+"``start_server` 或 ``start_simulation` 时未传递策略实例）现在应手动初始化 FedAvg，并将 "
+"`fraction_fit` 和 `fraction_evaluate` 设为 `0.1``。"
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:58
 msgid "Rename built-in strategy parameters (e.g., ``FedAvg``):"
-msgstr ""
+msgstr "重命名内置策略参数（例如，`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 ""
@@ -5270,16 +6571,21 @@ msgid ""
 "functions, for example, ``configure_fit``, ``aggregate_fit``, "
 "``configure_evaluate``, ``aggregate_evaluate``, and ``evaluate_fn``."
 msgstr ""
+"将 `rnd` 更名为 `server_round`。这会影响多个方法和函数，例如 "
+"``configure_fit``、``aggregate_fit``、``configure_evaluate``、`aggregate_evaluate``"
+" 和 ``evaluate_fn``。"
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:65
 msgid "Add ``server_round`` and ``config`` to ``evaluate_fn``:"
-msgstr ""
+msgstr "在 ``evaluate_fn` 中添加 ``server_round` 和 ``config`："
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:67
 msgid ""
 "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 ""
@@ -5287,10 +6593,13 @@ msgid ""
 "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 "定制策略"
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:73
 msgid ""
@@ -5300,41 +6609,51 @@ msgid ""
 "``List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]]`` (in "
 "``aggregate_evaluate``)"
 msgstr ""
+"参数``failures``的类型已从``List[BaseException]``变为``List[Union[Tuple[ClientProxy,"
+" FitRes], "
+"BaseException]]``（在``agregate_fit``中）和``List[Union[Tuple[ClientProxy, "
+"EvaluateRes], BaseException]]``（在``agregate_evaluate``中）"
 
 #: ../../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 ""
+msgstr "``Strategy``方法 的``evaluate``现在会接收当前一轮联邦学习/评估作为第一个参数："
 
 #: ../../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]]]:``"
 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 "可选的改进措施"
 
 #: ../../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 ""
+msgstr "除了上述必要的改动之外，还有一些潜在的改进措施："
 
 #: ../../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 "
-"empy placeholder implementations of ``evaluate`` are no longer necessary."
+"empty placeholder implementations of ``evaluate`` are no longer necessary."
 msgstr ""
+"删除 ``Client`` 或 ``NumPyClient`` 子类中的 \"占位符 "
+"\"方法。例如，如果你使用服务器端评估，那么就不再需要``evaluate``的 \"空占位符 \"实现。"
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:85
 msgid ""
@@ -5342,78 +6661,204 @@ msgid ""
 "``start_simulation(..., config=flwr.server.ServerConfig(num_rounds=3, "
 "round_timeout=600.0), ...)``"
 msgstr ""
+"通过 ``start_simulation`` 配置循环超时： ``start_simulation(..., "
+"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), "
+"...)``"
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:89
 msgid "Further help"
-msgstr ""
+msgstr "更多帮助"
 
 #: ../../source/how-to-upgrade-to-flower-1.0.rst:91
 msgid ""
 "Most official `Flower code examples "
 "<https://github.com/adap/flower/tree/main/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 questionsm, `join the Flower Slack "
-"<https://flower.dev/join-slack/>`_ and use the channgel ``#questions``."
+"API. If there are further questions, `join the Flower Slack "
+"<https://flower.ai/join-slack/>`_ and use the channel ``#questions``."
+msgstr ""
+"大多数官方的 `Flower 代码示例 <https://github.com/adap/flower/tree/main/examples>`_"
+" 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 "
+"<https://flower.ai/join-slack/>`_ 并使用 \"#questions``\"。"
+
+#: ../../source/how-to-use-built-in-mods.rst:2
+msgid "Use Built-in Mods"
+msgstr ""
+
+#: ../../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.**"
+msgstr ""
+
+#: ../../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/how-to-use-built-in-mods.rst:9
+msgid "What are Mods?"
+msgstr ""
+
+#: ../../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/how-to-use-built-in-mods.rst:18
+msgid "A typical mod function might look something like this:"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:31
+msgid "Using Mods"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:33
+msgid "To use mods in your ``ClientApp``, you can follow these steps:"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:36
+msgid "1. Import the required mods"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:38
+msgid "First, import the built-in mod you intend to use:"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:46
+msgid "2. Define your client function"
+msgstr ""
+
+#: ../../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/how-to-use-built-in-mods.rst:57
+msgid "3. Create the ``ClientApp`` with mods"
+msgstr ""
+
+#: ../../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/how-to-use-built-in-mods.rst:72
+#, fuzzy
+msgid "Order of execution"
+msgstr "停用"
+
+#: ../../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/how-to-use-built-in-mods.rst:76
+msgid "``example_mod_1`` (outermost mod)"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:77
+msgid "``example_mod_2`` (next mod)"
+msgstr ""
+
+#: ../../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/how-to-use-built-in-mods.rst:79
+msgid "``example_mod_2`` (on the way back)"
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:80
+msgid "``example_mod_1`` (outermost mod on the way back)"
+msgstr ""
+
+#: ../../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/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."
+msgstr ""
+
+#: ../../source/how-to-use-built-in-mods.rst:89
+msgid "Enjoy building more robust and flexible ``ClientApp``s with mods!"
 msgstr ""
 
 #: ../../source/how-to-use-strategies.rst:2
 msgid "Use strategies"
-msgstr ""
+msgstr "使用策略"
 
 #: ../../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 ""
+msgstr "Flower 允许通过 :code:`Strategy` 抽象类对学习过程进行完全定制。核心框架中提供了许多内置策略。"
 
 #: ../../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 ""
+msgstr "有三种方法可以自定义 Flower 在服务器端协调学习过程的方式："
 
 #: ../../source/how-to-use-strategies.rst:8
 msgid "Use an existing strategy, for example, :code:`FedAvg`"
-msgstr ""
+msgstr "使用现有策略，例如 :code:`FedAvg`"
 
 #: ../../source/how-to-use-strategies.rst:9
 #: ../../source/how-to-use-strategies.rst:40
 msgid "Customize an existing strategy with callback functions"
-msgstr ""
+msgstr "使用回调函数定制现有策略"
 
 #: ../../source/how-to-use-strategies.rst:10
 #: ../../source/how-to-use-strategies.rst:87
 msgid "Implement a novel strategy"
-msgstr ""
+msgstr "实施新策略"
 
 #: ../../source/how-to-use-strategies.rst:14
 msgid "Use an existing strategy"
-msgstr ""
+msgstr "使用现有策略"
 
 #: ../../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:"
-msgstr ""
+msgstr "Flower 内置了许多流行的联邦学习策略。内置策略的实例化方法如下："
 
 #: ../../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 ""
+msgstr "这会创建一个所有参数都保持默认值的策略，并将其传递给 :code:`start_server` 函数。通常建议在实例化过程中调整一些参数："
 
 #: ../../source/how-to-use-strategies.rst:42
 msgid ""
 "Existing strategies provide several ways to customize their behaviour. "
 "Callback functions allow strategies to call user-provided code during "
 "execution."
-msgstr ""
+msgstr "现有的策略提供了多种自定义行为的方法。回调函数允许策略在执行过程中调用用户提供的代码。"
 
 #: ../../source/how-to-use-strategies.rst:45
 msgid "Configuring client fit and client evaluate"
-msgstr ""
+msgstr "配置客户匹配和客户评估"
 
 #: ../../source/how-to-use-strategies.rst:47
 msgid ""
@@ -5421,10 +6866,13 @@ msgid ""
 "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 arbitraty configuration values  :code:`client.fit`"
+"return a dictionary of arbitrary configuration values  :code:`client.fit`"
 " and :code:`client.evaluate` functions during each round of federated "
 "learning."
 msgstr ""
+"服务器可以通过向 :code:`on_fit_config_fn` "
+"提供一个函数，在每一轮向客户端传递新的配置值。提供的函数将被策略调用，并且必须返回一个配置键值对的字典，该字典将被发送到客户端。在每一轮联邦学习期间，它必须返回一个任意配置值"
+" dictionary :code:`client.fit`和 :code:`client.evaluate`函数。"
 
 #: ../../source/how-to-use-strategies.rst:75
 msgid ""
@@ -5434,6 +6882,8 @@ msgid ""
 "the dictionary returned by the :code:`on_fit_config_fn` in its own "
 ":code:`client.fit()` function."
 msgstr ""
+":code:`on_fit_config_fn`可用于将任意配置值从服务器传递到客户端，并在每一轮改变这些值，例如，调整学习率。客户端将在自己的 "
+":code:`client.fit()` 函数中接收 :code:`on_fit_config_fn` 返回的字典。"
 
 #: ../../source/how-to-use-strategies.rst:78
 msgid ""
@@ -5441,16 +6891,18 @@ msgid ""
 ":code:`on_evaluate_config_fn` to customize the configuration sent to "
 ":code:`client.evaluate()`"
 msgstr ""
+"与 :code:`on_fit_config_fn` 类似，还有 :code:`on_evaluate_config_fn` 用于定制发送到 "
+":code:`client.evaluate()` 的配置"
 
 #: ../../source/how-to-use-strategies.rst:81
 msgid "Configuring server-side evaluation"
-msgstr ""
+msgstr "配置服务器端评估"
 
 #: ../../source/how-to-use-strategies.rst:83
 msgid ""
 "Server-side evaluation can be enabled by passing an evaluation function "
 "to :code:`evaluate_fn`."
-msgstr ""
+msgstr "服务器端评估可通过向 :code:`evaluate_fn` 传递评估函数来启用。"
 
 #: ../../source/how-to-use-strategies.rst:89
 msgid ""
@@ -5458,85 +6910,87 @@ msgid ""
 "the most flexibility. Read the `Implementing Strategies <how-to-"
 "implement-strategies.html>`_ guide to learn more."
 msgstr ""
+"编写完全自定义的策略涉及的内容较多，但灵活性最高。阅读 `实施策略 <how-to-implement-strategies.html>_ "
+"指南，了解更多信息。"
 
 #: ../../source/index.rst:34
 msgid "Tutorial"
-msgstr ""
+msgstr "教程"
 
 #: ../../source/index.rst:44
 msgid "Quickstart tutorials"
-msgstr ""
+msgstr "快速入门教程"
 
 #: ../../source/index.rst:75 ../../source/index.rst:79
 msgid "How-to guides"
-msgstr ""
+msgstr "操作指南"
 
-#: ../../source/index.rst:95
+#: ../../source/index.rst:97
 msgid "Legacy example guides"
-msgstr ""
+msgstr "旧版指南范例"
 
-#: ../../source/index.rst:106 ../../source/index.rst:110
+#: ../../source/index.rst:108 ../../source/index.rst:112
 msgid "Explanations"
-msgstr ""
+msgstr "说明"
 
-#: ../../source/index.rst:122
+#: None:-1
 msgid "API reference"
-msgstr ""
+msgstr "应用程序接口参考"
 
-#: ../../source/index.rst:129
+#: ../../source/index.rst:137
 msgid "Reference docs"
-msgstr ""
+msgstr "参考文档"
 
-#: ../../source/index.rst:145
+#: ../../source/index.rst:153
 msgid "Contributor tutorials"
-msgstr ""
+msgstr "贡献者教程"
 
-#: ../../source/index.rst:152
+#: ../../source/index.rst:160
 msgid "Contributor how-to guides"
-msgstr ""
+msgstr "投稿指南"
 
-#: ../../source/index.rst:164
+#: ../../source/index.rst:173
 msgid "Contributor explanations"
-msgstr ""
+msgstr "贡献者解释"
 
-#: ../../source/index.rst:170
+#: ../../source/index.rst:179
 msgid "Contributor references"
-msgstr ""
+msgstr "贡献者参考资料"
 
 #: ../../source/index.rst:-1
 msgid ""
 "Check out the documentation of the main Flower Framework enabling easy "
 "Python development for Federated Learning."
-msgstr ""
+msgstr "查看主 Flower Framework 的文档，轻松实现联邦学习的 Python 开发。"
 
 #: ../../source/index.rst:2
 msgid "Flower Framework Documentation"
-msgstr ""
+msgstr "Flower 框架文档"
 
 #: ../../source/index.rst:7
 msgid ""
-"Welcome to Flower's documentation. `Flower <https://flower.dev>`_ is a "
+"Welcome to Flower's documentation. `Flower <https://flower.ai>`_ is a "
 "friendly federated learning framework."
-msgstr ""
+msgstr "欢迎访问 Flower 文档。`Flower <https://flower.ai>`_ 是一个友好的联邦学习框架。"
 
 #: ../../source/index.rst:11
 msgid "Join the Flower Community"
-msgstr ""
+msgstr "加入 Flower 社区"
 
 #: ../../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 ""
+msgstr "Flower 社区发展迅速--我们是一个由研究人员、工程师、学生、专业人士、学者和其他爱好者组成的友好团体。"
 
 #: ../../source/index.rst:15
 msgid "Join us on Slack"
-msgstr ""
+msgstr "在 Slack 上加入我们"
 
 #: ../../source/index.rst:23
 msgid "Flower Framework"
-msgstr ""
+msgstr "Flower 框架"
 
 #: ../../source/index.rst:25
 msgid ""
@@ -5545,16 +6999,18 @@ msgid ""
 "setting. One of Flower's design goals was to make this simple. Read on to"
 " learn more."
 msgstr ""
+"该用户指南面向希望使用 Flower 将现有机器学习工作负载引入联邦环境的研究人员和开发人员。Flower "
+"的设计目标之一就是让这一切变得简单。请继续阅读，了解更多信息。"
 
 #: ../../source/index.rst:30
 msgid "Tutorials"
-msgstr ""
+msgstr "教程"
 
 #: ../../source/index.rst:32
 msgid ""
 "A learning-oriented series of federated learning tutorials, the best "
 "place to start."
-msgstr ""
+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
 msgid ""
@@ -5568,82 +7024,357 @@ msgid ""
 "<tutorial-quickstart-xgboost>` | :doc:`Android <tutorial-quickstart-"
 "android>` | :doc:`iOS <tutorial-quickstart-ios>`"
 msgstr ""
+"快速入门教程: :doc:`PyTorch <tutorial-quickstart-pytorch>` | :doc:`TensorFlow "
+"<tutorial-quickstart-tensorflow>` | :doc:`🤗 Transformers <tutorial-"
+"quickstart-huggingface>` | :doc:`JAX <tutorial-quickstart-jax>` | "
+":doc:`Pandas <tutorial-quickstart-pandas>` | :doc:`fastai <tutorial-"
+"quickstart-fastai>` | :doc:`PyTorch Lightning <tutorial-quickstart-"
+"pytorch-lightning>` | :doc:`MXNet <tutorial-quickstart-mxnet>` | :doc"
+":`scikit-learn <tutorial-quickstart-scikitlearn>` | :doc:`XGBoost "
+"<tutorial-quickstart-xgboost>` | :doc:`Android <tutorial-quickstart-"
+"android>` | :doc:`iOS <tutorial-quickstart-ios>`"
 
 #: ../../source/index.rst:64
 msgid "We also made video tutorials for PyTorch:"
-msgstr ""
+msgstr "我们还为 PyTorch 制作了视频教程："
 
 #: ../../source/index.rst:69
 msgid "And TensorFlow:"
-msgstr ""
+msgstr "还有 TensorFlow："
 
 #: ../../source/index.rst:77
 msgid ""
 "Problem-oriented how-to guides show step-by-step how to achieve a "
 "specific goal."
-msgstr ""
+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:108
+#: ../../source/index.rst:110
 msgid ""
 "Understanding-oriented concept guides explain and discuss key topics and "
 "underlying ideas behind Flower and collaborative AI."
-msgstr ""
+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:118
+#: ../../source/index.rst:120
 msgid "References"
-msgstr ""
+msgstr "参考资料"
 
-#: ../../source/index.rst:120
+#: ../../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:131:<autosummary>:1
+msgid ":py:obj:`flwr <flwr>`\\"
 msgstr ""
 
-#: ../../source/index.rst:140
-msgid "Contributor docs"
+#: ../../source/index.rst:131:<autosummary>:1 flwr:1 of
+msgid "Flower main package."
 msgstr ""
 
-#: ../../source/index.rst:142
+#: ../../source/index.rst:148
+msgid "Contributor docs"
+msgstr "贡献者文档"
+
+#: ../../source/index.rst:150
 msgid ""
 "The Flower community welcomes contributions. The following docs are "
 "intended to help along the way."
-msgstr ""
+msgstr "Flower 社区欢迎您的贡献。以下文档旨在为您提供帮助。"
 
 #: ../../source/ref-api-cli.rst:2
 msgid "Flower CLI reference"
-msgstr ""
+msgstr "Flower CLI 参考"
 
 #: ../../source/ref-api-cli.rst:7
-msgid "flower-server"
-msgstr ""
+msgid "flower-superlink"
+msgstr "flower-superlink"
 
 #: ../../source/ref-api-cli.rst:17
 msgid "flower-driver-api"
-msgstr ""
+msgstr "flower-driver-api"
 
 #: ../../source/ref-api-cli.rst:27
 msgid "flower-fleet-api"
+msgstr "flower-fleet-api"
+
+#: ../../source/ref-api/flwr.rst:2
+#, fuzzy
+msgid "flwr"
+msgstr "Flower"
+
+#: ../../source/ref-api/flwr.rst:25 ../../source/ref-api/flwr.server.rst:48
+msgid "Modules"
+msgstr ""
+
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1
+msgid ":py:obj:`flwr.client <flwr.client>`\\"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:2
-msgid "flwr (Python API reference)"
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1 flwr.client:1 of
+msgid "Flower client."
+msgstr "Flower 客户端。"
+
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1
+msgid ":py:obj:`flwr.common <flwr.common>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.rst:35:<autosummary>: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:<autosummary>:1
+msgid ":py:obj:`flwr.server <flwr.server>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1 flwr.server:1
+#: flwr.server.server.Server:1 of
+msgid "Flower server."
+msgstr "Flower 服务器。"
+
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1
+msgid ":py:obj:`flwr.simulation <flwr.simulation>`\\"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:8
+#: ../../source/ref-api/flwr.rst:35:<autosummary>:1 flwr.simulation:1 of
+#, fuzzy
+msgid "Flower simulation."
+msgstr "运行模拟"
+
+#: ../../source/ref-api/flwr.client.rst:2
 msgid "client"
+msgstr "客户端"
+
+#: ../../source/ref-api/flwr.client.rst:13
+#: ../../source/ref-api/flwr.common.rst:13
+#: ../../source/ref-api/flwr.server.driver.rst:13
+#: ../../source/ref-api/flwr.server.rst:13
+#: ../../source/ref-api/flwr.simulation.rst:13
+#, fuzzy
+msgid "Functions"
+msgstr "四种函数："
+
+#: ../../source/ref-api/flwr.client.rst:24:<autosummary>:1
+msgid ":py:obj:`run_client_app <flwr.client.run_client_app>`\\ \\(\\)"
 msgstr ""
 
-#: flwr.client:1 of
-msgid "Flower client."
+#: ../../source/ref-api/flwr.client.rst:24:<autosummary>:1
+#: flwr.client.app.run_client_app:1 of
+#, fuzzy
+msgid "Run Flower client app."
+msgstr "Flower 客户端。"
+
+#: ../../source/ref-api/flwr.client.rst:24:<autosummary>:1
+msgid ""
+":py:obj:`start_client <flwr.client.start_client>`\\ \\(\\*\\, "
+"server\\_address\\[\\, client\\_fn\\, ...\\]\\)"
 msgstr ""
 
+#: ../../source/ref-api/flwr.client.rst:24:<autosummary>: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:<autosummary>:1
+msgid ""
+":py:obj:`start_numpy_client <flwr.client.start_numpy_client>`\\ \\(\\*\\,"
+" server\\_address\\, client\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:24:<autosummary>: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.common.rst:31
+#: ../../source/ref-api/flwr.server.driver.rst:24
+#: ../../source/ref-api/flwr.server.rst:28
+#: ../../source/ref-api/flwr.server.strategy.rst:17
+msgid "Classes"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
+msgid ":py:obj:`Client <flwr.client.Client>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>: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:<autosummary>:1
+msgid ""
+":py:obj:`ClientApp <flwr.client.ClientApp>`\\ \\(client\\_fn\\[\\, "
+"mods\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
+#: flwr.client.client_app.ClientApp:1 of
+#, fuzzy
+msgid "Flower ClientApp."
+msgstr "Flower 客户端。"
+
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>:1
+msgid ":py:obj:`NumPyClient <flwr.client.NumPyClient>`\\ \\(\\)"
 msgstr ""
 
+#: ../../source/ref-api/flwr.client.rst:33:<autosummary>: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。"
+
+#: 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`"
+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.ClientMessage.rst:15
+#: ../../source/ref-api/flwr.common.DisconnectRes.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.Parameters.rst:15
+#: ../../source/ref-api/flwr.common.ReconnectIns.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.History.rst:15
+#: ../../source/ref-api/flwr.server.Server.rst:15
+#: ../../source/ref-api/flwr.server.ServerConfig.rst:15
+#: ../../source/ref-api/flwr.server.SimpleClientManager.rst:15
+#: ../../source/ref-api/flwr.server.driver.Driver.rst:15
+#: ../../source/ref-api/flwr.server.driver.GrpcDriver.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.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
+msgid "Methods"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`evaluate <flwr.client.Client.evaluate>`\\ \\(ins\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>: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 "使用本地数据集评估所提供的参数。"
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`fit <flwr.client.Client.fit>`\\ \\(ins\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: flwr.client.client.Client.fit:1 of
+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:<autosummary>:1
+msgid ":py:obj:`get_context <flwr.client.Client.get_context>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+#: flwr.client.client.Client.get_context:1
+#: flwr.client.numpy_client.NumPyClient.get_context:1 of
+#, fuzzy
+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:<autosummary>:1
+msgid ":py:obj:`get_parameters <flwr.client.Client.get_parameters>`\\ \\(ins\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>: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 "返回当前本地模型参数。"
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`get_properties <flwr.client.Client.get_properties>`\\ \\(ins\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: flwr.client.client.Client.get_properties:1 of
+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:<autosummary>:1
+msgid ":py:obj:`set_context <flwr.client.Client.set_context>`\\ \\(context\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>: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.client.Client.rst:44:<autosummary>:1
+msgid ":py:obj:`to_client <flwr.client.Client.to_client>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.client.Client.rst:44:<autosummary>:1
+#: flwr.client.client.Client.to_client:1 of
+msgid "Return client (itself)."
+msgstr "返回客户端（本身）。"
+
+#: ../../source/ref-api/flwr.client.Client.rst:46
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:46
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:25
+#: ../../source/ref-api/flwr.common.Code.rst:19
+#: ../../source/ref-api/flwr.common.DisconnectRes.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.Parameters.rst:25
+#: ../../source/ref-api/flwr.common.ReconnectIns.rst:25
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:25
+#: ../../source/ref-api/flwr.common.Status.rst:25
+#: ../../source/ref-api/flwr.server.ServerConfig.rst:25
+msgid "Attributes"
+msgstr ""
+
+#: flwr.client.client.Client.evaluate:1:<autosummary>:1 of
+msgid ":py:obj:`context <flwr.client.Client.context>`\\"
+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
@@ -5652,20 +7383,23 @@ msgstr ""
 #: flwr.client.numpy_client.NumPyClient.fit
 #: flwr.client.numpy_client.NumPyClient.get_parameters
 #: flwr.client.numpy_client.NumPyClient.get_properties
-#: flwr.server.app.start_server flwr.server.strategy.bulyan.Bulyan.__init__
+#: 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.app.start_driver flwr.server.driver.driver.Driver
+#: flwr.server.strategy.bulyan.Bulyan
 #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate
 #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__
-#: flwr.server.strategy.fedadam.FedAdam.__init__
-#: flwr.server.strategy.fedavg.FedAvg.__init__
-#: flwr.server.strategy.fedavgm.FedAvgM.__init__
-#: flwr.server.strategy.fedopt.FedOpt.__init__
-#: flwr.server.strategy.fedprox.FedProx.__init__
-#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__
-#: flwr.server.strategy.fedyogi.FedYogi.__init__
-#: flwr.server.strategy.krum.Krum.__init__
-#: flwr.server.strategy.qfedavg.QFedAvg.__init__
+#: 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
@@ -5674,14 +7408,14 @@ msgstr ""
 #: flwr.server.strategy.strategy.Strategy.initialize_parameters
 #: flwr.simulation.app.start_simulation of
 msgid "Parameters"
-msgstr ""
+msgstr "参数"
 
 #: 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 ""
+msgstr "评估指令包含从服务器接收的（全局）模型参数，以及用于定制本地评估流程的配置值字典。"
 
 #: flwr.client.client.Client.evaluate flwr.client.client.Client.fit
 #: flwr.client.client.Client.get_parameters
@@ -5691,6 +7425,12 @@ msgstr ""
 #: flwr.client.numpy_client.NumPyClient.get_parameters
 #: flwr.client.numpy_client.NumPyClient.get_properties
 #: 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.app.start_driver
 #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate
 #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit
 #: flwr.server.strategy.strategy.Strategy.aggregate_evaluate
@@ -5701,13 +7441,13 @@ msgstr ""
 #: flwr.server.strategy.strategy.Strategy.initialize_parameters
 #: flwr.simulation.app.start_simulation of
 msgid "Returns"
-msgstr ""
+msgstr "返回"
 
 #: 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 ""
+msgstr "评估结果包含本地数据集上的损失值和其他详细信息，如用于评估的本地数据的数量。"
 
 #: flwr.client.client.Client.evaluate flwr.client.client.Client.fit
 #: flwr.client.client.Client.get_parameters
@@ -5715,6 +7455,12 @@ msgstr ""
 #: flwr.client.numpy_client.NumPyClient.get_parameters
 #: flwr.client.numpy_client.NumPyClient.get_properties
 #: 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.app.start_driver
 #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate
 #: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit
 #: flwr.server.strategy.strategy.Strategy.aggregate_evaluate
@@ -5725,130 +7471,142 @@ msgstr ""
 #: flwr.server.strategy.strategy.Strategy.initialize_parameters
 #: flwr.simulation.app.start_simulation of
 msgid "Return type"
-msgstr ""
-
-#: flwr.client.client.Client.fit:1 of
-msgid "Refine the provided parameters using the locally held dataset."
-msgstr ""
+msgstr "返回类型"
 
 #: 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 ""
+msgstr "训练指令，包含从服务器接收的（全局）模型参数，以及用于定制本地训练过程的配置值字典。"
 
 #: 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 ""
-
-#: 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 "训练结果包含更新的参数和其他详细信息，如用于训练的本地训练示例的数量。"
 
 #: flwr.client.client.Client.get_parameters:3 of
 msgid ""
 "The get parameters instructions received from the server containing a "
 "dictionary of configuration values."
-msgstr ""
+msgstr "从服务器接收的获取参数指令包含配置值字典。"
 
 #: flwr.client.client.Client.get_parameters:7 of
 msgid "The current local model parameters."
-msgstr ""
-
-#: flwr.client.client.Client.get_properties:1 of
-msgid "Return set of client's properties."
-msgstr ""
+msgstr "当前的本地模型参数。"
 
 #: 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 "从服务器接收的获取属性指令包含配置值字典。"
 
 #: flwr.client.client.Client.get_properties:7 of
 msgid "The current client properties."
-msgstr ""
-
-#: flwr.client.client.Client.to_client:1 of
-msgid "Return client (itself)."
-msgstr ""
+msgstr "当前客户端属性。"
+
+#: ../../source/ref-api/flwr.client.ClientApp.rst:2
+#, fuzzy
+msgid "ClientApp"
+msgstr "客户端"
+
+#: flwr.client.client_app.ClientApp: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.app.ServerConfig:1
+#: flwr.server.driver.driver.Driver:1
+#: flwr.server.driver.grpc_driver.GrpcDriver:1 flwr.server.history.History:1
+#: flwr.server.server.Server:1 of
+msgid "Bases: :py:class:`object`"
+msgstr ""
+
+#: flwr.client.app.start_client:33 flwr.client.app.start_numpy_client:36
+#: flwr.client.client_app.ClientApp:4 flwr.server.app.start_server:41
+#: flwr.server.driver.app.start_driver:30 of
+msgid "Examples"
+msgstr "实例"
 
-#: ../../source/ref-api-flwr.rst:24
-msgid "start_client"
+#: 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 ""
 
-#: flwr.client.app.start_client:1 of
-msgid "Start a Flower client node which connects to a Flower server."
+#: 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 ""
 
-#: flwr.client.app.start_client:3 flwr.client.app.start_numpy_client:3 of
+#: flwr.client.client_app.ClientApp:21 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\"`."
+"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 ""
 
-#: flwr.client.app.start_client:7 of
-msgid "..."
-msgstr ""
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:2
+msgid "NumPyClient"
+msgstr "NumPyClient"
 
-#: flwr.client.app.start_client:9 of
-msgid "A callable that instantiates a Client. (default: None)"
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+msgid ""
+":py:obj:`evaluate <flwr.client.NumPyClient.evaluate>`\\ \\(parameters\\, "
+"config\\)"
 msgstr ""
 
-#: flwr.client.app.start_client:11 of
-msgid ""
-"An implementation of the abstract base class `flwr.client.Client` "
-"(default: None)"
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+msgid ":py:obj:`fit <flwr.client.NumPyClient.fit>`\\ \\(parameters\\, config\\)"
 msgstr ""
 
-#: flwr.client.app.start_client:14 flwr.client.app.start_numpy_client:9 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.client.NumPyClient.rst:44:<autosummary>:1
+#: flwr.client.numpy_client.NumPyClient.fit:1 of
+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:<autosummary>:1
+msgid ":py:obj:`get_context <flwr.client.NumPyClient.get_context>`\\ \\(\\)"
 msgstr ""
 
-#: flwr.client.app.start_client:21 flwr.client.app.start_numpy_client:16 of
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
 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."
+":py:obj:`get_parameters <flwr.client.NumPyClient.get_parameters>`\\ "
+"\\(config\\)"
 msgstr ""
 
-#: flwr.client.app.start_client:25 flwr.client.app.start_numpy_client:20 of
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
 msgid ""
-"Configure the transport layer. Allowed values: - 'grpc-bidi': gRPC, "
-"bidirectional streaming - 'grpc-rere': gRPC, request-response "
-"(experimental) - 'rest': HTTP (experimental)"
+":py:obj:`get_properties <flwr.client.NumPyClient.get_properties>`\\ "
+"\\(config\\)"
 msgstr ""
 
-#: flwr.client.app.start_client:32 flwr.client.app.start_numpy_client:27
-#: flwr.server.app.start_server:41 of
-msgid "Examples"
-msgstr ""
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+#: flwr.client.numpy_client.NumPyClient.get_properties:1 of
+msgid "Return a client's set of properties."
+msgstr "返回客户端的属性集。"
 
-#: flwr.client.app.start_client:33 of
-msgid "Starting a gRPC client with an insecure server connection:"
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+msgid ""
+":py:obj:`set_context <flwr.client.NumPyClient.set_context>`\\ "
+"\\(context\\)"
 msgstr ""
 
-#: flwr.client.app.start_client:43 flwr.client.app.start_numpy_client:35 of
-msgid "Starting an SSL-enabled gRPC client:"
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+msgid ":py:obj:`to_client <flwr.client.NumPyClient.to_client>`\\ \\(\\)"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:32
-msgid "NumPyClient"
-msgstr ""
+#: ../../source/ref-api/flwr.client.NumPyClient.rst:44:<autosummary>:1
+#: flwr.client.numpy_client.NumPyClient.to_client:1 of
+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:1 of
-msgid "Abstract base class for Flower clients using NumPy."
+#: flwr.client.numpy_client.NumPyClient.evaluate:1:<autosummary>:1 of
+msgid ":py:obj:`context <flwr.client.NumPyClient.context>`\\"
 msgstr ""
 
 #: flwr.client.numpy_client.NumPyClient.evaluate:3
@@ -5859,7 +7617,7 @@ 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 "当前（全局）模型参数。"
 
 #: flwr.client.numpy_client.NumPyClient.evaluate:5 of
 msgid ""
@@ -5867,7 +7625,7 @@ msgid ""
 "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 ""
+msgstr "允许服务器影响客户端评估的配置参数。它可用于将任意值从服务器传送到客户端，例如，影响用于评估的示例数量。"
 
 #: flwr.client.numpy_client.NumPyClient.evaluate:11 of
 msgid ""
@@ -5877,16 +7635,19 @@ msgid ""
 "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*) -- 模型在本地数据集上的评估损失值。**num_examples** (*int*) -- "
+"用于评估的示例数量。**metrics** (*Dict[str, Scalar]*) -- 将任意字符串键映射到 "
+"bool、bytes、float、int 或 str 类型值的字典。它可用于将任意值传回服务器。"
 
 #: flwr.client.numpy_client.NumPyClient.evaluate:11 of
 msgid ""
 "**loss** (*float*) -- The evaluation loss of the model on the local "
 "dataset."
-msgstr ""
+msgstr "**loss** (*float*) -- 模型在本地数据集上的评估损失值。"
 
 #: flwr.client.numpy_client.NumPyClient.evaluate:12 of
 msgid "**num_examples** (*int*) -- The number of examples used for evaluation."
-msgstr ""
+msgstr "**num_examples** (*int*) -- 用于评估的示例数量。"
 
 #: flwr.client.numpy_client.NumPyClient.evaluate:13
 #: flwr.client.numpy_client.NumPyClient.fit:13 of
@@ -5895,6 +7656,8 @@ msgid ""
 "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]*) -- 将任意字符串键映射到 bool、bytes、float、int 或 "
+"str 类型值的字典。它可用于将任意值传回服务器。"
 
 #: flwr.client.numpy_client.NumPyClient.evaluate:19 of
 msgid ""
@@ -5902,10 +7665,8 @@ msgid ""
 "format (int, float, float, Dict[str, Scalar]) have been deprecated and "
 "removed since Flower 0.19."
 msgstr ""
-
-#: flwr.client.numpy_client.NumPyClient.fit:1 of
-msgid "Train the provided parameters using the locally held dataset."
-msgstr ""
+"自 Flower 0.19 起，之前的返回类型格式（int、float、float）和扩展格式（int、float、float、Dict[str,"
+" Scalar]）已被弃用和移除。"
 
 #: flwr.client.numpy_client.NumPyClient.fit:5 of
 msgid ""
@@ -5913,7 +7674,7 @@ msgid ""
 "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 ""
+msgstr "允许服务器影响客户端训练的配置参数。它可用于将任意值从服务器传送到客户端，例如设置（本地）训练遍历数。"
 
 #: flwr.client.numpy_client.NumPyClient.fit:11 of
 msgid ""
@@ -5923,36 +7684,35 @@ msgid ""
 "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*) -- 本地更新的模型参数。**num_examples** (*int*) -- "
+"用于训练的示例数量。**metrics** (*Dict[str, Scalar]*) -- 将任意字符串键映射到 "
+"bool、bytes、float、int 或 str 类型值的字典。它可用于将任意值传回服务器。"
 
 #: flwr.client.numpy_client.NumPyClient.fit:11 of
 msgid "**parameters** (*NDArrays*) -- The locally updated model parameters."
-msgstr ""
+msgstr "**parameters** (*NDArrays*) -- 本地更新的模型参数。"
 
 #: flwr.client.numpy_client.NumPyClient.fit:12 of
 msgid "**num_examples** (*int*) -- The number of examples used for training."
-msgstr ""
+msgstr "**num_examples** (*int*) -- 用于训练的数据数量。"
 
 #: 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 ""
+msgstr "服务器请求的配置参数。这可以用来告诉客户端需要哪些参数以及一些标量属性。"
 
 #: flwr.client.numpy_client.NumPyClient.get_parameters:8 of
 msgid "**parameters** -- The local model parameters as a list of NumPy ndarrays."
-msgstr ""
-
-#: flwr.client.numpy_client.NumPyClient.get_properties:1 of
-msgid "Return a client's set of properties."
-msgstr ""
+msgstr "**parameters** -- NumPy ndarrays 的本地模型参数列表。"
 
 #: 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 ""
+msgstr "服务器请求的配置参数。这可以用来告诉客户端需要哪些属性以及一些标量属性。"
 
 #: flwr.client.numpy_client.NumPyClient.get_properties:8 of
 msgid ""
@@ -5960,1275 +7720,4600 @@ msgid ""
 " type bool, bytes, float, int, or str. It can be used to communicate "
 "arbitrary property values back to the server."
 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。"
 
-#: flwr.client.numpy_client.NumPyClient.to_client:1 of
-msgid "Convert to object to Client type and return it."
-msgstr ""
-
-#: ../../source/ref-api-flwr.rst:41
-msgid "start_numpy_client"
+#: ../../source/ref-api/flwr.client.run_client_app.rst:2
+msgid "run\\_client\\_app"
 msgstr ""
 
-#: flwr.client.app.start_numpy_client:1 of
-msgid "Start a Flower NumPyClient which connects to a gRPC server."
-msgstr ""
-
-#: flwr.client.app.start_numpy_client:7 of
-msgid "An implementation of the abstract base class `flwr.client.NumPyClient`."
-msgstr ""
-
-#: flwr.client.app.start_numpy_client:28 of
-msgid "Starting a client with an insecure server connection:"
-msgstr ""
+#: ../../source/ref-api/flwr.client.start_client.rst:2
+#, fuzzy
+msgid "start\\_client"
+msgstr "启动客户端"
 
-#: ../../source/ref-api-flwr.rst:49
-msgid "start_simulation"
+#: 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 ""
+"服务器的 IPv4 或 IPv6 地址：如果 Flower 服务器在同一台机器上运行，端口为 "
+"8080，则`server_address`应为`\"[::]:8080\"`。"
 
-#: flwr.simulation.app.start_simulation:1 of
-msgid "Start a Ray-based Flower simulation server."
-msgstr ""
+#: flwr.client.app.start_client:7 of
+msgid "A callable that instantiates a Client. (default: None)"
+msgstr "用于实例化客户端的可调用程序。(默认值：无）"
 
-#: flwr.simulation.app.start_simulation:3 of
+#: flwr.client.app.start_client:9 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 ""
+"An implementation of the abstract base class `flwr.client.Client` "
+"(default: None)"
+msgstr "抽象基类 `flwr.client.Client` 的实现（默认值：无）"
 
-#: flwr.simulation.app.start_simulation:13 of
+#: flwr.client.app.start_client:12 flwr.client.app.start_numpy_client:15 of
 msgid ""
-"The total number of clients in this simulation. This must be set if "
-"`clients_ids` is not set and vice-versa."
+"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 ""
+"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.simulation.app.start_simulation:16 of
+#: flwr.client.app.start_client:19 flwr.client.app.start_numpy_client:22
+#: flwr.server.driver.app.start_driver:21 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 ""
+"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 "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.simulation.app.start_simulation:20 of
+#: flwr.client.app.start_client:23 flwr.client.app.start_numpy_client:26 of
 msgid ""
-"\"num_gpus\": 0.0}` 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."
+"Starts an insecure gRPC connection when True. Enables HTTPS connection "
+"when False, using system certificates if `root_certificates` is None."
 msgstr ""
 
-#: flwr.simulation.app.start_simulation:25 of
+#: flwr.client.app.start_client:26 flwr.client.app.start_numpy_client:29 of
 msgid ""
-"An implementation of the abstract base class `flwr.server.Server`. If no "
-"instance is provided, then `start_server` will create one."
+"Configure the transport layer. Allowed values: - 'grpc-bidi': gRPC, "
+"bidirectional streaming - 'grpc-rere': gRPC, request-response "
+"(experimental) - 'rest': HTTP (experimental)"
 msgstr ""
+"配置传输层：允许的值包括 - 'grpc-bidi': gRPC，双向流 - 'grpc-rere': gRPC，请求-响应（实验性） - "
+"'rest': HTTP（实验性）"
+
+#: flwr.client.app.start_client:34 flwr.client.app.start_numpy_client:37 of
+msgid "Starting a gRPC client with an insecure server connection:"
+msgstr "使用不安全的服务器连接启动 gRPC 客户端："
+
+#: flwr.client.app.start_client:41 flwr.client.app.start_numpy_client:44 of
+#, fuzzy
+msgid "Starting an SSL-enabled gRPC client using system certificates:"
+msgstr "启动支持 SSL 的 gRPC 客户端："
+
+#: flwr.client.app.start_client:52 flwr.client.app.start_numpy_client:52 of
+#, fuzzy
+msgid "Starting an SSL-enabled gRPC client using provided certificates:"
+msgstr "启动支持 SSL 的 gRPC 客户端："
+
+#: ../../source/ref-api/flwr.client.start_numpy_client.rst:2
+#, fuzzy
+msgid "start\\_numpy\\_client"
+msgstr "start_numpy_client"
 
-#: flwr.server.app.start_server:9 flwr.simulation.app.start_simulation:28 of
+#: flwr.client.app.start_numpy_client:5 of
 msgid ""
-"Currently supported values are `num_rounds` (int, default: 1) and "
-"`round_timeout` in seconds (float, default: None)."
+"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 ""
 
-#: 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`."
-msgstr ""
+#: flwr.client.app.start_numpy_client:13 of
+msgid "An implementation of the abstract base class `flwr.client.NumPyClient`."
+msgstr "抽象基类 `flwr.client.NumPyClient` 的实现。"
 
-#: 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`."
+#: ../../source/ref-api/flwr.common.rst:2
+msgid "common"
+msgstr "常见"
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+msgid ":py:obj:`bytes_to_ndarray <flwr.common.bytes_to_ndarray>`\\ \\(tensor\\)"
 msgstr ""
 
-#: flwr.simulation.app.start_simulation:39 of
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.parameter.bytes_to_ndarray:1 of
+msgid "Deserialize NumPy ndarray from bytes."
+msgstr "从字节反序列化 NumPy ndarray。"
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
 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:`configure <flwr.common.configure>`\\ \\(identifier\\[\\, "
+"filename\\, host\\]\\)"
 msgstr ""
 
-#: flwr.simulation.app.start_simulation:39 of
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.logger.configure:1 of
+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:29:<autosummary>:1
 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:"
+":py:obj:`event <flwr.common.event>`\\ \\(event\\_type\\[\\, "
+"event\\_details\\]\\)"
 msgstr ""
 
-#: flwr.simulation.app.start_simulation:43 of
-msgid "{ \"ignore_reinit_error\": True, \"include_dashboard\": False }"
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.telemetry.event:1 of
+msgid "Submit create_event to ThreadPoolExecutor to avoid blocking."
 msgstr ""
 
-#: flwr.simulation.app.start_simulation:45 of
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
 msgid ""
-"An empty dictionary can be used (ray_init_args={}) to prevent any "
-"arguments from being passed to ray.init."
+":py:obj:`log <flwr.common.log>`\\ \\(level\\, msg\\, \\*args\\, "
+"\\*\\*kwargs\\)"
 msgstr ""
 
-#: flwr.simulation.app.start_simulation:48 of
-msgid ""
-"Set to True to prevent `ray.shutdown()` in case "
-"`ray.is_initialized()=True`."
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1 logging.Logger.log:1
+#: of
+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:29:<autosummary>:1
+msgid ":py:obj:`ndarray_to_bytes <flwr.common.ndarray_to_bytes>`\\ \\(ndarray\\)"
 msgstr ""
 
-#: flwr.simulation.app.start_simulation:50 of
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.parameter.ndarray_to_bytes:1 of
+msgid "Serialize NumPy ndarray to bytes."
+msgstr "将 NumPy ndarray 序列化为字节。"
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+msgid ":py:obj:`now <flwr.common.now>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.date.now:1 of
+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:29:<autosummary>:1
 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)."
+":py:obj:`ndarrays_to_parameters <flwr.common.ndarrays_to_parameters>`\\ "
+"\\(ndarrays\\)"
 msgstr ""
 
-#: flwr.simulation.app.start_simulation:54 of
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.parameter.ndarrays_to_parameters:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.ndarrays_to_parameters:1
+#: of
+msgid "Convert NumPy ndarrays to parameters object."
+msgstr "将 NumPy ndarrays 转换为参数对象。"
+
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
 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:`parameters_to_ndarrays <flwr.common.parameters_to_ndarrays>`\\ "
+"\\(parameters\\)"
 msgstr ""
 
-#: flwr.simulation.app.start_simulation:57 of
+#: ../../source/ref-api/flwr.common.rst:29:<autosummary>:1
+#: flwr.common.parameter.parameters_to_ndarrays:1 of
+msgid "Convert parameters object to NumPy ndarrays."
+msgstr "将参数对象转换为 NumPy ndarrays。"
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
 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:`ClientMessage <flwr.common.ClientMessage>`\\ "
+"\\(\\[get\\_properties\\_res\\, ...\\]\\)"
 msgstr ""
 
-#: flwr.simulation.app.start_simulation:66 of
-msgid "**hist** -- Object containing metrics from training."
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.ClientMessage:1 of
+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:52:<autosummary>:1
+msgid ":py:obj:`Code <flwr.common.Code>`\\ \\(value\\)"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:57
-msgid "server"
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.Code:1 of
+msgid "Client status codes."
+msgstr "客户端状态代码。"
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`DisconnectRes <flwr.common.DisconnectRes>`\\ \\(reason\\)"
 msgstr ""
 
-#: flwr.server:1 of
-msgid "Flower server."
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.DisconnectRes:1 of
+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:52:<autosummary>:1
+msgid ""
+":py:obj:`EvaluateIns <flwr.common.EvaluateIns>`\\ \\(parameters\\, "
+"config\\)"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:65
-msgid "server.start_server"
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.EvaluateIns:1 of
+msgid "Evaluate instructions for a client."
+msgstr "评估客户端的指示。"
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ""
+":py:obj:`EvaluateRes <flwr.common.EvaluateRes>`\\ \\(status\\, loss\\, "
+"num\\_examples\\, metrics\\)"
 msgstr ""
 
-#: flwr.server.app.start_server:1 of
-msgid "Start a Flower server using the gRPC transport layer."
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.EvaluateRes:1 of
+msgid "Evaluate response from a client."
+msgstr "评估客户端的反应。"
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`EventType <flwr.common.EventType>`\\ \\(value\\)"
 msgstr ""
 
-#: flwr.server.app.start_server:3 of
-msgid "The IPv4 or IPv6 address of the server. Defaults to `\"[::]:8080\"`."
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.telemetry.EventType:1 of
+msgid "Types of telemetry events."
+msgstr "遥测事件类型。"
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`FitIns <flwr.common.FitIns>`\\ \\(parameters\\, config\\)"
 msgstr ""
 
-#: flwr.server.app.start_server:5 of
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.FitIns:1 of
+msgid "Fit instructions for a client."
+msgstr "为客户提供安装说明。"
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>: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:`FitRes <flwr.common.FitRes>`\\ \\(status\\, parameters\\, "
+"num\\_examples\\, metrics\\)"
 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`."
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.FitRes:1 of
+msgid "Fit response from a client."
+msgstr "来自客户端的合适回复。"
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`GetParametersIns <flwr.common.GetParametersIns>`\\ \\(config\\)"
 msgstr ""
 
-#: flwr.server.app.start_server:16 of
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.GetParametersIns:1 of
+msgid "Parameters request for a client."
+msgstr "客户端的参数请求。"
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>: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:`GetParametersRes <flwr.common.GetParametersRes>`\\ \\(status\\, "
+"parameters\\)"
 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."
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.GetParametersRes:1 of
+msgid "Response when asked to return parameters."
+msgstr "要求返回参数时的响应。"
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`GetPropertiesIns <flwr.common.GetPropertiesIns>`\\ \\(config\\)"
 msgstr ""
 
-#: flwr.server.app.start_server:28 of
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.GetPropertiesIns:1 of
+msgid "Properties request for a client."
+msgstr "客户端的属性请求。"
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
 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:`GetPropertiesRes <flwr.common.GetPropertiesRes>`\\ \\(status\\, "
+"properties\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.GetPropertiesRes:1 of
+msgid "Properties response from a client."
+msgstr "来自客户端的属性响应。"
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`NDArray <flwr.common.NDArray>`\\"
 msgstr ""
 
-#: flwr.server.app.start_server:28 of
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
 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:"
+"alias of :py:class:`~numpy.ndarray`\\ [:py:obj:`~typing.Any`, "
+":py:class:`~numpy.dtype`\\ [:py:obj:`~typing.Any`]]"
 msgstr ""
 
-#: flwr.server.app.start_server:32 of
-msgid "CA certificate."
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ""
+":py:obj:`Parameters <flwr.common.Parameters>`\\ \\(tensors\\, "
+"tensor\\_type\\)"
 msgstr ""
 
-#: flwr.server.app.start_server:33 of
-msgid "server certificate."
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.Parameters:1 of
+msgid "Model parameters."
+msgstr "模型参数。"
+
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+msgid ":py:obj:`ReconnectIns <flwr.common.ReconnectIns>`\\ \\(seconds\\)"
 msgstr ""
 
-#: flwr.server.app.start_server:34 of
-msgid "server private key."
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.ReconnectIns:1 of
+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:52:<autosummary>:1
+msgid ""
+":py:obj:`ServerMessage <flwr.common.ServerMessage>`\\ "
+"\\(\\[get\\_properties\\_ins\\, ...\\]\\)"
 msgstr ""
 
-#: flwr.server.app.start_server:37 of
-msgid "**hist** -- Object containing training and evaluation metrics."
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.ServerMessage:1 of
+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:52:<autosummary>:1
+msgid ":py:obj:`Status <flwr.common.Status>`\\ \\(code\\, message\\)"
 msgstr ""
 
-#: flwr.server.app.start_server:42 of
-msgid "Starting an insecure server:"
+#: ../../source/ref-api/flwr.common.rst:52:<autosummary>:1
+#: flwr.common.typing.Status:1 of
+msgid "Client status."
+msgstr "客户端状态。"
+
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:2
+#, fuzzy
+msgid "ClientMessage"
+msgstr "客户端"
+
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:31:<autosummary>:1
+msgid ":py:obj:`evaluate_res <flwr.common.ClientMessage.evaluate_res>`\\"
 msgstr ""
 
-#: flwr.server.app.start_server:46 of
-msgid "Starting an SSL-enabled server:"
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:31:<autosummary>:1
+msgid ":py:obj:`fit_res <flwr.common.ClientMessage.fit_res>`\\"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:73
-msgid "server.strategy"
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:31:<autosummary>:1
+msgid ""
+":py:obj:`get_parameters_res "
+"<flwr.common.ClientMessage.get_parameters_res>`\\"
 msgstr ""
 
-#: flwr.server.strategy:1 of
-msgid "Contains the strategy abstraction and different implementations."
+#: ../../source/ref-api/flwr.common.ClientMessage.rst:31:<autosummary>:1
+msgid ""
+":py:obj:`get_properties_res "
+"<flwr.common.ClientMessage.get_properties_res>`\\"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:81
-msgid "server.strategy.Strategy"
+#: ../../source/ref-api/flwr.common.Code.rst:2
+msgid "Code"
 msgstr ""
 
-#: flwr.server.strategy.strategy.Strategy:1 of
-msgid "Abstract base class for server strategy implementations."
+#: flwr.common.typing.Code:1 of
+msgid "Bases: :py:class:`~enum.Enum`"
 msgstr ""
 
-#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1 of
-msgid "Aggregate evaluation results."
+#: ../../source/ref-api/flwr.common.Code.rst:26:<autosummary>:1
+msgid ":py:obj:`OK <flwr.common.Code.OK>`\\"
 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."
+#: ../../source/ref-api/flwr.common.Code.rst:26:<autosummary>:1
+msgid ""
+":py:obj:`GET_PROPERTIES_NOT_IMPLEMENTED "
+"<flwr.common.Code.GET_PROPERTIES_NOT_IMPLEMENTED>`\\"
 msgstr ""
 
-#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:5 of
+#: ../../source/ref-api/flwr.common.Code.rst:26:<autosummary>:1
 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:`GET_PARAMETERS_NOT_IMPLEMENTED "
+"<flwr.common.Code.GET_PARAMETERS_NOT_IMPLEMENTED>`\\"
 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."
+#: ../../source/ref-api/flwr.common.Code.rst:26:<autosummary>:1
+msgid ":py:obj:`FIT_NOT_IMPLEMENTED <flwr.common.Code.FIT_NOT_IMPLEMENTED>`\\"
 msgstr ""
 
-#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:16 of
+#: ../../source/ref-api/flwr.common.Code.rst:26:<autosummary>:1
 msgid ""
-"**aggregation_result** -- The aggregated evaluation result. Aggregation "
-"typically uses some variant of a weighted average."
+":py:obj:`EVALUATE_NOT_IMPLEMENTED "
+"<flwr.common.Code.EVALUATE_NOT_IMPLEMENTED>`\\"
 msgstr ""
 
-#: flwr.server.strategy.strategy.Strategy.aggregate_fit:1 of
-msgid "Aggregate training results."
+#: ../../source/ref-api/flwr.common.DisconnectRes.rst:2
+msgid "DisconnectRes"
 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`."
+#: ../../source/ref-api/flwr.common.DisconnectRes.rst:28:<autosummary>:1
+msgid ":py:obj:`reason <flwr.common.DisconnectRes.reason>`\\"
 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.common.EvaluateIns.rst:2
+#, fuzzy
+msgid "EvaluateIns"
+msgstr "说明"
+
+#: ../../source/ref-api/flwr.common.EvaluateIns.rst:29:<autosummary>:1
+msgid ":py:obj:`parameters <flwr.common.EvaluateIns.parameters>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedavg.FedAvg.configure_evaluate:1
-#: flwr.server.strategy.qfedavg.QFedAvg.configure_evaluate:1
-#: flwr.server.strategy.strategy.Strategy.configure_evaluate:1 of
-msgid "Configure the next round of evaluation."
+#: ../../source/ref-api/flwr.common.EvaluateIns.rst:29:<autosummary>:1
+msgid ":py:obj:`config <flwr.common.EvaluateIns.config>`\\"
 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."
+#: ../../source/ref-api/flwr.common.EvaluateRes.rst:2
+msgid "EvaluateRes"
 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."
+#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31:<autosummary>:1
+msgid ":py:obj:`status <flwr.common.EvaluateRes.status>`\\"
 msgstr ""
 
-#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.configure_fit:1
-#: flwr.server.strategy.fedavg.FedAvg.configure_fit:1
-#: flwr.server.strategy.fedprox.FedProx.configure_fit:1
-#: flwr.server.strategy.qfedavg.QFedAvg.configure_fit:1
-#: flwr.server.strategy.strategy.Strategy.configure_fit:1 of
-msgid "Configure the next round of training."
+#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31:<autosummary>:1
+msgid ":py:obj:`loss <flwr.common.EvaluateRes.loss>`\\"
 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."
+#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31:<autosummary>:1
+msgid ":py:obj:`num_examples <flwr.common.EvaluateRes.num_examples>`\\"
 msgstr ""
 
-#: flwr.server.strategy.strategy.Strategy.evaluate:1 of
-msgid "Evaluate the current model parameters."
+#: ../../source/ref-api/flwr.common.EvaluateRes.rst:31:<autosummary>:1
+msgid ":py:obj:`metrics <flwr.common.EvaluateRes.metrics>`\\"
 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."
+#: ../../source/ref-api/flwr.common.EventType.rst:2
+#, fuzzy
+msgid "EventType"
+msgstr "返回类型"
+
+#: flwr.common.telemetry.EventType:1 of
+msgid "Bases: :py:class:`str`, :py:class:`~enum.Enum`"
 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)."
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`PING <flwr.common.EventType.PING>`\\"
 msgstr ""
 
-#: flwr.server.strategy.strategy.Strategy.initialize_parameters:1 of
-msgid "Initialize the (global) model parameters."
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_CLIENT_ENTER <flwr.common.EventType.START_CLIENT_ENTER>`\\"
 msgstr ""
 
-#: flwr.server.strategy.strategy.Strategy.initialize_parameters:6 of
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_CLIENT_LEAVE <flwr.common.EventType.START_CLIENT_LEAVE>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_SERVER_ENTER <flwr.common.EventType.START_SERVER_ENTER>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_SERVER_LEAVE <flwr.common.EventType.START_SERVER_LEAVE>`\\"
+msgstr ""
+
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
 msgid ""
-"**parameters** -- If parameters are returned, then the server will treat "
-"these as the initial global model parameters."
+":py:obj:`RUN_DRIVER_API_ENTER "
+"<flwr.common.EventType.RUN_DRIVER_API_ENTER>`\\"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:90
-msgid "server.strategy.FedAvg"
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_DRIVER_API_LEAVE "
+"<flwr.common.EventType.RUN_DRIVER_API_LEAVE>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedavg.FedAvg:1 of
-msgid "Configurable FedAvg strategy implementation."
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_FLEET_API_ENTER "
+"<flwr.common.EventType.RUN_FLEET_API_ENTER>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:1
-#: flwr.server.strategy.fedavg.FedAvg.__init__:1
-#: flwr.server.strategy.qfedavg.QFedAvg.__init__:1 of
-msgid "Federated Averaging strategy."
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_FLEET_API_LEAVE "
+"<flwr.common.EventType.RUN_FLEET_API_LEAVE>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:3
-#: flwr.server.strategy.fedavg.FedAvg.__init__:3
-#: flwr.server.strategy.qfedavg.QFedAvg.__init__:3 of
-msgid "Implementation based on https://arxiv.org/abs/1602.05629"
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_SUPERLINK_ENTER "
+"<flwr.common.EventType.RUN_SUPERLINK_ENTER>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:5
-#: flwr.server.strategy.fedavg.FedAvg.__init__:5
-#: flwr.server.strategy.fedprox.FedProx.__init__:37
-#: flwr.server.strategy.qfedavg.QFedAvg.__init__:5 of
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
 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:`RUN_SUPERLINK_LEAVE "
+"<flwr.common.EventType.RUN_SUPERLINK_LEAVE>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:9
-#: flwr.server.strategy.fedavg.FedAvg.__init__:9
-#: flwr.server.strategy.fedprox.FedProx.__init__:41
-#: flwr.server.strategy.qfedavg.QFedAvg.__init__:9 of
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
 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."
+":py:obj:`START_SIMULATION_ENTER "
+"<flwr.common.EventType.START_SIMULATION_ENTER>`\\"
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan.__init__:9
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:13
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:9
-#: flwr.server.strategy.fedadam.FedAdam.__init__:9
-#: flwr.server.strategy.fedavg.FedAvg.__init__:13
-#: flwr.server.strategy.fedavgm.FedAvgM.__init__:9
-#: flwr.server.strategy.fedopt.FedOpt.__init__:9
-#: flwr.server.strategy.fedprox.FedProx.__init__:45
-#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:7
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:9
-#: flwr.server.strategy.krum.Krum.__init__:7
-#: flwr.server.strategy.qfedavg.QFedAvg.__init__:13 of
-msgid "Minimum number of clients used during training. Defaults to 2."
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`START_SIMULATION_LEAVE "
+"<flwr.common.EventType.START_SIMULATION_LEAVE>`\\"
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan.__init__:11
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:15
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:11
-#: flwr.server.strategy.fedadam.FedAdam.__init__:11
-#: flwr.server.strategy.fedavg.FedAvg.__init__:15
-#: flwr.server.strategy.fedavgm.FedAvgM.__init__:11
-#: flwr.server.strategy.fedopt.FedOpt.__init__:11
-#: flwr.server.strategy.fedprox.FedProx.__init__:47
-#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:9
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:11
-#: flwr.server.strategy.krum.Krum.__init__:9
-#: flwr.server.strategy.qfedavg.QFedAvg.__init__:15 of
-msgid "Minimum number of clients used during validation. Defaults to 2."
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`DRIVER_CONNECT <flwr.common.EventType.DRIVER_CONNECT>`\\"
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan.__init__:13
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:17
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:13
-#: flwr.server.strategy.fedadam.FedAdam.__init__:13
-#: flwr.server.strategy.fedavg.FedAvg.__init__:17
-#: flwr.server.strategy.fedavgm.FedAvgM.__init__:13
-#: flwr.server.strategy.fedopt.FedOpt.__init__:13
-#: flwr.server.strategy.fedprox.FedProx.__init__:49
-#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:11
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:13
-#: flwr.server.strategy.krum.Krum.__init__:11
-#: flwr.server.strategy.qfedavg.QFedAvg.__init__:17 of
-msgid "Minimum number of total clients in the system. Defaults to 2."
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`DRIVER_DISCONNECT <flwr.common.EventType.DRIVER_DISCONNECT>`\\"
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan.__init__:17
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:19
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:15
-#: flwr.server.strategy.fedadam.FedAdam.__init__:15
-#: flwr.server.strategy.fedavg.FedAvg.__init__:19
-#: flwr.server.strategy.fedavgm.FedAvgM.__init__:15
-#: flwr.server.strategy.fedopt.FedOpt.__init__:15
-#: flwr.server.strategy.fedprox.FedProx.__init__:51
-#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:13
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:15
-#: flwr.server.strategy.krum.Krum.__init__:18
-#: flwr.server.strategy.qfedavg.QFedAvg.__init__:19 of
-msgid "Optional function used for validation. Defaults to None."
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_DRIVER_ENTER <flwr.common.EventType.START_DRIVER_ENTER>`\\"
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan.__init__:19
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:21
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:17
-#: flwr.server.strategy.fedadam.FedAdam.__init__:17
-#: flwr.server.strategy.fedavg.FedAvg.__init__:21
-#: flwr.server.strategy.fedavgm.FedAvgM.__init__:17
-#: flwr.server.strategy.fedopt.FedOpt.__init__:17
-#: flwr.server.strategy.fedprox.FedProx.__init__:53
-#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:15
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:17
-#: flwr.server.strategy.krum.Krum.__init__:20
-#: flwr.server.strategy.qfedavg.QFedAvg.__init__:21 of
-msgid "Function used to configure training. Defaults to None."
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ":py:obj:`START_DRIVER_LEAVE <flwr.common.EventType.START_DRIVER_LEAVE>`\\"
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan.__init__:21
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:23
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:19
-#: flwr.server.strategy.fedadam.FedAdam.__init__:19
-#: flwr.server.strategy.fedavg.FedAvg.__init__:23
-#: flwr.server.strategy.fedavgm.FedAvgM.__init__:19
-#: flwr.server.strategy.fedopt.FedOpt.__init__:19
-#: flwr.server.strategy.fedprox.FedProx.__init__:55
-#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:17
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:19
-#: flwr.server.strategy.krum.Krum.__init__:22
-#: flwr.server.strategy.qfedavg.QFedAvg.__init__:23 of
-msgid "Function used to configure validation. Defaults to None."
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_CLIENT_APP_ENTER "
+"<flwr.common.EventType.RUN_CLIENT_APP_ENTER>`\\"
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan.__init__:23
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:25
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:25
-#: flwr.server.strategy.fedadam.FedAdam.__init__:21
-#: flwr.server.strategy.fedavg.FedAvg.__init__:25
-#: flwr.server.strategy.fedavgm.FedAvgM.__init__:21
-#: flwr.server.strategy.fedopt.FedOpt.__init__:21
-#: flwr.server.strategy.fedprox.FedProx.__init__:57
-#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:19
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:21
-#: flwr.server.strategy.krum.Krum.__init__:24
-#: flwr.server.strategy.qfedavg.QFedAvg.__init__:25 of
-msgid "Whether or not accept rounds containing failures. Defaults to True."
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_CLIENT_APP_LEAVE "
+"<flwr.common.EventType.RUN_CLIENT_APP_LEAVE>`\\"
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan.__init__:25
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:27
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:27
-#: flwr.server.strategy.fedadam.FedAdam.__init__:23
-#: flwr.server.strategy.fedavg.FedAvg.__init__:27
-#: flwr.server.strategy.fedavgm.FedAvgM.__init__:23
-#: flwr.server.strategy.fedopt.FedOpt.__init__:23
-#: flwr.server.strategy.fedprox.FedProx.__init__:59
-#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:21
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:23
-#: flwr.server.strategy.krum.Krum.__init__:26
-#: flwr.server.strategy.qfedavg.QFedAvg.__init__:27 of
-msgid "Initial global model parameters."
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_SERVER_APP_ENTER "
+"<flwr.common.EventType.RUN_SERVER_APP_ENTER>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:29
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.__init__:31
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:21
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:23
-#: flwr.server.strategy.fedadam.FedAdam.__init__:25
-#: flwr.server.strategy.fedadam.FedAdam.__init__:27
-#: flwr.server.strategy.fedavg.FedAvg.__init__:29
-#: flwr.server.strategy.fedavg.FedAvg.__init__:31
-#: flwr.server.strategy.fedopt.FedOpt.__init__:25
-#: flwr.server.strategy.fedopt.FedOpt.__init__:27
-#: flwr.server.strategy.fedprox.FedProx.__init__:61
-#: flwr.server.strategy.fedprox.FedProx.__init__:63
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:25
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:27
-#: flwr.server.strategy.qfedavg.QFedAvg.__init__:29
-#: flwr.server.strategy.qfedavg.QFedAvg.__init__:31 of
-msgid "Metrics aggregation function, optional."
+#: ../../source/ref-api/flwr.common.EventType.rst:42:<autosummary>:1
+msgid ""
+":py:obj:`RUN_SERVER_APP_LEAVE "
+"<flwr.common.EventType.RUN_SERVER_APP_LEAVE>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1
-#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1
-#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1 of
-msgid "Aggregate evaluation losses using weighted average."
+#: ../../source/ref-api/flwr.common.FitIns.rst:2
+msgid "FitIns"
 msgstr ""
 
-#: 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_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_fit:1 of
-msgid "Aggregate fit results using weighted average."
+#: ../../source/ref-api/flwr.common.FitIns.rst:29:<autosummary>:1
+msgid ":py:obj:`parameters <flwr.common.FitIns.parameters>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedavg.FedAvg.evaluate:1
-#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.evaluate:1 of
-msgid "Evaluate model parameters using an evaluation function."
+#: ../../source/ref-api/flwr.common.FitIns.rst:29:<autosummary>:1
+msgid ":py:obj:`config <flwr.common.FitIns.config>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedavg.FedAvg.initialize_parameters:1
-#: flwr.server.strategy.fedavgm.FedAvgM.initialize_parameters:1 of
-msgid "Initialize global model parameters."
+#: ../../source/ref-api/flwr.common.FitRes.rst:2
+msgid "FitRes"
 msgstr ""
 
-#: flwr.server.strategy.fedavg.FedAvg.num_evaluation_clients:1
-#: flwr.server.strategy.qfedavg.QFedAvg.num_evaluation_clients:1 of
-msgid "Use a fraction of available clients for evaluation."
+#: ../../source/ref-api/flwr.common.FitRes.rst:31:<autosummary>:1
+msgid ":py:obj:`status <flwr.common.FitRes.status>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedavg.FedAvg.num_fit_clients:1
-#: flwr.server.strategy.qfedavg.QFedAvg.num_fit_clients:1 of
-msgid "Return the sample size and the required number of available clients."
+#: ../../source/ref-api/flwr.common.FitRes.rst:31:<autosummary>:1
+msgid ":py:obj:`parameters <flwr.common.FitRes.parameters>`\\"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:101
-msgid "server.strategy.FedAvgM"
+#: ../../source/ref-api/flwr.common.FitRes.rst:31:<autosummary>:1
+msgid ":py:obj:`num_examples <flwr.common.FitRes.num_examples>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedavgm.FedAvgM:1
-#: flwr.server.strategy.fedmedian.FedMedian:1 of
-msgid "Configurable FedAvg with Momentum strategy implementation."
+#: ../../source/ref-api/flwr.common.FitRes.rst:31:<autosummary>:1
+msgid ":py:obj:`metrics <flwr.common.FitRes.metrics>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedavgm.FedAvgM.__init__:1 of
-msgid "Federated Averaging with Momentum strategy."
+#: ../../source/ref-api/flwr.common.GetParametersIns.rst:2
+#, fuzzy
+msgid "GetParametersIns"
+msgstr "参数"
+
+#: ../../source/ref-api/flwr.common.GetParametersIns.rst:28:<autosummary>:1
+msgid ":py:obj:`config <flwr.common.GetParametersIns.config>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedavgm.FedAvgM.__init__:3 of
-msgid "Implementation based on https://arxiv.org/pdf/1909.06335.pdf"
+#: ../../source/ref-api/flwr.common.GetParametersRes.rst:2
+#, fuzzy
+msgid "GetParametersRes"
+msgstr "参数"
+
+#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29:<autosummary>:1
+msgid ":py:obj:`status <flwr.common.GetParametersRes.status>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedavgm.FedAvgM.__init__:5
-#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:3
-#: flwr.server.strategy.krum.Krum.__init__:3 of
-msgid "Fraction of clients used during training. Defaults to 0.1."
+#: ../../source/ref-api/flwr.common.GetParametersRes.rst:29:<autosummary>:1
+msgid ":py:obj:`parameters <flwr.common.GetParametersRes.parameters>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedavgm.FedAvgM.__init__:7
-#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:5
-#: flwr.server.strategy.krum.Krum.__init__:5 of
-msgid "Fraction of clients used during validation. Defaults to 0.1."
+#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:2
+msgid "GetPropertiesIns"
 msgstr ""
 
-#: flwr.server.strategy.fedavgm.FedAvgM.__init__:25 of
-msgid ""
-"Server-side learning rate used in server-side optimization. Defaults to "
-"1.0."
+#: ../../source/ref-api/flwr.common.GetPropertiesIns.rst:28:<autosummary>:1
+msgid ":py:obj:`config <flwr.common.GetPropertiesIns.config>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedavgm.FedAvgM.__init__:28 of
-msgid "Server-side momentum factor used for FedAvgM. Defaults to 0.0."
+#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:2
+msgid "GetPropertiesRes"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:112
-msgid "server.strategy.FedMedian"
+#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29:<autosummary>:1
+msgid ":py:obj:`status <flwr.common.GetPropertiesRes.status>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedmedian.FedMedian.aggregate_fit:1 of
-msgid "Aggregate fit results using median."
+#: ../../source/ref-api/flwr.common.GetPropertiesRes.rst:29:<autosummary>:1
+msgid ":py:obj:`properties <flwr.common.GetPropertiesRes.properties>`\\"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:122
-msgid "server.strategy.QFedAvg"
+#: ../../source/ref-api/flwr.common.NDArray.rst:2
+msgid "NDArray"
 msgstr ""
 
-#: flwr.server.strategy.qfedavg.QFedAvg:1 of
-msgid "Configurable QFedAvg strategy implementation."
+#: ../../source/ref-api/flwr.common.Parameters.rst:29:<autosummary>:1
+msgid ":py:obj:`tensors <flwr.common.Parameters.tensors>`\\"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:133
-msgid "server.strategy.FaultTolerantFedAvg"
+#: ../../source/ref-api/flwr.common.Parameters.rst:29:<autosummary>:1
+msgid ":py:obj:`tensor_type <flwr.common.Parameters.tensor_type>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg:1 of
-msgid "Configurable fault-tolerant FedAvg strategy implementation."
+#: ../../source/ref-api/flwr.common.ReconnectIns.rst:2
+#, fuzzy
+msgid "ReconnectIns"
+msgstr "启用 SSL 连接"
+
+#: ../../source/ref-api/flwr.common.ReconnectIns.rst:28:<autosummary>:1
+msgid ":py:obj:`seconds <flwr.common.ReconnectIns.seconds>`\\"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:144
-msgid "server.strategy.FedOpt"
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:2
+#, fuzzy
+msgid "ServerMessage"
+msgstr "服务器端"
+
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:31:<autosummary>:1
+msgid ":py:obj:`evaluate_ins <flwr.common.ServerMessage.evaluate_ins>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedopt.FedOpt:1 of
-msgid "Configurable FedAdagrad strategy implementation."
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:31:<autosummary>:1
+msgid ":py:obj:`fit_ins <flwr.common.ServerMessage.fit_ins>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedopt.FedOpt.__init__:1 of
-msgid "Federated Optim strategy interface."
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:31:<autosummary>:1
+msgid ""
+":py:obj:`get_parameters_ins "
+"<flwr.common.ServerMessage.get_parameters_ins>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:3
-#: flwr.server.strategy.fedadam.FedAdam.__init__:3
-#: flwr.server.strategy.fedopt.FedOpt.__init__:3
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:3 of
-msgid "Implementation based on https://arxiv.org/abs/2003.00295v5"
+#: ../../source/ref-api/flwr.common.ServerMessage.rst:31:<autosummary>:1
+msgid ""
+":py:obj:`get_properties_ins "
+"<flwr.common.ServerMessage.get_properties_ins>`\\"
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan.__init__:5
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:5
-#: flwr.server.strategy.fedadam.FedAdam.__init__:5
-#: flwr.server.strategy.fedopt.FedOpt.__init__:5
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:5 of
-msgid "Fraction of clients used during training. Defaults to 1.0."
+#: ../../source/ref-api/flwr.common.Status.rst:2
+#, fuzzy
+msgid "Status"
+msgstr "客户端状态。"
+
+#: ../../source/ref-api/flwr.common.Status.rst:29:<autosummary>:1
+msgid ":py:obj:`code <flwr.common.Status.code>`\\"
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan.__init__:7
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:7
-#: flwr.server.strategy.fedadam.FedAdam.__init__:7
-#: flwr.server.strategy.fedopt.FedOpt.__init__:7
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:7 of
-msgid "Fraction of clients used during validation. Defaults to 1.0."
+#: ../../source/ref-api/flwr.common.Status.rst:29:<autosummary>:1
+msgid ":py:obj:`message <flwr.common.Status.message>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:29
-#: flwr.server.strategy.fedadam.FedAdam.__init__:29
-#: flwr.server.strategy.fedopt.FedOpt.__init__:29
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:29 of
-msgid "Server-side learning rate. Defaults to 1e-1."
+#: ../../source/ref-api/flwr.common.bytes_to_ndarray.rst:2
+msgid "bytes\\_to\\_ndarray"
 msgstr ""
 
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:31
-#: flwr.server.strategy.fedadam.FedAdam.__init__:31
-#: flwr.server.strategy.fedopt.FedOpt.__init__:31
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:31 of
-msgid "Client-side learning rate. Defaults to 1e-1."
+#: ../../source/ref-api/flwr.common.configure.rst:2
+#, fuzzy
+msgid "configure"
+msgstr "配置日志记录"
+
+#: ../../source/ref-api/flwr.common.event.rst:2
+msgid "event"
 msgstr ""
 
-#: flwr.server.strategy.fedopt.FedOpt.__init__:33 of
-msgid "Momentum parameter. Defaults to 0.0."
+#: ../../source/ref-api/flwr.common.log.rst:2
+msgid "log"
 msgstr ""
 
-#: flwr.server.strategy.fedopt.FedOpt.__init__:35 of
-msgid "Second moment parameter. Defaults to 0.0."
+#: logging.Logger.log:3 of
+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。"
+
+#: 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)"
+
+#: ../../source/ref-api/flwr.common.ndarray_to_bytes.rst:2
+msgid "ndarray\\_to\\_bytes"
 msgstr ""
 
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:33
-#: flwr.server.strategy.fedadam.FedAdam.__init__:37
-#: flwr.server.strategy.fedopt.FedOpt.__init__:37
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:37 of
-msgid "Controls the algorithm's degree of adaptability. Defaults to 1e-9."
+#: ../../source/ref-api/flwr.common.ndarrays_to_parameters.rst:2
+msgid "ndarrays\\_to\\_parameters"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:155
-msgid "server.strategy.FedProx"
+#: ../../source/ref-api/flwr.common.now.rst:2
+msgid "now"
 msgstr ""
 
-#: flwr.server.strategy.fedprox.FedProx:1 of
-msgid "Configurable FedProx strategy implementation."
+#: ../../source/ref-api/flwr.common.parameters_to_ndarrays.rst:2
+msgid "parameters\\_to\\_ndarrays"
 msgstr ""
 
-#: flwr.server.strategy.fedprox.FedProx.__init__:1 of
-msgid "Federated Optimization strategy."
+#: ../../source/ref-api/flwr.server.rst:2
+msgid "server"
+msgstr "服务器"
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+msgid ":py:obj:`run_driver_api <flwr.server.run_driver_api>`\\ \\(\\)"
 msgstr ""
 
-#: flwr.server.strategy.fedprox.FedProx.__init__:3 of
-msgid "Implementation based on https://arxiv.org/abs/1812.06127"
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+#: flwr.server.app.run_driver_api:1 of
+#, fuzzy
+msgid "Run Flower server (Driver API)."
+msgstr "flower-driver-api"
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+msgid ":py:obj:`run_fleet_api <flwr.server.run_fleet_api>`\\ \\(\\)"
 msgstr ""
 
-#: flwr.server.strategy.fedprox.FedProx.__init__: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:"
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+#: flwr.server.app.run_fleet_api:1 of
+#, fuzzy
+msgid "Run Flower server (Fleet API)."
+msgstr "Flower 服务器。"
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+msgid ":py:obj:`run_server_app <flwr.server.run_server_app>`\\ \\(\\)"
 msgstr ""
 
-#: flwr.server.strategy.fedprox.FedProx.__init__:9 of
-msgid ""
-"\\\\frac{\\\\mu}{2} || w - w^t ||^2\n"
-"\n"
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+#: flwr.server.app.run_server_app:1 of
+#, fuzzy
+msgid "Run Flower server app."
+msgstr "Flower 服务器。"
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+msgid ":py:obj:`run_superlink <flwr.server.run_superlink>`\\ \\(\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
+#: flwr.server.app.run_superlink:1 of
+msgid "Run Flower server (Driver API and Fleet API)."
 msgstr ""
 
-#: flwr.server.strategy.fedprox.FedProx.__init__:12 of
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>:1
 msgid ""
-"Where $w^t$ are the global parameters and $w$ are the local weights the "
-"function will be optimized with."
+":py:obj:`start_server <flwr.server.start_server>`\\ \\(\\*\\[\\, "
+"server\\_address\\, server\\, ...\\]\\)"
 msgstr ""
 
-#: flwr.server.strategy.fedprox.FedProx.__init__:15 of
-msgid "In PyTorch, for example, the loss would go from:"
+#: ../../source/ref-api/flwr.server.rst:26:<autosummary>: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。"
+
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+msgid ":py:obj:`ClientManager <flwr.server.ClientManager>`\\ \\(\\)"
 msgstr ""
 
-#: flwr.server.strategy.fedprox.FedProx.__init__:21 of
-msgid "To:"
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+#: flwr.server.client_manager.ClientManager:1 of
+#, fuzzy
+msgid "Abstract base class for managing Flower clients."
+msgstr "Flower 客户端的抽象基类。"
+
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+msgid ":py:obj:`History <flwr.server.History>`\\ \\(\\)"
 msgstr ""
 
-#: flwr.server.strategy.fedprox.FedProx.__init__:30 of
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>: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.rst:37:<autosummary>:1
 msgid ""
-"With `global_params` being a copy of the parameters before the training "
-"takes place."
+":py:obj:`Server <flwr.server.Server>`\\ \\(\\*\\, client\\_manager\\[\\, "
+"strategy\\]\\)"
 msgstr ""
 
-#: flwr.server.strategy.fedprox.FedProx.__init__:65 of
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+#, 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:`ServerConfig <flwr.server.ServerConfig>`\\ \\(\\[num\\_rounds\\,"
+" round\\_timeout\\]\\)"
 msgstr ""
+"Flower 1.0: ``start_server(..., "
+"config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), "
+"...)``"
 
-#: flwr.server.strategy.fedprox.FedProx.configure_fit:3 of
-msgid "Sends the proximal factor mu to the clients"
-msgstr ""
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+#: flwr.server.app.ServerConfig:1 of
+#, fuzzy
+msgid "Flower server config."
+msgstr "Flower 服务器。"
 
-#: ../../source/ref-api-flwr.rst:166
-msgid "server.strategy.FedAdagrad"
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+msgid ":py:obj:`SimpleClientManager <flwr.server.SimpleClientManager>`\\ \\(\\)"
 msgstr ""
 
-#: flwr.server.strategy.fedadagrad.FedAdagrad:1 of
-msgid "FedAdagrad strategy - Adaptive Federated Optimization using Adagrad."
+#: ../../source/ref-api/flwr.server.rst:37:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager:1 of
+#, fuzzy
+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.rst:56:<autosummary>:1
+msgid ":py:obj:`flwr.server.driver <flwr.server.driver>`\\"
 msgstr ""
 
-#: flwr.server.strategy.fedadagrad.FedAdagrad:3
-#: flwr.server.strategy.fedadam.FedAdam:3
-#: flwr.server.strategy.fedyogi.FedYogi:5 of
-msgid "Paper: https://arxiv.org/abs/2003.00295"
+#: ../../source/ref-api/flwr.server.rst:56:<autosummary>:1 flwr.server.driver:1
+#: of
+#, fuzzy
+msgid "Flower driver SDK."
+msgstr "Flower 服务器。"
+
+#: ../../source/ref-api/flwr.server.rst:56:<autosummary>:1
+#, fuzzy
+msgid ":py:obj:`flwr.server.strategy <flwr.server.strategy>`\\"
+msgstr "server.strategy.Strategy"
+
+#: ../../source/ref-api/flwr.server.rst:56:<autosummary>: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.ClientManager.rst:2
+#, fuzzy
+msgid "ClientManager"
+msgstr "客户端"
+
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
+msgid ":py:obj:`all <flwr.server.ClientManager.all>`\\ \\(\\)"
 msgstr ""
 
-#: flwr.server.strategy.fedadagrad.FedAdagrad.__init__:1
-#: flwr.server.strategy.fedadam.FedAdam.__init__:1 of
-msgid "Federated learning strategy using Adagrad on server-side."
+#: flwr.server.client_manager.ClientManager.all:1
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.all:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
+msgid "Return all available clients."
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:177
-msgid "server.strategy.FedAdam"
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
+msgid ":py:obj:`num_available <flwr.server.ClientManager.num_available>`\\ \\(\\)"
 msgstr ""
 
-#: flwr.server.strategy.fedadam.FedAdam:1 of
-msgid "FedAdam - Adaptive Federated Optimization using Adam."
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.ClientManager.num_available:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>: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。"
+
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
+msgid ":py:obj:`register <flwr.server.ClientManager.register>`\\ \\(client\\)"
 msgstr ""
 
-#: flwr.server.strategy.fedadam.FedAdam.__init__:33
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:33 of
-msgid "Momentum parameter. Defaults to 0.9."
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.ClientManager.register:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.register:1 of
+msgid "Register Flower ClientProxy instance."
 msgstr ""
 
-#: flwr.server.strategy.fedadam.FedAdam.__init__:35
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:35 of
-msgid "Second moment parameter. Defaults to 0.99."
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
+msgid ""
+":py:obj:`sample <flwr.server.ClientManager.sample>`\\ "
+"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:188
-msgid "server.strategy.FedYogi"
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.ClientManager.sample:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.sample:1 of
+msgid "Sample a number of Flower ClientProxy instances."
 msgstr ""
 
-#: flwr.server.strategy.fedyogi.FedYogi:1 of
-msgid "FedYogi [Reddi et al., 2020] strategy."
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
+msgid ":py:obj:`unregister <flwr.server.ClientManager.unregister>`\\ \\(client\\)"
 msgstr ""
 
-#: flwr.server.strategy.fedyogi.FedYogi:3 of
-msgid "Adaptive Federated Optimization using Yogi."
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.ClientManager.unregister:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.unregister:1 of
+msgid "Unregister Flower ClientProxy instance."
 msgstr ""
 
-#: flwr.server.strategy.fedyogi.FedYogi.__init__:1 of
-msgid "Federated learning strategy using Yogi on server-side."
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1 of
+msgid ""
+":py:obj:`wait_for <flwr.server.ClientManager.wait_for>`\\ "
+"\\(num\\_clients\\, timeout\\)"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:199
-msgid "server.strategy.FedTrimmedAvg"
+#: flwr.server.client_manager.ClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.ClientManager.wait_for:1
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1
+#: flwr.server.client_manager.SimpleClientManager.wait_for:1 of
+msgid "Wait until at least `num_clients` are available."
 msgstr ""
 
-#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg:1
-#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:1 of
-msgid "Federated Averaging with Trimmed Mean [Dong Yin, et al., 2021]."
+#: 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.fedtrimmedavg.FedTrimmedAvg:3 of
-msgid "Paper: https://arxiv.org/abs/1803.01498"
+#: 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 ""
 
-#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.__init__:23 of
-msgid "Fraction to cut off of both tails of the distribution. Defaults to 0.2."
+#: flwr.server.client_manager.ClientManager.unregister:3
+#: flwr.server.client_manager.SimpleClientManager.unregister:3 of
+msgid "This method is idempotent."
 msgstr ""
 
-#: flwr.server.strategy.fedtrimmedavg.FedTrimmedAvg.aggregate_fit:1 of
-msgid "Aggregate fit results using trimmed average."
+#: ../../source/ref-api/flwr.server.History.rst:2
+msgid "History"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:210
-msgid "server.strategy.Krum"
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1 of
+msgid ""
+":py:obj:`add_loss_centralized "
+"<flwr.server.History.add_loss_centralized>`\\ \\(server\\_round\\, "
+"loss\\)"
 msgstr ""
 
-#: flwr.server.strategy.krum.Krum:1 of
-msgid "Configurable Krum strategy implementation."
+#: flwr.server.history.History.add_loss_centralized:1
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1 of
+#, fuzzy
+msgid "Add one loss entry (from centralized evaluation)."
+msgstr "集中评估"
+
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1 of
+msgid ""
+":py:obj:`add_loss_distributed "
+"<flwr.server.History.add_loss_distributed>`\\ \\(server\\_round\\, "
+"loss\\)"
 msgstr ""
 
-#: flwr.server.strategy.krum.Krum.__init__:1 of
-msgid "Krum strategy."
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1
+#: flwr.server.history.History.add_loss_distributed:1 of
+msgid "Add one loss entry (from distributed evaluation)."
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan.__init__:15
-#: flwr.server.strategy.krum.Krum.__init__:13 of
-msgid "Number of malicious clients in the system. Defaults to 0."
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1 of
+msgid ""
+":py:obj:`add_metrics_centralized "
+"<flwr.server.History.add_metrics_centralized>`\\ \\(server\\_round\\, "
+"metrics\\)"
 msgstr ""
 
-#: flwr.server.strategy.krum.Krum.__init__:15 of
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>: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.history.History.add_loss_centralized:1:<autosummary>:1 of
 msgid ""
-"Number of clients to keep before averaging (MultiKrum). Defaults to 0, in"
-" that case classical Krum is applied."
+":py:obj:`add_metrics_distributed "
+"<flwr.server.History.add_metrics_distributed>`\\ \\(server\\_round\\, "
+"metrics\\)"
 msgstr ""
 
-#: flwr.server.strategy.krum.Krum.aggregate_fit:1 of
-msgid "Aggregate fit results using Krum."
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>: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.history.History.add_loss_centralized:1:<autosummary>:1 of
+msgid ""
+":py:obj:`add_metrics_distributed_fit "
+"<flwr.server.History.add_metrics_distributed_fit>`\\ \\(server\\_round\\,"
+" ...\\)"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:220
-msgid "server.strategy.Bulyan"
+#: flwr.server.history.History.add_loss_centralized:1:<autosummary>:1
+#: flwr.server.history.History.add_metrics_distributed_fit:1 of
+msgid "Add metrics entries (from distributed fit)."
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan:1 of
-msgid "Bulyan strategy implementation."
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
+msgid ":py:obj:`client_manager <flwr.server.Server.client_manager>`\\ \\(\\)"
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan.__init__:1 of
-msgid "Bulyan strategy."
+#: flwr.server.server.Server.client_manager:1
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
+#, fuzzy
+msgid "Return ClientManager."
+msgstr "返回客户端（本身）。"
+
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
+msgid ""
+":py:obj:`disconnect_all_clients "
+"<flwr.server.Server.disconnect_all_clients>`\\ \\(timeout\\)"
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan.__init__:3 of
-msgid "Implementation based on https://arxiv.org/abs/1802.07927."
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1
+#: flwr.server.server.Server.disconnect_all_clients:1 of
+msgid "Send shutdown signal to all clients."
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan.__init__:27 of
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
 msgid ""
-"Byzantine resilient aggregation rule that is used as the first step of "
-"the Bulyan (e.g., Krum)"
+":py:obj:`evaluate_round <flwr.server.Server.evaluate_round>`\\ "
+"\\(server\\_round\\, timeout\\)"
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan.__init__:29 of
-msgid "arguments to the first_aggregation rule"
+#: flwr.server.server.Server.client_manager:1:<autosummary>: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.server.Server.client_manager:1:<autosummary>:1 of
+msgid ":py:obj:`fit <flwr.server.Server.fit>`\\ \\(num\\_rounds\\, timeout\\)"
 msgstr ""
 
-#: flwr.server.strategy.bulyan.Bulyan.aggregate_fit:1 of
-msgid "Aggregate fit results using Bulyan."
+#: flwr.server.server.Server.client_manager:1:<autosummary>: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:<autosummary>:1 of
+msgid ""
+":py:obj:`fit_round <flwr.server.Server.fit_round>`\\ \\(server\\_round\\,"
+" timeout\\)"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:231
-msgid "server.strategy.FedXgbNnAvg"
+#: flwr.server.server.Server.client_manager:1:<autosummary>: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.server.Server.client_manager:1:<autosummary>:1 of
+msgid ""
+":py:obj:`set_max_workers <flwr.server.Server.set_max_workers>`\\ "
+"\\(max\\_workers\\)"
 msgstr ""
 
-#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg:1 of
-msgid "Configurable FedXgbNnAvg strategy implementation."
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1
+#: flwr.server.server.Server.set_max_workers:1 of
+msgid "Set the max_workers used by ThreadPoolExecutor."
 msgstr ""
 
-#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.__init__:1 of
-msgid "Federated XGBoost [Ma et al., 2023] strategy."
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1 of
+msgid ":py:obj:`set_strategy <flwr.server.Server.set_strategy>`\\ \\(strategy\\)"
 msgstr ""
 
-#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.__init__:3 of
-msgid "Implementation based on https://arxiv.org/abs/2304.07537."
+#: flwr.server.server.Server.client_manager:1:<autosummary>:1
+#: flwr.server.server.Server.set_strategy:1 of
+#, fuzzy
+msgid "Replace server strategy."
+msgstr "server.strategy"
+
+#: ../../source/ref-api/flwr.server.ServerConfig.rst:2
+#, fuzzy
+msgid "ServerConfig"
+msgstr "服务器"
+
+#: flwr.server.app.ServerConfig:3 of
+msgid ""
+"All attributes have default values which allows users to configure just "
+"the ones they care about."
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:242
-msgid "server.strategy.DPFedAvgAdaptive"
+#: ../../source/ref-api/flwr.server.ServerConfig.rst:29:<autosummary>:1
+msgid ":py:obj:`num_rounds <flwr.server.ServerConfig.num_rounds>`\\"
 msgstr ""
 
-#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive:1 of
-msgid "Wrapper for configuring a Strategy for DP with Adaptive Clipping."
+#: ../../source/ref-api/flwr.server.ServerConfig.rst:29:<autosummary>:1
+msgid ":py:obj:`round_timeout <flwr.server.ServerConfig.round_timeout>`\\"
 msgstr ""
 
-#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.aggregate_fit:1 of
-msgid "Aggregate training results as in DPFedAvgFixed and update clip norms."
+#: ../../source/ref-api/flwr.server.SimpleClientManager.rst:2
+msgid "SimpleClientManager"
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:253
-msgid "server.strategy.DPFedAvgFixed"
+#: flwr.server.client_manager.SimpleClientManager:1 of
+msgid "Bases: :py:class:`~flwr.server.client_manager.ClientManager`"
 msgstr ""
 
-#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed:1 of
-msgid "Wrapper for configuring a Strategy for DP with Fixed Clipping."
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
+msgid ":py:obj:`all <flwr.server.SimpleClientManager.all>`\\ \\(\\)"
 msgstr ""
 
-#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1 of
-msgid "Aggregate evaluation losses using the given strategy."
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_available <flwr.server.SimpleClientManager.num_available>`\\"
+" \\(\\)"
 msgstr ""
 
-#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_fit:1 of
-msgid "Aggregate training results using unweighted aggregation."
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
+msgid ""
+":py:obj:`register <flwr.server.SimpleClientManager.register>`\\ "
+"\\(client\\)"
 msgstr ""
 
-#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_evaluate:1 of
-msgid "Configure the next round of evaluation using the specified strategy."
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
+msgid ""
+":py:obj:`sample <flwr.server.SimpleClientManager.sample>`\\ "
+"\\(num\\_clients\\[\\, min\\_num\\_clients\\, criterion\\]\\)"
 msgstr ""
 
-#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:1 of
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
 msgid ""
-"Configure the next round of training incorporating Differential Privacy "
-"(DP)."
+":py:obj:`unregister <flwr.server.SimpleClientManager.unregister>`\\ "
+"\\(client\\)"
 msgstr ""
 
-#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:3 of
+#: flwr.server.client_manager.SimpleClientManager.all:1:<autosummary>:1 of
 msgid ""
-"Configuration of the next training round includes information related to "
-"DP, such as clip norm and noise stddev."
+":py:obj:`wait_for <flwr.server.SimpleClientManager.wait_for>`\\ "
+"\\(num\\_clients\\[\\, timeout\\]\\)"
 msgstr ""
 
-#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.evaluate:1 of
-msgid "Evaluate model parameters using an evaluation function from the strategy."
+#: 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 ""
 
-#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.initialize_parameters:1 of
-msgid "Initialize global model parameters using given strategy."
+#: flwr.server.client_manager.SimpleClientManager.wait_for:6 of
+msgid "The number of clients to wait for."
 msgstr ""
 
-#: ../../source/ref-api-flwr.rst:261
-msgid "common"
+#: flwr.server.client_manager.SimpleClientManager.wait_for:8 of
+msgid "The time in seconds to wait for, defaults to 86400 (24h)."
 msgstr ""
 
-#: flwr.common:1 of
-msgid "Common components shared between server and client."
+#: flwr.server.client_manager.SimpleClientManager.wait_for:11 of
+msgid "**success**"
 msgstr ""
 
-#: flwr.common.typing.ClientMessage:1 of
-msgid "ClientMessage is a container used to hold one result message."
+#: ../../source/ref-api/flwr.server.driver.rst:2
+#, fuzzy
+msgid "driver"
+msgstr "服务器"
+
+#: ../../source/ref-api/flwr.server.driver.rst:22:<autosummary>:1
+msgid ""
+":py:obj:`start_driver <flwr.server.driver.start_driver>`\\ \\(\\*\\[\\, "
+"server\\_address\\, server\\, ...\\]\\)"
 msgstr ""
 
-#: flwr.common.typing.Code:1 of
-msgid "Client status codes."
+#: ../../source/ref-api/flwr.server.driver.rst:22:<autosummary>:1
+#: flwr.server.driver.app.start_driver:1 of
+#, fuzzy
+msgid "Start a Flower Driver API server."
+msgstr "启动基于 Ray 的Flower模拟服务器。"
+
+#: ../../source/ref-api/flwr.server.driver.rst:30:<autosummary>:1
+msgid ""
+":py:obj:`Driver <flwr.server.driver.Driver>`\\ "
+"\\(\\[driver\\_service\\_address\\, ...\\]\\)"
 msgstr ""
 
-#: flwr.common.typing.DisconnectRes:1 of
-msgid "DisconnectRes message from client to server."
+#: ../../source/ref-api/flwr.server.driver.rst:30:<autosummary>:1
+#: flwr.server.driver.driver.Driver:1 of
+msgid "`Driver` class provides an interface to the Driver API."
 msgstr ""
 
-#: flwr.common.typing.EvaluateIns:1 of
-msgid "Evaluate instructions for a client."
+#: ../../source/ref-api/flwr.server.driver.rst:30:<autosummary>:1
+msgid ""
+":py:obj:`GrpcDriver <flwr.server.driver.GrpcDriver>`\\ "
+"\\(\\[driver\\_service\\_address\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.driver.rst:30:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver:1 of
+msgid "`GrpcDriver` provides access to the gRPC Driver API/service."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.driver.Driver.rst:2
+#, fuzzy
+msgid "Driver"
+msgstr "服务器"
+
+#: flwr.server.driver.driver.Driver:3 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\"。"
+
+#: 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."
+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.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:"
+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.app.start_server:32 flwr.server.driver.driver.Driver:10 of
+msgid "CA certificate."
+msgstr "CA 证书。"
+
+#: flwr.server.app.start_server:33 flwr.server.driver.driver.Driver:11 of
+msgid "server certificate."
+msgstr "服务器证书。"
+
+#: 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。"
+
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1 of
+msgid ":py:obj:`get_nodes <flwr.server.driver.Driver.get_nodes>`\\ \\(\\)"
+msgstr ""
+
+#: flwr.server.driver.driver.Driver.get_nodes:1
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1 of
+msgid "Get node IDs."
+msgstr ""
+
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1 of
+msgid ""
+":py:obj:`pull_task_res <flwr.server.driver.Driver.pull_task_res>`\\ "
+"\\(task\\_ids\\)"
+msgstr ""
+
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1
+#: flwr.server.driver.driver.Driver.pull_task_res:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.pull_task_res:1 of
+#, fuzzy
+msgid "Get task results."
+msgstr "汇总训练结果。"
+
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1 of
+msgid ""
+":py:obj:`push_task_ins <flwr.server.driver.Driver.push_task_ins>`\\ "
+"\\(task\\_ins\\_list\\)"
+msgstr ""
+
+#: flwr.server.driver.driver.Driver.get_nodes:1:<autosummary>:1
+#: flwr.server.driver.driver.Driver.push_task_ins:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.push_task_ins:1 of
+msgid "Schedule tasks."
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.driver.GrpcDriver.rst:2
+msgid "GrpcDriver"
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid ":py:obj:`connect <flwr.server.driver.GrpcDriver.connect>`\\ \\(\\)"
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid "Connect to the Driver API."
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid ""
+":py:obj:`create_run <flwr.server.driver.GrpcDriver.create_run>`\\ "
+"\\(req\\)"
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.create_run:1 of
+#, fuzzy
+msgid "Request for run ID."
+msgstr "Flower 基线申请"
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid ":py:obj:`disconnect <flwr.server.driver.GrpcDriver.disconnect>`\\ \\(\\)"
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.disconnect:1 of
+msgid "Disconnect from the Driver API."
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid ":py:obj:`get_nodes <flwr.server.driver.GrpcDriver.get_nodes>`\\ \\(req\\)"
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1
+#: flwr.server.driver.grpc_driver.GrpcDriver.get_nodes:1 of
+#, fuzzy
+msgid "Get client IDs."
+msgstr "返回客户端（本身）。"
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid ""
+":py:obj:`pull_task_res <flwr.server.driver.GrpcDriver.pull_task_res>`\\ "
+"\\(req\\)"
+msgstr ""
+
+#: flwr.server.driver.grpc_driver.GrpcDriver.connect:1:<autosummary>:1 of
+msgid ""
+":py:obj:`push_task_ins <flwr.server.driver.GrpcDriver.push_task_ins>`\\ "
+"\\(req\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.driver.start_driver.rst:2
+#, fuzzy
+msgid "start\\_driver"
+msgstr "启动客户端"
+
+#: flwr.server.driver.app.start_driver:3 of
+#, 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\"。"
+
+#: flwr.server.driver.app.start_driver:6 of
+#, 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。"
+
+#: flwr.server.app.start_server:9 flwr.server.driver.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）。"
+
+#: flwr.server.app.start_server:12 flwr.server.driver.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 ""
+"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.driver.app.start_driver:17 of
+#, 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`。"
+
+#: flwr.server.app.start_server:37 flwr.server.driver.app.start_driver:26 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.driver.app.start_driver:31 of
+#, fuzzy
+msgid "Starting a driver that connects to an insecure server:"
+msgstr "启动不安全的服务器："
+
+#: flwr.server.driver.app.start_driver:35 of
+#, fuzzy
+msgid "Starting a driver that connects to an SSL-enabled server:"
+msgstr "启动支持 SSL 的服务器："
+
+#: ../../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"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.run_server_app.rst:2
+msgid "run\\_server\\_app"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.run_superlink.rst:2
+#, fuzzy
+msgid "run\\_superlink"
+msgstr "flower-superlink"
+
+#: ../../source/ref-api/flwr.server.start_server.rst:2
+#, fuzzy
+msgid "start\\_server"
+msgstr "server.start_server"
+
+#: 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.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。"
+
+#: 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`."
+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."
+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.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 "strategy"
+msgstr "Krum 策略。"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FaultTolerantFedAvg "
+"<flwr.server.strategy.FaultTolerantFedAvg>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>: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。"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedAdagrad <flwr.server.strategy.FedAdagrad>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>: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:41:<autosummary>:1
+msgid ""
+":py:obj:`FedAdam <flwr.server.strategy.FedAdam>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>: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.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedAvg <flwr.server.strategy.FedAvg>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>: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。"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedXgbNnAvg <flwr.server.strategy.FedXgbNnAvg>`\\ \\(\\*args\\, "
+"\\*\\*kwargs\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>: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.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedXgbBagging <flwr.server.strategy.FedXgbBagging>`\\ "
+"\\(\\[evaluate\\_function\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>: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。"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedXgbCyclic <flwr.server.strategy.FedXgbCyclic>`\\ "
+"\\(\\*\\*kwargs\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>: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:41:<autosummary>:1
+msgid ""
+":py:obj:`FedAvgAndroid <flwr.server.strategy.FedAvgAndroid>`\\ "
+"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedAvgM <flwr.server.strategy.FedAvgM>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedavgm.FedAvgM:1 of
+msgid "Federated Averaging with Momentum strategy."
+msgstr "联邦平均动量策略。"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedOpt <flwr.server.strategy.FedOpt>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedopt.FedOpt:1 of
+#, fuzzy
+msgid "Federated Optim strategy."
+msgstr "联邦优化策略。"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedProx <flwr.server.strategy.FedProx>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedprox.FedProx:1 of
+msgid "Federated Optimization strategy."
+msgstr "联邦优化策略。"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedYogi <flwr.server.strategy.FedYogi>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedyogi.FedYogi:1 of
+msgid "FedYogi [Reddi et al., 2020] strategy."
+msgstr "FedYogi [Reddi 等人，2020] 策略。"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`QFedAvg <flwr.server.strategy.QFedAvg>`\\ \\(\\*\\[\\, "
+"q\\_param\\, qffl\\_learning\\_rate\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>: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.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedMedian <flwr.server.strategy.FedMedian>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.fedmedian.FedMedian:1 of
+#, fuzzy
+msgid "Configurable FedMedian strategy implementation."
+msgstr "可配置的 FedAvg 策略实施。"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`FedTrimmedAvg <flwr.server.strategy.FedTrimmedAvg>`\\ "
+"\\(\\*\\[\\, fraction\\_fit\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>: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]。"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`Krum <flwr.server.strategy.Krum>`\\ \\(\\*\\[\\, "
+"fraction\\_fit\\, fraction\\_evaluate\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.krum.Krum:1 of
+#, fuzzy
+msgid "Krum [Blanchard et al., 2017] strategy."
+msgstr "FedYogi [Reddi 等人，2020] 策略。"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`Bulyan <flwr.server.strategy.Bulyan>`\\ \\(\\*\\, "
+"fraction\\_fit\\, fraction\\_evaluate\\, ...\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+#: flwr.server.strategy.bulyan.Bulyan:1 of
+msgid "Bulyan strategy."
+msgstr "Bulyan 策略。"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>:1
+msgid ""
+":py:obj:`DPFedAvgAdaptive <flwr.server.strategy.DPFedAvgAdaptive>`\\ "
+"\\(strategy\\, num\\_sampled\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>: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:41:<autosummary>:1
+msgid ""
+":py:obj:`DPFedAvgFixed <flwr.server.strategy.DPFedAvgFixed>`\\ "
+"\\(strategy\\, num\\_sampled\\_clients\\, ...\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>: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.strategy.rst:41:<autosummary>:1
+#, fuzzy
+msgid ":py:obj:`Strategy <flwr.server.strategy.Strategy>`\\ \\(\\)"
+msgstr "server.strategy.Strategy"
+
+#: ../../source/ref-api/flwr.server.strategy.rst:41:<autosummary>: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。"
+
+#: ../../source/ref-api/flwr.server.strategy.Bulyan.rst:2
+msgid "Bulyan"
+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
+#, fuzzy
+msgid "Bases: :py:class:`~flwr.server.strategy.fedavg.FedAvg`"
+msgstr "server.strategy.DPFedAvgFixed"
+
+#: 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.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.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.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.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.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.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.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.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.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.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.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:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.Bulyan.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:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.Bulyan.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.bulyan.Bulyan.aggregate_fit:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.Bulyan.configure_evaluate>`\\ \\(server\\_round\\, "
+"parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.configure_evaluate:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.configure_evaluate:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_evaluate:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.configure_evaluate:1
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.Bulyan.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.configure_fit:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.configure_fit:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.configure_fit:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.configure_fit:1
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.Bulyan.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.evaluate:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.evaluate:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.evaluate:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_nn_avg.FedXgbNnAvg.evaluate:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.Bulyan.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.initialize_parameters:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.Bulyan.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.num_evaluation_clients:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_evaluation_clients:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>: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.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients <flwr.server.strategy.Bulyan.num_fit_clients>`\\"
+" \\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.num_fit_clients:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.num_fit_clients:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>: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.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."
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.DPFedAvgAdaptive.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.DPFedAvgAdaptive.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_adaptive.DPFedAvgAdaptive.aggregate_fit:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.DPFedAvgAdaptive.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.DPFedAvgAdaptive.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.DPFedAvgAdaptive.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.DPFedAvgAdaptive.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>: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.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 ""
+"**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。"
+
+#: 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.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.DPFedAvgFixed.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.DPFedAvgFixed.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>: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.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.DPFedAvgFixed.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.DPFedAvgFixed.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.configure_fit:1 of
+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。"
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.DPFedAvgFixed.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.dpfedavg_fixed.DPFedAvgFixed.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.DPFedAvgFixed.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."
+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。"
+
+#: ../../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:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FaultTolerantFedAvg.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FaultTolerantFedAvg.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_fit:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>: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:<autosummary>: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:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FaultTolerantFedAvg.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FaultTolerantFedAvg.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FaultTolerantFedAvg.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FaultTolerantFedAvg.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FaultTolerantFedAvg.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fault_tolerant_fedavg.FaultTolerantFedAvg.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FaultTolerantFedAvg.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedAdagrad.rst:2
+#: ../../source/ref-changelog.md:839
+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 ""
+
+#: 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.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:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedAdagrad.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedAdagrad.aggregate_fit>`\\"
+" \\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedAdagrad.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.FedAdagrad.configure_fit>`\\"
+" \\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedAdagrad.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedAdagrad.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedAdagrad.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedAdagrad.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../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 "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:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedAdam.aggregate_evaluate>`\\ \\(server\\_round\\,"
+" results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedAdam.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedAdam.configure_evaluate>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.FedAdam.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedAdam.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedAdam.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedAdam.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedAdam.num_fit_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.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 ""
+"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.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.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedAvg.aggregate_evaluate>`\\ \\(server\\_round\\, "
+"results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedAvg.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedAvg.configure_evaluate>`\\ \\(server\\_round\\, "
+"parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.FedAvg.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedAvg.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedAvg.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedAvg.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients <flwr.server.strategy.FedAvg.num_fit_clients>`\\"
+" \\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedAvgAndroid.rst:2
+#, fuzzy
+msgid "FedAvgAndroid"
+msgstr "DPFedAvgAdaptive"
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedAvgAndroid.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FedAvgAndroid.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`bytes_to_ndarray "
+"<flwr.server.strategy.FedAvgAndroid.bytes_to_ndarray>`\\ \\(tensor\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedAvgAndroid.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FedAvgAndroid.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedAvgAndroid.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedAvgAndroid.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`ndarray_to_bytes "
+"<flwr.server.strategy.FedAvgAndroid.ndarray_to_bytes>`\\ \\(ndarray\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`ndarrays_to_parameters "
+"<flwr.server.strategy.FedAvgAndroid.ndarrays_to_parameters>`\\ "
+"\\(ndarrays\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedAvgAndroid.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedAvgAndroid.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`parameters_to_ndarrays "
+"<flwr.server.strategy.FedAvgAndroid.parameters_to_ndarrays>`\\ "
+"\\(parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedavg_android.FedAvgAndroid.parameters_to_ndarrays:1
+#: of
+#, fuzzy
+msgid "Convert parameters object to NumPy weights."
+msgstr "将参数对象转换为 NumPy ndarrays。"
+
+#: ../../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 ""
+"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:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedAvgM.aggregate_evaluate>`\\ \\(server\\_round\\,"
+" results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedAvgM.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedAvgM.configure_evaluate>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.FedAvgM.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedAvgM.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedAvgM.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedAvgM.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedAvgM.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedMedian.rst:2
+msgid "FedMedian"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedMedian.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedMedian.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedMedian.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.FedMedian.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedMedian.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedMedian.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedMedian.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedMedian.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。"
+
+#: 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:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedOpt.aggregate_evaluate>`\\ \\(server\\_round\\, "
+"results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedOpt.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedOpt.configure_evaluate>`\\ \\(server\\_round\\, "
+"parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.FedOpt.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedOpt.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedOpt.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedOpt.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients <flwr.server.strategy.FedOpt.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 "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.fedprox.FedProx:9 of
+msgid ""
+"\\\\frac{\\\\mu}{2} || w - w^t ||^2\n"
+"\n"
+msgstr ""
+"\\\\frac{\\\\mu}{2} || w - w^t ||^2\n"
+"\n"
+
+#: 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 "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："
+
+#: flwr.server.strategy.fedprox.FedProx:30 of
+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。"
+
+#: 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:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedProx.aggregate_evaluate>`\\ \\(server\\_round\\,"
+" results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedProx.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedProx.configure_evaluate>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.FedProx.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedProx.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedProx.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedProx.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedProx.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"
+msgstr "向客户发送近端因子mu"
+
+#: ../../source/ref-api/flwr.server.strategy.FedTrimmedAvg.rst:2
+#, fuzzy
+msgid "FedTrimmedAvg"
+msgstr "server.strategy.FedTrimmedAvg"
+
+#: 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:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedTrimmedAvg.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FedTrimmedAvg.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>: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.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedTrimmedAvg.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FedTrimmedAvg.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedTrimmedAvg.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedTrimmedAvg.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedTrimmedAvg.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedTrimmedAvg.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedXgbBagging.rst:2
+msgid "FedXgbBagging"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedXgbBagging.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FedXgbBagging.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_fit:1
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>: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.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedXgbBagging.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FedXgbBagging.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedXgbBagging.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedXgbBagging.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedXgbBagging.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_bagging.FedXgbBagging.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedXgbBagging.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedXgbCyclic.rst:2
+msgid "FedXgbCyclic"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedXgbCyclic.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FedXgbCyclic.aggregate_fit>`\\ \\(server\\_round\\,"
+" results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedXgbCyclic.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FedXgbCyclic.configure_fit>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedXgbCyclic.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedXgbCyclic.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedXgbCyclic.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedxgb_cyclic.FedXgbCyclic.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedXgbCyclic.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedXgbNnAvg.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 ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedXgbNnAvg.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit "
+"<flwr.server.strategy.FedXgbNnAvg.aggregate_fit>`\\ \\(server\\_round\\, "
+"results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedXgbNnAvg.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit "
+"<flwr.server.strategy.FedXgbNnAvg.configure_fit>`\\ \\(server\\_round\\, "
+"parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedXgbNnAvg.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedXgbNnAvg.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedXgbNnAvg.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedXgbNnAvg.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.FedYogi.rst:2
+msgid "FedYogi"
+msgstr ""
+
+#: 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
+#, fuzzy
+msgid "Client-side learning rate. Defaults to 0.0316."
+msgstr "客户端学习率。默认为 1e-1。"
+
+#: 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。"
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.FedYogi.aggregate_evaluate>`\\ \\(server\\_round\\,"
+" results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.FedYogi.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.FedYogi.configure_evaluate>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.FedYogi.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.FedYogi.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.FedYogi.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.FedYogi.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.FedYogi.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
+#, fuzzy
+msgid "Implementation based on https://arxiv.org/abs/1703.02757"
+msgstr "实施基于 https://arxiv.org/abs/2304.07537。"
+
+#: 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."
+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.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.Krum.aggregate_evaluate>`\\ \\(server\\_round\\, "
+"results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.Krum.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>: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.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.Krum.configure_evaluate>`\\ \\(server\\_round\\, "
+"parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.Krum.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.Krum.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.Krum.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.Krum.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.fedavg.FedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients <flwr.server.strategy.Krum.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.QFedAvg.rst:2
+#, fuzzy
+msgid "QFedAvg"
+msgstr "DP-FedAvg"
+
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.QFedAvg.aggregate_evaluate>`\\ \\(server\\_round\\,"
+" results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.QFedAvg.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.QFedAvg.configure_evaluate>`\\ \\(server\\_round\\,"
+" parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.QFedAvg.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.QFedAvg.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.QFedAvg.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_evaluation_clients "
+"<flwr.server.strategy.QFedAvg.num_evaluation_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: flwr.server.strategy.qfedavg.QFedAvg.aggregate_evaluate:1:<autosummary>:1 of
+msgid ""
+":py:obj:`num_fit_clients "
+"<flwr.server.strategy.QFedAvg.num_fit_clients>`\\ "
+"\\(num\\_available\\_clients\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.server.strategy.Strategy.rst:2
+#, fuzzy
+msgid "Strategy"
+msgstr "Krum 策略。"
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_evaluate "
+"<flwr.server.strategy.Strategy.aggregate_evaluate>`\\ "
+"\\(server\\_round\\, results\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid "Aggregate evaluation results."
+msgstr "聚合评估结果。"
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`aggregate_fit <flwr.server.strategy.Strategy.aggregate_fit>`\\ "
+"\\(server\\_round\\, results\\, failures\\)"
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_evaluate "
+"<flwr.server.strategy.Strategy.configure_evaluate>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`configure_fit <flwr.server.strategy.Strategy.configure_fit>`\\ "
+"\\(server\\_round\\, parameters\\, ...\\)"
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`evaluate <flwr.server.strategy.Strategy.evaluate>`\\ "
+"\\(server\\_round\\, parameters\\)"
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>: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:<autosummary>:1
+#: of
+msgid ""
+":py:obj:`initialize_parameters "
+"<flwr.server.strategy.Strategy.initialize_parameters>`\\ "
+"\\(client\\_manager\\)"
+msgstr ""
+
+#: flwr.server.strategy.strategy.Strategy.aggregate_evaluate:1:<autosummary>: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 ""
+"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 ""
+"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`。"
+
+#: 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 ""
+"**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\"。"
+
+#: 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."
+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。"
+
+#: 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."
+msgstr "该函数可用于对模型参数进行集中（即服务器端）评估。"
+
+#: 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.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.simulation.rst:2
+#, fuzzy
+msgid "simulation"
+msgstr "运行模拟"
+
+#: ../../source/ref-api/flwr.simulation.rst:17:<autosummary>:1
+msgid ""
+":py:obj:`start_simulation <flwr.simulation.start_simulation>`\\ \\(\\*\\,"
+" client\\_fn\\[\\, ...\\]\\)"
+msgstr ""
+
+#: ../../source/ref-api/flwr.simulation.rst:17:<autosummary>: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-api/flwr.simulation.start_simulation.rst:2
+#, fuzzy
+msgid "start\\_simulation"
+msgstr "start_simulation"
+
+#: 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, "
+"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。"
+
+#: 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 "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."
+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。"
+
+#: flwr.simulation.app.start_simulation:20 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."
+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。"
+
+#: 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.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`."
+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`。"
+
+#: 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 ""
+"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`。"
+
+#: 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:  { \"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 ""
+"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()` 。"
+
+#: flwr.simulation.app.start_simulation:50 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 "
+"running the clients' jobs (i.e. their `fit()` method)."
+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。"
+
+#: 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."
+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。"
+
+#: 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"
+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"
+
+#: ../../source/ref-changelog.md:9
+#, 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"
+
+#: ../../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
+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）："
+
+#: ../../source/ref-changelog.md:15
+#, 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` <!---TOKEN_v1.7.0-->"
+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` <!---"
+"TOKEN_v1.5.0-->"
+
+#: ../../source/ref-changelog.md:19
+#, 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))"
+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))"
+
+#: ../../source/ref-changelog.md:21
+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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:23
+#, 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))"
+
+#: ../../source/ref-changelog.md:25
+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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:27
+#, fuzzy
+msgid ""
+"**Support Federated Learning with Apple MLX and Flower** "
+"([#2693](https://github.com/adap/flower/pull/2693))"
+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: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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:31
+#, fuzzy
+msgid ""
+"**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: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 ""
+
+#: ../../source/ref-changelog.md:35
+#, 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))"
+
+#: ../../source/ref-changelog.md:37
+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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:39
+#, fuzzy
+msgid ""
+"**Update gRPC and ProtoBuf dependencies** "
+"([#2814](https://github.com/adap/flower/pull/2814))"
+msgstr ""
+"**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:41
+msgid ""
+"The `grpcio` and `protobuf` dependencies were updated to their latest "
+"versions for improved security and performance."
+msgstr ""
+
+#: ../../source/ref-changelog.md:43
+#, 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 ""
+"** 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:45
+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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:47
+#, fuzzy
+msgid ""
+"**Introduce** `flower-via-docker-compose` **example** "
+"([#2626](https://github.com/adap/flower/pull/2626))"
+msgstr ""
+"**介绍Flower Android SDK** "
+"([#2131](https://github.com/adap/flower/pull/2131))"
+
+#: ../../source/ref-changelog.md:49
+#, fuzzy
+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)）"
+
+#: ../../source/ref-changelog.md:51
+#, fuzzy
+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)）"
+
+#: ../../source/ref-changelog.md:53
+#, 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))"
+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)"
+
+#: ../../source/ref-changelog.md:55
+msgid ""
+"Several code examples were updated to use [Flower "
+"Datasets](https://flower.ai/docs/datasets/)."
 msgstr ""
 
-#: flwr.common.typing.EvaluateRes:1 of
-msgid "Evaluate response from a client."
-msgstr ""
+#: ../../source/ref-changelog.md:57
+#, 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))"
 
-#: flwr.common.telemetry.EventType:1 of
-msgid "Types of telemetry events."
+#: ../../source/ref-changelog.md:59
+msgid "Many Flower code examples received substantial updates."
 msgstr ""
 
-#: flwr.common.typing.FitIns:1 of
-msgid "Fit instructions for a client."
-msgstr ""
+#: ../../source/ref-changelog.md:61 ../../source/ref-changelog.md:154
+msgid "**Update Flower Baselines**"
+msgstr "**更新 Flower Baselines**"
 
-#: flwr.common.typing.FitRes:1 of
-msgid "Fit response from a client."
+#: ../../source/ref-changelog.md:63
+#, fuzzy
+msgid ""
+"HFedXGBoost ([#2226](https://github.com/adap/flower/pull/2226), "
+"[#2771](https://github.com/adap/flower/pull/2771))"
 msgstr ""
+"FedBN ([#2608](https://github.com/adap/flower/pull/2608), "
+"[#2615](https://github.com/adap/flower/pull/2615))"
 
-#: flwr.common.typing.GetParametersIns:1 of
-msgid "Parameters request for a client."
-msgstr ""
+#: ../../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)"
 
-#: flwr.common.typing.GetParametersRes:1 of
-msgid "Response when asked to return parameters."
-msgstr ""
+#: ../../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)"
 
-#: flwr.common.typing.GetPropertiesIns:1 of
-msgid "Properties request for a client."
-msgstr ""
+#: ../../source/ref-changelog.md:66
+#, fuzzy
+msgid "HeteroFL ([#2439](https://github.com/adap/flower/pull/2439))"
+msgstr "FedMeta [#2438](https://github.com/adap/flower/pull/2438)"
 
-#: flwr.common.typing.GetPropertiesRes:1 of
-msgid "Properties response from a client."
-msgstr ""
+#: ../../source/ref-changelog.md:67
+#, 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:68
+#, 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:70
+#, 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."
+msgstr ""
+
+#: ../../source/ref-changelog.md:76
+#, 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))"
 
-#: flwr.common.typing.Parameters:1 of
-msgid "Model parameters."
+#: ../../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 ""
+
+#: ../../source/ref-changelog.md:82
+#, 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))"
 
-#: flwr.common.typing.ReconnectIns:1 of
-msgid "ReconnectIns message from server to client."
+#: ../../source/ref-changelog.md:84
+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 ""
 
-#: flwr.common.typing.ServerMessage:1 of
-msgid "ServerMessage is a container used to hold one instruction message."
-msgstr ""
+#: ../../source/ref-changelog.md:86
+#, 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)）"
 
-#: flwr.common.typing.Status:1 of
-msgid "Client status."
+#: ../../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."
 msgstr ""
 
-#: flwr.common.parameter.bytes_to_ndarray:1 of
-msgid "Deserialize NumPy ndarray from bytes."
+#: ../../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))"
 msgstr ""
+"**从** `start_client` 中移除** `rest` **实验参数 "
+"([#2324](https://github.com/adap/flower/pull/2324))"
 
-#: flwr.common.logger.configure:1 of
-msgid "Configure logging to file and/or remote log server."
+#: ../../source/ref-changelog.md:92
+#, 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))"
 
-#: logging.Logger.log:1 of
-msgid "Log 'msg % args' with the integer severity 'level'."
+#: ../../source/ref-changelog.md:94
+#, 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))"
 
-#: logging.Logger.log:3 of
+#: ../../source/ref-changelog.md:96
 msgid ""
-"To pass exception information, use the keyword argument exc_info with a "
-"true value, e.g."
+"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 ""
 
-#: logging.Logger.log:6 of
-#, python-format
-msgid "logger.log(level, \"We have a %s\", \"mysterious problem\", exc_info=1)"
-msgstr ""
+#: ../../source/ref-changelog.md:98
+#, 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))"
 
-#: flwr.common.parameter.ndarray_to_bytes:1 of
-msgid "Serialize NumPy ndarray to bytes."
+#: ../../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."
 msgstr ""
 
-#: flwr.common.parameter.ndarrays_to_parameters:1 of
-msgid "Convert NumPy ndarrays to parameters object."
-msgstr ""
+#: ../../source/ref-changelog.md:102
+#, fuzzy
+msgid "v1.6.0 (2023-11-28)"
+msgstr "v1.4.0 (2023-04-21)"
 
-#: flwr.common.date.now:1 of
-msgid "Construct a datetime from time.time() with time zone set to UTC."
+#: ../../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` <!---TOKEN_v1.6.0-->"
 msgstr ""
 
-#: flwr.common.parameter.parameters_to_ndarrays:1 of
-msgid "Convert parameters object to NumPy ndarrays."
+#: ../../source/ref-changelog.md:112
+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))"
 
-#: ../../source/ref-changelog.md:1
-msgid "Changelog"
+#: ../../source/ref-changelog.md:114
+#, 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))"
 
-#: ../../source/ref-changelog.md:3
-msgid "Unreleased"
+#: ../../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 ""
 
-#: ../../source/ref-changelog.md:5 ../../source/ref-changelog.md:83
-#: ../../source/ref-changelog.md:167 ../../source/ref-changelog.md:231
-#: ../../source/ref-changelog.md:289 ../../source/ref-changelog.md:358
-#: ../../source/ref-changelog.md:487 ../../source/ref-changelog.md:529
-#: ../../source/ref-changelog.md:596 ../../source/ref-changelog.md:662
-#: ../../source/ref-changelog.md:707 ../../source/ref-changelog.md:746
-#: ../../source/ref-changelog.md:779 ../../source/ref-changelog.md:829
-msgid "What's new?"
-msgstr ""
+#: ../../source/ref-changelog.md:118
+#, 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)）"
 
-#: ../../source/ref-changelog.md:7
+#: ../../source/ref-changelog.md:120
 msgid ""
-"**Add experimental support for Python 3.12** "
-"([#2565](https://github.com/adap/flower/pull/2565))"
+"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:9
+#: ../../source/ref-changelog.md:122
 msgid ""
 "**Support custom** `ClientManager` **in** `start_driver()` "
 "([#2292](https://github.com/adap/flower/pull/2292))"
-msgstr ""
+msgstr "**在***`start_driver()`中支持自定义***`ClientManager([#2292](https://github.com/adap/flower/pull/2292))"
 
-#: ../../source/ref-changelog.md:11
+#: ../../source/ref-changelog.md:124
 msgid ""
 "**Update REST API to support create and delete nodes** "
 "([#2283](https://github.com/adap/flower/pull/2283))"
 msgstr ""
+"**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:126
+#, fuzzy
+msgid ""
+"**Update the Android SDK** "
+"([#2187](https://github.com/adap/flower/pull/2187))"
+msgstr ""
+"**介绍Flower Android SDK** "
+"([#2131](https://github.com/adap/flower/pull/2131))"
+
+#: ../../source/ref-changelog.md:128
+#, 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:13
+#: ../../source/ref-changelog.md:130
+#, fuzzy
 msgid ""
 "**Update the C++ SDK** "
-"([#2537](https://github/com/adap/flower/pull/2537), "
-"[#2528](https://github/com/adap/flower/pull/2528), "
+"([#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 ""
+"** 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:15
+#: ../../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。"
+
+#: ../../source/ref-changelog.md:134
+#, fuzzy
+msgid ""
+"**Make HTTPS the new default** "
+"([#2591](https://github.com/adap/flower/pull/2591), "
+"[#2636](https://github.com/adap/flower/pull/2636))"
 msgstr ""
+"Baselines文档（[#2290](https://github.com/adap/flower/pull/2290), "
+"[#2400](https://github.com/adap/flower/pull/2400)"
 
-#: ../../source/ref-changelog.md:17
+#: ../../source/ref-changelog.md:136
 msgid ""
-"**Fix the incorrect return types of Strategy** "
-"([#2432](https://github.com/adap/flower/pull/2432/files))"
+"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:19
+#: ../../source/ref-changelog.md:138
 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."
+"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:21
+#: ../../source/ref-changelog.md:140
 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))"
 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))"
 
-#: ../../source/ref-changelog.md:23
+#: ../../source/ref-changelog.md:142
+#, 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. Calling `start_numpy_client` is now "
-"deprecated."
+" defined and instantiated. The `to_client()` function is introduced to "
+"convert a `NumPyClient` to a `Client`."
 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。"
 
-#: ../../source/ref-changelog.md:25
-msgid "**Update Flower Baselines**"
+#: ../../source/ref-changelog.md:144
+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)"
 
-#: ../../source/ref-changelog.md:27
+#: ../../source/ref-changelog.md:146
+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"
+
+#: ../../source/ref-changelog.md:148
+#, 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)）"
+
+#: ../../source/ref-changelog.md:150 ../../source/ref-changelog.md:152
+#, fuzzy
+msgid ""
+"**Introduce `WorkloadState`** "
+"([#2564](https://github.com/adap/flower/pull/2564), "
+"[#2632](https://github.com/adap/flower/pull/2632))"
+msgstr ""
+"**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:156
 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))"
 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))"
 
-#: ../../source/ref-changelog.md:29
+#: ../../source/ref-changelog.md:158
 msgid ""
 "Baselines Docs ([#2290](https://github.com/adap/flower/pull/2290), "
 "[#2400](https://github.com/adap/flower/pull/2400))"
 msgstr ""
+"Baselines文档（[#2290](https://github.com/adap/flower/pull/2290), "
+"[#2400](https://github.com/adap/flower/pull/2400)"
 
-#: ../../source/ref-changelog.md:31
+#: ../../source/ref-changelog.md:160
 msgid ""
 "FedMLB ([#2340](https://github.com/adap/flower/pull/2340), "
 "[#2507](https://github.com/adap/flower/pull/2507))"
 msgstr ""
+"FedMLB ([#2340](https://github.com/adap/flower/pull/2340), "
+"[#2507](https://github.com/adap/flower/pull/2507))"
 
-#: ../../source/ref-changelog.md:33
+#: ../../source/ref-changelog.md:162
 msgid ""
 "TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), "
 "[#2508](https://github.com/adap/flower/pull/2508))"
 msgstr ""
+"TAMUNA ([#2254](https://github.com/adap/flower/pull/2254), "
+"[#2508](https://github.com/adap/flower/pull/2508))"
 
-#: ../../source/ref-changelog.md:35
+#: ../../source/ref-changelog.md:164
 msgid "FedMeta [#2438](https://github.com/adap/flower/pull/2438)"
-msgstr ""
+msgstr "FedMeta [#2438](https://github.com/adap/flower/pull/2438)"
 
-#: ../../source/ref-changelog.md:37
+#: ../../source/ref-changelog.md:166
 msgid "FjORD [#2431](https://github.com/adap/flower/pull/2431)"
-msgstr ""
+msgstr "FjORD [#2431](https://github.com/adap/flower/pull/2431)"
 
-#: ../../source/ref-changelog.md:39
+#: ../../source/ref-changelog.md:168
 msgid "MOON [#2421](https://github.com/adap/flower/pull/2421)"
-msgstr ""
+msgstr "MOON [#2421](https://github.com/adap/flower/pull/2421)"
 
-#: ../../source/ref-changelog.md:41
+#: ../../source/ref-changelog.md:170
 msgid "DepthFL [#2295](https://github.com/adap/flower/pull/2295)"
-msgstr ""
+msgstr "DepthFL [#2295](https://github.com/adap/flower/pull/2295)"
 
-#: ../../source/ref-changelog.md:43
+#: ../../source/ref-changelog.md:172
 msgid "FedPer [#2266](https://github.com/adap/flower/pull/2266)"
-msgstr ""
+msgstr "FedPer [#2266](https://github.com/adap/flower/pull/2266)"
 
-#: ../../source/ref-changelog.md:45
+#: ../../source/ref-changelog.md:174
 msgid "FedWav2vec [#2551](https://github.com/adap/flower/pull/2551)"
-msgstr ""
+msgstr "FedWav2vec [#2551](https://github.com/adap/flower/pull/2551)"
 
-#: ../../source/ref-changelog.md:47
+#: ../../source/ref-changelog.md:176
 msgid "niid-Bench [#2428](https://github.com/adap/flower/pull/2428)"
-msgstr ""
+msgstr "niid-Bench [#2428](https://github.com/adap/flower/pull/2428)"
 
-#: ../../source/ref-changelog.md:49
+#: ../../source/ref-changelog.md:178
 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))"
 
-#: ../../source/ref-changelog.md:51
+#: ../../source/ref-changelog.md:180
+#, fuzzy
 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))"
+"**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))"
 
-#: ../../source/ref-changelog.md:53
+#: ../../source/ref-changelog.md:182
+#, fuzzy
 msgid ""
-"**General updates to baselines** "
+"**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))"
-msgstr ""
-
-#: ../../source/ref-changelog.md:55
-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 ""
-
-#: ../../source/ref-changelog.md:57
-msgid ""
-"**General improvements** "
-"([#2309](https://github.com/adap/flower/pull/2309), "
+"[#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), "
@@ -7237,37 +12322,78 @@ msgid ""
 "[#2402](https://github.com/adap/flower/pull/2402), "
 "[#2446](https://github.com/adap/flower/pull/2446) "
 "[#2561](https://github.com/adap/flower/pull/2561))"
-msgstr ""
-
-#: ../../source/ref-changelog.md:59 ../../source/ref-changelog.md:153
-#: ../../source/ref-changelog.md:217 ../../source/ref-changelog.md:271
-#: ../../source/ref-changelog.md:338
-msgid "Flower received many improvements under the hood, too many to list here."
-msgstr ""
 
-#: ../../source/ref-changelog.md:61
+#: ../../source/ref-changelog.md:184
+#, fuzzy
 msgid ""
-"**Add new** `Bulyan` **strategy** "
-"([#1817](https://github.com/adap/flower/pull/1817), "
-"[#1891](https://github.com/adap/flower/pull/1891))"
+"**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 ""
+"**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:63
+#: ../../source/ref-changelog.md:186
+#, fuzzy
 msgid ""
-"The new `Bulyan` strategy implements Bulyan by [El Mhamdi et al., "
-"2018](https://arxiv.org/abs/1802.07927)"
+"**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 ""
+"**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:65 ../../source/ref-changelog.md:155
-#: ../../source/ref-changelog.md:219 ../../source/ref-changelog.md:277
-#: ../../source/ref-changelog.md:346 ../../source/ref-changelog.md:408
-#: ../../source/ref-changelog.md:427 ../../source/ref-changelog.md:583
-#: ../../source/ref-changelog.md:654 ../../source/ref-changelog.md:691
-#: ../../source/ref-changelog.md:734
-msgid "Incompatible changes"
-msgstr ""
+#: ../../source/ref-changelog.md:188
+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: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-changelog.md:67
+#: ../../source/ref-changelog.md:194
 msgid ""
 "**Remove support for Python 3.7** "
 "([#2280](https://github.com/adap/flower/pull/2280), "
@@ -7277,44 +12403,42 @@ msgid ""
 "[#2355](https://github.com/adap/flower/pull/2355), "
 "[#2356](https://github.com/adap/flower/pull/2356))"
 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))"
 
-#: ../../source/ref-changelog.md:69
+#: ../../source/ref-changelog.md:196
 msgid ""
 "Python 3.7 support was deprecated in Flower 1.5, and this release removes"
 " support. Flower now requires Python 3.8."
-msgstr ""
+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:71
+#: ../../source/ref-changelog.md:198
 msgid ""
 "**Remove experimental argument** `rest` **from** `start_client` "
 "([#2324](https://github.com/adap/flower/pull/2324))"
 msgstr ""
+"**从** `start_client` 中移除** `rest` **实验参数 "
+"([#2324](https://github.com/adap/flower/pull/2324))"
 
-#: ../../source/ref-changelog.md:73
+#: ../../source/ref-changelog.md:200
 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 ""
+"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:75
+#: ../../source/ref-changelog.md:202
 msgid "v1.5.0 (2023-08-31)"
-msgstr ""
+msgstr "v1.5.0 (2023-08-31)"
 
-#: ../../source/ref-changelog.md:77 ../../source/ref-changelog.md:161
-#: ../../source/ref-changelog.md:225 ../../source/ref-changelog.md:283
-#: ../../source/ref-changelog.md:352 ../../source/ref-changelog.md:421
-msgid "Thanks to our contributors"
-msgstr ""
-
-#: ../../source/ref-changelog.md:79 ../../source/ref-changelog.md:163
-#: ../../source/ref-changelog.md:227 ../../source/ref-changelog.md:285
-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:81
+#: ../../source/ref-changelog.md:208
 msgid ""
 "`Adam Narozniak`, `Anass Anhari`, `Charles Beauville`, `Dana-Farber`, "
 "`Daniel J. Beutel`, `Daniel Nata Nugraha`, `Edoardo Gabrielli`, `Gustavo "
@@ -7322,16 +12446,24 @@ msgid ""
 "Topal`, `achiverram28`, `danielnugraha`, `eunchung`, `ruthgal` <!---"
 "TOKEN_v1.5.0-->"
 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` <!---"
+"TOKEN_v1.5.0-->"
 
-#: ../../source/ref-changelog.md:85
+#: ../../source/ref-changelog.md:212
 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))"
 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:87
+#: ../../source/ref-changelog.md:214
 msgid ""
 "The new simulation engine has been rewritten from the ground up, yet it "
 "remains fully backwards compatible. It offers much improved stability and"
@@ -7339,19 +12471,27 @@ msgid ""
 "transparently adapt to different settings to scale simulation in CPU-"
 "only, CPU+GPU, multi-GPU, or multi-node multi-GPU environments."
 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。"
 
-#: ../../source/ref-changelog.md:89
+#: ../../source/ref-changelog.md:216
 msgid ""
 "Comprehensive documentation includes a new [how-to run "
-"simulations](https://flower.dev/docs/framework/how-to-run-"
+"simulations](https://flower.ai/docs/framework/how-to-run-"
 "simulations.html) guide, new [simulation-"
-"pytorch](https://flower.dev/docs/examples/simulation-pytorch.html) and "
-"[simulation-tensorflow](https://flower.dev/docs/examples/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 ""
+"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:91
+#: ../../source/ref-changelog.md:218
 msgid ""
 "**Restructure Flower Docs** "
 "([#1824](https://github.com/adap/flower/pull/1824), "
@@ -7382,43 +12522,82 @@ msgid ""
 "[#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:93
+#: ../../source/ref-changelog.md:220
 msgid ""
 "Much effort went into a completely restructured Flower docs experience. "
-"The documentation on [flower.dev/docs](flower.dev/docs) is now divided "
+"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 ""
+"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:95
+#: ../../source/ref-changelog.md:222
 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))"
 
-#: ../../source/ref-changelog.md:97
+#: ../../source/ref-changelog.md:224
 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。"
 
-#: ../../source/ref-changelog.md:99
+#: ../../source/ref-changelog.md:226
 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:101
+#: ../../source/ref-changelog.md:228
 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。"
 
-#: ../../source/ref-changelog.md:103
+#: ../../source/ref-changelog.md:230
 msgid ""
 "**Introduce new end-to-end testing infrastructure** "
 "([#1842](https://github.com/adap/flower/pull/1842), "
@@ -7439,43 +12618,64 @@ msgid ""
 "[#2137](https://github.com/adap/flower/pull/2137), "
 "[#2165](https://github.com/adap/flower/pull/2165))"
 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))"
 
-#: ../../source/ref-changelog.md:105
+#: ../../source/ref-changelog.md:232
 msgid ""
 "A new testing infrastructure ensures that new changes stay compatible "
 "with existing framework integrations or strategies."
-msgstr ""
+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:107
+#: ../../source/ref-changelog.md:234
 msgid "**Deprecate Python 3.7**"
-msgstr ""
+msgstr "** 过时的 Python 3.7**"
 
-#: ../../source/ref-changelog.md:109
+#: ../../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 ""
+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:111
+#: ../../source/ref-changelog.md:238
 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)"
 
-#: ../../source/ref-changelog.md:113
+#: ../../source/ref-changelog.md:240
 msgid ""
 "The new `FedTrimmedAvg` strategy implements Trimmed Mean by [Dong Yin, "
 "2018](https://arxiv.org/abs/1803.01498)."
 msgstr ""
+"新的 \"FedTrimmedAvg \"策略实现了[Dong Yin, "
+"2018]（https://arxiv.org/abs/1803.01498）的 \"Trimmed Mean\"。"
 
-#: ../../source/ref-changelog.md:115
+#: ../../source/ref-changelog.md:242
 msgid ""
 "**Introduce start_driver** "
 "([#1697](https://github.com/adap/flower/pull/1697))"
-msgstr ""
+msgstr "**引入 start_driver**（[#1697](https://github.com/adap/flower/pull/1697)）"
 
-#: ../../source/ref-changelog.md:117
+#: ../../source/ref-changelog.md:244
 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` "
@@ -7483,68 +12683,87 @@ msgid ""
 " the `mt-pytorch` code example to see a working example using "
 "`start_driver`."
 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:119
+#: ../../source/ref-changelog.md:246
 msgid ""
 "**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:121
+#: ../../source/ref-changelog.md:248
 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 ""
+"`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:123
+#: ../../source/ref-changelog.md:250
 msgid ""
 "**Migrate experimental REST API to Starlette** "
 "([2171](https://github.com/adap/flower/pull/2171))"
 msgstr ""
+"**将实验性 REST API 移植到 Starlette** "
+"([2171](https://github.com/adap/flower/pull/2171))"
 
-#: ../../source/ref-changelog.md:125
+#: ../../source/ref-changelog.md:252
 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 ""
+"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:127
+#: ../../source/ref-changelog.md:254
 msgid ""
 "Please note: The REST request-response API is still experimental and will"
 " likely change significantly over time."
-msgstr ""
+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:129
+#: ../../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))"
 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)"
 
-#: ../../source/ref-changelog.md:131
+#: ../../source/ref-changelog.md:258
 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 ""
+"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:133
+#: ../../source/ref-changelog.md:260
 msgid ""
 "Please note: The gRPC request-response API is still experimental and will"
 " likely change significantly over time."
-msgstr ""
+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:135
+#: ../../source/ref-changelog.md:262
 msgid ""
 "**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:137
+#: ../../source/ref-changelog.md:264
 msgid ""
 "The (experimental) `start_client` argument `rest` was deprecated in "
 "favour of a new argument `transport`. `start_client(transport=\"rest\")` "
@@ -7552,31 +12771,35 @@ msgid ""
 "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:139
+#: ../../source/ref-changelog.md:266
 msgid ""
 "**Add a new gRPC option** "
 "([#2197](https://github.com/adap/flower/pull/2197))"
-msgstr ""
+msgstr "** 添加一个新的 gRPC 选项**（[#2197](https://github.com/adap/flower/pull/2197)）"
 
-#: ../../source/ref-changelog.md:141
+#: ../../source/ref-changelog.md:268
 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 ""
+"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:143
+#: ../../source/ref-changelog.md:270
 msgid ""
 "**Improve example notebooks** "
 "([#2005](https://github.com/adap/flower/pull/2005))"
-msgstr ""
+msgstr "**改进示例笔记** ([#2005](https://github.com/adap/flower/pull/2005))"
 
-#: ../../source/ref-changelog.md:145
+#: ../../source/ref-changelog.md:272
 msgid "There's a new 30min Federated Learning PyTorch tutorial!"
-msgstr ""
+msgstr "有一个新的 30 分钟的联邦学习 PyTorch 教程！"
 
-#: ../../source/ref-changelog.md:147
+#: ../../source/ref-changelog.md:274
 msgid ""
 "**Example updates** ([#1772](https://github.com/adap/flower/pull/1772), "
 "[#1873](https://github.com/adap/flower/pull/1873), "
@@ -7590,8 +12813,19 @@ msgid ""
 "[#2225](https://github.com/adap/flower/pull/2225), "
 "[#2183](https://github.com/adap/flower/pull/2183))"
 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:149
+#: ../../source/ref-changelog.md:276
 msgid ""
 "Many examples have received significant updates, including simplified "
 "advanced-tensorflow and advanced-pytorch examples, improved macOS "
@@ -7599,8 +12833,11 @@ msgid ""
 " major upgrade is that all code examples now have a `requirements.txt` "
 "(in addition to `pyproject.toml`)."
 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）。"
 
-#: ../../source/ref-changelog.md:151
+#: ../../source/ref-changelog.md:278
 msgid ""
 "**General improvements** "
 "([#1872](https://github.com/adap/flower/pull/1872), "
@@ -7610,18 +12847,24 @@ msgid ""
 "[#1477](https://github.com/adap/flower/pull/1477), "
 "[#2171](https://github.com/adap/flower/pull/2171))"
 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:157 ../../source/ref-changelog.md:221
-#: ../../source/ref-changelog.md:279 ../../source/ref-changelog.md:348
-#: ../../source/ref-changelog.md:410
+#: ../../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 ""
+msgstr "无"
 
-#: ../../source/ref-changelog.md:159
+#: ../../source/ref-changelog.md:286
 msgid "v1.4.0 (2023-04-21)"
-msgstr ""
+msgstr "v1.4.0 (2023-04-21)"
 
-#: ../../source/ref-changelog.md:165
+#: ../../source/ref-changelog.md:292
 msgid ""
 "`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, "
 "`Chenyang Ma (Danny)`, `Daniel J. Beutel`, `Edoardo`, `Gautam Jajoo`, "
@@ -7630,8 +12873,14 @@ msgid ""
 "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:169
+#: ../../source/ref-changelog.md:296
 msgid ""
 "**Introduce support for XGBoost (**`FedXgbNnAvg` **strategy and "
 "example)** ([#1694](https://github.com/adap/flower/pull/1694), "
@@ -7641,26 +12890,38 @@ msgid ""
 "[#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:171
+#: ../../source/ref-changelog.md:298
 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/quickstart_xgboost_horizontal)"
+"example](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart)"
 " that demonstrates the usage of this new strategy in an XGBoost project."
 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:173
+#: ../../source/ref-changelog.md:300
 msgid ""
 "**Introduce iOS SDK (preview)** "
 "([#1621](https://github.com/adap/flower/pull/1621), "
 "[#1764](https://github.com/adap/flower/pull/1764))"
 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:175
+#: ../../source/ref-changelog.md:302
 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 "
@@ -7670,24 +12931,34 @@ msgid ""
 "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:177
+#: ../../source/ref-changelog.md:304
 msgid ""
 "**Introduce new \"What is Federated Learning?\" tutorial** "
 "([#1657](https://github.com/adap/flower/pull/1657), "
 "[#1721](https://github.com/adap/flower/pull/1721))"
 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:179
+#: ../../source/ref-changelog.md:306
 msgid ""
-"A new [entry-level tutorial](https://flower.dev/docs/framework/tutorial-"
+"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 ""
+"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:181
+#: ../../source/ref-changelog.md:308
 msgid ""
 "**Introduce new Flower Baseline: FedProx MNIST** "
 "([#1513](https://github.com/adap/flower/pull/1513), "
@@ -7695,30 +12966,42 @@ msgid ""
 "[#1681](https://github.com/adap/flower/pull/1681), "
 "[#1679](https://github.com/adap/flower/pull/1679))"
 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:183
+#: ../../source/ref-changelog.md:310
 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 heterogenous settings."
+" which aims at making convergence more robust in heterogeneous settings."
 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:185
+#: ../../source/ref-changelog.md:312
 msgid ""
 "**Introduce new Flower Baseline: FedAvg FEMNIST** "
 "([#1655](https://github.com/adap/flower/pull/1655))"
 msgstr ""
+"**引入新的 Flower Baseline： FedAvg FEMNIST** "
+"([#1655](https://github.com/adap/flower/pull/1655))"
 
-#: ../../source/ref-changelog.md:187
+#: ../../source/ref-changelog.md:314
 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 ""
+"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:189
+#: ../../source/ref-changelog.md:316
 msgid ""
 "**Introduce (experimental) REST API** "
 "([#1594](https://github.com/adap/flower/pull/1594), "
@@ -7729,21 +13012,28 @@ msgid ""
 "[#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:191
+#: ../../source/ref-changelog.md:318
 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 ""
+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:193
+#: ../../source/ref-changelog.md:320
 msgid ""
 "Please note: The REST API is still experimental and will likely change "
 "significantly over time."
-msgstr ""
+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:195
+#: ../../source/ref-changelog.md:322
 msgid ""
 "**Improve the (experimental) Driver API** "
 "([#1663](https://github.com/adap/flower/pull/1663), "
@@ -7756,8 +13046,17 @@ msgid ""
 "[#1662](https://github.com/adap/flower/pull/1662), "
 "[#1794](https://github.com/adap/flower/pull/1794))"
 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:197
+#: ../../source/ref-changelog.md:324
 msgid ""
 "The Driver API is still an experimental feature, but this release "
 "introduces some major upgrades. One of the main improvements is the "
@@ -7766,59 +13065,70 @@ msgid ""
 "results) that have been delivered will now be deleted. This greatly "
 "improves the memory efficiency of a long-running Flower server."
 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。"
 
-#: ../../source/ref-changelog.md:199
+#: ../../source/ref-changelog.md:326
 msgid ""
 "**Fix spilling issues related to Ray during simulations** "
 "([#1698](https://github.com/adap/flower/pull/1698))"
-msgstr ""
+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:201
+#: ../../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 ""
+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:203
+#: ../../source/ref-changelog.md:330
 msgid ""
 "**Add new example using** `TabNet` **and Flower** "
 "([#1725](https://github.com/adap/flower/pull/1725))"
 msgstr ""
+"** 添加使用** `TabNet` ** 的新示例** "
+"([#1725](https://github.com/adap/flower/pull/1725))"
 
-#: ../../source/ref-changelog.md:205
+#: ../../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: "
-"[https://github.com/adap/flower/tree/main/examples/tabnet](https://github.com/adap/flower/tree/main/examples/quickstart_tabnet)."
+"[quickstart-tabnet](https://github.com/adap/flower/tree/main/examples/quickstart-tabnet)."
 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)。"
 
-#: ../../source/ref-changelog.md:207
+#: ../../source/ref-changelog.md:334
 msgid ""
 "**Add new how-to guide for monitoring simulations** "
 "([#1649](https://github.com/adap/flower/pull/1649))"
-msgstr ""
+msgstr "** 添加新的模拟监控指南** ([#1649](https://github.com/adap/flower/pull/1649))"
 
-#: ../../source/ref-changelog.md:209
+#: ../../source/ref-changelog.md:336
 msgid ""
 "We now have a documentation guide to help users monitor their performance"
 " during simulations."
-msgstr ""
+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:211
+#: ../../source/ref-changelog.md:338
 msgid ""
 "**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:213
+#: ../../source/ref-changelog.md:340
 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!"
 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！"
 
-#: ../../source/ref-changelog.md:215
+#: ../../source/ref-changelog.md:342
 msgid ""
 "**General improvements** "
 "([#1659](https://github.com/adap/flower/pull/1659), "
@@ -7871,24 +13181,77 @@ msgid ""
 "[#1804](https://github.com/adap/flower/pull/1804), "
 "[#1805](https://github.com/adap/flower/pull/1805))"
 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:223
+#: ../../source/ref-changelog.md:350
 msgid "v1.3.0 (2023-02-06)"
-msgstr ""
+msgstr "v1.3.0 (2023-02-06)"
 
-#: ../../source/ref-changelog.md:229
+#: ../../source/ref-changelog.md:356
 msgid ""
 "`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, "
 "`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`"
 msgstr ""
+"`Adam Narozniak`, `Alexander Viala Bellander`, `Charles Beauville`, "
+"`Daniel J. Beutel`, `JDRanpariya`, `Lennart Behme`, `Taner Topal`"
 
-#: ../../source/ref-changelog.md:233
+#: ../../source/ref-changelog.md:360
 msgid ""
 "**Add support for** `workload_id` **and** `group_id` **in Driver API** "
 "([#1595](https://github.com/adap/flower/pull/1595))"
 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))"
 
-#: ../../source/ref-changelog.md:235
+#: ../../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"
@@ -7896,65 +13259,84 @@ msgid ""
 "training round. Both the `workload_id` and `group_id` enable client nodes"
 " to decide whether they want to handle a task or not."
 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。"
 
-#: ../../source/ref-changelog.md:237
+#: ../../source/ref-changelog.md:364
 msgid ""
 "**Make Driver API and Fleet API address configurable** "
 "([#1637](https://github.com/adap/flower/pull/1637))"
 msgstr ""
+"**使Driver API 和Fleet "
+"API地址可配置**（[#1637](https://github.com/adap/flower/pull/1637)）"
 
-#: ../../source/ref-changelog.md:239
+#: ../../source/ref-changelog.md:366
 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:"
 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："
 
-#: ../../source/ref-changelog.md:241
+#: ../../source/ref-changelog.md:368
+#, 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:243
+#: ../../source/ref-changelog.md:370
 msgid "Both IPv4 and IPv6 addresses are supported."
-msgstr ""
+msgstr "支持 IPv4 和 IPv6 地址。"
 
-#: ../../source/ref-changelog.md:245
+#: ../../source/ref-changelog.md:372
 msgid ""
 "**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:247
+#: ../../source/ref-changelog.md:374
 msgid ""
-"A new code example (`quickstart_fastai`) demonstrates federated learning "
+"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 ""
+"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:249
+#: ../../source/ref-changelog.md:376
 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))"
 
-#: ../../source/ref-changelog.md:251
+#: ../../source/ref-changelog.md:378
 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."
 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。"
 
-#: ../../source/ref-changelog.md:253
+#: ../../source/ref-changelog.md:380
 msgid ""
 "**Add new `FedProx` strategy** "
 "([#1619](https://github.com/adap/flower/pull/1619))"
-msgstr ""
+msgstr "**添加新的`FedProx`策略** （[#1619](https://github.com/adap/flower/pull/1619)）"
 
-#: ../../source/ref-changelog.md:255
+#: ../../source/ref-changelog.md:382
 msgid ""
 "This "
 "[strategy](https://github.com/adap/flower/blob/main/src/py/flwr/server/strategy/fedprox.py)"
@@ -7965,48 +13347,56 @@ msgid ""
 "parameter called `proximal_mu` to regularize the local models with "
 "respect to the global models."
 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。"
 
-#: ../../source/ref-changelog.md:257
+#: ../../source/ref-changelog.md:384
 msgid ""
 "**Add new metrics to telemetry events** "
 "([#1640](https://github.com/adap/flower/pull/1640))"
-msgstr ""
+msgstr "**为遥测事件添加新指标**（[#1640](https://github.com/adap/flower/pull/1640)）"
 
-#: ../../source/ref-changelog.md:259
+#: ../../source/ref-changelog.md:386
 msgid ""
 "An updated event structure allows, for example, the clustering of events "
 "within the same workload."
-msgstr ""
+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:261
+#: ../../source/ref-changelog.md:388
 msgid ""
 "**Add new custom strategy tutorial section** "
 "[#1623](https://github.com/adap/flower/pull/1623)"
-msgstr ""
+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:263
+#: ../../source/ref-changelog.md:390
 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)"
 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:265
+#: ../../source/ref-changelog.md:392
 msgid ""
 "**Add new custom serialization tutorial section** "
 "([#1622](https://github.com/adap/flower/pull/1622))"
-msgstr ""
+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:267
+#: ../../source/ref-changelog.md:394
 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 ""
+"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:269
+#: ../../source/ref-changelog.md:396
 msgid ""
 "**General improvements** "
 "([#1638](https://github.com/adap/flower/pull/1638), "
@@ -8043,8 +13433,41 @@ msgid ""
 "[#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:273
+#: ../../source/ref-changelog.md:400
 msgid ""
 "**Updated documentation** "
 "([#1629](https://github.com/adap/flower/pull/1629), "
@@ -8055,103 +13478,137 @@ msgid ""
 "[#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:275 ../../source/ref-changelog.md:342
+#: ../../source/ref-changelog.md:402 ../../source/ref-changelog.md:469
 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 ""
+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:281
+#: ../../source/ref-changelog.md:408
 msgid "v1.2.0 (2023-01-13)"
-msgstr ""
+msgstr "v1.2.0 (2023-01-13)"
 
-#: ../../source/ref-changelog.md:287
+#: ../../source/ref-changelog.md:414
 msgid ""
 "`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L."
 " Jiang`, `Ragy`, `Taner Topal`, `dannymcy`"
 msgstr ""
+"`Adam Narozniak`, `Charles Beauville`, `Daniel J. Beutel`, `Edoardo`, `L."
+" Jiang`, `Ragy`, `Taner Topal`, `dannymcy`"
 
-#: ../../source/ref-changelog.md:291
+#: ../../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))"
 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:293
+#: ../../source/ref-changelog.md:420
 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.dev/blog/2023-01-12-fl-starter-"
+"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:295
+#: ../../source/ref-changelog.md:422
 msgid ""
 "**Improve GPU support in simulations** "
 "([#1555](https://github.com/adap/flower/pull/1555))"
-msgstr ""
+msgstr "**改进模拟中的 GPU 支持**（[#1555](https://github.com/adap/flower/pull/1555)）"
 
-#: ../../source/ref-changelog.md:297
+#: ../../source/ref-changelog.md:424
 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 ""
+"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:299
+#: ../../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))"
 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))"
 
-#: ../../source/ref-changelog.md:301
+#: ../../source/ref-changelog.md:428
 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："
 
-#: ../../source/ref-changelog.md:303
+#: ../../source/ref-changelog.md:430
 msgid ""
-"[An Introduction to Federated Learning](https://flower.dev/docs/framework"
+"[An Introduction to Federated Learning](https://flower.ai/docs/framework"
 "/tutorial-get-started-with-flower-pytorch.html)"
 msgstr ""
+"[联邦学习简介](https://flower.ai/docs/framework/tutorial-get-started-with-"
+"flower-pytorch.html)"
 
-#: ../../source/ref-changelog.md:304
+#: ../../source/ref-changelog.md:431
 msgid ""
-"[Strategies in Federated Learning](https://flower.dev/docs/framework"
+"[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)"
 
-#: ../../source/ref-changelog.md:305
+#: ../../source/ref-changelog.md:432
 msgid ""
-"[Building a Strategy](https://flower.dev/docs/framework/tutorial-build-a"
+"[Building a Strategy](https://flower.ai/docs/framework/tutorial-build-a"
 "-strategy-from-scratch-pytorch.html)"
 msgstr ""
+"[制定策略](https://flower.ai/docs/framework/tutorial-build-a-strategy-from-"
+"scratch-pytorch.html)"
 
-#: ../../source/ref-changelog.md:306
+#: ../../source/ref-changelog.md:433
 msgid ""
-"[Client and NumPyClient](https://flower.dev/docs/framework/tutorial-"
+"[Client and NumPyClient](https://flower.ai/docs/framework/tutorial-"
 "customize-the-client-pytorch.html)"
 msgstr ""
+"[客户端和 NumPyClient](https://flower.ai/docs/framework/tutorial-customize-"
+"the-client-pytorch.html)"
 
-#: ../../source/ref-changelog.md:308
+#: ../../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))"
 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)"
 
-#: ../../source/ref-changelog.md:310
+#: ../../source/ref-changelog.md:437
 msgid ""
 "After a [request for "
 "feedback](https://github.com/adap/flower/issues/1534) from the community,"
@@ -8160,16 +13617,21 @@ msgid ""
 "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。"
 
-#: ../../source/ref-changelog.md:312
+#: ../../source/ref-changelog.md:439
 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.dev/docs/telemetry.html)."
+"[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:314
+#: ../../source/ref-changelog.md:441
 msgid ""
 "**Introduce (experimental) Driver API** "
 "([#1520](https://github.com/adap/flower/pull/1520), "
@@ -8180,8 +13642,15 @@ msgid ""
 "[#1551](https://github.com/adap/flower/pull/1551), "
 "[#1567](https://github.com/adap/flower/pull/1567))"
 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:316
+#: ../../source/ref-changelog.md:443
 msgid ""
 "Flower now has a new (experimental) Driver API which will enable fully "
 "programmable, async, and multi-tenant Federated Learning and Federated "
@@ -8189,67 +13658,83 @@ msgid ""
 " 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。"
 
-#: ../../source/ref-changelog.md:318
+#: ../../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."
 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。"
 
-#: ../../source/ref-changelog.md:320
+#: ../../source/ref-changelog.md:447
 msgid ""
 "To learn more, check out the `mt-pytorch` code example. We look forward "
 "to you feedback!"
-msgstr ""
+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:322
+#: ../../source/ref-changelog.md:449
 msgid ""
 "Please note: *The Driver API is still experimental and will likely change"
 " significantly over time.*"
-msgstr ""
+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:324
+#: ../../source/ref-changelog.md:451
 msgid ""
 "**Add new Federated Analytics with Pandas example** "
 "([#1469](https://github.com/adap/flower/pull/1469), "
 "[#1535](https://github.com/adap/flower/pull/1535))"
 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:326
+#: ../../source/ref-changelog.md:453
 msgid ""
-"A new code example (`quickstart_pandas`) demonstrates federated analytics"
+"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)."
+"[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:328
+#: ../../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))"
 
-#: ../../source/ref-changelog.md:330
+#: ../../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:332
+#: ../../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:334
+#: ../../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 ""
+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:336
+#: ../../source/ref-changelog.md:463
 msgid ""
 "**General improvements** "
 "([#1491](https://github.com/adap/flower/pull/1491), "
@@ -8266,8 +13751,21 @@ msgid ""
 "[#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:340
+#: ../../source/ref-changelog.md:467
 msgid ""
 "**Updated documentation** "
 "([#1494](https://github.com/adap/flower/pull/1494), "
@@ -8280,40 +13778,57 @@ msgid ""
 "[#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:344
+#: ../../source/ref-changelog.md:471
 msgid ""
 "One highlight is the new [first time contributor "
-"guide](https://flower.dev/docs/first-time-contributors.html): if you've "
+"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 ""
+"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:350
+#: ../../source/ref-changelog.md:477
 msgid "v1.1.0 (2022-10-31)"
-msgstr ""
+msgstr "v1.1.0 (2022-10-31)"
 
-#: ../../source/ref-changelog.md:354
+#: ../../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 ""
+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:356
+#: ../../source/ref-changelog.md:483
 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`"
 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`"
 
-#: ../../source/ref-changelog.md:360
+#: ../../source/ref-changelog.md:487
 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))"
 
-#: ../../source/ref-changelog.md:362
+#: ../../source/ref-changelog.md:489
 msgid ""
 "The first (experimental) preview of pluggable Differential Privacy "
 "wrappers enables easy configuration and usage of differential privacy "
@@ -8321,101 +13836,115 @@ msgid ""
 "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 ""
+"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:364
+#: ../../source/ref-changelog.md:491
 msgid ""
 "**New iOS CoreML code example** "
 "([#1289](https://github.com/adap/flower/pull/1289))"
-msgstr ""
+msgstr "**新的 iOS CoreML 代码示例**（[#1289](https://github.com/adap/flower/pull/1289)）"
 
-#: ../../source/ref-changelog.md:366
+#: ../../source/ref-changelog.md:493
 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."
 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。"
 
-#: ../../source/ref-changelog.md:368
+#: ../../source/ref-changelog.md:495
 msgid ""
 "**New FedMedian strategy** "
 "([#1461](https://github.com/adap/flower/pull/1461))"
-msgstr ""
+msgstr "**新的联邦医疗策略** ([#1461](https://github.com/adap/flower/pull/1461))"
 
-#: ../../source/ref-changelog.md:370
+#: ../../source/ref-changelog.md:497
 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)。"
 
-#: ../../source/ref-changelog.md:372
+#: ../../source/ref-changelog.md:499
 msgid ""
 "**Log** `Client` **exceptions in Virtual Client Engine** "
 "([#1493](https://github.com/adap/flower/pull/1493))"
-msgstr ""
+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:374
+#: ../../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)."
-msgstr ""
+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:376
+#: ../../source/ref-changelog.md:503
 msgid ""
 "**Improve Virtual Client Engine internals** "
 "([#1401](https://github.com/adap/flower/pull/1401), "
 "[#1453](https://github.com/adap/flower/pull/1453))"
-msgstr ""
+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:378
+#: ../../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."
 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。"
 
-#: ../../source/ref-changelog.md:380
+#: ../../source/ref-changelog.md:507
 msgid ""
 "**Support optional** `Client`**/**`NumPyClient` **methods in Virtual "
 "Client Engine**"
-msgstr ""
+msgstr "**支持虚拟客户端引擎中的可选** `Client`**/**`NumPyClient` **方法**"
 
-#: ../../source/ref-changelog.md:382
+#: ../../source/ref-changelog.md:509
 msgid ""
 "The Virtual Client Engine now has full support for optional `Client` (and"
 " `NumPyClient`) methods."
-msgstr ""
+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:384
+#: ../../source/ref-changelog.md:511
 msgid ""
 "**Provide type information to packages using** `flwr` "
 "([#1377](https://github.com/adap/flower/pull/1377))"
 msgstr ""
+"**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:386
+#: ../../source/ref-changelog.md:513
 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`."
 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。"
 
-#: ../../source/ref-changelog.md:388
+#: ../../source/ref-changelog.md:515
 msgid ""
 "**Updated code example** "
 "([#1344](https://github.com/adap/flower/pull/1344), "
 "[#1347](https://github.com/adap/flower/pull/1347))"
 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:390
+#: ../../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."
-msgstr ""
+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:392
+#: ../../source/ref-changelog.md:519
 msgid ""
 "**Updated documentation** "
 "([#1355](https://github.com/adap/flower/pull/1355), "
@@ -8436,41 +13965,60 @@ msgid ""
 "[#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:394
+#: ../../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 ""
+msgstr "文档更新的数量之多，甚至没有必要逐一列出。"
 
-#: ../../source/ref-changelog.md:396
+#: ../../source/ref-changelog.md:523
 msgid ""
 "**Restructured documentation** "
 "([#1387](https://github.com/adap/flower/pull/1387))"
-msgstr ""
+msgstr "**重构文档**（[#1387](https://github.com/adap/flower/pull/1387)）"
 
-#: ../../source/ref-changelog.md:398
+#: ../../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!"
-msgstr ""
+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-changelog.md:400
+#: ../../source/ref-changelog.md:527
 msgid ""
 "**Open in Colab button** "
 "([#1389](https://github.com/adap/flower/pull/1389))"
-msgstr ""
+msgstr "**在 Colab 中打开按钮** ([#1389](https://github.com/adap/flower/pull/1389))"
 
-#: ../../source/ref-changelog.md:402
+#: ../../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."
 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。"
 
-#: ../../source/ref-changelog.md:404
+#: ../../source/ref-changelog.md:531
 msgid ""
 "**Improved tutorial** ([#1468](https://github.com/adap/flower/pull/1468),"
 " [#1470](https://github.com/adap/flower/pull/1470), "
@@ -8479,49 +14027,59 @@ msgid ""
 "[#1474](https://github.com/adap/flower/pull/1474), "
 "[#1475](https://github.com/adap/flower/pull/1475))"
 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)))"
 
-#: ../../source/ref-changelog.md:406
+#: ../../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)."
 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:412
+#: ../../source/ref-changelog.md:539
 msgid "v1.0.0 (2022-07-28)"
-msgstr ""
+msgstr "v1.0.0 (2022-07-28)"
 
-#: ../../source/ref-changelog.md:414
+#: ../../source/ref-changelog.md:541
 msgid "Highlights"
-msgstr ""
+msgstr "亮点"
 
-#: ../../source/ref-changelog.md:416
+#: ../../source/ref-changelog.md:543
 msgid "Stable **Virtual Client Engine** (accessible via `start_simulation`)"
-msgstr ""
+msgstr "稳定的**虚拟客户端引擎**（可通过`start_simulation`访问）"
 
-#: ../../source/ref-changelog.md:417
+#: ../../source/ref-changelog.md:544
 msgid "All `Client`/`NumPyClient` methods are now optional"
-msgstr ""
+msgstr "所有 `Client`/`NumPyClient` 方法现在都是可选的了"
 
-#: ../../source/ref-changelog.md:418
+#: ../../source/ref-changelog.md:545
 msgid "Configurable `get_parameters`"
-msgstr ""
+msgstr "可配置的`get_parameters`"
 
-#: ../../source/ref-changelog.md:419
+#: ../../source/ref-changelog.md:546
 msgid ""
 "Tons of small API cleanups resulting in a more coherent developer "
 "experience"
-msgstr ""
+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:423
+#: ../../source/ref-changelog.md:550
 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 ""
+"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-changelog.md:425
+#: ../../source/ref-changelog.md:552
 msgid ""
 "[@rtaiello](https://github.com/rtaiello), "
 "[@g-pichler](https://github.com/g-pichler), [@rob-"
@@ -8546,7 +14104,7 @@ msgid ""
 "[@mrinaald](https://github.com/mrinaald), "
 "[@zliel](https://github.com/zliel), "
 "[@MeiruiJiang](https://github.com/MeiruiJiang), "
-"[@sandracl72](https://github.com/sandracl72), "
+"[@sancarlim](https://github.com/sancarlim), "
 "[@gubertoli](https://github.com/gubertoli), "
 "[@Vingt100](https://github.com/Vingt100), "
 "[@MakGulati](https://github.com/MakGulati), "
@@ -8560,14 +14118,50 @@ msgid ""
 "[@tanertopal](https://github.com/tanertopal), "
 "[@danieljanes](https://github.com/danieljanes)."
 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)."
 
-#: ../../source/ref-changelog.md:429
+#: ../../source/ref-changelog.md:556
 msgid ""
 "**All arguments must be passed as keyword arguments** "
 "([#1338](https://github.com/adap/flower/pull/1338))"
-msgstr ""
+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-changelog.md:431
+#: ../../source/ref-changelog.md:558
 msgid ""
 "Pass all arguments as keyword arguments, positional arguments are not "
 "longer supported. Code that uses positional arguments (e.g., "
@@ -8576,15 +14170,20 @@ msgid ""
 "`start_client(server_address=\"127.0.0.1:8080\", "
 "client=FlowerClient())`)."
 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())`）。"
 
-#: ../../source/ref-changelog.md:433
+#: ../../source/ref-changelog.md:560
 msgid ""
 "**Introduce configuration object** `ServerConfig` **in** `start_server` "
 "**and** `start_simulation` "
 "([#1317](https://github.com/adap/flower/pull/1317))"
 msgstr ""
+"**在*** `start_server` ***和*** `start_simulation` 中引入配置对象*** "
+"`ServerConfig` ([#1317](https://github.com/adap/flower/pull/1317))"
 
-#: ../../source/ref-changelog.md:435
+#: ../../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"
@@ -8592,38 +14191,42 @@ msgid ""
 " arguments that as the previous config dict, but it makes writing type-"
 "safe code easier and the default parameters values more transparent."
 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:437
+#: ../../source/ref-changelog.md:564
 msgid ""
 "**Rename built-in strategy parameters for clarity** "
 "([#1334](https://github.com/adap/flower/pull/1334))"
-msgstr ""
+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-changelog.md:439
+#: ../../source/ref-changelog.md:566
 msgid ""
 "The following built-in strategy parameters were renamed to improve "
 "readability and consistency with other API's:"
-msgstr ""
+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:441
+#: ../../source/ref-changelog.md:568
 msgid "`fraction_eval` --> `fraction_evaluate`"
-msgstr ""
+msgstr "`fraction_eval` --> `fraction_evaluate`"
 
-#: ../../source/ref-changelog.md:442
+#: ../../source/ref-changelog.md:569
 msgid "`min_eval_clients` --> `min_evaluate_clients`"
-msgstr ""
+msgstr "`min_eval_clients` --> `min_evaluate_clients`"
 
-#: ../../source/ref-changelog.md:443
+#: ../../source/ref-changelog.md:570
 msgid "`eval_fn` --> `evaluate_fn`"
-msgstr ""
+msgstr "`eval_fn` --> `evaluate_fn`"
 
-#: ../../source/ref-changelog.md:445
+#: ../../source/ref-changelog.md:572
 msgid ""
 "**Update default arguments of built-in strategies** "
 "([#1278](https://github.com/adap/flower/pull/1278))"
-msgstr ""
+msgstr "**更新内置策略的默认参数** ([#1278](https://github.com/adap/flower/pull/1278))"
 
-#: ../../source/ref-changelog.md:447
+#: ../../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 "
@@ -8631,44 +14234,54 @@ msgid ""
 "the previous default values can get the previous behaviour by "
 "initializing the strategy in the following way:"
 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:449
+#: ../../source/ref-changelog.md:576
 msgid "`strategy = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`"
-msgstr ""
+msgstr "`strategy = FedAvg(fraction_fit=0.1, fraction_evaluate=0.1)`"
 
-#: ../../source/ref-changelog.md:451
+#: ../../source/ref-changelog.md:578
 msgid ""
 "**Add** `server_round` **to** `Strategy.evaluate` "
 "([#1334](https://github.com/adap/flower/pull/1334))"
 msgstr ""
+"**添加*** `server_round` ***到*** `Strategy.evaluate` "
+"([#1334](https://github.com/adap/flower/pull/1334))"
 
-#: ../../source/ref-changelog.md:453
+#: ../../source/ref-changelog.md:580
 msgid ""
 "The `Strategy` method `evaluate` now receives the current round of "
 "federated learning/evaluation as the first parameter."
-msgstr ""
+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-changelog.md:455
+#: ../../source/ref-changelog.md:582
 msgid ""
 "**Add** `server_round` **and** `config` **parameters to** `evaluate_fn` "
 "([#1334](https://github.com/adap/flower/pull/1334))"
 msgstr ""
+"**将*** `server_round` **和*** `config` **参数添加到*** `evaluate_fn` "
+"([#1334](https://github.com/adap/flower/pull/1334))"
 
-#: ../../source/ref-changelog.md:457
+#: ../../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`)."
 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`)。"
 
-#: ../../source/ref-changelog.md:459
+#: ../../source/ref-changelog.md:586
 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-changelog.md:461
+#: ../../source/ref-changelog.md:588
 msgid ""
 "Several Flower methods and functions (`evaluate_fn`, `configure_fit`, "
 "`aggregate_fit`, `configure_evaluate`, `aggregate_evaluate`) receive the "
@@ -8676,174 +14289,210 @@ msgid ""
 "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:463
+#: ../../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:465
+#: ../../source/ref-changelog.md:592
 msgid "The experimental package `flwr.dataset` was migrated to Flower Baselines."
-msgstr ""
+msgstr "实验软件包 `flwr.dataset` 已迁移至 Flower Baselines。"
 
-#: ../../source/ref-changelog.md:467
+#: ../../source/ref-changelog.md:594
 msgid ""
 "**Remove experimental strategies** "
 "([#1280](https://github.com/adap/flower/pull/1280))"
-msgstr ""
+msgstr "**删除实验策略** ([#1280](https://github.com/adap/flower/pull/1280))"
 
-#: ../../source/ref-changelog.md:469
+#: ../../source/ref-changelog.md:596
 msgid ""
 "Remove unmaintained experimental strategies (`FastAndSlow`, `FedFSv0`, "
 "`FedFSv1`)."
-msgstr ""
+msgstr "移除未维护的试验性策略（`FastAndSlow`、`FedFSv0`、`FedFSv1`）。"
 
-#: ../../source/ref-changelog.md:471
+#: ../../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:473
+#: ../../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 ""
+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:475
+#: ../../source/ref-changelog.md:602
 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))"
 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))"
 
-#: ../../source/ref-changelog.md:477
+#: ../../source/ref-changelog.md:604
 msgid ""
 "The `start_server` parameter `force_final_distributed_eval` has long been"
 " a historic artefact, in this release it is finally gone for good."
 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。"
 
-#: ../../source/ref-changelog.md:479
+#: ../../source/ref-changelog.md:606
 msgid ""
 "**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-changelog.md:481
+#: ../../source/ref-changelog.md:608
 msgid ""
 "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-changelog.md:483
+#: ../../source/ref-changelog.md:610
 msgid ""
 "**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-changelog.md:485
+#: ../../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."
 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:489
+#: ../../source/ref-changelog.md:616
 msgid ""
 "**Support Python 3.10** "
 "([#1320](https://github.com/adap/flower/pull/1320))"
-msgstr ""
+msgstr "** 支持 Python 3.10** ([#1320](https://github.com/adap/flower/pull/1320))"
 
-#: ../../source/ref-changelog.md:491
+#: ../../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 ""
+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-changelog.md:493
+#: ../../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:495
+#: ../../source/ref-changelog.md:622
 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 ""
+"`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-changelog.md:497
+#: ../../source/ref-changelog.md:624
 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))"
 
-#: ../../source/ref-changelog.md:499
+#: ../../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."
 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-changelog.md:501
+#: ../../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))"
 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:503
+#: ../../source/ref-changelog.md:630
 msgid ""
 "Many code examples received small or even large maintenance updates, "
 "among them are"
-msgstr ""
+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:505
+#: ../../source/ref-changelog.md:632
 msgid "`scikit-learn`"
-msgstr ""
+msgstr "`scikit-learn`"
 
-#: ../../source/ref-changelog.md:506
+#: ../../source/ref-changelog.md:633
 msgid "`simulation_pytorch`"
-msgstr ""
+msgstr "`simulation_pytorch`"
 
-#: ../../source/ref-changelog.md:507
+#: ../../source/ref-changelog.md:634
 msgid "`quickstart_pytorch`"
-msgstr ""
+msgstr "`quickstart_pytorch`"
 
-#: ../../source/ref-changelog.md:508
+#: ../../source/ref-changelog.md:635
 msgid "`quickstart_simulation`"
-msgstr ""
+msgstr "`quickstart_simulation`"
 
-#: ../../source/ref-changelog.md:509
+#: ../../source/ref-changelog.md:636
 msgid "`quickstart_tensorflow`"
-msgstr ""
+msgstr "`quickstart_tensorflow`"
 
-#: ../../source/ref-changelog.md:510
+#: ../../source/ref-changelog.md:637
 msgid "`advanced_tensorflow`"
-msgstr ""
+msgstr "`advanced_tensorflow`"
 
-#: ../../source/ref-changelog.md:512
+#: ../../source/ref-changelog.md:639
 msgid ""
 "**Remove the obsolete simulation example** "
 "([#1328](https://github.com/adap/flower/pull/1328))"
-msgstr ""
+msgstr "**删除过时的模拟示例** ([#1328](https://github.com/adap/flower/pull/1328))"
 
-#: ../../source/ref-changelog.md:514
+#: ../../source/ref-changelog.md:641
 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/ref-changelog.md:516
+#: ../../source/ref-changelog.md:643
 msgid ""
 "**Update documentation** "
 "([#1223](https://github.com/adap/flower/pull/1223), "
@@ -8857,8 +14506,18 @@ msgid ""
 "[#1305](https://github.com/adap/flower/pull/1305), "
 "[#1307](https://github.com/adap/flower/pull/1307))"
 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))"
 
-#: ../../source/ref-changelog.md:518
+#: ../../source/ref-changelog.md:645
 msgid ""
 "One substantial documentation update fixes multiple smaller rendering "
 "issues, makes titles more succinct to improve navigation, removes a "
@@ -8867,25 +14526,30 @@ msgid ""
 "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/ref-changelog.md:520 ../../source/ref-changelog.md:575
-#: ../../source/ref-changelog.md:644 ../../source/ref-changelog.md:683
+#: ../../source/ref-changelog.md:647 ../../source/ref-changelog.md:702
+#: ../../source/ref-changelog.md:771 ../../source/ref-changelog.md:810
 msgid "**Minor updates**"
-msgstr ""
+msgstr "**小规模更新**"
 
-#: ../../source/ref-changelog.md:522
+#: ../../source/ref-changelog.md:649
 msgid ""
 "Add round number to fit and evaluate log messages "
 "([#1266](https://github.com/adap/flower/pull/1266))"
-msgstr ""
+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/ref-changelog.md:523
+#: ../../source/ref-changelog.md:650
 msgid ""
 "Add secure gRPC connection to the `advanced_tensorflow` code example "
 "([#847](https://github.com/adap/flower/pull/847))"
 msgstr ""
+"为 `advanced_tensorflow` 代码示例添加安全 gRPC 连接 "
+"([#847](https://github.com/adap/flower/pull/847))"
 
-#: ../../source/ref-changelog.md:524
+#: ../../source/ref-changelog.md:651
 msgid ""
 "Update developer tooling "
 "([#1231](https://github.com/adap/flower/pull/1231), "
@@ -8893,85 +14557,109 @@ msgid ""
 "[#1301](https://github.com/adap/flower/pull/1301), "
 "[#1310](https://github.com/adap/flower/pull/1310))"
 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)"
 
-#: ../../source/ref-changelog.md:525
+#: ../../source/ref-changelog.md:652
 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 ""
+"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/ref-changelog.md:527
+#: ../../source/ref-changelog.md:654
 msgid "v0.19.0 (2022-05-18)"
-msgstr ""
+msgstr "v0.19.0 (2022-05-18)"
 
-#: ../../source/ref-changelog.md:531
+#: ../../source/ref-changelog.md:658
 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 ""
+"**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/ref-changelog.md:533
+#: ../../source/ref-changelog.md:660
 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.dev/docs/using-baselines.html). "
+"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.dev/docs/contributing-"
+"[contribute their own baselines](https://flower.ai/docs/contributing-"
 "baselines.html)."
 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)。"
 
-#: ../../source/ref-changelog.md:535
+#: ../../source/ref-changelog.md:662
 msgid ""
 "**C++ client SDK (preview) and code example** "
 "([#1111](https://github.com/adap/flower/pull/1111))"
-msgstr ""
+msgstr "**C++客户端SDK（预览版）和代码示例**（[#1111](https://github.com/adap/flower/pull/1111)）"
 
-#: ../../source/ref-changelog.md:537
+#: ../../source/ref-changelog.md:664
 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。"
 
-#: ../../source/ref-changelog.md:539
+#: ../../source/ref-changelog.md:666
 msgid ""
 "**Add experimental support for Python 3.10 and Python 3.11** "
 "([#1135](https://github.com/adap/flower/pull/1135))"
 msgstr ""
+"** 增加对 Python 3.10 和 Python 3.11 的实验支持** "
+"([#1135](https://github.com/adap/flower/pull/1135))"
 
-#: ../../source/ref-changelog.md:541
+#: ../../source/ref-changelog.md:668
 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 ""
+"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/ref-changelog.md:543
+#: ../../source/ref-changelog.md:670
 msgid ""
 "**Aggregate custom metrics through user-provided functions** "
 "([#1144](https://github.com/adap/flower/pull/1144))"
-msgstr ""
+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/ref-changelog.md:545
+#: ../../source/ref-changelog.md:672
 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/ref-changelog.md:547
+#: ../../source/ref-changelog.md:674
 msgid ""
 "**User-configurable round timeout** "
 "([#1162](https://github.com/adap/flower/pull/1162))"
-msgstr ""
+msgstr "**用户可配置的回合超时**（[#1162](https://github.com/adap/flower/pull/1162)）"
 
-#: ../../source/ref-changelog.md:549
+#: ../../source/ref-changelog.md:676
 msgid ""
 "A new configuration value allows the round timeout to be set for "
 "`start_server` and `start_simulation`. If the `config` dictionary "
@@ -8979,171 +14667,208 @@ msgid ""
 "server will wait *at least* `round_timeout` seconds before it closes the "
 "connection."
 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。"
 
-#: ../../source/ref-changelog.md:551
+#: ../../source/ref-changelog.md:678
 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:553
+#: ../../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`."
 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/ref-changelog.md:555
+#: ../../source/ref-changelog.md:682
 msgid ""
 "**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/ref-changelog.md:557
+#: ../../source/ref-changelog.md:684
 msgid ""
 "Two Jupyter Notebook tutorials (compatible with Google Colab) explain "
 "basic and intermediate Flower features:"
-msgstr ""
+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/ref-changelog.md:559
+#: ../../source/ref-changelog.md:686
 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)"
 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)"
 
-#: ../../source/ref-changelog.md:561
+#: ../../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)"
 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/ref-changelog.md:563
+#: ../../source/ref-changelog.md:690
 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))"
 
-#: ../../source/ref-changelog.md:565
+#: ../../source/ref-changelog.md:692
 msgid ""
 "The new `FedAvgM` strategy implements Federated Averaging with Server "
 "Momentum \\[Hsu et al., 2019\\]."
-msgstr ""
+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/ref-changelog.md:567
+#: ../../source/ref-changelog.md:694
 msgid ""
 "**New advanced PyTorch code example** "
 "([#1007](https://github.com/adap/flower/pull/1007))"
-msgstr ""
+msgstr "**新的 PyTorch 高级代码示例** ([#1007](https://github.com/adap/flower/pull/1007))"
 
-#: ../../source/ref-changelog.md:569
+#: ../../source/ref-changelog.md:696
 msgid ""
 "A new code example (`advanced_pytorch`) demonstrates advanced Flower "
 "concepts with PyTorch."
-msgstr ""
+msgstr "新代码示例 (`advanced_pytorch`) 演示了 PyTorch 的高级 Flower 概念。"
 
-#: ../../source/ref-changelog.md:571
+#: ../../source/ref-changelog.md:698
 msgid ""
 "**New JAX code example** "
 "([#906](https://github.com/adap/flower/pull/906), "
 "[#1143](https://github.com/adap/flower/pull/1143))"
 msgstr ""
+"**新的 JAX 代码示例**（[#906](https://github.com/adap/flower/pull/906), "
+"[#1143](https://github.com/adap/flower/pull/1143)"
 
-#: ../../source/ref-changelog.md:573
+#: ../../source/ref-changelog.md:700
 msgid ""
 "A new code example (`jax_from_centralized_to_federated`) shows federated "
 "learning with JAX and Flower."
-msgstr ""
+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/ref-changelog.md:577
+#: ../../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:578
+#: ../../source/ref-changelog.md:705
 msgid ""
 "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/ref-changelog.md:579
+#: ../../source/ref-changelog.md:706
 msgid ""
 "New documentation for [implementing "
-"strategies](https://flower.dev/docs/framework/how-to-implement-"
+"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 ""
+"[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:580
+#: ../../source/ref-changelog.md:707
 msgid ""
 "New mobile-friendly documentation theme "
 "([#1174](https://github.com/adap/flower/pull/1174))"
-msgstr ""
+msgstr "新的移动友好型文档主题 ([#1174](https://github.com/adap/flower/pull/1174))"
 
-#: ../../source/ref-changelog.md:581
+#: ../../source/ref-changelog.md:708
 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 ""
+"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/ref-changelog.md:585
+#: ../../source/ref-changelog.md:712
 msgid ""
 "**Remove deprecated support for Python 3.6** "
 "([#871](https://github.com/adap/flower/pull/871))"
-msgstr ""
+msgstr "**删除对 Python 3.6 的过时支持** ([#871](https://github.com/adap/flower/pull/871))"
 
-#: ../../source/ref-changelog.md:586
+#: ../../source/ref-changelog.md:713
 msgid ""
 "**Remove deprecated KerasClient** "
 "([#857](https://github.com/adap/flower/pull/857))"
-msgstr ""
+msgstr "**移除过时的 KerasClient**（[#857](https://github.com/adap/flower/pull/857)）"
 
-#: ../../source/ref-changelog.md:587
+#: ../../source/ref-changelog.md:714
 msgid ""
 "**Remove deprecated no-op extra installs** "
 "([#973](https://github.com/adap/flower/pull/973))"
-msgstr ""
+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:588
+#: ../../source/ref-changelog.md:715
 msgid ""
 "**Remove deprecated proto fields from** `FitRes` **and** `EvaluateRes` "
 "([#869](https://github.com/adap/flower/pull/869))"
 msgstr ""
+"**从** `FitRes` **和** `EvaluateRes` 中移除已废弃的 proto 字段 "
+"([#869](https://github.com/adap/flower/pull/869))"
 
-#: ../../source/ref-changelog.md:589
+#: ../../source/ref-changelog.md:716
 msgid ""
 "**Remove deprecated QffedAvg strategy (replaced by QFedAvg)** "
 "([#1107](https://github.com/adap/flower/pull/1107))"
 msgstr ""
+"**移除过时的 QffedAvg 策略（由 QFedAvg 取代）** "
+"([#1107](https://github.com/adap/flower/pull/1107))"
 
-#: ../../source/ref-changelog.md:590
+#: ../../source/ref-changelog.md:717
 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))"
 
-#: ../../source/ref-changelog.md:591
+#: ../../source/ref-changelog.md:718
 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/ref-changelog.md:592
+#: ../../source/ref-changelog.md:719
 msgid ""
 "**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/ref-changelog.md:594
+#: ../../source/ref-changelog.md:721
 msgid "v0.18.0 (2022-02-28)"
-msgstr ""
+msgstr "v0.18.0 (2022-02-28)"
 
-#: ../../source/ref-changelog.md:598
+#: ../../source/ref-changelog.md:725
 msgid ""
 "**Improved Virtual Client Engine compatibility with Jupyter Notebook / "
 "Google Colab** ([#866](https://github.com/adap/flower/pull/866), "
@@ -9151,55 +14876,69 @@ msgid ""
 "[#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/ref-changelog.md:600
+#: ../../source/ref-changelog.md:727
 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 ""
+"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/ref-changelog.md:602
+#: ../../source/ref-changelog.md:729
 msgid ""
 "**New Jupyter Notebook code example** "
 "([#833](https://github.com/adap/flower/pull/833))"
 msgstr ""
+"**新的 Jupyter Notebook 代码示例** "
+"([#833](https://github.com/adap/flower/pull/833))"
 
-#: ../../source/ref-changelog.md:604
+#: ../../source/ref-changelog.md:731
 msgid ""
 "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/ref-changelog.md:606
+#: ../../source/ref-changelog.md:733
 msgid ""
 "**Client properties (feature preview)** "
 "([#795](https://github.com/adap/flower/pull/795))"
-msgstr ""
+msgstr "**客户端属性（功能预览）** ([#795](https://github.com/adap/flower/pull/795))"
 
-#: ../../source/ref-changelog.md:608
+#: ../../source/ref-changelog.md:735
 msgid ""
 "Clients can implement a new method `get_properties` to enable server-side"
 " strategies to query client properties."
-msgstr ""
+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/ref-changelog.md:610
+#: ../../source/ref-changelog.md:737
 msgid ""
 "**Experimental Android support with TFLite** "
 "([#865](https://github.com/adap/flower/pull/865))"
-msgstr ""
+msgstr "** 使用 TFLite 实验性支持安卓系统** ([#865](https://github.com/adap/flower/pull/865))"
 
-#: ../../source/ref-changelog.md:612
+#: ../../source/ref-changelog.md:739
 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."
 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。"
 
-#: ../../source/ref-changelog.md:614
+#: ../../source/ref-changelog.md:741
 msgid ""
 "The example uses TFLite on the client side, along with a new "
 "`FedAvgAndroid` strategy. The Android client and `FedAvgAndroid` are "
@@ -9207,46 +14946,57 @@ msgid ""
 "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/ref-changelog.md:616
+#: ../../source/ref-changelog.md:743
 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))"
 
-#: ../../source/ref-changelog.md:618
+#: ../../source/ref-changelog.md:745
 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。"
 
-#: ../../source/ref-changelog.md:620
+#: ../../source/ref-changelog.md:747
 msgid ""
 "**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/ref-changelog.md:622
+#: ../../source/ref-changelog.md:749
 msgid ""
 "A new code example (`opacus`) demonstrates differentially-private "
 "federated learning with Opacus, PyTorch, and Flower."
-msgstr ""
+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/ref-changelog.md:624
+#: ../../source/ref-changelog.md:751
 msgid ""
 "**New Hugging Face Transformers code example** "
 "([#863](https://github.com/adap/flower/pull/863))"
 msgstr ""
+"**新的Hugging Face Transformers代码示例** "
+"([#863](https://github.com/adap/flower/pull/863))"
 
-#: ../../source/ref-changelog.md:626
+#: ../../source/ref-changelog.md:753
 msgid ""
 "A new code example (`quickstart_huggingface`) demonstrates usage of "
 "Hugging Face Transformers with Flower."
-msgstr ""
+msgstr "新的代码示例(`quickstart_huggingface`)证明了结合Flower和Hugging Face Transformers的实用性。"
 
-#: ../../source/ref-changelog.md:628
+#: ../../source/ref-changelog.md:755
 msgid ""
 "**New MLCube code example** "
 "([#779](https://github.com/adap/flower/pull/779), "
@@ -9254,14 +15004,18 @@ msgid ""
 "[#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))"
 
-#: ../../source/ref-changelog.md:630
+#: ../../source/ref-changelog.md:757
 msgid ""
 "A new code example (`quickstart_mlcube`) demonstrates usage of MLCube "
 "with Flower."
-msgstr ""
+msgstr "新代码示例（\"quickstart_mlcube\"）演示了 MLCube 与 Flower 的用法。"
 
-#: ../../source/ref-changelog.md:632
+#: ../../source/ref-changelog.md:759
 msgid ""
 "**SSL-enabled server and client** "
 "([#842](https://github.com/adap/flower/pull/842),  "
@@ -9271,34 +15025,45 @@ msgid ""
 "[#993](https://github.com/adap/flower/pull/993), "
 "[#994](https://github.com/adap/flower/pull/994))"
 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))"
 
-#: ../../source/ref-changelog.md:634
+#: ../../source/ref-changelog.md:761
 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 ""
+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:636
+#: ../../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))"
 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/ref-changelog.md:638
+#: ../../source/ref-changelog.md:765
 msgid ""
 "`FedAdam` and `FedAdam` match the latest version of the Adaptive "
 "Federated Optimization paper."
-msgstr ""
+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:640
+#: ../../source/ref-changelog.md:767
 msgid ""
 "**Initialize** `start_simulation` **with a list of client IDs** "
 "([#860](https://github.com/adap/flower/pull/860))"
 msgstr ""
+"**初始化** `start_simulation` **使用客户端 ID 列表** "
+"([#860](https://github.com/adap/flower/pull/860))"
 
-#: ../../source/ref-changelog.md:642
+#: ../../source/ref-changelog.md:769
 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 "
@@ -9306,56 +15071,69 @@ msgid ""
 "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/ref-changelog.md:646
+#: ../../source/ref-changelog.md:773
 msgid ""
 "Update `num_examples` calculation in PyTorch code examples in "
 "([#909](https://github.com/adap/flower/pull/909))"
 msgstr ""
+"更新 PyTorch 代码示例中的 \"num_examples \"计算 "
+"([#909](https://github.com/adap/flower/pull/909))"
 
-#: ../../source/ref-changelog.md:647
+#: ../../source/ref-changelog.md:774
 msgid ""
 "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/ref-changelog.md:648
+#: ../../source/ref-changelog.md:775
 msgid ""
 "`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/ref-changelog.md:649
+#: ../../source/ref-changelog.md:776
 msgid ""
 "Make `max_workers` (used by `ThreadPoolExecutor`) configurable "
 "([#978](https://github.com/adap/flower/pull/978))"
 msgstr ""
+"使 `max_workers`（由 "
+"`ThreadPoolExecutor`使用）可配置（[#978](https://github.com/adap/flower/pull/978)）"
 
-#: ../../source/ref-changelog.md:650
+#: ../../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 ""
+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:651
+#: ../../source/ref-changelog.md:778
 msgid ""
 "Added a new FAQ section to the documentation "
 "([#948](https://github.com/adap/flower/pull/948))"
-msgstr ""
+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/ref-changelog.md:652
+#: ../../source/ref-changelog.md:779
 msgid ""
 "And many more under-the-hood changes, library updates, documentation "
 "changes, and tooling improvements!"
-msgstr ""
+msgstr "还有更多底层更改、库更新、文档更改和工具改进！"
 
-#: ../../source/ref-changelog.md:656
+#: ../../source/ref-changelog.md:783
 msgid ""
 "**Removed** `flwr_example` **and** `flwr_experimental` **from release "
 "build** ([#869](https://github.com/adap/flower/pull/869))"
 msgstr ""
+"**从发布版中删除**`flwr_example`**和**`flwr_experimental`** "
+"([#869](https://github.com/adap/flower/pull/869))"
 
-#: ../../source/ref-changelog.md:658
+#: ../../source/ref-changelog.md:785
 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 "
@@ -9363,20 +15141,26 @@ msgid ""
 "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/ref-changelog.md:660
+#: ../../source/ref-changelog.md:787
 msgid "v0.17.0 (2021-09-24)"
-msgstr ""
+msgstr "v0.17.0 (2021-09-24)"
 
-#: ../../source/ref-changelog.md:664
+#: ../../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))"
 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))"
 
-#: ../../source/ref-changelog.md:666
+#: ../../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"
@@ -9385,125 +15169,142 @@ msgid ""
 "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/ref-changelog.md:668
+#: ../../source/ref-changelog.md:795
 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。"
 
-#: ../../source/ref-changelog.md:670
+#: ../../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))"
 msgstr ""
+"**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:672
+#: ../../source/ref-changelog.md:799
 msgid ""
 "FedYogi - Federated learning strategy using Yogi on server-side. "
 "Implementation based on https://arxiv.org/abs/2003.00295"
-msgstr ""
+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/ref-changelog.md:673
+#: ../../source/ref-changelog.md:800
 msgid ""
 "FedAdam - Federated learning strategy using Adam on server-side. "
 "Implementation based on https://arxiv.org/abs/2003.00295"
-msgstr ""
+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/ref-changelog.md:675
+#: ../../source/ref-changelog.md:802
 msgid ""
 "**New PyTorch Lightning code example** "
 "([#617](https://github.com/adap/flower/pull/617))"
 msgstr ""
+"**新的 PyTorch Lightning 代码示例** "
+"([#617](https://github.com/adap/flower/pull/617))"
 
-#: ../../source/ref-changelog.md:677
+#: ../../source/ref-changelog.md:804
 msgid ""
 "**New Variational Auto-Encoder code example** "
 "([#752](https://github.com/adap/flower/pull/752))"
-msgstr ""
+msgstr "**新的变分自动编码器代码示例** ([#752](https://github.com/adap/flower/pull/752))"
 
-#: ../../source/ref-changelog.md:679
+#: ../../source/ref-changelog.md:806
 msgid ""
 "**New scikit-learn code example** "
 "([#748](https://github.com/adap/flower/pull/748))"
-msgstr ""
+msgstr "**新的 scikit-learn 代码示例** ([#748](https://github.com/adap/flower/pull/748))"
 
-#: ../../source/ref-changelog.md:681
+#: ../../source/ref-changelog.md:808
 msgid ""
 "**New experimental TensorBoard strategy** "
 "([#789](https://github.com/adap/flower/pull/789))"
-msgstr ""
+msgstr "**新的实验性 TensorBoard 策略**（[#789](https://github.com/adap/flower/pull/789)）"
 
-#: ../../source/ref-changelog.md:685
+#: ../../source/ref-changelog.md:812
 msgid ""
 "Improved advanced TensorFlow code example "
 "([#769](https://github.com/adap/flower/pull/769))"
-msgstr ""
+msgstr "改进的高级 TensorFlow 代码示例（[#769](https://github.com/adap/flower/pull/769)"
 
-#: ../../source/ref-changelog.md:686
+#: ../../source/ref-changelog.md:813
 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/ref-changelog.md:687
+#: ../../source/ref-changelog.md:814
 msgid ""
 "Improved gRPC server docs "
 "([#841](https://github.com/adap/flower/pull/841))"
-msgstr ""
+msgstr "改进了 gRPC 服务器文档（[#841](https://github.com/adap/flower/pull/841)）"
 
-#: ../../source/ref-changelog.md:688
+#: ../../source/ref-changelog.md:815
 msgid ""
 "Improved error message in `NumPyClient` "
 "([#851](https://github.com/adap/flower/pull/851))"
-msgstr ""
+msgstr "改进了 `NumPyClient` 中的错误信息 ([#851](https://github.com/adap/flower/pull/851))"
 
-#: ../../source/ref-changelog.md:689
+#: ../../source/ref-changelog.md:816
 msgid ""
 "Improved PyTorch quickstart code example "
 "([#852](https://github.com/adap/flower/pull/852))"
-msgstr ""
+msgstr "改进的 PyTorch 快速启动代码示例 ([#852](https://github.com/adap/flower/pull/852))"
 
-#: ../../source/ref-changelog.md:693
+#: ../../source/ref-changelog.md:820
 msgid ""
 "**Disabled final distributed evaluation** "
 "([#800](https://github.com/adap/flower/pull/800))"
-msgstr ""
+msgstr "**禁用最终分布式评价** ([#800](https://github.com/adap/flower/pull/800))"
 
-#: ../../source/ref-changelog.md:695
+#: ../../source/ref-changelog.md:822
 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`."
 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。"
 
-#: ../../source/ref-changelog.md:697
+#: ../../source/ref-changelog.md:824
 msgid ""
 "**Renamed q-FedAvg strategy** "
 "([#802](https://github.com/adap/flower/pull/802))"
-msgstr ""
+msgstr "**更名为 q-FedAvg 策略** ([#802](https://github.com/adap/flower/pull/802))"
 
-#: ../../source/ref-changelog.md:699
+#: ../../source/ref-changelog.md:826
 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 the original (now "
+"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 ""
+"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/ref-changelog.md:701
+#: ../../source/ref-changelog.md:828
 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/ref-changelog.md:703
+#: ../../source/ref-changelog.md:830
 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 "
@@ -9511,32 +15312,30 @@ msgid ""
 " 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/ref-changelog.md:705
+#: ../../source/ref-changelog.md:832
 msgid "v0.16.0 (2021-05-11)"
-msgstr ""
+msgstr "v0.16.0 (2021-05-11)"
 
-#: ../../source/ref-changelog.md:709
+#: ../../source/ref-changelog.md:836
 msgid ""
 "**New built-in strategies** "
 "([#549](https://github.com/adap/flower/pull/549))"
-msgstr ""
+msgstr "**新的内置策略** ([#549](https://github.com/adap/flower/pull/549))"
 
-#: ../../source/ref-changelog.md:711
+#: ../../source/ref-changelog.md:838
 msgid "(abstract) FedOpt"
-msgstr ""
-
-#: ../../source/ref-changelog.md:712
-msgid "FedAdagrad"
-msgstr ""
+msgstr "（摘要） FedOpt"
 
-#: ../../source/ref-changelog.md:714
+#: ../../source/ref-changelog.md:841
 msgid ""
 "**Custom metrics for server and strategies** "
 "([#717](https://github.com/adap/flower/pull/717))"
-msgstr ""
+msgstr "**服务器和策略的自定义指标** ([#717](https://github.com/adap/flower/pull/717))"
 
-#: ../../source/ref-changelog.md:716
+#: ../../source/ref-changelog.md:843
 msgid ""
 "The Flower server is now fully task-agnostic, all remaining instances of "
 "task-specific metrics (such as `accuracy`) have been replaced by custom "
@@ -9544,38 +15343,46 @@ msgid ""
 "dictionary containing custom metrics from client to server. As of this "
 "release, custom metrics replace task-specific metrics on the server."
 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。"
 
-#: ../../source/ref-changelog.md:718
+#: ../../source/ref-changelog.md:845
 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 build-in strategies (via "
+"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 ""
+"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/ref-changelog.md:720
+#: ../../source/ref-changelog.md:847
 msgid ""
-"Stratey implementations should migrate their `aggregate_fit` and "
+"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 ""
+"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:722
+#: ../../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 ""
+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/ref-changelog.md:724
+#: ../../source/ref-changelog.md:851
 msgid ""
 "**Migration warnings for deprecated functionality** "
 "([#690](https://github.com/adap/flower/pull/690))"
-msgstr ""
+msgstr "** 过时功能的迁移警告** ([#690](https://github.com/adap/flower/pull/690))"
 
-#: ../../source/ref-changelog.md:726
+#: ../../source/ref-changelog.md:853
 msgid ""
 "Earlier versions of Flower were often migrated to new APIs, while "
 "maintaining compatibility with legacy APIs. This release introduces "
@@ -9583,34 +15390,43 @@ msgid ""
 "new warning messages often provide details on how to migrate to more "
 "recent APIs, thus easing the transition from one release to another."
 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。"
 
-#: ../../source/ref-changelog.md:728
+#: ../../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 ""
+"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:730
+#: ../../source/ref-changelog.md:857
 msgid "MXNet example and documentation"
-msgstr ""
+msgstr "MXNet 示例和文档"
 
-#: ../../source/ref-changelog.md:732
+#: ../../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))"
 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))"
 
-#: ../../source/ref-changelog.md:736
+#: ../../source/ref-changelog.md:863
 msgid ""
 "**Serialization-agnostic server** "
 "([#721](https://github.com/adap/flower/pull/721))"
-msgstr ""
+msgstr "**序列化无关服务器** ([#721](https://github.com/adap/flower/pull/721))"
 
-#: ../../source/ref-changelog.md:738
+#: ../../source/ref-changelog.md:865
 msgid ""
 "The Flower server is now fully serialization-agnostic. Prior usage of "
 "class `Weights` (which represents parameters as deserialized NumPy "
@@ -9620,96 +15436,118 @@ msgid ""
 "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/ref-changelog.md:740
+#: ../../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 slighly changed Strategy method "
+"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 ""
+"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/ref-changelog.md:742
+#: ../../source/ref-changelog.md:869
 msgid ""
 "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/ref-changelog.md:744
+#: ../../source/ref-changelog.md:871
 msgid "v0.15.0 (2021-03-12)"
-msgstr ""
+msgstr "v0.15.0 (2021-03-12)"
 
-#: ../../source/ref-changelog.md:748
+#: ../../source/ref-changelog.md:875
 msgid ""
 "**Server-side parameter initialization** "
 "([#658](https://github.com/adap/flower/pull/658))"
-msgstr ""
+msgstr "**服务器端参数初始化** ([#658](https://github.com/adap/flower/pull/658))"
 
-#: ../../source/ref-changelog.md:750
+#: ../../source/ref-changelog.md:877
 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/ref-changelog.md:752
+#: ../../source/ref-changelog.md:879
 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 ""
+"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/ref-changelog.md:771
+#: ../../source/ref-changelog.md:898
 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 ""
+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:773
+#: ../../source/ref-changelog.md:900
 msgid "Deprecations"
-msgstr ""
+msgstr "停用"
 
-#: ../../source/ref-changelog.md:775
+#: ../../source/ref-changelog.md:902
 msgid ""
 "Deprecate `flwr.server.strategy.DefaultStrategy` (migrate to "
 "`flwr.server.strategy.FedAvg`, which is equivalent)"
 msgstr ""
+"停用 `flwr.server.strategy.DefaultStrategy`（迁移到等价的 "
+"`flwr.server.strategy.FedAvg`）"
 
-#: ../../source/ref-changelog.md:777
+#: ../../source/ref-changelog.md:904
 msgid "v0.14.0 (2021-02-18)"
-msgstr ""
+msgstr "v0.14.0 (2021-02-18)"
 
-#: ../../source/ref-changelog.md:781
+#: ../../source/ref-changelog.md:908
 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))"
 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))"
 
-#: ../../source/ref-changelog.md:783
+#: ../../source/ref-changelog.md:910
 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!"
 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/ref-changelog.md:785
+#: ../../source/ref-changelog.md:912
 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。"
 
-#: ../../source/ref-changelog.md:787
+#: ../../source/ref-changelog.md:914
 msgid ""
 "In case you wondered: this feature is compatible with existing projects, "
 "the additional dictionary return value is optional. New code should "
@@ -9718,124 +15556,142 @@ msgid ""
 "`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/ref-changelog.md:789
+#: ../../source/ref-changelog.md:916
 msgid ""
 "*Code example:* note the additional dictionary return values in both "
 "`FlwrClient.fit` and `FlwrClient.evaluate`:"
-msgstr ""
+msgstr "*代码示例：* 注意 `FlwrClient.fit` 和 `FlwrClient.evaluate` 中的附加字典返回值："
 
-#: ../../source/ref-changelog.md:804
+#: ../../source/ref-changelog.md:931
 msgid ""
 "**Generalized** `config` **argument in** `Client.fit` **and** "
 "`Client.evaluate` ([#595](https://github.com/adap/flower/pull/595))"
 msgstr ""
+"**在**`Client.fit` "
+"**和**`Client.evaluate`中泛化**`config`参数（[#595](https://github.com/adap/flower/pull/595)）"
 
-#: ../../source/ref-changelog.md:806
+#: ../../source/ref-changelog.md:933
 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`。"
 
-#: ../../source/ref-changelog.md:808
+#: ../../source/ref-changelog.md:935
 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!"
 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/ref-changelog.md:810
+#: ../../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`:"
-msgstr ""
+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:827
+#: ../../source/ref-changelog.md:954
 msgid "v0.13.0 (2021-01-08)"
-msgstr ""
+msgstr "v0.13.0 (2021-01-08)"
 
-#: ../../source/ref-changelog.md:831
+#: ../../source/ref-changelog.md:958
 msgid ""
 "New example: PyTorch From Centralized To Federated "
 "([#549](https://github.com/adap/flower/pull/549))"
-msgstr ""
+msgstr "新示例： PyTorch 从集中到联邦 ([#549](https://github.com/adap/flower/pull/549))"
 
-#: ../../source/ref-changelog.md:832
+#: ../../source/ref-changelog.md:959
 msgid "Improved documentation"
-msgstr ""
+msgstr "改进文档"
 
-#: ../../source/ref-changelog.md:833
+#: ../../source/ref-changelog.md:960
 msgid "New documentation theme ([#551](https://github.com/adap/flower/pull/551))"
-msgstr ""
+msgstr "新文档主题 ([#551](https://github.com/adap/flower/pull/551))"
 
-#: ../../source/ref-changelog.md:834
+#: ../../source/ref-changelog.md:961
 msgid "New API reference ([#554](https://github.com/adap/flower/pull/554))"
-msgstr ""
+msgstr "新的 API 参考 ([#554](https://github.com/adap/flower/pull/554))"
 
-#: ../../source/ref-changelog.md:835
+#: ../../source/ref-changelog.md:962
 msgid ""
 "Updated examples documentation "
 "([#549](https://github.com/adap/flower/pull/549))"
-msgstr ""
+msgstr "更新了示例文档 ([#549](https://github.com/adap/flower/pull/549))"
 
-#: ../../source/ref-changelog.md:836
+#: ../../source/ref-changelog.md:963
 msgid ""
 "Removed obsolete documentation "
 "([#548](https://github.com/adap/flower/pull/548))"
-msgstr ""
+msgstr "删除了过时的文档 ([#548](https://github.com/adap/flower/pull/548))"
 
-#: ../../source/ref-changelog.md:838
+#: ../../source/ref-changelog.md:965
 msgid "Bugfix:"
-msgstr ""
+msgstr "错误修正："
 
-#: ../../source/ref-changelog.md:840
+#: ../../source/ref-changelog.md:967
 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 ""
+"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:842
+#: ../../source/ref-changelog.md:969
 msgid "v0.12.0 (2020-12-07)"
-msgstr ""
+msgstr "v0.12.0 (2020-12-07)"
 
-#: ../../source/ref-changelog.md:844 ../../source/ref-changelog.md:860
+#: ../../source/ref-changelog.md:971 ../../source/ref-changelog.md:987
 msgid "Important changes:"
-msgstr ""
+msgstr "重要变更："
 
-#: ../../source/ref-changelog.md:846
+#: ../../source/ref-changelog.md:973
 msgid ""
 "Added an example for embedded devices "
 "([#507](https://github.com/adap/flower/pull/507))"
-msgstr ""
+msgstr "添加了嵌入式设备示例 ([#507](https://github.com/adap/flower/pull/507))"
 
-#: ../../source/ref-changelog.md:847
+#: ../../source/ref-changelog.md:974
 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 ""
+"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/ref-changelog.md:848
+#: ../../source/ref-changelog.md:975
 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 ""
+"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/ref-changelog.md:850
+#: ../../source/ref-changelog.md:977
 msgid "v0.11.0 (2020-11-30)"
-msgstr ""
+msgstr "v0.11.0 (2020-11-30)"
 
-#: ../../source/ref-changelog.md:852
+#: ../../source/ref-changelog.md:979
 msgid "Incompatible changes:"
-msgstr ""
+msgstr "不兼容的更改："
 
-#: ../../source/ref-changelog.md:854
+#: ../../source/ref-changelog.md:981
 msgid ""
 "Renamed strategy methods "
 "([#486](https://github.com/adap/flower/pull/486)) to unify the naming of "
@@ -9844,57 +15700,68 @@ msgid ""
 "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:855
+#: ../../source/ref-changelog.md:982
 msgid "`on_configure_evaluate` => `configure_evaluate`"
-msgstr ""
+msgstr "`on_configure_evaluate` => `configure_evaluate`"
 
-#: ../../source/ref-changelog.md:856
+#: ../../source/ref-changelog.md:983
 msgid "`on_aggregate_evaluate` => `aggregate_evaluate`"
-msgstr ""
+msgstr "`on_aggregate_evaluate` => `aggregate_evaluate`"
 
-#: ../../source/ref-changelog.md:857
+#: ../../source/ref-changelog.md:984
 msgid "`on_configure_fit` => `configure_fit`"
-msgstr ""
+msgstr "`on_configure_fit` => `configure_fit`"
 
-#: ../../source/ref-changelog.md:858
+#: ../../source/ref-changelog.md:985
 msgid "`on_aggregate_fit` => `aggregate_fit`"
-msgstr ""
+msgstr "`on_aggregate_fit` => `aggregate_fit`"
 
-#: ../../source/ref-changelog.md:862
+#: ../../source/ref-changelog.md:989
 msgid ""
 "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/ref-changelog.md:863
+#: ../../source/ref-changelog.md:990
 msgid ""
 "Simplified examples and baselines "
 "([#484](https://github.com/adap/flower/pull/484))."
-msgstr ""
+msgstr "简化示例和baselines（[#484](https://github.com/adap/flower/pull/484)）。"
 
-#: ../../source/ref-changelog.md:864
+#: ../../source/ref-changelog.md:991
 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)）。"
 
-#: ../../source/ref-changelog.md:865
+#: ../../source/ref-changelog.md:992
 msgid ""
 "Set minimal Python version to 3.6.1 instead of 3.6.9 "
 "([#471](https://github.com/adap/flower/pull/471))."
 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))."
 
-#: ../../source/ref-changelog.md:866
+#: ../../source/ref-changelog.md:993
 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 ""
+msgstr "项目实例"
 
 #: ../../source/ref-example-projects.rst:4
 msgid ""
@@ -9904,6 +15771,9 @@ msgid ""
 "frameworks such as `PyTorch <https://pytorch.org/>`_ or `TensorFlow "
 "<https://www.tensorflow.org/>`_."
 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 <https://pytorch.org/>`_ 或 `TensorFlow "
+"<https://www.tensorflow.org/>`_。"
 
 #: ../../source/ref-example-projects.rst:11
 msgid ""
@@ -9912,20 +15782,23 @@ msgid ""
 "to make them easier to use. All new examples are based in the directory "
 "`examples <https://github.com/adap/flower/tree/main/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 "
+"<https://github.com/adap/flower/tree/main/examples>`_。"
 
 #: ../../source/ref-example-projects.rst:16
 msgid "The following examples are available as standalone projects."
-msgstr ""
+msgstr "以下示例可作为独立项目使用。"
 
 #: ../../source/ref-example-projects.rst:20
 msgid "Quickstart TensorFlow/Keras"
-msgstr ""
+msgstr "快速入门 TensorFlow/Keras"
 
 #: ../../source/ref-example-projects.rst:22
 msgid ""
 "The TensorFlow/Keras quickstart example shows CIFAR-10 image "
 "classification with MobileNetV2:"
-msgstr ""
+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/ref-example-projects.rst:25
 msgid ""
@@ -9933,51 +15806,61 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples/quickstart-"
 "tensorflow>`_"
 msgstr ""
+"`TensorFlow快速入门 (代码) <https://github.com/adap/flower/tree/main/examples"
+"/quickstart-tensorflow>`_"
 
 #: ../../source/ref-example-projects.rst:26
 msgid ""
-"`Quickstart TensorFlow (Tutorial) <https://flower.dev/docs/framework"
+"`Quickstart TensorFlow (Tutorial) <https://flower.ai/docs/framework"
 "/tutorial-quickstart-tensorflow.html>`_"
 msgstr ""
+"`TensorFlow快速入门 (教程) <https://flower.ai/docs/framework/tutorial-"
+"quickstart-tensorflow.html>`_"
 
 #: ../../source/ref-example-projects.rst:27
 msgid ""
-"`Quickstart TensorFlow (Blog Post) <https://flower.dev/blog/2020-12-11"
+"`Quickstart TensorFlow (Blog Post) <https://flower.ai/blog/2020-12-11"
 "-federated-learning-in-less-than-20-lines-of-code>`_"
 msgstr ""
+"`TensorFlow快速入门 (博客) <https://flower.ai/blog/2020-12-11-federated-"
+"learning-in-less-than-20-lines-of-code>`_"
 
 #: ../../source/ref-example-projects.rst:31
 #: ../../source/tutorial-quickstart-pytorch.rst:5
 msgid "Quickstart PyTorch"
-msgstr ""
+msgstr "PyTorch快速入门"
 
 #: ../../source/ref-example-projects.rst:33
 msgid ""
 "The PyTorch quickstart example shows CIFAR-10 image classification with a"
 " simple Convolutional Neural Network:"
-msgstr ""
+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/ref-example-projects.rst:36
 msgid ""
 "`Quickstart PyTorch (Code) "
 "<https://github.com/adap/flower/tree/main/examples/quickstart-pytorch>`_"
 msgstr ""
+"`PyTorch快速入门 (代码) <https://github.com/adap/flower/tree/main/examples"
+"/quickstart-pytorch>`_"
 
 #: ../../source/ref-example-projects.rst:37
 msgid ""
-"`Quickstart PyTorch (Tutorial) <https://flower.dev/docs/framework"
+"`Quickstart PyTorch (Tutorial) <https://flower.ai/docs/framework"
 "/tutorial-quickstart-pytorch.html>`_"
 msgstr ""
+"`PyTorch快速入门 (教程) <https://flower.ai/docs/framework/tutorial-quickstart-"
+"pytorch.html>`_"
 
 #: ../../source/ref-example-projects.rst:41
 msgid "PyTorch: From Centralized To Federated"
-msgstr ""
+msgstr "PyTorch： 从集中式到联邦式"
 
 #: ../../source/ref-example-projects.rst:43
 msgid ""
 "This example shows how a regular PyTorch project can be federated using "
 "Flower:"
-msgstr ""
+msgstr "本例展示了如何使用 Flower 联邦化一个普通的 PyTorch 项目："
 
 #: ../../source/ref-example-projects.rst:45
 msgid ""
@@ -9985,39 +15868,47 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples/pytorch-from-"
 "centralized-to-federated>`_"
 msgstr ""
+"PyTorch： 从集中式到联邦式（代码） <https://github.com/adap/flower/tree/main/examples"
+"/pytorch-from-centralized-to-federated>`_"
 
 #: ../../source/ref-example-projects.rst:46
 msgid ""
 "`PyTorch: From Centralized To Federated (Tutorial) "
-"<https://flower.dev/docs/framework/example-pytorch-from-centralized-to-"
+"<https://flower.ai/docs/framework/example-pytorch-from-centralized-to-"
 "federated.html>`_"
 msgstr ""
+"PyTorch： 从集中式到联邦式（教程） <https://flower.ai/docs/framework/example-pytorch-"
+"from-centralized-to-federated.html>`_"
 
 #: ../../source/ref-example-projects.rst:50
 msgid "Federated Learning on Raspberry Pi and Nvidia Jetson"
-msgstr ""
+msgstr "树莓派和 Nvidia Jetson 上的联邦学习"
 
 #: ../../source/ref-example-projects.rst:52
 msgid ""
 "This example shows how Flower can be used to build a federated learning "
 "system that run across Raspberry Pi and Nvidia Jetson:"
-msgstr ""
+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/ref-example-projects.rst:54
 msgid ""
 "`Federated Learning on Raspberry Pi and Nvidia Jetson (Code) "
 "<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_"
 msgstr ""
+"Raspberry Pi 和 Nvidia Jetson 上的联邦学习（代码） "
+"<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_"
 
 #: ../../source/ref-example-projects.rst:55
 msgid ""
 "`Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
 msgstr ""
+"Raspberry Pi和 Nvidia Jetson 上的联邦学习（博客） "
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
 
 #: ../../source/ref-example-projects.rst:60
 msgid "Legacy Examples (`flwr_example`)"
-msgstr ""
+msgstr "传统示例 (`flwr_example`)"
 
 #: ../../source/ref-example-projects.rst:63
 msgid ""
@@ -10025,10 +15916,12 @@ msgid ""
 "in the future. New examples are provided as standalone projects in "
 "`examples <https://github.com/adap/flower/tree/main/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 "
+"<https://github.com/adap/flower/tree/main/examples>`_ 中提供。"
 
 #: ../../source/ref-example-projects.rst:69
 msgid "Extra Dependencies"
-msgstr ""
+msgstr "额外依赖"
 
 #: ../../source/ref-example-projects.rst:71
 msgid ""
@@ -10036,39 +15929,41 @@ msgid ""
 "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 ""
+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。"
 
 #: ../../source/ref-example-projects.rst:75
 msgid "For PyTorch examples::"
-msgstr ""
+msgstr "PyTorch 示例：："
 
 #: ../../source/ref-example-projects.rst:79
 msgid "For TensorFlow examples::"
-msgstr ""
+msgstr "TensorFlow 示例：："
 
 #: ../../source/ref-example-projects.rst:83
 msgid "For both PyTorch and TensorFlow examples::"
-msgstr ""
+msgstr "PyTorch 和 TensorFlow 示例：："
 
 #: ../../source/ref-example-projects.rst:87
 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]`）。"
 
 #: ../../source/ref-example-projects.rst:92
 msgid "PyTorch Examples"
-msgstr ""
+msgstr "PyTorch 示例"
 
 #: ../../source/ref-example-projects.rst:94
 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 ""
+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。"
 
 #: ../../source/ref-example-projects.rst:98
 msgid "CIFAR-10 Image Classification"
-msgstr ""
+msgstr "CIFAR-10 图像分类"
 
 #: ../../source/ref-example-projects.rst:100
 msgid ""
@@ -10077,34 +15972,37 @@ msgid ""
 "to train a simple CNN classifier in a federated learning setup with two "
 "clients."
 msgstr ""
+"CIFAR-10 和 CIFAR-100 <https://www.cs.toronto.edu/~kriz/cifar.html>``_ "
+"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。"
 
 #: ../../source/ref-example-projects.rst:104
 #: ../../source/ref-example-projects.rst:121
 #: ../../source/ref-example-projects.rst:146
 msgid "First, start a Flower server:"
-msgstr ""
+msgstr "首先，启动 Flower 服务器："
 
 #: ../../source/ref-example-projects.rst:106
 msgid "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh"
-msgstr ""
+msgstr "$ ./src/py/flwr_example/pytorch_cifar/run-server.sh"
 
 #: ../../source/ref-example-projects.rst:108
 #: ../../source/ref-example-projects.rst:125
 #: ../../source/ref-example-projects.rst:150
 msgid "Then, start the two clients in a new terminal window:"
-msgstr ""
+msgstr "然后，在新的终端窗口中启动两个客户端："
 
 #: ../../source/ref-example-projects.rst:110
 msgid "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh"
-msgstr ""
+msgstr "$ ./src/py/flwr_example/pytorch_cifar/run-clients.sh"
 
 #: ../../source/ref-example-projects.rst:112
 msgid "For more details, see :code:`src/py/flwr_example/pytorch_cifar`."
-msgstr ""
+msgstr "更多详情，请参阅 :code:`src/py/flwr_example/pytorch_cifar`。"
 
 #: ../../source/ref-example-projects.rst:115
 msgid "ImageNet-2012 Image Classification"
-msgstr ""
+msgstr "ImageNet-2012 图像分类"
 
 #: ../../source/ref-example-projects.rst:117
 msgid ""
@@ -10112,32 +16010,34 @@ msgid ""
 " 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 <http://www.image-net.org/>`_ 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。"
 
 #: ../../source/ref-example-projects.rst:123
 msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh"
-msgstr ""
+msgstr "$ ./src/py/flwr_example/pytorch_imagenet/run-server.sh"
 
 #: ../../source/ref-example-projects.rst:127
 msgid "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh"
-msgstr ""
+msgstr "$ ./src/py/flwr_example/pytorch_imagenet/run-clients.sh"
 
 #: ../../source/ref-example-projects.rst:129
 msgid "For more details, see :code:`src/py/flwr_example/pytorch_imagenet`."
-msgstr ""
+msgstr "更多详情，请参阅 :code:`src/py/flwr_example/pytorch_imagenet`。"
 
 #: ../../source/ref-example-projects.rst:133
 msgid "TensorFlow Examples"
-msgstr ""
+msgstr "TensorFlow 示例"
 
 #: ../../source/ref-example-projects.rst:135
 msgid ""
 "Our TensorFlow examples are based on TensorFlow 2.0 or newer. So far, we "
 "provide the following examples."
-msgstr ""
+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。"
 
 #: ../../source/ref-example-projects.rst:139
 msgid "Fashion-MNIST Image Classification"
-msgstr ""
+msgstr "Fashion-MNIST 图像分类"
 
 #: ../../source/ref-example-projects.rst:141
 msgid ""
@@ -10147,36 +16047,39 @@ msgid ""
 " Fashion-MNIST and trains a simple image classification model over those "
 "partitions."
 msgstr ""
+"`Fashion-MNIST <https://github.com/zalandoresearch/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。"
 
 #: ../../source/ref-example-projects.rst:148
 msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh"
-msgstr ""
+msgstr "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-server.sh"
 
 #: ../../source/ref-example-projects.rst:152
 msgid "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh"
-msgstr ""
+msgstr "$ ./src/py/flwr_example/tensorflow_fashion_mnist/run-clients.sh"
 
 #: ../../source/ref-example-projects.rst:154
 msgid ""
 "For more details, see "
 ":code:`src/py/flwr_example/tensorflow_fashion_mnist`."
-msgstr ""
+msgstr "更多详情，请参阅 :code:`src/py/flwr_example/tensorflow_fashion_mnist`。"
 
 #: ../../source/ref-faq.rst:4
 msgid ""
 "This page collects answers to commonly asked questions about Federated "
 "Learning with Flower."
-msgstr ""
+msgstr "本页收集了有关 \"Flower 联邦学习 \"常见问题的答案。"
 
 #: ../../source/ref-faq.rst
 msgid ":fa:`eye,mr-1` Can Flower run on Juptyter Notebooks / Google Colab?"
-msgstr ""
+msgstr ":fa:`eye,mr-1` Flower 可以在 Juptyter Notebooks / Google Colab 上运行吗？"
 
 #: ../../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:"
-msgstr ""
+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/ref-faq.rst:10
 msgid ""
@@ -10184,6 +16087,9 @@ msgid ""
 "<https://colab.research.google.com/github/adap/flower/blob/main/examples"
 "/simulation-pytorch/sim.ipynb>`_"
 msgstr ""
+"`Flower 模拟 PyTorch "
+"<https://colab.research.google.com/github/adap/flower/blob/main/examples"
+"/simulation-pytorch/sim.ipynb>`_"
 
 #: ../../source/ref-faq.rst:11
 msgid ""
@@ -10191,67 +16097,84 @@ msgid ""
 "<https://colab.research.google.com/github/adap/flower/blob/main/examples"
 "/simulation-tensorflow/sim.ipynb>`_"
 msgstr ""
+"`Flower模拟TensorFlow/Keras "
+"<https://colab.research.google.com/github/adap/flower/blob/main/examples"
+"/simulation-tensorflow/sim.ipynb>`_"
 
 #: ../../source/ref-faq.rst
 msgid ":fa:`eye,mr-1` How can I run Federated Learning on a Raspberry Pi?"
-msgstr ""
+msgstr ":fa:`eye,mr-1` 如何在 Raspberry Pi 上运行联邦学习？"
 
 #: ../../source/ref-faq.rst:15
 msgid ""
 "Find the `blog post about federated learning on embedded device here "
-"<https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
+"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_"
 " and the corresponding `GitHub code example "
 "<https://github.com/adap/flower/tree/main/examples/embedded-devices>`_."
 msgstr ""
+"请点击此处查看有关嵌入式设备联邦学习的 "
+"\"博文\"<https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_和相应的"
+" \"GitHub 代码示例\"<https://github.com/adap/flower/tree/main/examples"
+"/embedded-devices>`_。"
 
 #: ../../source/ref-faq.rst
 msgid ":fa:`eye,mr-1` Does Flower support federated learning on Android devices?"
-msgstr ""
+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 ""
 "Yes, it does. Please take a look at our `blog post "
-"<https://flower.dev/blog/2021-12-15-federated-learning-on-android-"
+"<https://flower.ai/blog/2021-12-15-federated-learning-on-android-"
 "devices-with-flower>`_ or check out the code examples:"
 msgstr ""
+"是的，确实如此。请查看我们的 \"博客文章 <https://flower.ai/blog/2021-12-15-federated-"
+"learning-on-android-devices-with-flower>`_\" 或查看代码示例："
 
 #: ../../source/ref-faq.rst:21
 msgid ""
-"`Android Kotlin example <https://flower.dev/docs/examples/android-"
+"`Android Kotlin example <https://flower.ai/docs/examples/android-"
 "kotlin.html>`_"
 msgstr ""
+"`Android Kotlin 示例 <https://flower.ai/docs/examples/android-"
+"kotlin.html>`_"
 
 #: ../../source/ref-faq.rst:22
-msgid "`Android Java example <https://flower.dev/docs/examples/android.html>`_"
-msgstr ""
+msgid "`Android Java example <https://flower.ai/docs/examples/android.html>`_"
+msgstr "Android Java 示例 <https://flower.ai/docs/examples/android.html>`_"
 
 #: ../../source/ref-faq.rst
 msgid ":fa:`eye,mr-1` Can I combine federated learning with blockchain?"
-msgstr ""
+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 ""
 "Yes, of course. A list of available examples using Flower within a "
 "blockchain environment is available here:"
-msgstr ""
+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/ref-faq.rst:28
 msgid ""
 "`Flower meets Nevermined GitHub Repository <https://github.com"
 "/nevermined-io/fl-demo/tree/master/image-classification-flower>`_."
 msgstr ""
+"`Flower meets Nevermined GitHub Repository <https://github.com"
+"/nevermined-io/fl-demo/tree/master/image-classification-flower>`_."
 
 #: ../../source/ref-faq.rst:29
 msgid ""
 "`Flower meets Nevermined YouTube video "
 "<https://www.youtube.com/watch?v=A0A9hSlPhKI>`_."
 msgstr ""
+"`Flower meets Nevermined YouTube 视频 "
+"<https://www.youtube.com/watch?v=A0A9hSlPhKI>`_."
 
 #: ../../source/ref-faq.rst:30
 msgid ""
 "`Flower meets KOSMoS <https://www.kosmos-bmbf.de/wp-"
 "content/uploads/sites/13/2021/05/Talk-Flower-Summit-2021.pdf>`_."
 msgstr ""
+"`Flower meets KOSMoS <https://www.kosmos-bmbf.de/wp-"
+"content/uploads/sites/13/2021/05/Talk-Flower-Summit-2021.pdf>`_."
 
 #: ../../source/ref-faq.rst:31
 msgid ""
@@ -10259,16 +16182,21 @@ msgid ""
 "learning-same-mask-different-faces-imen-"
 "ayari/?trackingId=971oIlxLQ9%2BA9RB0IQ73XQ%3D%3D>`_ ."
 msgstr ""
+"`Flower meets Talan博文 <https://www.linkedin.com/pulse/federated-learning-"
+"same-mask-different-faces-imen-"
+"ayari/?trackingId=971oIlxLQ9%2BA9RB0IQ73XQ%3D%3D>`_ 。"
 
 #: ../../source/ref-faq.rst:32
 msgid ""
 "`Flower meets Talan GitHub Repository "
 "<https://gitlab.com/Talan_Innovation_Factory/food-waste-prevention>`_ ."
 msgstr ""
+"`Flower meets Talan GitHub Repository "
+"<https://gitlab.com/Talan_Innovation_Factory/food-waste-prevention>`_ ."
 
 #: ../../source/ref-telemetry.md:1
 msgid "Telemetry"
-msgstr ""
+msgstr "遥测功能"
 
 #: ../../source/ref-telemetry.md:3
 msgid ""
@@ -10277,27 +16205,29 @@ msgid ""
 "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/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."
-msgstr ""
+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 ""
+msgstr "原则"
 
 #: ../../source/ref-telemetry.md:9
 msgid "We follow strong principles guarding anonymous usage metrics collection:"
-msgstr ""
+msgstr "我们遵循严格的匿名使用指标收集原则："
 
 #: ../../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)”."
-msgstr ""
+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/ref-telemetry.md:12
 msgid ""
@@ -10306,6 +16236,8 @@ msgid ""
 "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。"
 
 #: ../../source/ref-telemetry.md:13
 msgid ""
@@ -10313,17 +16245,19 @@ msgid ""
 "reported; see the section “[How to inspect what is being reported](#how-"
 "to-inspect-what-is-being-reported)”"
 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"
 
 #: ../../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."
-msgstr ""
+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 ""
+msgstr "如何退出"
 
 #: ../../source/ref-telemetry.md:18
 msgid ""
@@ -10332,6 +16266,9 @@ msgid ""
 "`FLWR_TELEMETRY_ENABLED=0`. Assuming you are starting a Flower server or "
 "client, simply do so by prepending your command as in:"
 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："
 
 #: ../../source/ref-telemetry.md:24
 msgid ""
@@ -10339,14 +16276,16 @@ msgid ""
 " `.bashrc` (or whatever configuration file applies to your environment) "
 "to disable Flower telemetry permanently."
 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 ""
+msgstr "收集的指标"
 
 #: ../../source/ref-telemetry.md:28
 msgid "Flower telemetry collects the following metrics:"
-msgstr ""
+msgstr "Flower 遥测技术收集以下指标："
 
 #: ../../source/ref-telemetry.md:30
 msgid ""
@@ -10354,13 +16293,13 @@ msgid ""
 "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 ""
+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/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 ""
+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/ref-telemetry.md:34
 msgid ""
@@ -10368,21 +16307,21 @@ msgid ""
 "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 ""
+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/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 ""
+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/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 ""
+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/ref-telemetry.md:40
 msgid ""
@@ -10391,6 +16330,8 @@ msgid ""
 "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。"
 
 #: ../../source/ref-telemetry.md:42
 msgid ""
@@ -10403,14 +16344,18 @@ msgid ""
 "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/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.dev`. All events "
+"request mentioning the source ID to `telemetry@flower.ai`. All events "
 "related to that source ID will then be permanently deleted."
 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。"
 
 #: ../../source/ref-telemetry.md:46
 msgid ""
@@ -10420,17 +16365,19 @@ msgid ""
 "any changes to the metrics collected and publish changes in the "
 "changelog."
 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。"
 
 #: ../../source/ref-telemetry.md:48
 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 ""
+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 ""
+msgstr "如何检查报告中的内容"
 
 #: ../../source/ref-telemetry.md:52
 msgid ""
@@ -10441,40 +16388,45 @@ msgid ""
 "`FLWR_TELEMETRY_ENABLED` so that you can inspect the telemetry feature "
 "without sending any metrics."
 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。"
 
 #: ../../source/ref-telemetry.md:58
 msgid ""
 "The inspect Flower telemetry without sending any anonymous usage metrics,"
 " use both environment variables:"
-msgstr ""
+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/ref-telemetry.md:64
 msgid "How to contact us"
-msgstr ""
+msgstr "如何联系我们"
 
 #: ../../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.dev/join-slack/) (channel `#telemetry`) or email "
-"(`telemetry@flower.dev`)."
+"[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/tutorial-quickstart-android.rst:-1
 msgid ""
 "Read this Federated Learning quickstart tutorial for creating an Android "
 "app using Flower."
-msgstr ""
+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-quickstart-android.rst:5
 msgid "Quickstart Android"
-msgstr ""
+msgstr "快速入门 Android"
 
 #: ../../source/tutorial-quickstart-android.rst:10
 msgid ""
 "Let's build a federated learning system using TFLite and Flower on "
 "Android!"
-msgstr ""
+msgstr "让我们在 Android 上使用 TFLite 和 Flower 构建一个联邦学习系统！"
 
 #: ../../source/tutorial-quickstart-android.rst:12
 msgid ""
@@ -10482,20 +16434,22 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples/android>`_ to learn "
 "more."
 msgstr ""
+"请参阅`完整代码示例 "
+"<https://github.com/adap/flower/tree/main/examples/android>`_了解更多信息。"
 
 #: ../../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."
-msgstr ""
+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 ""
+msgstr "快速入门 fastai"
 
 #: ../../source/tutorial-quickstart-fastai.rst:10
 msgid "Let's build a federated learning system using fastai and Flower!"
-msgstr ""
+msgstr "让我们用 fastai 和 Flower 建立一个联邦学习系统！"
 
 #: ../../source/tutorial-quickstart-fastai.rst:12
 msgid ""
@@ -10503,22 +16457,24 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples/quickstart-fastai>`_ "
 "to learn more."
 msgstr ""
+"请参阅 `完整代码示例 <https://github.com/adap/flower/tree/main/examples"
+"/quickstart-fastai>`_了解更多信息。"
 
 #: ../../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."
-msgstr ""
+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-quickstart-huggingface.rst:5
 msgid "Quickstart 🤗 Transformers"
-msgstr ""
+msgstr "🤗 Transformers快速入门"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:10
 msgid ""
 "Let's build a federated learning system using Hugging Face Transformers "
 "and Flower!"
-msgstr ""
+msgstr "让我们用Hugging Face Transformers和Flower来构建一个联邦学习系统！"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:12
 msgid ""
@@ -10528,10 +16484,12 @@ msgid ""
 " 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-quickstart-huggingface.rst:18
 msgid "Dependencies"
-msgstr ""
+msgstr "依赖关系"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:20
 msgid ""
@@ -10540,14 +16498,16 @@ msgid ""
 ":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-quickstart-huggingface.rst:30
 msgid "Standard Hugging Face workflow"
-msgstr ""
+msgstr "标准Hugging Face工作流程"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:33
 msgid "Handling the data"
-msgstr ""
+msgstr "处理数据"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:35
 msgid ""
@@ -10555,10 +16515,12 @@ msgid ""
 "library. We then need to tokenize the data and create :code:`PyTorch` "
 "dataloaders, this is all done in the :code:`load_data` function:"
 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："
 
 #: ../../source/tutorial-quickstart-huggingface.rst:81
 msgid "Training and testing the model"
-msgstr ""
+msgstr "训练和测试模型"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:83
 msgid ""
@@ -10566,24 +16528,28 @@ msgid ""
 "take care of the training and testing. This is very similar to any "
 ":code:`PyTorch` training or testing loop:"
 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 ""
+msgstr "创建模型本身"
 
 #: ../../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` :"
 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 ""
+msgstr "将示例联邦化"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:139
 msgid "Creating the IMDBClient"
-msgstr ""
+msgstr "创建 IMDBClient"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:141
 msgid ""
@@ -10591,6 +16557,8 @@ msgid ""
 "Flower client class (inheriting from :code:`flwr.client.NumPyClient`). "
 "This is very easy, as our model is a standard :code:`PyTorch` model:"
 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："
 
 #: ../../source/tutorial-quickstart-huggingface.rst:169
 msgid ""
@@ -10601,10 +16569,12 @@ msgid ""
 ":code:`evaluate` function tests the model locally and returns the "
 "relevant metrics."
 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 ""
+msgstr "启动服务器"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:177
 msgid ""
@@ -10615,6 +16585,9 @@ msgid ""
 "all the clients' weights at each round) and then using the "
 ":code:`flwr.server.start_server` function:"
 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："
 
 #: ../../source/tutorial-quickstart-huggingface.rst:205
 msgid ""
@@ -10622,20 +16595,22 @@ msgid ""
 "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 ""
+msgstr "把所有东西放在一起"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:211
 msgid "We can now start client instances using:"
-msgstr ""
+msgstr "现在我们可以使用："
 
 #: ../../source/tutorial-quickstart-huggingface.rst:221
 msgid ""
 "And they will be able to connect to the server and start the federated "
 "training."
-msgstr ""
+msgstr "他们就能连接到服务器，开始联邦训练。"
 
 #: ../../source/tutorial-quickstart-huggingface.rst:223
 msgid ""
@@ -10645,49 +16620,56 @@ msgid ""
 "huggingface](https://github.com/adap/flower/tree/main/examples"
 "/quickstart-huggingface)."
 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)."
 
 #: ../../source/tutorial-quickstart-huggingface.rst:227
 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 ""
+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-quickstart-huggingface.rst:230
 msgid ""
 "Note that in this example we used :code:`PyTorch`, but we could have very"
 " well used :code:`TensorFlow`."
-msgstr ""
+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-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 ""
+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 ""
+msgstr "快速入门 iOS"
 
 #: ../../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."
-msgstr ""
+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-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 `virtualenv "
-"<https://flower.dev/docs/recommended-env-setup.html>`_. For the Flower "
+"<https://flower.ai/docs/recommended-env-setup.html>`_. For the Flower "
 "client implementation in iOS, it is recommended to use Xcode as our IDE."
 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 "
+"<https://flower.ai/docs/recommended-env-setup.html>`_ 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-quickstart-ios.rst:15
 msgid ""
 "Our example consists of one Python *server* and two iPhone *clients* that"
 " all have the same model."
-msgstr ""
+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-quickstart-ios.rst:17
 msgid ""
@@ -10696,18 +16678,18 @@ msgid ""
 "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 ""
+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-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 ""
+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 ""
+msgstr "或者Poetry："
 
 #: ../../source/tutorial-quickstart-ios.rst:36
 msgid ""
@@ -10717,6 +16699,9 @@ msgid ""
 "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-quickstart-ios.rst:72
 msgid ""
@@ -10728,10 +16713,14 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples/ios>`_ to learn more "
 "about the app."
 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 "
+"<https://github.com/adap/flower/tree/main/examples/ios>`_ 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 ""
+msgstr "在 :code:`FLiOSModel.swift` 中导入 Flower 和 CoreML 相关软件包："
 
 #: ../../source/tutorial-quickstart-ios.rst:83
 msgid ""
@@ -10743,6 +16732,10 @@ msgid ""
 "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-quickstart-ios.rst:99
 msgid ""
@@ -10753,18 +16746,20 @@ msgid ""
 "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-quickstart-ios.rst:102
 msgid ""
 "After we have all of the necessary informations, let's create our Flower "
 "client."
-msgstr ""
+msgstr "获得所有必要信息后，让我们创建 Flower 客户端。"
 
 #: ../../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 ""
+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 ""
@@ -10775,9 +16770,12 @@ msgid ""
 "in the application before clicking the start button to start the "
 "federated learning process."
 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。"
 
 #: ../../source/tutorial-quickstart-ios.rst:131
-#: ../../source/tutorial-quickstart-mxnet.rst:226
+#: ../../source/tutorial-quickstart-mxnet.rst:228
 #: ../../source/tutorial-quickstart-pytorch.rst:205
 #: ../../source/tutorial-quickstart-tensorflow.rst:100
 msgid ""
@@ -10785,24 +16783,26 @@ msgid ""
 "configuration possibilities at their default values. In a file named "
 ":code:`server.py`, import Flower and start the server:"
 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："
 
 #: ../../source/tutorial-quickstart-ios.rst:142
-#: ../../source/tutorial-quickstart-mxnet.rst:237
+#: ../../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 ""
+msgstr "联邦训练模型！"
 
 #: ../../source/tutorial-quickstart-ios.rst:144
 #: ../../source/tutorial-quickstart-pytorch.rst:218
 #: ../../source/tutorial-quickstart-tensorflow.rst:114
-#: ../../source/tutorial-quickstart-xgboost.rst:522
+#: ../../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:"
-msgstr ""
+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-quickstart-ios.rst:152
 msgid ""
@@ -10813,6 +16813,10 @@ msgid ""
 "<https://developer.apple.com/documentation/xcode/running-your-app-in-"
 "simulator-or-on-a-device>`_."
 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 "
+"<https://developer.apple.com/documentation/xcode/running-your-app-in-"
+"simulator-or-on-a-device>`_。"
 
 #: ../../source/tutorial-quickstart-ios.rst:156
 msgid ""
@@ -10821,42 +16825,55 @@ msgid ""
 "<https://github.com/adap/flower/blob/main/examples/ios>`_ 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 "
+"<https://github.com/adap/flower/blob/main/examples/ios>`_ 可在 "
+":code:`examples/ios` 中找到。"
 
 #: ../../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."
-msgstr ""
+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-quickstart-jax.rst:5
 msgid "Quickstart JAX"
-msgstr ""
+msgstr "快速入门 JAX"
 
 #: ../../source/tutorial-quickstart-mxnet.rst:-1
 msgid ""
 "Check out this Federated Learning quickstart tutorial for using Flower "
 "with MXNet to train a Sequential model on MNIST."
-msgstr ""
+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。"
 
 #: ../../source/tutorial-quickstart-mxnet.rst:5
 msgid "Quickstart MXNet"
+msgstr "快速入门 MXNet"
+
+#: ../../source/tutorial-quickstart-mxnet.rst:7
+msgid ""
+"MXNet is no longer maintained and has been moved into `Attic "
+"<https://attic.apache.org/projects/mxnet.html>`_. 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 ""
 
-#: ../../source/tutorial-quickstart-mxnet.rst:10
+#: ../../source/tutorial-quickstart-mxnet.rst:12
 msgid ""
 "In this tutorial, we will learn how to train a :code:`Sequential` model "
 "on MNIST using Flower and MXNet."
-msgstr ""
+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。"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:12
+#: ../../source/tutorial-quickstart-mxnet.rst:14
 #: ../../source/tutorial-quickstart-scikitlearn.rst:12
 msgid ""
 "It is recommended to create a virtual environment and run everything "
-"within this `virtualenv <https://flower.dev/docs/recommended-env-"
+"within this `virtualenv <https://flower.ai/docs/recommended-env-"
 "setup.html>`_."
 msgstr ""
+"建议创建一个虚拟环境，并在此 `virtualenv <https://flower.ai/docs/recommended-env-"
+"setup.html>`_ 中运行所有内容。"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:16
+#: ../../source/tutorial-quickstart-mxnet.rst:18
 #: ../../source/tutorial-quickstart-scikitlearn.rst:16
 msgid ""
 "*Clients* are responsible for generating individual model parameter "
@@ -10865,20 +16882,20 @@ msgid ""
 " 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 ""
+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-quickstart-mxnet.rst:20
+#: ../../source/tutorial-quickstart-mxnet.rst:22
 #: ../../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 ""
+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-quickstart-mxnet.rst:26
+#: ../../source/tutorial-quickstart-mxnet.rst:28
 msgid "Since we want to use MXNet, let's go ahead and install it:"
-msgstr ""
+msgstr "既然我们要使用 MXNet，那就继续安装吧："
 
-#: ../../source/tutorial-quickstart-mxnet.rst:36
+#: ../../source/tutorial-quickstart-mxnet.rst:38
 msgid ""
 "Now that we have all our dependencies installed, let's run a simple "
 "distributed training with two clients and one server. Our training "
@@ -10886,54 +16903,61 @@ msgid ""
 "Digit Recognition tutorial "
 "<https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html>`_."
 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 "
+"<https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html>`_\"。"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:38
+#: ../../source/tutorial-quickstart-mxnet.rst:40
 msgid ""
 "In a file called :code:`client.py`, import Flower and MXNet related "
 "packages:"
-msgstr ""
+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:53
+#: ../../source/tutorial-quickstart-mxnet.rst:55
 msgid "In addition, define the device allocation in MXNet with:"
-msgstr ""
+msgstr "此外，还可以在 MXNet 中定义设备分配："
 
-#: ../../source/tutorial-quickstart-mxnet.rst:59
+#: ../../source/tutorial-quickstart-mxnet.rst:61
 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 ""
+"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。"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:73
+#: ../../source/tutorial-quickstart-mxnet.rst:75
 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 ""
+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。"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:111
+#: ../../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."
-msgstr ""
+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。"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:135
+#: ../../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."
-msgstr ""
+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:137
+#: ../../source/tutorial-quickstart-mxnet.rst:139
 msgid "Our Flower clients will use a simple :code:`Sequential` model:"
-msgstr ""
+msgstr "我们的 Flower 客户端将使用简单的 :code:`Sequential` 模型："
 
-#: ../../source/tutorial-quickstart-mxnet.rst:156
+#: ../../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."
 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。"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:158
+#: ../../source/tutorial-quickstart-mxnet.rst:160
 #: ../../source/tutorial-quickstart-pytorch.rst:144
 #: ../../source/tutorial-quickstart-tensorflow.rst:54
 msgid ""
@@ -10943,8 +16967,11 @@ msgid ""
 "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）。"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:164
+#: ../../source/tutorial-quickstart-mxnet.rst:166
 msgid ""
 "Flower provides a convenience class called :code:`NumPyClient` which "
 "makes it easier to implement the :code:`Client` interface when your "
@@ -10952,63 +16979,65 @@ msgid ""
 "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）："
 
-#: ../../source/tutorial-quickstart-mxnet.rst:170
+#: ../../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 ""
+msgstr "以 NumPy ndarrays 列表形式返回模型参数"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:171
+#: ../../source/tutorial-quickstart-mxnet.rst:173
 #: ../../source/tutorial-quickstart-pytorch.rst:157
 #: ../../source/tutorial-quickstart-scikitlearn.rst:111
 msgid ":code:`set_parameters` (optional)"
-msgstr ""
+msgstr ":code:`set_parameters` （可选）"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:172
+#: ../../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 ""
+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:174
+#: ../../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 ""
+msgstr "设置本地模型参数"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:175
+#: ../../source/tutorial-quickstart-mxnet.rst:177
 #: ../../source/tutorial-quickstart-pytorch.rst:161
 #: ../../source/tutorial-quickstart-scikitlearn.rst:115
 msgid "train the local model"
-msgstr ""
+msgstr "训练本地模型"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:176
+#: ../../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 ""
+msgstr "接收更新的本地模型参数"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:178
+#: ../../source/tutorial-quickstart-mxnet.rst:180
 #: ../../source/tutorial-quickstart-pytorch.rst:164
 #: ../../source/tutorial-quickstart-scikitlearn.rst:118
 msgid "test the local model"
-msgstr ""
+msgstr "测试本地模型"
 
-#: ../../source/tutorial-quickstart-mxnet.rst:180
+#: ../../source/tutorial-quickstart-mxnet.rst:182
 msgid "They can be implemented in the following way:"
-msgstr ""
+msgstr "它们可以通过以下方式实现："
 
-#: ../../source/tutorial-quickstart-mxnet.rst:210
+#: ../../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:"
-msgstr ""
+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-quickstart-mxnet.rst:217
-#: ../../source/tutorial-quickstart-scikitlearn.rst:150
+#: ../../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 "
@@ -11019,43 +17048,49 @@ msgid ""
 "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-quickstart-mxnet.rst:239
+#: ../../source/tutorial-quickstart-mxnet.rst:241
 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 ""
+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-quickstart-mxnet.rst:247
+#: ../../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:530
+#: ../../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:"
-msgstr ""
+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:254
+#: ../../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:537
+#: ../../source/tutorial-quickstart-xgboost.rst:540
 msgid "Open another terminal and start the second client:"
-msgstr ""
+msgstr "打开另一台终端，启动第二个客户端："
 
-#: ../../source/tutorial-quickstart-mxnet.rst:260
+#: ../../source/tutorial-quickstart-mxnet.rst:262
 #: ../../source/tutorial-quickstart-pytorch.rst:239
 #: ../../source/tutorial-quickstart-scikitlearn.rst:237
-#: ../../source/tutorial-quickstart-xgboost.rst:543
+#: ../../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):"
-msgstr ""
+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-quickstart-mxnet.rst:292
+#: ../../source/tutorial-quickstart-mxnet.rst:294
 msgid ""
 "Congratulations! You've successfully built and run your first federated "
 "learning system. The full `source code "
@@ -11063,20 +17098,23 @@ msgid ""
 "mxnet/client.py>`_ 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 "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"mxnet/client.py>`_ 可在 :code:`examples/quickstart-mxnet` 中找到。"
 
 #: ../../source/tutorial-quickstart-pandas.rst:-1
 msgid ""
 "Check out this Federated Learning quickstart tutorial for using Flower "
 "with Pandas to perform Federated Analytics."
-msgstr ""
+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 ""
+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 ""
+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 ""
@@ -11084,32 +17122,36 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples/quickstart-pandas>`_ "
 "to learn more."
 msgstr ""
+"请参阅 `完整代码示例 <https://github.com/adap/flower/tree/main/examples"
+"/quickstart-pandas>`_\" 了解更多信息。"
 
 #: ../../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 ""
+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-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 ""
+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-quickstart-pytorch.rst:15
-#: ../../source/tutorial-quickstart-xgboost.rst:36
+#: ../../source/tutorial-quickstart-xgboost.rst:39
 msgid ""
 "First of all, it is recommended to create a virtual environment and run "
-"everything within a `virtualenv <https://flower.dev/docs/recommended-env-"
+"everything within a `virtualenv <https://flower.ai/docs/recommended-env-"
 "setup.html>`_."
 msgstr ""
+"首先，建议创建一个虚拟环境，并在 `virtualenv <https://flower.ai/docs/recommended-env-"
+"setup.html>`_ 中运行一切。"
 
 #: ../../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:"
-msgstr ""
+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-quickstart-pytorch.rst:39
 msgid ""
@@ -11119,16 +17161,19 @@ msgid ""
 "with PyTorch "
 "<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_."
 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 "
+"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_》。"
 
 #: ../../source/tutorial-quickstart-pytorch.rst:41
 msgid ""
 "In a file called :code:`client.py`, import Flower and PyTorch related "
 "packages:"
-msgstr ""
+msgstr "在名为 :code:`client.py` 的文件中，导入 Flower 和 PyTorch 相关软件包："
 
 #: ../../source/tutorial-quickstart-pytorch.rst:56
 msgid "In addition, we define the device allocation in PyTorch with:"
-msgstr ""
+msgstr "此外，我们还在 PyTorch 中定义了设备分配："
 
 #: ../../source/tutorial-quickstart-pytorch.rst:62
 msgid ""
@@ -11136,37 +17181,39 @@ msgid ""
 "dataset for machine learning. The PyTorch :code:`DataLoader()` downloads "
 "the training and test data that are then normalized."
 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。"
 
 #: ../../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 ""
+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-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 ""
+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
 msgid ""
 "After defining the training and testing of a PyTorch machine learning "
 "model, we use the functions for the Flower clients."
-msgstr ""
+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-quickstart-pytorch.rst:115
 msgid ""
 "The Flower clients will use a simple CNN adapted from 'PyTorch: A 60 "
 "Minute Blitz':"
-msgstr ""
+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-quickstart-pytorch.rst:142
 msgid ""
 "After loading the data set with :code:`load_data()` we define the Flower "
 "interface."
-msgstr ""
+msgstr "使用 :code:`load_data()` 加载数据集后，我们定义了 Flower 接口。"
 
 #: ../../source/tutorial-quickstart-pytorch.rst:150
 msgid ""
@@ -11176,30 +17223,41 @@ msgid ""
 "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 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 ""
+msgstr "可以通过以下方式实现："
 
 #: ../../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:"
-msgstr ""
+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-quickstart-pytorch.rst:196
 #: ../../source/tutorial-quickstart-tensorflow.rst:90
+#, 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:`\"[::]: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:`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`。"
 
 #: ../../source/tutorial-quickstart-pytorch.rst:271
 msgid ""
@@ -11209,22 +17267,25 @@ msgid ""
 "pytorch/client.py>`_ 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 "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"pytorch/client.py>`_ 可以在 :code:`examples/quickstart-pytorch` 中找到。"
 
 #: ../../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 ""
+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-quickstart-pytorch-lightning.rst:5
 msgid "Quickstart PyTorch Lightning"
-msgstr ""
+msgstr "快速入门 PyTorch Lightning"
 
 #: ../../source/tutorial-quickstart-pytorch-lightning.rst:10
 msgid ""
 "Let's build a horizontal federated learning system using PyTorch "
 "Lightning and Flower!"
-msgstr ""
+msgstr "让我们使用 PyTorch Lightning 和 Flower 构建一个水平联邦学习系统！"
 
 #: ../../source/tutorial-quickstart-pytorch-lightning.rst:12
 msgid ""
@@ -11232,30 +17293,34 @@ msgid ""
 "<https://github.com/adap/flower/tree/main/examples/quickstart-pytorch-"
 "lightning>`_ to learn more."
 msgstr ""
+"请参阅 `完整代码示例 <https://github.com/adap/flower/tree/main/examples"
+"/quickstart-pytorch-lightning>`_ 了解更多信息。"
 
 #: ../../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."
-msgstr ""
+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-quickstart-scikitlearn.rst:5
 msgid "Quickstart scikit-learn"
-msgstr ""
+msgstr "scikit-learn快速入门"
 
 #: ../../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 ""
+"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
 msgid "Since we want to use scikt-learn, let's go ahead and install it:"
-msgstr ""
+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 ""
+msgstr "或者直接使用 Poetry 安装所有依赖项："
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:42
 msgid ""
@@ -11266,54 +17331,56 @@ msgid ""
 ":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-quickstart-scikitlearn.rst:45
 msgid ":code:`get_model_parameters()`"
-msgstr ""
+msgstr ":code:`get_model_parameters()`"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:46
-msgid "Returns the paramters of a :code:`sklearn` LogisticRegression model"
-msgstr ""
+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 ""
+msgstr ":code:`set_model_params()`"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:48
 msgid "Sets the parameters of a :code:`sklean` LogisticRegression model"
-msgstr ""
+msgstr "设置:code:`sklean`的LogisticRegression模型的参数"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:49
 msgid ":code:`set_initial_params()`"
-msgstr ""
+msgstr ":code:`set_initial_params()`"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:50
 msgid "Initializes the model parameters that the Flower server will ask for"
-msgstr ""
+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 ""
+msgstr ":code:`load_mnist()`"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:52
 msgid "Loads the MNIST dataset using OpenML"
-msgstr ""
+msgstr "使用 OpenML 加载 MNIST 数据集"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:53
 msgid ":code:`shuffle()`"
-msgstr ""
+msgstr ":code:`shuffle()`"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:54
 msgid "Shuffles data and its label"
-msgstr ""
+msgstr "对数据及其标签进行洗牌"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:56
 msgid ":code:`partition()`"
-msgstr ""
+msgstr ":code:`partition()`"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:56
 msgid "Splits datasets into a number of partitions"
-msgstr ""
+msgstr "将数据集分割成多个分区"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:58
 msgid ""
@@ -11323,6 +17390,10 @@ msgid ""
 " 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 "
+"<https://github.com/adap/flower/blob/main/examples/sklearn-logreg-"
+"mnist/utils.py>`_。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-quickstart-scikitlearn.rst:73
 msgid ""
@@ -11332,12 +17403,15 @@ msgid ""
 "and test data. The training set is split afterwards into 10 partitions "
 "with :code:`utils.partition()`."
 msgstr ""
+"我们从 `OpenML <https://www.openml.org/d/554>`_ 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。"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:85
 msgid ""
 "Next, the logistic regression model is defined and initialized with "
 ":code:`utils.set_initial_params()`."
-msgstr ""
+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 ""
@@ -11347,6 +17421,9 @@ msgid ""
 "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 ""
+"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-quickstart-scikitlearn.rst:103
 msgid ""
@@ -11356,20 +17433,43 @@ msgid ""
 "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-quickstart-scikitlearn.rst:112
 msgid "is directly imported with :code:`utils.set_model_params()`"
-msgstr ""
+msgstr "直接导入 :code:`utils.set_model_params()`"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:120
 msgid "The methods can be implemented in the following way:"
-msgstr ""
+msgstr "这些方法可以通过以下方式实现："
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:143
 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："
+
+#: ../../source/tutorial-quickstart-scikitlearn.rst:150
+#, 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 ""
+"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-quickstart-scikitlearn.rst:159
 msgid ""
@@ -11377,10 +17477,12 @@ msgid ""
 "evaluation function for the server-side evaluation. First, we import "
 "again all required libraries such as Flower and scikit-learn."
 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。"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:162
 msgid ":code:`server.py`, import Flower and start the server:"
-msgstr ""
+msgstr ":code:`server.py`, 导入 Flower 并启动服务器："
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:173
 msgid ""
@@ -11389,6 +17491,8 @@ msgid ""
 "function is called after each federated learning round and gives you "
 "information about loss and accuracy."
 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。"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:198
 msgid ""
@@ -11400,6 +17504,11 @@ msgid ""
 " :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。"
 
 #: ../../source/tutorial-quickstart-scikitlearn.rst:217
 msgid ""
@@ -11407,7 +17516,7 @@ msgid ""
 "federated learning in action. Federated learning systems usually have a "
 "server and multiple clients. We, therefore, have to start the server "
 "first:"
-msgstr ""
+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-quickstart-scikitlearn.rst:271
 msgid ""
@@ -11417,34 +17526,37 @@ msgid ""
 "mnist>`_ for this example can be found in :code:`examples/sklearn-logreg-"
 "mnist`."
 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 "
+"<https://github.com/adap/flower/tree/main/examples/sklearn-logreg-"
+"mnist>`_ 可以在 :code:`examples/sklearn-logreg-mnist` 中找到。"
 
 #: ../../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."
-msgstr ""
+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 ""
+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 ""
+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 ""
+msgstr "在导入 Flower 之前，我们必须先安装它："
 
 #: ../../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:"
-msgstr ""
+msgstr "由于我们要使用 TensorFlow (TF) 的 Keras API，因此还必须安装 TF："
 
 #: ../../source/tutorial-quickstart-tensorflow.rst:31
 msgid "Next, in a file called :code:`client.py`, import Flower and TensorFlow:"
-msgstr ""
+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 ""
@@ -11454,12 +17566,15 @@ msgid ""
 "it locally, and then returns the entire training and test set as NumPy "
 "ndarrays."
 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。"
 
 #: ../../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 ""
+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-quickstart-tensorflow.rst:60
 msgid ""
@@ -11468,16 +17583,18 @@ msgid ""
 "workload uses Keras. The :code:`NumPyClient` interface defines three "
 "methods which can be implemented in the following way:"
 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 ""
+msgstr "每个客户都有自己的数据集。"
 
 #: ../../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 ""
+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-quickstart-tensorflow.rst:169
 msgid ""
@@ -11487,22 +17604,26 @@ msgid ""
 "tensorflow/client.py>`_ for this can be found in :code:`examples"
 "/quickstart-tensorflow/client.py`."
 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 "
+"<https://github.com/adap/flower/blob/main/examples/quickstart-"
+"tensorflow/client.py>`_ 可以在 :code:`examples/quickstart-"
+"tensorflow/client.py` 中找到。"
 
 #: ../../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 ""
+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 ""
+msgstr "XGBoost快速入门"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:11
+#: ../../source/tutorial-quickstart-xgboost.rst:14
 msgid "Federated XGBoost"
-msgstr ""
+msgstr "联邦化 XGBoost"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:13
+#: ../../source/tutorial-quickstart-xgboost.rst:16
 msgid ""
 "EXtreme Gradient Boosting (**XGBoost**) is a robust and efficient "
 "implementation of gradient-boosted decision tree (**GBDT**), that "
@@ -11511,27 +17632,30 @@ msgid ""
 "speed of machine learning models. In XGBoost, trees are constructed "
 "concurrently, unlike the sequential approach taken by GBDT."
 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。"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:17
+#: ../../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 ""
+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:20
+#: ../../source/tutorial-quickstart-xgboost.rst:23
 msgid "Why federated XGBoost?"
-msgstr ""
+msgstr "为什么选择联邦 XGBoost？"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:22
+#: ../../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 ""
+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-quickstart-xgboost.rst:24
+#: ../../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 "
@@ -11539,8 +17663,10 @@ msgid ""
 "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-quickstart-xgboost.rst:27
+#: ../../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 "
@@ -11551,104 +17677,120 @@ msgid ""
 "comprehensive <https://github.com/adap/flower/tree/main/examples/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 "
+"<https://github.com/adap/flower/tree/main/examples/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 <https://github.com/adap/flower/tree/main/examples/xgboost-"
+"comprehensive>`_)，以运行各种实验。"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:34
+#: ../../source/tutorial-quickstart-xgboost.rst:37
 msgid "Environment Setup"
-msgstr ""
+msgstr "环境设定"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:38
+#: ../../source/tutorial-quickstart-xgboost.rst:41
 msgid ""
 "We first need to install Flower and Flower Datasets. You can do this by "
 "running :"
-msgstr ""
+msgstr "我们首先需要安装 Flower 和 Flower Datasets。您可以通过运行 ："
 
-#: ../../source/tutorial-quickstart-xgboost.rst:44
+#: ../../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 ""
+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-quickstart-xgboost.rst:54
+#: ../../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 ""
+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-quickstart-xgboost.rst:57
+#: ../../source/tutorial-quickstart-xgboost.rst:60
 msgid ""
 "In a file called :code:`client.py`, import xgboost, Flower, Flower "
 "Datasets and other related functions:"
-msgstr ""
+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-quickstart-xgboost.rst:84
+#: ../../source/tutorial-quickstart-xgboost.rst:87
 msgid "Dataset partition and hyper-parameter selection"
-msgstr ""
+msgstr "数据集划分和超参数选择"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:86
+#: ../../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 ""
+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-quickstart-xgboost.rst:99
+#: ../../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-quickstart-xgboost.rst:118
+#: ../../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 ""
+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-quickstart-xgboost.rst:131
+#: ../../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 ""
+msgstr ":code:`train_test_split` 和 :code:`transform_dataset_too_dmatrix` 的函数定义如下："
 
-#: ../../source/tutorial-quickstart-xgboost.rst:155
+#: ../../source/tutorial-quickstart-xgboost.rst:158
 msgid "Finally, we define the hyper-parameters used for XGBoost training."
-msgstr ""
+msgstr "最后，我们定义了用于 XGBoost 训练的超参数。"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:171
+#: ../../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 ""
+"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-quickstart-xgboost.rst:178
+#: ../../source/tutorial-quickstart-xgboost.rst:181
 msgid "Flower client definition for XGBoost"
-msgstr ""
+msgstr "用于 XGBoost 的 Flower 客户端定义"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:180
+#: ../../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 ""
+"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-quickstart-xgboost.rst:190
+#: ../../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 ""
+"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-quickstart-xgboost.rst:193
+#: ../../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 ""
+"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-quickstart-xgboost.rst:207
+#: ../../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 "
@@ -11657,8 +17799,11 @@ msgid ""
 ":code:`get_parameters` when it is called by the server at the first "
 "round."
 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。"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:248
+#: ../../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 "
@@ -11667,27 +17812,33 @@ msgid ""
 ":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/tutorial-quickstart-xgboost.rst:266
+#: ../../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-quickstart-xgboost.rst:288
+#: ../../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 ""
+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-quickstart-xgboost.rst:291
+#: ../../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 ""
+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-quickstart-xgboost.rst:297
+#: ../../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\"` "
@@ -11697,60 +17848,75 @@ msgid ""
 "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-quickstart-xgboost.rst:308
+#: ../../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-quickstart-xgboost.rst:311
+#: ../../source/tutorial-quickstart-xgboost.rst:314
 msgid ""
 "In a file named :code:`server.py`, import Flower and FedXgbBagging from "
 ":code:`flwr.server.strategy`."
 msgstr ""
+"在名为 :code:`server.py` 的文件中，从 :code:`flwr.server.strategy` 导入 Flower 和 "
+"FedXgbBagging。"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:313
+#: ../../source/tutorial-quickstart-xgboost.rst:316
 msgid "We first define a strategy for XGBoost bagging aggregation."
-msgstr ""
+msgstr "我们首先定义了 XGBoost bagging聚合策略。"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:336
+#: ../../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 ""
+"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:339
+#: ../../source/tutorial-quickstart-xgboost.rst:342
 msgid "Then, we start the server:"
-msgstr ""
+msgstr "然后，我们启动服务器："
 
-#: ../../source/tutorial-quickstart-xgboost.rst:351
+#: ../../source/tutorial-quickstart-xgboost.rst:354
 msgid "Tree-based bagging aggregation"
-msgstr ""
+msgstr "基于树的bagging聚合"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:353
+#: ../../source/tutorial-quickstart-xgboost.rst:356
 msgid ""
 "You must be curious about how bagging aggregation works. Let's look into "
 "the details."
-msgstr ""
+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-quickstart-xgboost.rst:355
+#: ../../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 ""
+"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-quickstart-xgboost.rst:451
+#: ../../source/tutorial-quickstart-xgboost.rst:454
 msgid ""
 "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-quickstart-xgboost.rst:510
+#: ../../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 "
@@ -11758,52 +17924,110 @@ msgid ""
 " 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-quickstart-xgboost.rst:515
+#: ../../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 ""
+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-quickstart-xgboost.rst:520
+#: ../../source/tutorial-quickstart-xgboost.rst:523
 msgid "Launch Federated XGBoost!"
-msgstr ""
+msgstr "启动联邦 XGBoost！"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:582
+#: ../../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-quickstart-xgboost.rst:587
+#: ../../source/tutorial-quickstart-xgboost.rst:590
 msgid ""
 "The full `source code <https://github.com/adap/flower/blob/main/examples"
 "/xgboost-quickstart/>`_ for this example can be found in :code:`examples"
 "/xgboost-quickstart`."
 msgstr ""
+"此示例的`完整源代码 <https://github.com/adap/flower/blob/main/examples/xgboost-"
+"quickstart/>`_ 可在 :code:`examples/xgboost-quickstart` 中找到。"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:591
+#: ../../source/tutorial-quickstart-xgboost.rst:594
 msgid "Comprehensive Federated XGBoost"
-msgstr ""
+msgstr "综合的联邦 XGBoost"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:593
+#: ../../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 "
 "<https://github.com/adap/flower/tree/main/examples/xgboost-"
 "comprehensive>`_), we provide more options to define various experimental"
-" setups, including data partitioning and centralised/distributed "
-"evaluation. Let's take a look!"
+" setups, including aggregation strategies, data partitioning and "
+"centralised/distributed evaluation. We also support `Flower simulation "
+"<https://flower.ai/docs/framework/how-to-run-simulations.html>`_ 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 "
+"<https://github.com/adap/flower/tree/main/examples/xgboost-"
+"comprehensive>`_)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-quickstart-xgboost.rst:603
+#, fuzzy
+msgid "Cyclic training"
+msgstr "集中式训练"
+
+#: ../../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:599
-msgid "Customised data partitioning"
+#: ../../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:601
+#: ../../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 "定制数据分区"
+
+#: ../../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 "
@@ -11811,106 +18035,187 @@ msgid ""
 "provide four supported partitioner type to simulate the uniformity/non-"
 "uniformity in data quantity (uniform, linear, square, exponential)."
 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。"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:632
+#: ../../source/tutorial-quickstart-xgboost.rst:790
 msgid "Customised centralised/distributed evaluation"
-msgstr ""
+msgstr "定制的集中/分布式评估"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:634
+#: ../../source/tutorial-quickstart-xgboost.rst:792
+#, fuzzy
 msgid ""
 "To facilitate centralised evaluation, we define a function in "
-":code:`server.py`:"
-msgstr ""
+":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-quickstart-xgboost.rst:666
+#: ../../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 ""
+"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-quickstart-xgboost.rst:669
+#: ../../source/tutorial-quickstart-xgboost.rst:827
+#, fuzzy
 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.py`."
+":code:`XgbClient` class in :code:`client_utils.py`."
 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。"
 
-#: ../../source/tutorial-quickstart-xgboost.rst:673
-msgid "Arguments parser"
+#: ../../source/tutorial-quickstart-xgboost.rst:831
+#, fuzzy
+msgid "Flower simulation"
+msgstr "运行模拟"
+
+#: ../../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:675
+#: ../../source/tutorial-quickstart-xgboost.rst:866
 msgid ""
-"In :code:`utils.py`, we define the arguments parsers for clients and "
-"server, allowing users to specify different experimental settings. Let's "
-"first see the sever side:"
+"After importing all required packages, we define a :code:`main()` "
+"function to perform the simulation process:"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-xgboost.rst:714
+#: ../../source/tutorial-quickstart-xgboost.rst:921
 msgid ""
-"This allows user to specify 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."
+"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:718
-msgid "Then, the argument parser on client side:"
+#: ../../source/tutorial-quickstart-xgboost.rst:924
+msgid "Then, we define the strategies and other hyper-parameters:"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-xgboost.rst:760
+#: ../../source/tutorial-quickstart-xgboost.rst:975
 msgid ""
-"This defines various options for client data partitioning. Besides, "
-"clients also have a option to conduct evaluation on centralised test set "
-"by setting :code:`--centralised-eval`."
+"After that, we start the simulation by calling "
+":code:`fl.simulation.start_simulation`:"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-xgboost.rst:764
-msgid "Example commands"
+#: ../../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 "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:766
+#: ../../source/tutorial-quickstart-xgboost.rst:1144
+#, fuzzy
 msgid ""
-"To run a centralised evaluated experiment on 5 clients with exponential "
-"distribution for 50 rounds, we first start the server as below:"
+"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:"
+msgstr ""
+
+#: ../../source/tutorial-quickstart-xgboost.rst:1226
+msgid "This integrates all arguments for both client and server sides."
 msgstr ""
 
-#: ../../source/tutorial-quickstart-xgboost.rst:773
+#: ../../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
+msgid "To run the same experiment with Flower simulation:"
 msgstr ""
 
-#: ../../source/tutorial-quickstart-xgboost.rst:779
+#: ../../source/tutorial-quickstart-xgboost.rst:1250
+#, fuzzy
 msgid ""
-"The full `source code <https://github.com/adap/flower/blob/main/examples"
+"The full `code <https://github.com/adap/flower/blob/main/examples"
 "/xgboost-comprehensive/>`_ for this comprehensive example can be found in"
 " :code:`examples/xgboost-comprehensive`."
 msgstr ""
+"此综合示例的全部`源代码 <https://github.com/adap/flower/blob/main/examples/xgboost-"
+"comprehensive/>`_ 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 ""
+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 <https://flower.dev/docs/framework/tutorial-"
+"PyTorch and Flower (`part 1 <https://flower.ai/docs/framework/tutorial-"
 "get-started-with-flower-pytorch.html>`__) and we learned how strategies "
 "can be used to customize the execution on both the server and the clients"
-" (`part 2 <https://flower.dev/docs/framework/tutorial-use-a-federated-"
+" (`part 2 <https://flower.ai/docs/framework/tutorial-use-a-federated-"
 "learning-strategy-pytorch.html>`__)."
 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 "
+"<https://flower.ai/docs/framework/tutorial-get-started-with-flower-"
+"pytorch.html>`__），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 "
+"<https://flower.ai/docs/framework/tutorial-use-a-federated-learning-"
+"strategy-pytorch.html>`__）。"
 
 #: ../../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 <https://flower.dev/>`__ and `PyTorch "
+" using `Flower <https://flower.ai/>`__ and `PyTorch "
 "<https://pytorch.org/>`__)."
 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 <https://flower.ai/>`__ 和 "
+"`PyTorch <https://pytorch.org/>`__），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
@@ -11919,19 +18224,22 @@ msgstr ""
 msgid ""
 "`Star Flower on GitHub <https://github.com/adap/flower>`__ ⭐️ and join "
 "the Flower community on Slack to connect, ask questions, and get help: "
-"`Join Slack <https://flower.dev/join-slack>`__ 🌼 We'd love to hear from "
+"`Join Slack <https://flower.ai/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 <https://github.com/adap/flower>`__ ⭐️ 并加入 Slack "
+"上的 Flower 社区，进行交流、提问并获得帮助： 加入 Slack <https://flower.ai/join-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 ""
+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 ""
+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
@@ -11939,20 +18247,20 @@ msgstr ""
 msgid ""
 "Before we begin with the actual code, let's make sure that we have "
 "everything we need."
-msgstr ""
+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 ""
+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 ""
+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
@@ -11961,11 +18269,10 @@ msgstr ""
 msgid ""
 "Now that we have all dependencies installed, we can import everything we "
 "need for this tutorial:"
-msgstr ""
+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-get-started-with-flower-pytorch.ipynb:104
 #: ../../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 "
@@ -11977,12 +18284,16 @@ msgid ""
 "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 ""
+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
@@ -11993,12 +18304,15 @@ msgid ""
 " ``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 ""
+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
@@ -12006,12 +18320,12 @@ msgstr ""
 msgid ""
 "Let's continue with the usual model definition (including "
 "``set_parameters`` and ``get_parameters``), training and test functions:"
-msgstr ""
+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 ""
+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
@@ -12021,14 +18335,17 @@ msgid ""
 "``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 ""
+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 ""
+msgstr "从零开始构建策略"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:341
 msgid ""
@@ -12038,18 +18355,20 @@ msgid ""
 " 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 ""
+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 ""
+msgstr "回顾"
 
 #: ../../source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb:536
 msgid ""
@@ -12061,51 +18380,66 @@ msgid ""
 "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:749
+#: ../../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 <https://flower.dev/join-slack/>`__"
+"`Join Slack <https://flower.ai/join-slack/>`__"
 msgstr ""
+"在继续之前，请务必加入 Slack 上的 Flower 社区：`Join Slack <https://flower.ai/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:751
+#: ../../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 ""
+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 "
-"<https://flower.dev/docs/framework/tutorial-customize-the-client-"
+"<https://flower.ai/docs/framework/tutorial-customize-the-client-"
 "pytorch.html>`__ introduces ``Client``, the flexible API underlying "
 "``NumPyClient``."
 msgstr ""
+"Flower联邦学习教程 - 第4部分 <https://flower.ai/docs/framework/tutorial-"
+"customize-the-client-pytorch.html>`__ "
+"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 ""
+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 <https://flower.dev/docs/framework"
+"with PyTorch and Flower (`part 1 <https://flower.ai/docs/framework"
 "/tutorial-get-started-with-flower-pytorch.html>`__), we learned how "
 "strategies can be used to customize the execution on both the server and "
-"the clients (`part 2 <https://flower.dev/docs/framework/tutorial-use-a"
+"the clients (`part 2 <https://flower.ai/docs/framework/tutorial-use-a"
 "-federated-learning-strategy-pytorch.html>`__), and we built our own "
-"custom strategy from scratch (`part 3 <https://flower.dev/docs/framework"
+"custom strategy from scratch (`part 3 <https://flower.ai/docs/framework"
 "/tutorial-build-a-strategy-from-scratch-pytorch.html>`__)."
 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 "
+"<https://flower.ai/docs/framework/tutorial-get-started-with-flower-"
+"pytorch.html>`__），了解了如何使用策略来定制服务器和客户端的执行（`part 2 "
+"<https://flower.ai/docs/framework/tutorial-use-a-federated-learning-"
+"strategy-pytorch.html>`__），并从头开始构建了我们自己的定制策略（`part 3 "
+"<https://flower.ai/docs/framework/tutorial-build-a-strategy-from-"
+"scratch-pytorch.html>`__）。"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:14
 msgid ""
@@ -12117,17 +18451,20 @@ msgid ""
 " 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 ""
+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 ""
+msgstr "步骤 0：准备工作"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:117
 msgid ""
@@ -12135,10 +18472,12 @@ msgid ""
 "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 ""
+msgstr "步骤 1：重温 NumPyClient"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:261
 msgid ""
@@ -12147,6 +18486,9 @@ msgid ""
 "``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 ""
@@ -12155,12 +18497,14 @@ msgid ""
 "``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 ""
+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 ""
@@ -12170,6 +18514,8 @@ msgid ""
 "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 ""
@@ -12181,34 +18527,39 @@ msgid ""
 "``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 ""
+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 ""
+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 ""
+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 ""
+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 ""
+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 ""
@@ -12225,6 +18576,10 @@ msgid ""
 "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 ""
@@ -12234,6 +18589,9 @@ msgid ""
 " 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 ""
@@ -12247,16 +18605,20 @@ msgid ""
 "``*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 ""
+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 ""
+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 ""
@@ -12266,6 +18628,8 @@ msgid ""
 "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 ""
@@ -12273,7 +18637,7 @@ msgid ""
 "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 ""
+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 ""
@@ -12285,10 +18649,13 @@ msgid ""
 "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 ""
+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 ""
@@ -12296,23 +18663,25 @@ msgid ""
 "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 ""
+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 ""
+msgstr "客户端"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:615
 msgid ""
-"To be able to able to serialize our ``ndarray``\\ s into sparse "
+"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 ""
+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 ""
@@ -12320,6 +18689,8 @@ msgid ""
 "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 ""
@@ -12328,16 +18699,18 @@ msgid ""
 "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 ""
+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 ""
+msgstr "服务器端"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:727
 msgid ""
@@ -12347,24 +18720,26 @@ msgid ""
 " 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 ""
+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 ""
+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 ""
+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 ""
+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 ""
@@ -12376,6 +18751,8 @@ msgid ""
 "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 ""
@@ -12383,77 +18760,105 @@ msgid ""
 "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 ""
+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 <https://flower.dev/docs/>`__"
-msgstr ""
+msgid "`Read Flower Docs <https://flower.ai/docs/>`__"
+msgstr "阅读Flower文档 <https://flower.ai/docs/>`__"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:955
 msgid ""
 "`Check out Flower Code Examples "
 "<https://github.com/adap/flower/tree/main/examples>`__"
-msgstr ""
+msgstr "查看 Flower 代码示例 <https://github.com/adap/flower/tree/main/examples>`__"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:956
 msgid ""
 "`Use Flower Baselines for your research "
-"<https://flower.dev/docs/baselines/>`__"
-msgstr ""
+"<https://flower.ai/docs/baselines/>`__"
+msgstr "使用 \"Flower Baselines \"进行研究 <https://flower.ai/docs/baselines/>`__"
 
 #: ../../source/tutorial-series-customize-the-client-pytorch.ipynb:957
 msgid ""
-"`Watch Flower Summit 2023 videos <https://flower.dev/conf/flower-"
+"`Watch Flower Summit 2023 videos <https://flower.ai/conf/flower-"
 "summit-2023/>`__"
-msgstr ""
+msgstr "观看 2023 年Flower峰会视频 <https://flower.ai/conf/flower-summit-2023/>`__"
 
 #: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:9
 msgid "Get started with Flower"
-msgstr ""
+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 ""
+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 "
-"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."
+"In this notebook, we'll build a federated learning system using Flower, "
+"`Flower Datasets <https://flower.ai/docs/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
 msgid "Let's get stated!"
-msgstr ""
+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 ""
+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``) and Flower (``flwr``):"
+"``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:117
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:118
 msgid "Loading the data"
-msgstr ""
+msgstr "加载数据"
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:119
+#: ../../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:"
+"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:150
+#: ../../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"
@@ -12463,23 +18868,26 @@ msgid ""
 "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:152
+#: ../../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 ""
+"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:172
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:144
 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:"
+"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:222
+#: ../../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 "
@@ -12490,14 +18898,17 @@ msgid ""
 "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:225
+#: ../../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 ""
+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:264
+#: ../../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 "
@@ -12505,12 +18916,14 @@ msgid ""
 "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:276
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:252
 msgid "Step 1: Centralized Training with PyTorch"
-msgstr ""
+msgstr "步骤 1：使用 PyTorch 进行集中训练"
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:287
+#: ../../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 "
@@ -12519,27 +18932,34 @@ msgid ""
 "MINUTE BLITZ "
 "<https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html>`__."
 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 "
+"<https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html>`__。"
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:299
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:275
 msgid "Defining the model"
-msgstr ""
+msgstr "定义模型"
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:301
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:277
 msgid ""
 "We use the simple CNN described in the `PyTorch tutorial "
 "<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#define-a"
 "-convolutional-neural-network>`__:"
 msgstr ""
+"我们使用` PyTorch 教程 "
+"<https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#define-a"
+"-convolutional-neural-network>`__ 中描述的简单 CNN："
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:338
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:314
 msgid "Let's continue with the usual training and test functions:"
-msgstr ""
+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:398
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:374
 msgid "Training the model"
-msgstr ""
+msgstr "训练模型"
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:400
+#: ../../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 "
@@ -12547,9 +18967,9 @@ msgid ""
 "(``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 ""
+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:430
+#: ../../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 "
@@ -12557,12 +18977,14 @@ msgid ""
 "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:442
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:418
 msgid "Step 2: Federated Learning with Flower"
-msgstr ""
+msgstr "步骤 2：使用 Flower 联邦学习"
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:444
+#: ../../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``)."
@@ -12570,12 +18992,14 @@ msgid ""
 "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:456
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:432
 msgid "Updating model parameters"
-msgstr ""
+msgstr "更新模型参数"
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:458
+#: ../../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 "
@@ -12584,17 +19008,19 @@ msgid ""
 "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 ""
+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:460
+#: ../../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:462
+#: ../../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 "
@@ -12602,12 +19028,14 @@ msgid ""
 " 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:490
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:466
 msgid "Implementing a Flower client"
-msgstr ""
+msgstr "实现 Flower 客户端"
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:492
+#: ../../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 "
@@ -12616,40 +19044,47 @@ msgid ""
 "``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:494
+#: ../../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:496
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:472
 msgid "``get_parameters``: Return the current local model parameters"
-msgstr ""
+msgstr "``get_parameters``： 返回当前本地模型参数"
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:497
+#: ../../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 ""
+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:498
+#: ../../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 ""
+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:500
+#: ../../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:537
+#: ../../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"
@@ -12663,12 +19098,18 @@ msgid ""
 " 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:541
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:517
 msgid "Using the Virtual Client Engine"
-msgstr ""
+msgstr "使用虚拟客户端引擎"
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:543
+#: ../../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 "
@@ -12678,8 +19119,11 @@ msgid ""
 "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:545
+#: ../../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 "
@@ -12694,12 +19138,18 @@ msgid ""
 "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:580
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:556
 msgid "Starting the training"
-msgstr ""
+msgstr "开始训练"
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:582
+#: ../../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 "
@@ -12707,8 +19157,11 @@ msgid ""
 "``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:584
+#: ../../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 "
@@ -12717,8 +19170,11 @@ msgid ""
 "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:586
+#: ../../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"
@@ -12727,16 +19183,18 @@ msgid ""
 "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:628
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:608
 msgid "Behind the scenes"
-msgstr ""
+msgstr "幕后"
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:630
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:610
 msgid "So how does this work? How does Flower execute this simulation?"
-msgstr ""
+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:632
+#: ../../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 "
@@ -12745,8 +19203,11 @@ msgid ""
 "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:634
+#: ../../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 "
@@ -12754,18 +19215,22 @@ msgid ""
 "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:646
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:626
 msgid "Where's the accuracy?"
-msgstr ""
+msgstr "准确度在哪里找？"
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:648
+#: ../../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:650
+#: ../../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 "
@@ -12774,8 +19239,10 @@ msgid ""
 "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:652
+#: ../../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 "
@@ -12784,41 +19251,47 @@ msgid ""
 " 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:654
+#: ../../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 ""
+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:680
+#: ../../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 ""
+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:717
+#: ../../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:719
+#: ../../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:731
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:711
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:351
 msgid "Final remarks"
-msgstr ""
+msgstr "结束语"
 
-#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:733
+#: ../../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 "
@@ -12827,60 +19300,70 @@ msgid ""
 " 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:735
+#: ../../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 ""
+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:753
+#: ../../source/tutorial-series-get-started-with-flower-pytorch.ipynb:733
 msgid ""
 "The `Flower Federated Learning Tutorial - Part 2 "
-"<https://flower.dev/docs/framework/tutorial-use-a-federated-learning-"
+"<https://flower.ai/docs/framework/tutorial-use-a-federated-learning-"
 "strategy-pytorch.html>`__ 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 <https://flower.ai/docs/framework/tutorial-use-a"
+"-federated-learning-strategy-pytorch.html>`__ 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 ""
+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 <https://flower.dev/docs/framework/tutorial-get-started-"
+" Flower (`part 1 <https://flower.ai/docs/framework/tutorial-get-started-"
 "with-flower-pytorch.html>`__)."
 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 "
+"<https://flower.ai/docs/framework/tutorial-get-started-with-flower-"
+"pytorch.html>`___）。"
 
 #: ../../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 "
-"<https://flower.dev/>`__ and `PyTorch <https://pytorch.org/>`__)."
+"<https://flower.ai/>`__ and `PyTorch <https://pytorch.org/>`__)."
 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 <https://flower.ai/>`__ 和 "
+"`PyTorch <https://pytorch.org/>`__）。"
 
 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:17
 msgid "Let's move beyond FedAvg with Flower strategies!"
-msgstr ""
+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 ""
+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 ""
+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 ""
+msgstr "服务器端参数 **初始化**"
 
 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:325
 msgid ""
@@ -12889,6 +19372,8 @@ msgid ""
 "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 ""
@@ -12897,10 +19382,13 @@ msgid ""
 "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 ""
+msgstr "从定制战略开始"
 
 #: ../../source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb:384
 msgid ""
@@ -12909,24 +19397,26 @@ msgid ""
 "``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 ""
+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 ""
+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 ""
+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 ""
@@ -12937,7 +19427,7 @@ msgid ""
 "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 ""
+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 ""
@@ -12953,7 +19443,7 @@ msgid ""
 "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 ""
+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 ""
@@ -12961,10 +19451,12 @@ msgid ""
 "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 ""
+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 ""
@@ -12980,6 +19472,11 @@ msgid ""
 " 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 ""
@@ -12989,12 +19486,14 @@ msgid ""
 "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 ""
+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 ""
@@ -13004,6 +19503,8 @@ msgid ""
 " 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 ""
@@ -13013,16 +19514,19 @@ msgid ""
 "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 ""
+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 ""
+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
@@ -13037,6 +19541,10 @@ msgid ""
 "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 ""
@@ -13044,7 +19552,7 @@ msgid ""
 "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 ""
+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 ""
@@ -13054,18 +19562,23 @@ msgid ""
 "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 "
-"<https://flower.dev/docs/framework/tutorial-build-a-strategy-from-"
+"<https://flower.ai/docs/framework/tutorial-build-a-strategy-from-"
 "scratch-pytorch.html>`__ shows how to build a fully custom ``Strategy`` "
 "from scratch."
 msgstr ""
+"`Flower 联邦学习教程 - 第 3 部分 <https://flower.ai/docs/framework/tutorial-"
+"build-a-strategy-from-scratch-pytorch.html>`__ 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 ""
+msgstr "什么是联邦学习？"
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:13
 msgid ""
@@ -13074,87 +19587,92 @@ msgid ""
 "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 ""
+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 <https://github.com/adap/flower>`__ ⭐️ and join "
 "the open-source Flower community on Slack to connect, ask questions, and "
-"get help: `Join Slack <https://flower.dev/join-slack>`__ 🌼 We'd love to "
+"get help: `Join Slack <https://flower.ai/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 <https://github.com/adap/flower>`__ ⭐️ 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 <https://flower.ai/join-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:19
 msgid "Let's get started!"
-msgstr ""
+msgstr "让我们开始吧！"
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:31
 msgid "Classic machine learning"
-msgstr ""
+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 ""
+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 ""
+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 "|e1dd4b4129b040bea23a894266227080|"
+msgid "|31e4b1afa87c4b968327bbeafbf184d4|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:109
 msgid "Model and data"
-msgstr ""
+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 ""
+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 "|c0d4cc6a442948dca8da40d2440068d9|"
+msgid "|c9d935b4284e4c389a33d86b33e07c0a|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:111
 msgid "Train model using data"
-msgstr ""
+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 ""
+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 ""
+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 "|174e1e4fa1f149a19bfbc8bc1126f46a|"
+msgid "|00727b5faffb468f84dd1b03ded88638|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:113
 msgid "Data on a phone"
-msgstr ""
+msgstr "手机上的数据"
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:73
 msgid ""
@@ -13163,14 +19681,16 @@ msgid ""
 " 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 "|4e021a3dc08249d2a89daa3ab03c2714|"
+msgid "|daf0cf0ff4c24fd29439af78416cf47b|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:115
 msgid "Data is on many devices"
-msgstr ""
+msgstr "数据存在于多种设备中"
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:85
 msgid ""
@@ -13178,34 +19698,34 @@ msgid ""
 "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 ""
+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 "|e74a1d5ce7eb49688651f2167a59065b|"
+msgid "|9f093007080d471d94ca90d3e9fde9b6|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:117
 msgid "Central data collection"
-msgstr ""
+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 ""
+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 "|eb29ec4c7aef4e93976795ed72df647e|"
+msgid "|46a26e6150e0479fbd3dfd655f36eb13|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:119
 msgid "Central model training"
-msgstr ""
+msgstr "集中模型训练"
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:130
 msgid "Challenges of classical machine learning"
-msgstr ""
+msgstr "经典机器学习面临的挑战"
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:132
 msgid ""
@@ -13213,37 +19733,37 @@ msgid ""
 " cases. Great examples include categorizing holiday photos, or analyzing "
 "web traffic. Cases, where all the data is naturally available on a "
 "centralized server."
-msgstr ""
+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 "|c2f699d8ac484f5081721a6f1511f70d|"
+msgid "|3daba297595c4c7fb845d90404a6179a|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:173
 msgid "Centralized possible"
-msgstr ""
+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 ""
+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 "|cf42accdacbf4e5eb4fa0503108ba7a7|"
+msgid "|5769874fa9c4455b80b2efda850d39d7|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:175
 msgid "Centralized impossible"
-msgstr ""
+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 ""
+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 ""
@@ -13257,6 +19777,8 @@ msgid ""
 " 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 ""
@@ -13267,6 +19789,8 @@ msgid ""
 "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 ""
@@ -13279,45 +19803,49 @@ msgid ""
 "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 ""
+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 ""
+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 ""
+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 ""
+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 ""
+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 "
 "<https://brave.com/>`__ browser or the `Signal <https://signal.org/>`__ "
 "messenger shows that users care about privacy. In fact, they choose the "
-"privacy-enhancing version over other alternatives, if such an alernative "
+"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 <https://brave.com/>`__浏览器或 `Signal "
+"<https://signal.org/>`__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 ""
+msgstr "联邦学习"
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:188
 msgid ""
@@ -13325,15 +19853,15 @@ msgid ""
 "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 ""
+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 ""
+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 ""
+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 ""
@@ -13347,42 +19875,42 @@ msgid ""
 " 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 ""
+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 ""
+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 ""
+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 ""
+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 ""
+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 "|5ec8356bc2564fa09178b1ceed5beccc|"
+msgid "|ba47ffb421814b0f8f9fa5719093d839|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:307
 msgid "Initialize global model"
-msgstr ""
+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 ""
+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 ""
@@ -13392,21 +19920,21 @@ msgid ""
 "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 ""
+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 "|7c9329e97bd0430bad335ab605a897a7|"
+msgid "|aeac5bf79cbf497082e979834717e01b|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:309
 msgid "Send global model"
-msgstr ""
+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 ""
+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 ""
@@ -13417,18 +19945,20 @@ msgid ""
 "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 "|88002bbce1094ba1a83c9151df18f707|"
+msgid "|ce27ed4bbe95459dba016afc42486ba2|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:311
 msgid "Train on local data"
-msgstr ""
+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 ""
+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 ""
@@ -13439,19 +19969,19 @@ msgid ""
 "server. The model updates they send can either be the full model "
 "parameters or just the gradients that were accumulated during local "
 "training."
-msgstr ""
+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 "|391766aee87c482c834c93f7c22225e2|"
+msgid "|ae94a7f71dda443cbec2385751427d41|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:313
 msgid "Send model updates"
-msgstr ""
+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 ""
+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 ""
@@ -13461,6 +19991,8 @@ msgid ""
 "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 ""
@@ -13478,18 +20010,24 @@ msgid ""
 "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 "
+"<https://arxiv.org/abs/1602.05629>`__)，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 "|93b9a15bd27f4e91b40f642c253dfaac|"
+msgid "|e61fce4d43d243e7bb08bdde97d81ce6|"
 msgstr ""
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:315
 msgid "Aggregate model updates"
-msgstr ""
+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 ""
+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 ""
@@ -13499,6 +20037,8 @@ msgid ""
 "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 ""
@@ -13510,6 +20050,8 @@ msgid ""
 "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 ""
@@ -13521,6 +20063,8 @@ msgid ""
 "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 ""
@@ -13530,10 +20074,12 @@ msgid ""
 "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 ""
+msgstr "联邦分析"
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:299
 msgid ""
@@ -13545,11 +20091,11 @@ msgid ""
 "conjunction with other privacy-enhancing technologies like secure "
 "aggregation to prevent the server from seeing the results submitted by "
 "individual client nodes."
-msgstr ""
+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:303
 msgid "Differential Privacy"
-msgstr ""
+msgstr "差分隐私"
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:305
 msgid ""
@@ -13561,10 +20107,12 @@ msgid ""
 "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 ""
+msgstr "Flower"
 
 #: ../../source/tutorial-series-what-is-federated-learning.ipynb:328
 msgid ""
@@ -13576,33 +20124,372 @@ msgid ""
 " 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 "|a23d9638f96342ef9d25209951e2d564|"
+msgid "|08cb60859b07461588fe44e55810b050|"
 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 ""
+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 ""
+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 ""
+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 "
-"<https://flower.dev/docs/framework/tutorial-get-started-with-flower-"
+"<https://flower.ai/docs/framework/tutorial-get-started-with-flower-"
 "pytorch.html>`__ 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 <https://flower.ai/docs/framework/tutorial-get-"
+"started-with-flower-pytorch.html>`__ 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 "
+#~ "<https://github.com/adap/flower/compare/v1.2.0...main>`_"
+#~ msgstr ""
+#~ "`GitHub: Compare v1.2.0...main "
+#~ "<https://github.com/adap/flower/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:`<version>`)::"
+#~ 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"
+#~ " <ref-api-flwr.html#start-numpy-client>`_)"
+#~ " in the sense that they can be"
+#~ " configure by creating a class "
+#~ "inheriting, for example, from "
+#~ "`flwr.client.NumPyClient <ref-api-"
+#~ "flwr.html#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 <ref-"
+#~ "api-flwr.html#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 "
+#~ "<ref-api-flwr.html#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："
diff --git a/doc/source/.gitignore b/doc/source/.gitignore
new file mode 100644
index 000000000000..e9341a1383b7
--- /dev/null
+++ b/doc/source/.gitignore
@@ -0,0 +1 @@
+ref-api/
diff --git a/doc/source/_static/DP/CDP.png b/doc/source/_static/DP/CDP.png
new file mode 100644
index 000000000000..13332fffd5c4
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diff --git a/doc/source/_static/DP/LDP.png b/doc/source/_static/DP/LDP.png
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diff --git a/doc/source/_static/DP/clientsideCDP.png b/doc/source/_static/DP/clientsideCDP.png
new file mode 100644
index 000000000000..2517c75690b6
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diff --git a/doc/source/_static/DP/clipping.png b/doc/source/_static/DP/clipping.png
new file mode 100644
index 000000000000..adb36daa1a3e
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diff --git a/doc/source/_static/DP/dp-intro.png b/doc/source/_static/DP/dp-intro.png
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index 000000000000..fffd345ad531
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diff --git a/doc/source/_static/DP/localdp.png b/doc/source/_static/DP/localdp.png
new file mode 100644
index 000000000000..b701b2ca9950
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diff --git a/doc/source/_static/DP/serversideCDP.png b/doc/source/_static/DP/serversideCDP.png
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index 000000000000..aae09d77fb6b
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diff --git a/doc/source/_static/flower-architecture.drawio.png b/doc/source/_static/flower-architecture.drawio.png
index 3f82332a0a32..a9c3914a1839 100755
Binary files a/doc/source/_static/flower-architecture.drawio.png and b/doc/source/_static/flower-architecture.drawio.png differ
diff --git a/doc/source/_templates/autosummary/base.rst b/doc/source/_templates/autosummary/base.rst
new file mode 100644
index 000000000000..5536fa10863f
--- /dev/null
+++ b/doc/source/_templates/autosummary/base.rst
@@ -0,0 +1,5 @@
+{{ name | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. auto{{ objtype }}:: {{ objname }}
diff --git a/doc/source/_templates/autosummary/class.rst b/doc/source/_templates/autosummary/class.rst
new file mode 100644
index 000000000000..b4b35789bc6f
--- /dev/null
+++ b/doc/source/_templates/autosummary/class.rst
@@ -0,0 +1,33 @@
+{{ name | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+   :members:
+   :show-inheritance:
+   :inherited-members:
+
+   {% block methods %}
+
+   {% if methods %}
+   .. rubric:: {{ _('Methods') }}
+
+   .. autosummary::
+   {% for item in methods %}
+      {% if item != "__init__" %}
+         ~{{ name }}.{{ item }}
+      {% endif %}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: {{ _('Attributes') }}
+
+   .. autosummary::
+   {% for item in attributes %}
+      ~{{ name }}.{{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
diff --git a/doc/source/_templates/autosummary/module.rst b/doc/source/_templates/autosummary/module.rst
new file mode 100644
index 000000000000..571db198d27c
--- /dev/null
+++ b/doc/source/_templates/autosummary/module.rst
@@ -0,0 +1,66 @@
+{{ name | escape | underline}}
+
+.. automodule:: {{ fullname }}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:
+   {% for item in attributes %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block functions %}
+   {% if functions %}
+   .. rubric:: {{ _('Functions') }}
+
+   .. autosummary::
+      :toctree:
+   {% for item in functions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block classes %}
+   {% if classes %}
+   .. rubric:: {{ _('Classes') }}
+
+   .. autosummary::
+      :toctree:
+      :template: autosummary/class.rst
+   {% for item in classes %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block exceptions %}
+   {% if exceptions %}
+   .. rubric:: {{ _('Exceptions') }}
+
+   .. autosummary::
+      :toctree:
+   {% for item in exceptions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+{% block modules %}
+{% if modules %}
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: autosummary/module.rst
+   :recursive:
+{% for item in modules %}
+   {{ item }}
+{%- endfor %}
+{% endif %}
+{% endblock %}
diff --git a/doc/source/_templates/base.html b/doc/source/_templates/base.html
index 0cbe6e9e4456..768c560f4f6a 100644
--- a/doc/source/_templates/base.html
+++ b/doc/source/_templates/base.html
@@ -6,9 +6,9 @@
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark">
     {% if current_language != 'en' %}
-      <link rel="canonical" href="https://flower.dev/docs/framework/main/{{ current_language }}/{{ pagename }}.html">
+      <link rel="canonical" href="https://flower.ai/docs/framework/main/{{ current_language }}/{{ pagename }}.html">
     {% else %}
-      <link rel="canonical" href="https://flower.dev/docs/framework/{{ pagename }}.html">
+      <link rel="canonical" href="https://flower.ai/docs/framework/{{ pagename }}.html">
     {% endif %}
 
     {%- if metatags %}{{ metatags }}{% endif -%}
@@ -105,6 +105,6 @@
     {%- endblock -%}
 
     {%- endblock scripts -%}
-    <script defer data-domain="flower.dev" data-api="/static/pla/api/event" src="/static/pla/js/script.js"></script>
+    <script defer data-domain="flower.ai" data-api="/static/pla/api/event" src="/static/pla/js/script.js"></script>
   </body>
 </html>
diff --git a/doc/source/_templates/sidebar/lang.html b/doc/source/_templates/sidebar/lang.html
index b5143bd7212b..b377a53f9c40 100644
--- a/doc/source/_templates/sidebar/lang.html
+++ b/doc/source/_templates/sidebar/lang.html
@@ -2,7 +2,8 @@
   <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/flag-icon-css/0.8.2/css/flag-icon.min.css">
 
   <div style="text-align: center; padding-bottom: 1rem;">
-    <a href="https://flower.dev/docs/framework/{{ current_version.url }}/en/{{ pagename }}.html" style="text-decoration:none; padding-right: 2rem; font-size: 150%;">&#127468;&#127463;</a>
-    <a href="https://flower.dev/docs/framework/{{ current_version.url }}/fr/{{ pagename }}.html" style="text-decoration:none; font-size: 150%;">&#127467;&#127479;</a>
+    <a href="https://flower.ai/docs/framework/{{ current_version.url }}/en/{{ pagename }}.html" style="text-decoration:none; padding-right: 2rem; font-size: 150%;">&#127468;&#127463;</a>
+    <a href="https://flower.ai/docs/framework/{{ current_version.url }}/fr/{{ pagename }}.html" style="text-decoration:none; padding-right: 2rem; font-size: 150%;">&#127467;&#127479;</a>
+    <a href="https://flower.ai/docs/framework/{{ current_version.url }}/zh_Hans/{{ pagename }}.html" style="text-decoration:none; font-size: 150%;">&#x1F1E8;&#x1F1F3;</a>
   </div>
 {% endif %}
diff --git a/doc/source/_templates/sidebar/versioning.html b/doc/source/_templates/sidebar/versioning.html
index de607b55373a..74f1cd8febb7 100644
--- a/doc/source/_templates/sidebar/versioning.html
+++ b/doc/source/_templates/sidebar/versioning.html
@@ -57,14 +57,40 @@
 }
 </style>
 
-<div class="wrap-collabsible"> 
-  <input id="collapsible" class="toggle" type="checkbox"> 
+<script>
+function checkPageExistence(versionName, current_language, pagename) {
+  var newUrl = `https://flower.ai/docs/framework/${versionName}/${current_language}/${pagename}.html`;
+  var fallbackUrl = `https://flower.ai/docs/framework/${versionName}/${current_language}/index.html#references`;
+
+  fetch(newUrl)
+    .then(response => {
+      if (response.ok) {
+        window.location.href = newUrl;
+      } else {
+        window.location.href = fallbackUrl;
+      }
+    })
+    .catch(error => {
+      console.error('Error:', error);
+      window.location.href = fallbackUrl;
+    });
+}
+</script>
+
+<div class="wrap-collabsible">
+  <input id="collapsible" class="toggle" type="checkbox">
   <label for="collapsible" class="lbl-toggle">Versions</label>
   <div class="collapsible-content">
     <div class="content-inner">
       <ul>
         {%- for item in versions %}
-        <li><a href="https://flower.dev/docs/framework/{{ item.name }}/{{ current_language }}/{{ pagename }}.html" style="text-decoration:none">{{ item.name }}</a></li>
+        <li>
+          <a href="javascript:void(0);"
+             onclick="checkPageExistence('{{ item.name }}', '{{ current_language }}', '{{ pagename }}')"
+             style="text-decoration:none">
+             {{ item.name }}
+          </a>
+        </li>
         {%- endfor %}
       </ul>
     </div>
diff --git a/doc/source/conf.py b/doc/source/conf.py
index 8077d26aa6ae..88cb5c05b1d8 100644
--- a/doc/source/conf.py
+++ b/doc/source/conf.py
@@ -14,6 +14,7 @@
 # ==============================================================================
 
 
+import datetime
 import os
 import sys
 from git import Repo
@@ -81,11 +82,11 @@
 # -- Project information -----------------------------------------------------
 
 project = "Flower"
-copyright = "2022 Flower Labs GmbH"
+copyright = f"{datetime.date.today().year} Flower Labs GmbH"
 author = "The Flower Authors"
 
 # The full version, including alpha/beta/rc tags
-release = "1.7.0"
+release = "1.8.0"
 
 # -- General configuration ---------------------------------------------------
 
@@ -95,6 +96,7 @@
 extensions = [
     "sphinx.ext.napoleon",
     "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
     "sphinx.ext.mathjax",
     "sphinx.ext.viewcode",
     "sphinx.ext.graphviz",
@@ -108,6 +110,44 @@
     "nbsphinx",
 ]
 
+# Generate .rst files
+autosummary_generate = True
+
+# Document ONLY the objects from __all__ (present in __init__ files).
+# It will be done recursively starting from flwr.__init__
+# Starting point is controlled in the index.rst file.
+autosummary_ignore_module_all = False
+
+# Each class and function docs start with the path to it
+# Make the flwr_datasets.federated_dataset.FederatedDataset appear as FederatedDataset
+# The full name is still at the top of the page
+add_module_names = False
+
+
+def find_test_modules(package_path):
+    """Go through the python files and exclude every *_test.py file."""
+    full_path_modules = []
+    for root, dirs, files in os.walk(package_path):
+        for file in files:
+            if file.endswith("_test.py"):
+                # Construct the module path relative to the package directory
+                full_path = os.path.join(root, file)
+                relative_path = os.path.relpath(full_path, package_path)
+                # Convert file path to dotted module path
+                module_path = os.path.splitext(relative_path)[0].replace(os.sep, ".")
+                full_path_modules.append(module_path)
+    modules = []
+    for full_path_module in full_path_modules:
+        parts = full_path_module.split(".")
+        for i in range(len(parts)):
+            modules.append(".".join(parts[i:]))
+    return modules
+
+
+# Stop from documenting the *_test.py files.
+# That's the only way to do that in autosummary (make the modules as mock_imports).
+autodoc_mock_imports = find_test_modules(os.path.abspath("../../src/py/flwr"))
+
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ["_templates"]
 
@@ -135,6 +175,7 @@
     "writing-documentation": "contributor-how-to-write-documentation.html",
     "apiref-binaries": "ref-api-cli.html",
     "fedbn-example-pytorch-from-centralized-to-federated": "example-fedbn-pytorch-from-centralized-to-federated.html",
+    "how-to-use-built-in-middleware-layers": "how-to-use-built-in-mods.html",
     # Restructuring: tutorials
     "tutorial/Flower-0-What-is-FL": "tutorial-series-what-is-federated-learning.html",
     "tutorial/Flower-1-Intro-to-FL-PyTorch": "tutorial-series-get-started-with-flower-pytorch.html",
@@ -173,7 +214,8 @@
     "evaluation": "explanation-federated-evaluation.html",
     "differential-privacy-wrappers": "explanation-differential-privacy.html",
     # Restructuring: references
-    "apiref-flwr": "ref-api-flwr.html",
+    "apiref-flwr": "ref-api/flwr.html",
+    "ref-api-flwr": "ref-api/flwr.html",
     "apiref-cli": "ref-api-cli.html",
     "examples": "ref-example-projects.html",
     "telemetry": "ref-telemetry.html",
@@ -209,7 +251,7 @@
 html_title = f"Flower Framework"
 html_logo = "_static/flower-logo.png"
 html_favicon = "_static/favicon.ico"
-html_baseurl = "https://flower.dev/docs/framework/"
+html_baseurl = "https://flower.ai/docs/framework/"
 
 html_theme_options = {
     #
diff --git a/doc/source/contributor-explanation-architecture.rst b/doc/source/contributor-explanation-architecture.rst
index 0e2ea1f6e66b..a20a84313118 100644
--- a/doc/source/contributor-explanation-architecture.rst
+++ b/doc/source/contributor-explanation-architecture.rst
@@ -4,7 +4,7 @@ Flower Architecture
 Edge Client Engine
 ------------------
 
-`Flower <https://flower.dev>`_ core framework architecture with Edge Client Engine
+`Flower <https://flower.ai>`_ core framework architecture with Edge Client Engine
 
 .. figure:: _static/flower-architecture-ECE.png
      :width: 80 %
@@ -12,7 +12,7 @@ Edge Client Engine
 Virtual Client Engine
 ---------------------
 
-`Flower <https://flower.dev>`_ core framework architecture with Virtual Client Engine
+`Flower <https://flower.ai>`_ core framework architecture with Virtual Client Engine
 
 .. figure:: _static/flower-architecture-VCE.png
      :width: 80 %
@@ -20,7 +20,7 @@ Virtual Client Engine
 Virtual Client Engine and Edge Client Engine in the same workload
 -----------------------------------------------------------------
 
-`Flower <https://flower.dev>`_ core framework architecture with both Virtual Client Engine and Edge Client Engine
+`Flower <https://flower.ai>`_ core framework architecture with both Virtual Client Engine and Edge Client Engine
 
 .. figure:: _static/flower-architecture.drawio.png
      :width: 80 %
diff --git a/doc/source/contributor-how-to-build-docker-images.rst b/doc/source/contributor-how-to-build-docker-images.rst
new file mode 100644
index 000000000000..5dead265bee2
--- /dev/null
+++ b/doc/source/contributor-how-to-build-docker-images.rst
@@ -0,0 +1,135 @@
+How to build Docker Flower images locally
+=========================================
+
+Flower provides pre-made docker images on `Docker Hub <https://hub.docker.com/r/flwr/server/tags>`_
+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.
+
+Before we can start, we need to meet a few prerequisites in our local development environment.
+
+#. Clone the flower repository.
+
+    .. code-block:: bash
+
+      $ git clone https://github.com/adap/flower.git && cd flower
+
+#. Verify the Docker daemon is running.
+
+    Please follow the first section on
+    :doc:`Run Flower using Docker <how-to-run-flower-using-docker>`
+    which covers this step in more detail.
+
+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``.
+
+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 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.
+
+Building the base image
+-----------------------
+
+.. list-table::
+   :widths: 25 45 15 15
+   :header-rows: 1
+
+   * - Build argument
+     - Description
+     - Required
+     - Example
+   * - ``PYTHON_VERSION``
+     - Version of ``python`` to be installed.
+     - Yes
+     - ``3.11``
+   * - ``PIP_VERSION``
+     - Version of ``pip`` to be installed.
+     - Yes
+     - ``23.0.1``
+   * - ``SETUPTOOLS_VERSION``
+     - Version of ``setuptools`` to be installed.
+     - Yes
+     - ``69.0.2``
+   * - ``UBUNTU_VERSION``
+     - Version of the official Ubuntu Docker image.
+     - Defaults to ``22.04``.
+     -
+
+The following example creates a base image with Python 3.11.0, pip 23.0.1 and setuptools 69.0.2:
+
+.. code-block:: bash
+
+  $ cd src/docker/base/
+  $ docker build \
+    --build-arg PYTHON_VERSION=3.11.0 \
+    --build-arg PIP_VERSION=23.0.1 \
+    --build-arg SETUPTOOLS_VERSION=69.0.2 \
+    -t flwr_base:0.1.0 .
+
+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 server image
+-------------------------
+
+.. list-table::
+   :widths: 25 45 15 15
+   :header-rows: 1
+
+   * - Build argument
+     - Description
+     - Required
+     - Example
+   * - ``BASE_REPOSITORY``
+     - The repository name of the base image.
+     - Defaults to ``flwr/server``.
+     -
+   * - ``BASE_IMAGE_TAG``
+     - The image tag of the base image.
+     - Defaults to ``py3.11-ubuntu22.04``.
+     -
+   * - ``FLWR_VERSION``
+     - Version of Flower to be installed.
+     - Yes
+     - ``1.7.0``
+
+The following example creates a server image with the official Flower base image py3.11-ubuntu22.04
+and Flower 1.7.0:
+
+.. code-block:: bash
+
+  $ cd src/docker/server/
+  $ docker build \
+    --build-arg BASE_IMAGE_TAG=py3.11-ubuntu22.04 \
+    --build-arg FLWR_VERSION=1.7.0 \
+    -t flwr_server:0.1.0 .
+
+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.
+
+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.
+
+.. code-block:: bash
+
+  $ cd src/docker/server/
+  $ docker build \
+    --build-arg BASE_REPOSITORY=flwr_base \
+    --build-arg BASE_IMAGE_TAG=0.1.0 \
+    --build-arg FLWR_VERSION=1.7.0 \
+    -t flwr_server:0.1.0 .
+
+After creating the image, we can test whether the image is working:
+
+.. code-block:: bash
+
+  $ docker run --rm flwr_server:0.1.0 --help
diff --git a/doc/source/contributor-how-to-contribute-translations.rst b/doc/source/contributor-how-to-contribute-translations.rst
index d97a2cb8c64f..ba59901cf1c4 100644
--- a/doc/source/contributor-how-to-contribute-translations.rst
+++ b/doc/source/contributor-how-to-contribute-translations.rst
@@ -2,13 +2,13 @@ Contribute translations
 =======================
 
 Since `Flower 1.5
-<https://flower.dev/docs/framework/ref-changelog.html#v1-5-0-2023-08-31>`_ we
+<https://flower.ai/docs/framework/ref-changelog.html#v1-5-0-2023-08-31>`_ 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.
+contributors with little prerequisites.
 
 Our translation project is publicly available over on `Weblate
 <https://hosted.weblate.org/projects/flower-docs/framework/>`_, this where most
@@ -44,7 +44,7 @@ This is what the interface looks like:
 
   .. image:: _static/weblate_interface.png
 
-You input your translation in the textbox at the top and then, once you are
+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
@@ -67,5 +67,5 @@ Add new languages
 -----------------
 
 If you want to add a new language, you will first have to contact us, either on
-`Slack <https://flower.dev/join-slack>`_, or by opening an issue on our `GitHub
+`Slack <https://flower.ai/join-slack>`_, or by opening an issue on our `GitHub
 repo <https://github.com/adap/flower>`_.
diff --git a/doc/source/contributor-how-create-new-messages.rst b/doc/source/contributor-how-to-create-new-messages.rst
similarity index 95%
rename from doc/source/contributor-how-create-new-messages.rst
rename to doc/source/contributor-how-to-create-new-messages.rst
index 24fa5f573158..3f1849bdce47 100644
--- a/doc/source/contributor-how-create-new-messages.rst
+++ b/doc/source/contributor-how-to-create-new-messages.rst
@@ -29,8 +29,8 @@ Let's now see what we need to implement in order to get this simple function bet
 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 <https://developers.google.com/protocol-buffers/docs/proto3>`_.
+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://protobuf.dev/programming-guides/proto3/>`_.
 
 Within the :code:`ServerMessage` block:
 
@@ -75,7 +75,7 @@ Once that is done, we will compile the file with:
 
   $ python -m flwr_tool.protoc
 
-If it compiles succesfully, you should see the following message:
+If it compiles successfully, you should see the following message:
 
 .. code-block:: shell
 
diff --git a/doc/source/contributor-how-to-develop-in-vscode-dev-containers.rst b/doc/source/contributor-how-to-develop-in-vscode-dev-containers.rst
index 19d46c5753c6..c861457b6edc 100644
--- a/doc/source/contributor-how-to-develop-in-vscode-dev-containers.rst
+++ b/doc/source/contributor-how-to-develop-in-vscode-dev-containers.rst
@@ -8,7 +8,7 @@ When working on the Flower framework we want to ensure that all contributors use
 
   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.
 
-Source: `Official VSCode documentation <https://code.visualstudio.com/docs/remote/containers>`_
+Source: `Official VSCode documentation <https://code.visualstudio.com/docs/devcontainers/containers>`_
 
 
 Getting started
@@ -20,5 +20,5 @@ Now you should be good to go. When starting VSCode, it will ask you to run in th
 
 In some cases your setup might be more involved. For those cases consult the following sources:
 
-* `Developing inside a Container <https://code.visualstudio.com/docs/remote/containers#_system-requirements>`_
-* `Remote development in Containers <https://code.visualstudio.com/docs/remote/containers-tutorial>`_
+* `Developing inside a Container <https://code.visualstudio.com/docs/devcontainers/containers#_system-requirements>`_
+* `Remote development in Containers <https://code.visualstudio.com/docs/devcontainers/tutorial>`_
diff --git a/doc/source/contributor-how-to-install-development-versions.rst b/doc/source/contributor-how-to-install-development-versions.rst
index 243f4ef97e8e..558ec7f8ec46 100644
--- a/doc/source/contributor-how-to-install-development-versions.rst
+++ b/doc/source/contributor-how-to-install-development-versions.rst
@@ -19,8 +19,8 @@ Install ``flwr`` from a local copy of the Flower source code via ``pyproject.tom
 
 Install ``flwr`` from a local wheel file via ``pyproject.toml``:
 
-- ``flwr = { path = "../../dist/flwr-1.0.0-py3-none-any.whl" }`` (without extras)
-- ``flwr = { path = "../../dist/flwr-1.0.0-py3-none-any.whl", extras = ["simulation"] }`` (with extras)
+- ``flwr = { path = "../../dist/flwr-1.8.0-py3-none-any.whl" }`` (without extras)
+- ``flwr = { path = "../../dist/flwr-1.8.0-py3-none-any.whl", extras = ["simulation"] }`` (with extras)
 
 Please refer to the Poetry documentation for further details: `Poetry Dependency Specification <https://python-poetry.org/docs/dependency-specification/>`_
 
@@ -59,5 +59,5 @@ Open a development version of the same notebook from branch `branch-name` by cha
 Install a `whl` on Google Colab:
 
 1. In the vertical icon grid on the left hand side, select ``Files`` > ``Upload to session storage``
-2. Upload the whl (e.g., ``flwr-1.7.0-py3-none-any.whl``)
-3. 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``
+2. Upload the whl (e.g., ``flwr-1.8.0-py3-none-any.whl``)
+3. 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``
diff --git a/doc/source/contributor-how-to-release-flower.rst b/doc/source/contributor-how-to-release-flower.rst
index 2eef165c0ed0..4853d87bc4c1 100644
--- a/doc/source/contributor-how-to-release-flower.rst
+++ b/doc/source/contributor-how-to-release-flower.rst
@@ -3,23 +3,15 @@ Release Flower
 
 This document describes the current release process. It may or may not change in the future.
 
-Before the release
-------------------
-
-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:
-
-`GitHub: Compare v1.2.0...main <https://github.com/adap/flower/compare/v1.2.0...main>`_
-
-Thank the authors who contributed since the last release. This can be done by running the ``./dev/add-shortlog.sh <new version>`` convenience script (it can be ran multiple times and will update the names in the list if new contributors were added in the meantime).
-
 During the release
 ------------------
 
 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):
 
-1. 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.
-2. 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.
-3. Check the draft release on GitHub, and if everything is good, publish it.
+1. Run ``python3 src/py/flwr_tool/update_changelog.py <YOUR_GH_TOKEN>`` in order to add every new change to the changelog (feel free to make manual changes to the changelog afterwards until it looks good).
+2. Once the changelog has been updated with all the changes, run ``./dev/prepare-release-changelog.sh v<NEW_VERSION>``, where ``<NEW_VERSION>`` 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<NEW_VERSION>`` (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.
 
 After the release
 -----------------
@@ -30,7 +22,7 @@ Create a pull request which contains the following changes:
 2. Update all files which contain the current version number if necessary.
 3. Add a new ``Unreleased`` section in ``changelog.md``.
 
-Merge the pull request on the same day (i.e., before a new nighly release gets published to PyPI).
+Merge the pull request on the same day (i.e., before a new nightly release gets published to PyPI).
 
 Publishing a pre-release
 ------------------------
@@ -38,11 +30,11 @@ Publishing a pre-release
 Pre-release naming
 ~~~~~~~~~~~~~~~~~~
 
-PyPI supports pre-releases (alpha, beta, release candiate). 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:
 
 - Alpha: ``MAJOR.MINOR.PATCHaN``
 - Beta: ``MAJOR.MINOR.PATCHbN``
-- Release candiate (RC): ``MAJOR.MINOR.PATCHrcN``
+- Release candidate (RC): ``MAJOR.MINOR.PATCHrcN``
 
 Examples include:
 
diff --git a/doc/source/contributor-ref-good-first-contributions.rst b/doc/source/contributor-ref-good-first-contributions.rst
index 523a4679c6ef..2b8ce88413f5 100644
--- a/doc/source/contributor-ref-good-first-contributions.rst
+++ b/doc/source/contributor-ref-good-first-contributions.rst
@@ -14,7 +14,7 @@ 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:
 
-- Documentation: What's missing? What could be expressed more clearly? 
+- Documentation: What's missing? What could be expressed more clearly?
 - Baselines: See below.
 - Examples: See below.
 
@@ -22,11 +22,11 @@ are:
 Request for Flower Baselines
 ----------------------------
 
-If you are not familiar with Flower Baselines, you should probably check-out our `contributing guide for baselines <https://flower.dev/docs/contributing-baselines.html>`_.
+If you are not familiar with Flower Baselines, you should probably check-out our `contributing guide for baselines <https://flower.ai/docs/baselines/how-to-contribute-baselines.html>`_.
 
-You should then check out the open 
+You should then check out the open
 `issues <https://github.com/adap/flower/issues?q=is%3Aopen+is%3Aissue+label%3A%22new+baseline%22>`_ 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!
+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!
 
 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!
 
diff --git a/doc/source/contributor-tutorial-contribute-on-github.rst b/doc/source/contributor-tutorial-contribute-on-github.rst
index 9aeb8229b412..6da81ce73662 100644
--- a/doc/source/contributor-tutorial-contribute-on-github.rst
+++ b/doc/source/contributor-tutorial-contribute-on-github.rst
@@ -3,9 +3,7 @@ Contribute on GitHub
 
 This guide is for people who want to get involved with Flower, but who are not used to contributing to GitHub projects.
 
-If you're familiar with how contributing on GitHub works, you can directly checkout our 
-`getting started guide for contributors <https://flower.dev/docs/getting-started-for-contributors.html>`_ 
-and examples of `good first contributions <https://flower.dev/docs/good-first-contributions.html>`_.
+If you're familiar with how contributing on GitHub works, you can directly checkout our :doc:`getting started guide for contributors <contributor-tutorial-get-started-as-a-contributor>`.
 
 
 Setting up the repository
@@ -13,21 +11,21 @@ Setting up the repository
 
 1. **Create a GitHub account and setup Git**
     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 <https://docs.github.com/en/get-started/quickstart/set-up-git>`_ to set it up.
+    It is a software that will need to be installed on your local machine, you can follow this `guide <https://docs.github.com/en/get-started/getting-started-with-git/set-up-git>`_ to set it up.
 
     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.
 
-    If you haven't already, you will need to create an account on `GitHub <https://github.com/signup>`_. 
+    If you haven't already, you will need to create an account on `GitHub <https://github.com/signup>`_.
 
-    The idea behind the generic Git and GitHub workflow boils down to this: 
+    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.
 
 2. **Forking the Flower repository**
-    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)
+    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.
 
     .. image:: _static/fork_button.png
-    
+
     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.
 
@@ -35,18 +33,18 @@ Setting up the repository
 
 3. **Cloning your forked repository**
     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, 
+    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.
 
     .. image:: _static/cloning_fork.png
 
     Once you copied the \<URL\>, you can open a terminal on your machine, navigate to the place you want to download the repository to and type:
 
-    .. code-block:: shell 
+    .. code-block:: shell
 
         $ git clone <URL>
 
-    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.
 
 4. **Add origin**
     You can then go into the repository folder:
@@ -59,17 +57,17 @@ Setting up the repository
     To obtain it, we can do as previously mentioned by going to our fork repository on our GitHub account and copying the link.
 
     .. image:: _static/cloning_fork.png
-    
+
     Once the \<URL\> is copied, we can type the following command in our terminal:
 
     .. code-block:: shell
 
         $ git remote add origin <URL>
 
-    
+
 5. **Add upstream**
     Now we will add an upstream address to our repository.
-    Still in the same directroy, we must run the following command:
+    Still in the same directory, we must run the following command:
 
     .. code-block:: shell
 
@@ -77,10 +75,10 @@ Setting up the repository
 
     The following diagram visually explains what we did in the previous steps:
 
-    .. image:: _static/github_schema.png 
+    .. image:: _static/github_schema.png
 
-    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 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.
 
     To make sure our local version of the fork is up-to-date with the latest changes from the Flower repository,
@@ -94,7 +92,7 @@ Setting up the repository
 Setting up the coding environment
 ---------------------------------
 
-This can be achieved by following this `getting started guide for contributors`_ (note that you won't need to clone the repository).
+This can be achieved by following this :doc:`getting started guide for contributors <contributor-tutorial-get-started-as-a-contributor>` (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!
 
 
@@ -114,9 +112,9 @@ And with Flower's repository:
     $ git pull upstream main
 
 1. **Create a new branch**
-    To make the history cleaner and easier to work with, it is good practice to 
+    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 do so, just run the following command inside the repository's directory:
 
     .. code-block:: shell
@@ -138,7 +136,7 @@ And with Flower's repository:
         $ ./dev/test.sh # to test that your code can be accepted
         $ ./baselines/dev/format.sh # same as above but for code added to baselines
         $ ./baselines/dev/test.sh # same as above but for code added to baselines
-    
+
 4. **Stage changes**
     Before creating a commit that will update your history, you must specify to Git which files it needs to take into account.
 
@@ -180,22 +178,26 @@ Creating and merging a pull request (PR)
 
     .. image:: _static/compare_and_pr.png
 
-    Otherwise you can always find this option in the `Branches` page.
+    Otherwise you can always find this option in the ``Branches`` page.
 
-    Once you click the `Compare & pull request` button, you should see something similar to this:
+    Once you click the ``Compare & pull request`` button, you should see something similar to this:
 
     .. image:: _static/creating_pr.png
-    
+
     At the top you have an explanation of which branch will be merged where:
 
     .. image:: _static/merging_branch.png
-    
+
     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.
 
-    The input box in the middle is there for you to describe what your PR does and to link it to existing issues. 
+    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.
 
-    At the bottom you will find the button to open the PR. This will notify reviewers that a new PR has been opened and 
+    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.
+
+    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.
 
     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:
@@ -215,7 +217,7 @@ Creating and merging a pull request (PR)
     Merging will be blocked if there are ongoing requested changes.
 
     .. image:: _static/changes_requested.png
-    
+
     To resolve them, just push the necessary changes to the branch associated with the PR:
 
     .. image:: _static/make_changes.png
@@ -253,54 +255,54 @@ Example of first contribution
 Problem
 *******
 
-For our documentation, we’ve started to use the `Diàtaxis framework <https://diataxis.fr/>`_.
+For our documentation, we've started to use the `Diàtaxis framework <https://diataxis.fr/>`_.
 
-Our “How to” guides should have titles that continue the sencence “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".
 
 Most of our guides do not follow this new format yet, and changing their title is (unfortunately) more involved than one might think.
 
-This issue is about changing the title of a doc from present continious to present simple.
+This issue is about changing the title of a doc from present continuous to present simple.
 
-Let's take the example of “Saving Progress” which we changed to “Save Progress”. Does this pass our check?
+Let's take the example of "Saving Progress" which we changed to "Save Progress". Does this pass our check?
 
-Before: ”How to saving progress” ❌
+Before: "How to saving progress" ❌
 
-After: ”How to save progress” ✅
+After: "How to save progress" ✅
 
 Solution
 ********
 
-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:
+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:
 
-- Find the source file in `doc/source`
-- Make the change in the `.rst` file (beware, the dashes under the title should be the same length as the title itself)
-- Build the docs and check the result: `<https://flower.dev/docs/writing-documentation.html#edit-an-existing-page>`_
+- Find the source file in ``doc/source``
+- Make the change in the ``.rst`` file (beware, the dashes under the title should be the same length as the title itself)
+- Build the docs and `check the result <contributor-how-to-write-documentation.html#edit-an-existing-page>`_
 
 Rename file
 :::::::::::
 
-You might have noticed that the file name still reflects the old wording. 
+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.
 
-Here’s how to change the file name:
+Here's how to change the file name:
 
-- Change the file name to `save-progress.rst`
-- Add a redirect rule to `doc/source/conf.py`
+- Change the file name to ``save-progress.rst``
+- Add a redirect rule to ``doc/source/conf.py``
 
-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.
 
 Apply changes in the index file
 :::::::::::::::::::::::::::::::
 
-For the lateral navigation bar to work properly, it is very important to update the `index.rst` file as well. 
+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.
 
-- Find and modify the file name in `index.rst`
+- Find and modify the file name in ``index.rst``
 
 Open PR
 :::::::
 
-- Commit the changes (commit messages are always imperative: “Do something”, in this case “Change …”)
+- Commit the changes (commit messages are always imperative: "Do something", in this case "Change …")
 - Push the changes to your fork
 - Open a PR (as shown above)
 - Wait for it to be approved!
@@ -331,7 +333,7 @@ Here are a few positive examples which provide helpful information without repea
 * Update docs banner to mention Flower Summit 2023
 * Remove unnecessary XGBoost dependency
 * Remove redundant attributes in strategies subclassing FedAvg
-* Add CI job to deploy the staging system when the `main` branch changes
+* Add CI job to deploy the staging system when the ``main`` branch changes
 * Add new amazing library which will be used to improve the simulation engine
 
 
@@ -340,4 +342,77 @@ Next steps
 
 Once you have made your first PR, and want to contribute more, be sure to check out the following :
 
-- `Good first contributions <https://flower.dev/docs/framework/contributor-ref-good-first-contributions.html>`_, where you should particularly look into the :code:`baselines` contributions.
+- :doc:`Good first contributions <contributor-ref-good-first-contributions>`, where you should particularly look into the :code:`baselines` contributions.
+
+
+Appendix
+--------
+
+.. _changelogentry:
+
+Changelog entry
+***************
+
+When opening a new PR, inside its description, there should be a ``Changelog entry`` header.
+
+Above this header you should see the following comment that explains how to write your changelog entry:
+
+    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.
+
+    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.
+
+    If the section contains some text other than tokens, it will use it to add a description to the change.
+
+    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:
+
+    <general> is for classifying a PR as a general improvement.
+
+    <skip> is to not add the PR to the changelog
+
+    <baselines> is to add a general baselines change to the PR
+
+    <examples> is to add a general examples change to the PR
+
+    <sdk> is to add a general sdk change to the PR
+
+    <simulations> is to add a general simulations change to the PR
+
+    Note that only one token should be used.
+
+Its content must have a specific format. We will break down what each possibility does:
+
+- If the ``### Changelog entry`` section contains nothing or doesn't exist, the following text will be added to the changelog::
+
+    - **PR TITLE** ([#PR_NUMBER](https://github.com/adap/flower/pull/PR_NUMBER))
+
+- If the ``### Changelog entry`` section contains a description (and no token), the following text will be added to the changelog::
+
+    - **PR TITLE** ([#PR_NUMBER](https://github.com/adap/flower/pull/PR_NUMBER))
+
+        DESCRIPTION FROM THE CHANGELOG ENTRY
+
+- If the ``### Changelog entry`` section contains ``<skip>``, nothing will change in the changelog.
+
+- If the ``### Changelog entry`` section contains ``<general>``, the following text will be added to the changelog::
+
+    - **General improvements** ([#PR_NUMBER](https://github.com/adap/flower/pull/PR_NUMBER))
+
+- If the ``### Changelog entry`` section contains ``<baselines>``, the following text will be added to the changelog::
+
+    - **General updates to Flower Baselines** ([#PR_NUMBER](https://github.com/adap/flower/pull/PR_NUMBER))
+
+- If the ``### Changelog entry`` section contains ``<examples>``, the following text will be added to the changelog::
+
+    - **General updates to Flower Examples** ([#PR_NUMBER](https://github.com/adap/flower/pull/PR_NUMBER))
+
+- If the ``### Changelog entry`` section contains ``<sdk>``, the following text will be added to the changelog::
+
+    - **General updates to Flower SDKs** ([#PR_NUMBER](https://github.com/adap/flower/pull/PR_NUMBER))
+
+- If the ``### Changelog entry`` section contains ``<simulations>``, the following text will be added to the changelog::
+
+    - **General updates to Flower Simulations** ([#PR_NUMBER](https://github.com/adap/flower/pull/PR_NUMBER))
+
+Note that only one token must be provided, otherwise, only the first action (in the order listed above), will be performed.
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 72c6df5fdbc7..9136fea96bf6 100644
--- a/doc/source/contributor-tutorial-get-started-as-a-contributor.rst
+++ b/doc/source/contributor-tutorial-get-started-as-a-contributor.rst
@@ -11,7 +11,7 @@ Prerequisites
 
 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 <https://www.python.org/dev/peps/pep-0517/>`_.
+supports `PEP 517 <https://peps.python.org/pep-0517/>`_.
 
 
 Developer Machine Setup
@@ -27,7 +27,7 @@ For macOS
 * Install `homebrew <https://brew.sh/>`_. Don't forget the post-installation actions to add `brew` to your PATH.
 * Install `xz` (to install different Python versions) and `pandoc` to build the
   docs::
-    
+
   $ brew install xz pandoc
 
 For Ubuntu
@@ -54,7 +54,7 @@ GitHub::
 * 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)::
 
   $ ./dev/setup-defaults.sh <version> # once completed, run the bootstrap script
-  
+
 * 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)::
 
   $ ./dev/venv-create.sh <version> # once completed, run the `bootstrap.sh` script
@@ -70,7 +70,7 @@ Convenience Scripts
 
 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 amonst the most
+subdirectory for a full list. The following scripts are amongst the most
 important ones:
 
 Create/Delete Virtual Environment
diff --git a/doc/source/example-fedbn-pytorch-from-centralized-to-federated.rst b/doc/source/example-fedbn-pytorch-from-centralized-to-federated.rst
index 5ebaa337dde8..0139f3b8dc31 100644
--- a/doc/source/example-fedbn-pytorch-from-centralized-to-federated.rst
+++ b/doc/source/example-fedbn-pytorch-from-centralized-to-federated.rst
@@ -3,11 +3,11 @@ Example: FedBN in PyTorch - From Centralized To Federated
 
 This tutorial will show you how to use Flower to build a federated version of an existing machine learning workload with `FedBN <https://github.com/med-air/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 <https://flower.dev/docs/examples/pytorch-from-centralized-to-federated.html>`_.
+When applying FedBN, only few changes needed compared to :doc:`Example: PyTorch - From Centralized To Federated <example-pytorch-from-centralized-to-federated>`.
 
 Centralized Training
 --------------------
-All files are revised based on `Example: PyTorch - From Centralized To Federated <https://flower.dev/docs/examples/pytorch-from-centralized-to-federated.html>`_.
+All files are revised based on :doc:`Example: PyTorch - From Centralized To Federated <example-pytorch-from-centralized-to-federated>`.
 The only thing to do is modifying the file called :code:`cifar.py`, revised part is shown below:
 
 The model architecture defined in class Net() is added with Batch Normalization layers accordingly.
@@ -45,13 +45,13 @@ You can now run your machine learning workload:
     python3 cifar.py
 
 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.
+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.
 
 Federated Training
 ------------------
 
-If you have read `Example: PyTorch - From Centralized To Federated <https://flower.dev/docs/examples/pytorch-from-centralized-to-federated.html>`_, 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 <https://flower.dev/docs/examples/pytorch-from-centralized-to-federated.html>`_. first.
+If you have read :doc:`Example: PyTorch - From Centralized To Federated <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 <example-pytorch-from-centralized-to-federated>`. first.
 
 Our example consists of one *server* and two *clients*. In FedBN, :code:`server.py` keeps unchanged, we can start the server directly.
 
@@ -66,7 +66,7 @@ Finally, we will revise our *client* logic by changing :code:`get_parameters` an
     class CifarClient(fl.client.NumPyClient):
         """Flower client implementing CIFAR-10 image classification using
         PyTorch."""
-        
+
         ...
 
         def get_parameters(self, config) -> List[np.ndarray]:
@@ -79,7 +79,7 @@ Finally, we will revise our *client* logic by changing :code:`get_parameters` an
             params_dict = zip(keys, parameters)
             state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
             self.model.load_state_dict(state_dict, strict=False)
-        
+
         ...
 
 Now, you can now open two additional terminal windows and run
diff --git a/doc/source/example-jax-from-centralized-to-federated.rst b/doc/source/example-jax-from-centralized-to-federated.rst
index 2b1823e9d408..6b06a288a67a 100644
--- a/doc/source/example-jax-from-centralized-to-federated.rst
+++ b/doc/source/example-jax-from-centralized-to-federated.rst
@@ -259,7 +259,7 @@ Having defined the federation process, we can run it.
 
         # Start Flower client
         client = FlowerClient(params, grad_fn, train_x, train_y, test_x, test_y)
-        fl.client.start_numpy_client(server_address="0.0.0.0:8080", client)
+        fl.client.start_client(server_address="0.0.0.0:8080", client.to_client())
 
     if __name__ == "__main__":
         main()
diff --git a/doc/source/example-mxnet-walk-through.rst b/doc/source/example-mxnet-walk-through.rst
index a395a8a723cb..c215f709ffb2 100644
--- a/doc/source/example-mxnet-walk-through.rst
+++ b/doc/source/example-mxnet-walk-through.rst
@@ -234,7 +234,7 @@ Our *client* needs to import :code:`flwr`, but also :code:`mxnet` to update the
 
 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 slighly 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:`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:
 
 #. :code:`set_parameters (optional)`
diff --git a/doc/source/example-pytorch-from-centralized-to-federated.rst b/doc/source/example-pytorch-from-centralized-to-federated.rst
index d649658667da..0c458a136a81 100644
--- a/doc/source/example-pytorch-from-centralized-to-federated.rst
+++ b/doc/source/example-pytorch-from-centralized-to-federated.rst
@@ -174,7 +174,7 @@ However, with Flower you can evolve your pre-existing code into a federated lear
 
 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 paramters.
+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. 
 
@@ -195,7 +195,7 @@ We can already start the *server*:
     python3 server.py
 
 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 paramters on our PyTorch model:
+Our *client* needs to import :code:`flwr`, but also :code:`torch` to update the parameters on our PyTorch model:
 
 .. code-block:: python
 
@@ -212,7 +212,7 @@ Our *client* needs to import :code:`flwr`, but also :code:`torch` to update the
 
 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 slighly 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:`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:
 
 #. :code:`set_parameters`
@@ -278,7 +278,7 @@ We included type annotations to give you a better understanding of the data type
             return float(loss), self.num_examples["testset"], {"accuracy": float(accuracy)}
 
 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 adress we used in :code:`server.py`: 
+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`: 
 
 .. code-block:: python
 
@@ -292,7 +292,7 @@ You load your data and model by using :code:`cifar.py`. Start :code:`CifarClient
 
         # Start client
         client = CifarClient(model, trainloader, testloader, num_examples)
-        fl.client.start_numpy_client(server_address="0.0.0.0:8080", client)
+        fl.client.start_client(server_address="0.0.0.0:8080", client.to_client())
 
 
     if __name__ == "__main__":
diff --git a/doc/source/example-walkthrough-pytorch-mnist.rst b/doc/source/example-walkthrough-pytorch-mnist.rst
index ab311813f5de..f8eacc8647fe 100644
--- a/doc/source/example-walkthrough-pytorch-mnist.rst
+++ b/doc/source/example-walkthrough-pytorch-mnist.rst
@@ -1,11 +1,11 @@
 Example: Walk-Through PyTorch & MNIST
 =====================================
 
-In this tutorial we will learn, how to train a Convolutional Neural Network on MNIST using Flower and PyTorch. 
+In this tutorial we will learn, how to train a Convolutional Neural Network on MNIST using Flower and PyTorch.
 
-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.
 
-*Clients* are responsible for generating individual weight-updates for the model based on their local datasets. 
+*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*.
 
@@ -15,7 +15,7 @@ Now that we have a rough idea of what is going on, let's get started. We first n
 
   $ pip install flwr
 
-Since we want to use PyTorch to solve a computer vision task, let's go ahead an install PyTorch and the **torchvision** library: 
+Since we want to use PyTorch to solve a computer vision task, let's go ahead an install PyTorch and the **torchvision** library:
 
 .. code-block:: shell
 
@@ -32,51 +32,51 @@ Go ahead and launch on a terminal the *run-server.sh* script first as follows:
 
 .. code-block:: shell
 
-  $ bash ./run-server.sh 
+  $ bash ./run-server.sh
 
 
-Now that the server is up and running, go ahead and launch the clients.  
+Now that the server is up and running, go ahead and launch the clients.
 
 .. code-block:: shell
 
-  $ bash ./run-clients.sh 
+  $ bash ./run-clients.sh
 
 
 Et voilà! You should be seeing the training procedure and, after a few iterations, the test accuracy for each client.
 
 .. code-block:: shell
 
-    Train Epoch: 10 [30000/30016 (100%)] Loss: 0.007014				
-    
-    Train Epoch: 10 [30000/30016 (100%)] Loss: 0.000403				
-    
-    Train Epoch: 11 [30000/30016 (100%)] Loss: 0.001280				
-    
-    Train Epoch: 11 [30000/30016 (100%)] Loss: 0.000641				
-    
-    Train Epoch: 12 [30000/30016 (100%)] Loss: 0.006784				
-    
-    Train Epoch: 12 [30000/30016 (100%)] Loss: 0.007134				
-    
-    Client 1 - Evaluate on 5000 samples: Average loss: 0.0290, Accuracy: 99.16%	
-    
+    Train Epoch: 10 [30000/30016 (100%)] Loss: 0.007014
+
+    Train Epoch: 10 [30000/30016 (100%)] Loss: 0.000403
+
+    Train Epoch: 11 [30000/30016 (100%)] Loss: 0.001280
+
+    Train Epoch: 11 [30000/30016 (100%)] Loss: 0.000641
+
+    Train Epoch: 12 [30000/30016 (100%)] Loss: 0.006784
+
+    Train Epoch: 12 [30000/30016 (100%)] Loss: 0.007134
+
+    Client 1 - Evaluate on 5000 samples: Average loss: 0.0290, Accuracy: 99.16%
+
     Client 0 - Evaluate on 5000 samples: Average loss: 0.0328, Accuracy: 99.14%
 
 
-Now, let's see what is really happening inside. 
+Now, let's see what is really happening inside.
 
 Flower Server
 -------------
 
 Inside the server helper script *run-server.sh* you will find the following code that basically runs the :code:`server.py`
 
-.. code-block:: bash 
+.. code-block:: bash
 
     python -m flwr_example.quickstart-pytorch.server
 
 
 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 <flwr-server-start_server-apiref>` function and leave all the configuration possibilities at their default values, as seen below.
+Flower Servers are very customizable, but for simple workloads, we can start a server using the `start_server <https://flower.ai/docs/framework/ref-api/flwr.server.start_server.html#flwr.server.start_server>`_ function and leave all the configuration possibilities at their default values, as seen below.
 
 .. code-block:: python
 
@@ -90,18 +90,18 @@ Flower Client
 
 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*.
 
-.. code-block:: bash 
+.. code-block:: bash
 
     python -m flwr_example.quickstart-pytorch.client \
       --cid=$i \
       --server_address=$SERVER_ADDRESS \
-      --nb_clients=$NUM_CLIENTS 
+      --nb_clients=$NUM_CLIENTS
 
 * **cid**: is the client ID. It is an integer that uniquely identifies client identifier.
-* **sever_address**: String that identifies IP and port of the server. 
+* **sever_address**: String that identifies IP and port of the server.
 * **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.
 
-Again, we can go deeper and look inside :code:`flwr_example/quickstart-pytorch/client.py`. 
+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).
 
@@ -152,7 +152,7 @@ Now, let's look closely into the :code:`PytorchMNISTClient` inside :code:`flwr_e
 
             Parameters
             ----------
-            weights: fl.common.NDArrays 
+            weights: fl.common.NDArrays
                 Weights received by the server and set to local model
 
 
@@ -179,8 +179,8 @@ Now, let's look closely into the :code:`PytorchMNISTClient` inside :code:`flwr_e
 
             Parameters
             ----------
-            ins: fl.common.FitIns 
-            Parameters sent by the server to be used during training. 
+            ins: fl.common.FitIns
+            Parameters sent by the server to be used during training.
 
             Returns
             -------
@@ -214,9 +214,9 @@ Now, let's look closely into the :code:`PytorchMNISTClient` inside :code:`flwr_e
 
             Parameters
             ----------
-            ins: fl.common.EvaluateIns 
-            Parameters sent by the server to be used during testing. 
-                
+            ins: fl.common.EvaluateIns
+            Parameters sent by the server to be used during testing.
+
 
             Returns
             -------
@@ -262,9 +262,9 @@ The code for the CNN is available under :code:`quickstart-pytorch.mnist` and it
 
             Parameters
             ----------
-            x: Tensor 
+            x: Tensor
                 Mini-batch of shape (N,28,28) containing images from MNIST dataset.
-                
+
 
             Returns
             -------
@@ -287,7 +287,7 @@ The code for the CNN is available under :code:`quickstart-pytorch.mnist` and it
             return output
 
 
-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:  
+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:
 
 .. code-block:: python
 
@@ -312,7 +312,7 @@ The second thing to notice is that :code:`PytorchMNISTClient` class inherits fro
             """Evaluate the provided weights using the locally held dataset."""
 
 
-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. 
+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.
 
 These functions can both be found inside the same :code:`quickstart-pytorch.mnist` module:
 
@@ -330,21 +330,21 @@ These functions can both be found inside the same :code:`quickstart-pytorch.mnis
         ----------
         model: torch.nn.ModuleList
             Neural network model used in this example.
-            
+
         train_loader: torch.utils.data.DataLoader
-            DataLoader used in traning.
-            
-        epochs: int 
-            Number of epochs to run in each round. 
-            
-        device: torch.device 
+            DataLoader used in training.
+
+        epochs: int
+            Number of epochs to run in each round.
+
+        device: torch.device
             (Default value = torch.device("cpu"))
             Device where the network will be trained within a client.
 
         Returns
         -------
         num_examples_train: int
-            Number of total samples used during traning.
+            Number of total samples used during training.
 
         """
         model.train()
@@ -399,10 +399,10 @@ These functions can both be found inside the same :code:`quickstart-pytorch.mnis
         ----------
         model: torch.nn.ModuleList :
             Neural network model used in this example.
-            
+
         test_loader: torch.utils.data.DataLoader :
             DataLoader used in test.
-            
+
         device: torch.device :
             (Default value = torch.device("cpu"))
             Device where the network will be tested within a client.
@@ -435,19 +435,19 @@ These functions can both be found inside the same :code:`quickstart-pytorch.mnis
 
 
 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? 
+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?
 
 
 
 Give It a Try
 -------------
-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. 
+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:
 
 - Try and change :code:`PytorchMNISTClient` so it can accept different architectures.
 - Modify the :code:`train` function so that it accepts different optimizers
 - Modify the :code:`test` function so that it proves not only the top-1 (regular accuracy) but also the top-5 accuracy?
-- Go larger! Try to adapt the code to larger images and datasets. Why not try training on ImageNet with a ResNet-50? 
+- Go larger! Try to adapt the code to larger images and datasets. Why not try training on ImageNet with a ResNet-50?
 
 You are ready now. Enjoy learning in a federated way!
diff --git a/doc/source/explanation-differential-privacy.rst b/doc/source/explanation-differential-privacy.rst
index fcebd81983cc..69fd333f9b13 100644
--- a/doc/source/explanation-differential-privacy.rst
+++ b/doc/source/explanation-differential-privacy.rst
@@ -1,100 +1,139 @@
-Differential privacy
+Differential 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.
 
-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. 
+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.
 
-.. warning::
-  Please note that these components are still experimental; the correct configuration of DP for a specific task is still an unsolved problem.
 
-.. note::
-  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.
+Differential Privacy
+--------------------
+Imagine two datasets that are identical except for a single record (for 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.
 
-DP-FedAvg
----------
+.. image:: ./_static/DP/dp-intro.png
+  :align: center
+  :width: 400
+  :alt: DP Intro
 
-DP-FedAvg, originally proposed by McMahan et al. [mcmahan]_ and extended by Andrew et al. [andrew]_, is essentially FedAvg with the following modifications. 
 
-* **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.
-* **Noising** :  Gaussian noise, calibrated to the clipping threshold, is added to the average computed at the server.
+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.
 
-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.
+Formal Definition
+~~~~~~~~~~~~~~~~~
+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):
 
-Simplifying Assumptions
-***********************
+.. math::
 
-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. 
+  \small
+  P[M(D_{1} \in A)] \leq e^{\delta} P[M(D_{2} \in A)] + \delta
 
-* **Fixed-size subsampling** :Fixed-size subsamples of the clients must be taken at each round, as opposed to variable-sized Poisson subsamples. 
-* **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.
-* **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. 
 
-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 :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.
 
-.. note::
-   These restrictions are in line with constraints imposed by Andrew et al. [andrew]_.
 
-Customizable Responsibility for Noise injection
-***********************************************
-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. 
+Differential Privacy in Machine Learning
+----------------------------------------
+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.
 
+Differential Privacy in Federated Learning
+------------------------------------------
+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 membership inference and property inference attacks, or model inversion attacks.
 
-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}`.
+DP can play a crucial role in federated learning to provide privacy for the clients' data.
 
-Wrapper-based approach
-----------------------
+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).
 
-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.
+- **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.
 
-Server-side logic
-*****************
+- **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.
 
-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.
+Central Differential Privacy
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+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.
 
-DPFedAvgFixed
-:::::::::::::
+.. image:: ./_static/DP/CDP.png
+  :align: center
+  :width: 400
+  :alt: Central Differential Privacy
 
-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.
+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`.
 
-#. :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()`.
-#. :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()`.
+.. image:: ./_static/DP/clipping.png
+  :align: center
+  :width: 300
+  :alt: clipping
 
-.. note::
-  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. 
+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)`.
 
-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. 
+Clipping
+^^^^^^^^
 
-DPFedAvgAdaptive
-::::::::::::::::
+There are two forms of clipping commonly used in Central DP: Fixed Clipping and Adaptive Clipping.
 
-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. 
+- **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.
 
-#. :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. 
+- **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.
 
-#. :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 choice between fixed and adaptive clipping depends on various factors such as privacy requirements, data distribution, model complexity, and others.
 
+Local Differential Privacy
+~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Client-side logic
-*****************
+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.
 
-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.
+.. image:: ./_static/DP/LDP.png
+  :align: center
+  :width: 400
+  :alt: Local Differential Privacy
 
-* :code:`dpfedavg_noise_stddev` : Generate and add the specified amount of noise to the clipped update.
-* :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.
 
+In this explainer, we focus on two forms of achieving Local DP:
 
-Performing the :math:`(\epsilon, \delta)` analysis
---------------------------------------------------
+- 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:
 
-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. 
+.. math::
+    \small
+    \frac{∆ \times \sqrt{2 \times \log\left(\frac{1.25}{\delta}\right)}}{\epsilon}
 
-.. code-block:: python
 
-   import tensorflow_privacy as tfp
-   max_order = 32
-   orders = range(2, max_order + 1)
-   rdp = tfp.compute_rdp_sample_without_replacement(q, z, n, orders)
-   eps, _, _ = tfp.rdp_accountant.get_privacy_spent(rdp, target_delta=delta)
+- 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.
 
-.. [mcmahan] McMahan et al. "Learning Differentially Private Recurrent Language Models." International Conference on Learning Representations (ICLR), 2017.
 
-.. [andrew] Andrew, Galen, et al. "Differentially Private Learning with Adaptive Clipping." Advances in Neural Information Processing Systems (NeurIPS), 2021.
+Please note that these two approaches are providing privacy at different levels.
+
+
+**References:**
+
+[1] Dwork et al. The Algorithmic Foundations of Differential Privacy.
+
+[2] McMahan et al. Learning Differentially Private Recurrent Language Models.
+
+[3] Geyer et al. Differentially Private Federated Learning: A Client Level Perspective.
+
+[4] Galen et al. Differentially Private Learning with Adaptive Clipping.
diff --git a/doc/source/explanation-federated-evaluation.rst b/doc/source/explanation-federated-evaluation.rst
index 632241f70d36..bcdca9bae700 100644
--- a/doc/source/explanation-federated-evaluation.rst
+++ b/doc/source/explanation-federated-evaluation.rst
@@ -120,7 +120,7 @@ Federated evaluation can be configured from the server side. Built-in strategies
 
     # Create strategy
     strategy = fl.server.strategy.FedAvg(
-        # ... other FedAvg agruments
+        # ... other FedAvg arguments
         fraction_evaluate=0.2,
         min_evaluate_clients=2,
         min_available_clients=10,
diff --git a/doc/source/how-to-configure-clients.rst b/doc/source/how-to-configure-clients.rst
index 26c132125ccf..ff0a2f4033df 100644
--- a/doc/source/how-to-configure-clients.rst
+++ b/doc/source/how-to-configure-clients.rst
@@ -13,7 +13,7 @@ Configuration values are represented as a dictionary with ``str`` keys and value
     config_dict = {
         "dropout": True,        # str key, bool value
         "learning_rate": 0.01,  # str key, float value
-        "batch_size": 32,       # str key, int value 
+        "batch_size": 32,       # str key, int value
         "optimizer": "sgd",     # str key, str value
     }
 
@@ -56,7 +56,7 @@ To make the built-in strategies use this function, we can pass it to ``FedAvg``
 One the client side, we receive the configuration dictionary in ``fit``:
 
 .. code-block:: python
-    
+
     class FlowerClient(flwr.client.NumPyClient):
         def fit(parameters, config):
             print(config["batch_size"])  # Prints `32`
@@ -86,7 +86,7 @@ Configuring individual clients
 
 In some cases, it is necessary to send different configuration values to different clients.
 
-This can be achieved by customizing an existing strategy or by `implementing a custom strategy from scratch <https://flower.dev/docs/framework/how-to-implement-strategies.html>`_. 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 <how-to-implement-strategies>`. 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):
 
 .. code-block:: python
 
diff --git a/doc/source/how-to-configure-logging.rst b/doc/source/how-to-configure-logging.rst
index 3fcfe85b9592..d5559429a73c 100644
--- a/doc/source/how-to-configure-logging.rst
+++ b/doc/source/how-to-configure-logging.rst
@@ -129,4 +129,4 @@ Log to a remote service
 
 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 whish to backup or analyze the logs somewhere else.
+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.
diff --git a/doc/source/how-to-enable-ssl-connections.rst b/doc/source/how-to-enable-ssl-connections.rst
index fa59d4423c5a..051dd5711497 100644
--- a/doc/source/how-to-enable-ssl-connections.rst
+++ b/doc/source/how-to-enable-ssl-connections.rst
@@ -75,9 +75,9 @@ We are now going to show how to write a client which uses the previously generat
   client = MyFlowerClient()
 
   # Start client
-  fl.client.start_numpy_client(
+  fl.client.start_client(
       "localhost:8080",
-      client=client,
+      client=client.to_client(),
       root_certificates=Path(".cache/certificates/ca.crt").read_bytes(),
   )
 
diff --git a/doc/source/how-to-implement-strategies.rst b/doc/source/how-to-implement-strategies.rst
index 7997503b65a8..01bbb3042973 100644
--- a/doc/source/how-to-implement-strategies.rst
+++ b/doc/source/how-to-implement-strategies.rst
@@ -233,7 +233,7 @@ The return value is a list of tuples, each representing the instructions that wi
 * Use the :code:`client_manager` to randomly sample all (or a subset of) available clients (each represented as a :code:`ClientProxy` object)
 * Pair each :code:`ClientProxy` with the same :code:`FitIns` holding the current global model :code:`parameters` and :code:`config` dict
 
-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`.
+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`.
 
 .. note::
 
@@ -280,7 +280,7 @@ The return value is a list of tuples, each representing the instructions that wi
 * Use the :code:`client_manager` to randomly sample all (or a subset of) available clients (each represented as a :code:`ClientProxy` object)
 * Pair each :code:`ClientProxy` with the same :code:`EvaluateIns` holding the current global model :code:`parameters` and :code:`config` dict
 
-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`.
+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`.
 
 .. note::
 
diff --git a/doc/source/how-to-install-flower.rst b/doc/source/how-to-install-flower.rst
index b2efde176fc9..aebe5f7316de 100644
--- a/doc/source/how-to-install-flower.rst
+++ b/doc/source/how-to-install-flower.rst
@@ -11,6 +11,9 @@ Flower requires at least `Python 3.8 <https://docs.python.org/3.8/>`_, but `Pyth
 Install stable release
 ----------------------
 
+Using pip
+~~~~~~~~~
+
 Stable releases are available on `PyPI <https://pypi.org/project/flwr/>`_::
 
   python -m pip install flwr
@@ -20,10 +23,29 @@ For simulations that use the Virtual Client Engine, ``flwr`` should be installed
   python -m pip install flwr[simulation]
 
 
+Using conda (or mamba)
+~~~~~~~~~~~~~~~~~~~~~~
+
+Flower can also be installed from the ``conda-forge`` channel.
+
+If you have not added ``conda-forge`` to your channels, you will first need to run the following::
+
+  conda config --add channels conda-forge
+  conda config --set channel_priority strict
+
+Once the ``conda-forge`` channel has been enabled, ``flwr`` can be installed with ``conda``::
+
+  conda install flwr
+
+or with ``mamba``::
+
+  mamba install flwr
+
+
 Verify installation
 -------------------
 
-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::
+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__)"
   1.5.0
@@ -32,6 +54,11 @@ The following command can be used to verfiy if Flower was successfully installed
 Advanced installation options
 -----------------------------
 
+Install via Docker
+~~~~~~~~~~~~~~~~~~
+
+:doc:`How to run Flower using Docker <how-to-run-flower-using-docker>`
+
 Install pre-release
 ~~~~~~~~~~~~~~~~~~~
 
diff --git a/doc/source/how-to-monitor-simulation.rst b/doc/source/how-to-monitor-simulation.rst
index 740004914eed..f6c26a701d94 100644
--- a/doc/source/how-to-monitor-simulation.rst
+++ b/doc/source/how-to-monitor-simulation.rst
@@ -161,7 +161,7 @@ However, you can overwrite the defaults. When starting a simulation, do the foll
   ram_memory = 16_000 * 1024 * 1024  # 16 GB
   fl.simulation.start_simulation(
       # ...
-      # all the args you were specyfing before
+      # all the args you were specifying before
       # ...
       ray_init_args = {
               "include_dashboard": True, # we need this one for tracking
@@ -187,7 +187,7 @@ Let’s also specify the resource for a single client.
 
   fl.simulation.start_simulation(
       # ...
-      # all the args you were specyfing before
+      # all the args you were specifying before
       # ...
       ray_init_args = {
               "include_dashboard": True, # we need this one for tracking
@@ -231,6 +231,6 @@ A: Either the simulation has already finished, or you still need to start Promet
 Resources
 ---------
 
-Ray Dashboard: `<https://docs.ray.io/en/latest/ray-core/ray-dashboard.html>`_
+Ray Dashboard: `<https://docs.ray.io/en/latest/ray-observability/getting-started.html>`_
 
-Ray Metrics: `<https://docs.ray.io/en/latest/ray-observability/ray-metrics.html>`_
+Ray Metrics: `<https://docs.ray.io/en/latest/cluster/metrics.html>`_
diff --git a/doc/source/how-to-run-flower-using-docker.rst b/doc/source/how-to-run-flower-using-docker.rst
new file mode 100644
index 000000000000..ed034c820142
--- /dev/null
+++ b/doc/source/how-to-run-flower-using-docker.rst
@@ -0,0 +1,144 @@
+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 <https://hub.docker.com/r/flwr/server/tags>`_.
+
+Before you start, make sure that the Docker daemon is running:
+
+.. code-block:: bash
+
+  $ docker -v
+  Docker version 24.0.7, build afdd53b
+
+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 <https://docs.docker.com/get-docker/>`_.
+
+.. note::
+
+  On Linux, Docker commands require ``sudo`` privilege. If you want to avoid using ``sudo``,
+  you can follow the `Post-installation steps <https://docs.docker.com/engine/install/linux-postinstall/>`_
+  on the official Docker website.
+
+Flower server
+-------------
+
+Quickstart
+~~~~~~~~~~
+
+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/server:1.7.0-py3.11-ubuntu22.04 \
+    --insecure
+
+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.
+
+.. note::
+
+  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.
+
+The ``-p <host>:<container>`` 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``.
+
+.. attention::
+
+  The ``--insecure`` flag enables insecure communication (using HTTP, not HTTPS) and should only be used
+  for testing purposes. We strongly recommend enabling
+  `SSL <https://flower.ai/docs/framework/how-to-run-flower-using-docker.html#enabling-ssl-for-secure-connections>`_
+  when deploying to a production environment.
+
+You can use ``--help`` to view all available flags that the server supports:
+
+.. code-block:: bash
+
+  $ docker run --rm flwr/server:1.7.0-py3.11-ubuntu22.04 --help
+
+Mounting a volume to store the state on the host system
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+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.
+
+.. code-block:: bash
+
+  $ docker run --rm \
+    -p 9091:9091 -p 9092:9092 -v ~/:/app/ flwr/server:1.7.0-py3.11-ubuntu22.04 \
+    --insecure \
+    --database state.db
+
+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.
+
+Enabling SSL for secure connections
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+To enable SSL, you will need a CA certificate, a server certificate and a server private key.
+
+.. note::
+  For testing purposes, you can generate your own self-signed certificates. The
+  `Enable SSL connections <https://flower.ai/docs/framework/how-to-enable-ssl-connections.html#certificates>`_
+  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
+``-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.
+
+.. code-block:: bash
+
+  $ docker run --rm \
+    -p 9091:9091 -p 9092:9092 -v ./certificates/:/app/ flwr/server:1.7.0-py3.11-ubuntu22.04 \
+    --certificates ca.crt server.pem server.key
+
+Using a different Flower or Python version
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+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 <https://hub.docker.com/r/flwr/server/tags>`_.
+
+Pinning a Docker image to a specific version
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+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 following command returns the current image hash referenced by the ``server:1.7.0-py3.11-ubuntu22.04`` tag:
+
+.. code-block:: bash
+
+  $ docker inspect --format='{{index .RepoDigests 0}}' flwr/server:1.7.0-py3.11-ubuntu22.04
+  flwr/server@sha256:c4be5012f9d73e3022e98735a889a463bb2f4f434448ebc19c61379920b1b327
+
+Next, we can pin the hash when running a new server container:
+
+.. code-block:: bash
+
+  $ docker run \
+    --rm flwr/server@sha256:c4be5012f9d73e3022e98735a889a463bb2f4f434448ebc19c61379920b1b327 \
+    --insecure
+
+Setting environment variables
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+To set a variable inside a Docker container, you can use the ``-e <name>=<value>`` flag.
+
+.. code-block:: bash
+
+  $ docker run -e FLWR_TELEMETRY_ENABLED=0 \
+    --rm flwr/server:1.7.0-py3.11-ubuntu22.04 --insecure
diff --git a/doc/source/how-to-run-simulations.rst b/doc/source/how-to-run-simulations.rst
index 707e3d3ffe84..d1dcb511ed51 100644
--- a/doc/source/how-to-run-simulations.rst
+++ b/doc/source/how-to-run-simulations.rst
@@ -7,7 +7,7 @@ Run simulations
 
 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 <contributor-explanation-architecture.html#virtual-client-engine>`_ or VCE.
 
-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 <ref-api-flwr.html#start-numpy-client>`_) in the sense that they can be configure by creating a class inheriting, for example, from `flwr.client.NumPyClient <ref-api-flwr.html#flwr.client.NumPyClient>`_ and therefore behave in an identical way. In addition to that, clients managed by the :code:`VirtualClientEngine` are:
+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 <ref-api-flwr.html#start-client>`_) in the sense that they can be configure by creating a class inheriting, for example, from `flwr.client.NumPyClient <ref-api-flwr.html#flwr.client.NumPyClient>`_ and therefore behave in an identical way. In addition to that, clients managed by the :code:`VirtualClientEngine` are:
 
 * 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.
 * 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.
@@ -29,7 +29,7 @@ Running Flower simulations still require you to define your client class, a stra
     
     def client_fn(cid: str):
         # Return a standard Flower client
-        return MyFlowerClient()
+        return MyFlowerClient().to_client()
 
     # Launch the simulation
     hist = fl.simulation.start_simulation(
diff --git a/doc/source/how-to-save-and-load-model-checkpoints.rst b/doc/source/how-to-save-and-load-model-checkpoints.rst
index 404df485fbae..0d711e375cd8 100644
--- a/doc/source/how-to-save-and-load-model-checkpoints.rst
+++ b/doc/source/how-to-save-and-load-model-checkpoints.rst
@@ -91,3 +91,7 @@ To load your progress, you simply append the following lines to your code. Note
     print("Loading pre-trained model from: ", latest_round_file)
     state_dict = torch.load(latest_round_file)
     net.load_state_dict(state_dict)
+    state_dict_ndarrays = [v.cpu().numpy() for v in net.state_dict().values()]
+    parameters = fl.common.ndarrays_to_parameters(state_dict_ndarrays)
+
+Return/use this object of type ``Parameters`` wherever necessary, such as in the ``initial_parameters`` when defining a ``Strategy``.
\ No newline at end of file
diff --git a/doc/source/how-to-upgrade-to-flower-1.0.rst b/doc/source/how-to-upgrade-to-flower-1.0.rst
index fd380e95d69c..3a55a1a953f5 100644
--- a/doc/source/how-to-upgrade-to-flower-1.0.rst
+++ b/doc/source/how-to-upgrade-to-flower-1.0.rst
@@ -50,7 +50,7 @@ Strategies / ``start_server`` / ``start_simulation``
 - Replace ``num_rounds=1`` in ``start_simulation`` with the new ``config=ServerConfig(...)`` (see previous item)
 - 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.
 - Rename parameter/ndarray conversion functions:
-  
+
   - ``parameters_to_weights`` --> ``parameters_to_ndarrays``
   - ``weights_to_parameters`` --> ``ndarrays_to_parameters``
 
@@ -81,11 +81,11 @@ Optional improvements
 
 Along with the necessary changes above, there are a number of potential improvements that just became possible:
 
-- Remove "placeholder" methods from subclasses of ``Client`` or ``NumPyClient``. If you, for example, use server-side evaluation, then empy placeholder implementations of ``evaluate`` are no longer necessary.
+- 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.
 - Configure the round timeout via ``start_simulation``: ``start_simulation(..., config=flwr.server.ServerConfig(num_rounds=3, round_timeout=600.0), ...)``
 
 
 Further help
 ------------
 
-Most official `Flower code examples <https://github.com/adap/flower/tree/main/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 questionsm, `join the Flower Slack <https://flower.dev/join-slack/>`_ and use the channgel ``#questions``.
+Most official `Flower code examples <https://github.com/adap/flower/tree/main/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 <https://flower.ai/join-slack/>`_ and use the channel ``#questions``.
diff --git a/doc/source/how-to-use-built-in-mods.rst b/doc/source/how-to-use-built-in-mods.rst
new file mode 100644
index 000000000000..341139175074
--- /dev/null
+++ b/doc/source/how-to-use-built-in-mods.rst
@@ -0,0 +1,89 @@
+Use Built-in Mods
+=================
+
+**Note: This tutorial covers experimental features. The functionality and interfaces may change in future versions.**
+
+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``.
+
+What are Mods?
+--------------
+
+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:
+
+.. code-block:: python
+
+    ClientApp = Callable[[Message, Context], Message]
+    Mod = Callable[[Message, Context, ClientApp], Message]
+
+A typical mod function might look something like this:
+
+.. code-block:: python
+
+    def example_mod(msg: Message, ctx: Context, nxt: ClientApp) -> Message:
+        # Do something with incoming Message (or Context)
+        # before passing to the inner ``ClientApp``
+        msg = nxt(msg, ctx)
+        # Do something with outgoing Message (or Context)
+        # before returning
+        return msg
+
+Using Mods
+----------
+
+To use mods in your ``ClientApp``, you can follow these steps:
+
+1. Import the required mods
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+First, import the built-in mod you intend to use:
+
+.. code-block:: python
+
+    import flwr as fl
+    from flwr.client.mod import example_mod_1, example_mod_2
+
+2. Define your client function
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Define your client function (``client_fn``) that will be wrapped by the mod(s):
+
+.. code-block:: python
+
+    def client_fn(cid):
+        # Your client code goes here.
+        return # your client
+
+3. Create the ``ClientApp`` with mods
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Create your ``ClientApp`` and pass the mods as a list to the ``mods`` argument. The order in which you provide the mods matters:
+
+.. code-block:: python
+
+    app = fl.client.ClientApp(
+        client_fn=client_fn,
+        mods=[
+            example_mod_1,  # Mod 1
+            example_mod_2,  # Mod 2
+        ]
+    )
+
+Order of execution
+------------------
+
+When the ``ClientApp`` runs, the mods are executed in the order they are provided in the list:
+
+1. ``example_mod_1`` (outermost mod)
+2. ``example_mod_2`` (next mod)
+3. Message handler (core function that handles the incoming ``Message`` and returns the outgoing ``Message``)
+4. ``example_mod_2`` (on the way back)
+5. ``example_mod_1`` (outermost mod on the way back)
+
+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.
+
+Conclusion
+----------
+
+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.
+
+Enjoy building a more robust and flexible ``ClientApp`` with mods!
diff --git a/doc/source/how-to-use-differential-privacy.rst b/doc/source/how-to-use-differential-privacy.rst
new file mode 100644
index 000000000000..c8901bd906cc
--- /dev/null
+++ b/doc/source/how-to-use-differential-privacy.rst
@@ -0,0 +1,126 @@
+Use 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`.
+
+.. warning::
+
+   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.
+
+
+Central Differential Privacy
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+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.
+
+- **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.
+- **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.
+
+
+
+Server-side Clipping
+^^^^^^^^^^^^^^^^^^^^
+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.
+
+.. image:: ./_static/DP/serversideCDP.png
+  :align: center
+  :width: 700
+  :alt: server side clipping
+
+
+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.
+
+.. code-block:: python
+
+  from flwr.server.strategy import DifferentialPrivacyClientSideFixedClipping
+
+  # Create the strategy
+  strategy = fl.server.strategy.FedAvg(...)
+
+  # Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping wrapper
+  dp_strategy = DifferentialPrivacyServerSideFixedClipping(
+      strategy,
+      cfg.noise_multiplier,
+      cfg.clipping_norm,
+      cfg.num_sampled_clients,
+  )
+
+
+
+Client-side Clipping
+^^^^^^^^^^^^^^^^^^^^
+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`.
+
+.. image:: ./_static/DP/clientsideCDP.png
+  :align: center
+  :width: 800
+  :alt: client side clipping
+
+
+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`:
+
+.. code-block:: python
+
+  from flwr.server.strategy import DifferentialPrivacyClientSideFixedClipping
+
+  # Create the strategy
+  strategy = fl.server.strategy.FedAvg(...)
+
+  # Wrap the strategy with the DifferentialPrivacyClientSideFixedClipping wrapper
+  dp_strategy = DifferentialPrivacyClientSideFixedClipping(
+      strategy,
+      cfg.noise_multiplier,
+      cfg.clipping_norm,
+      cfg.num_sampled_clients,
+  )
+
+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:
+
+.. code-block:: python
+
+  from flwr.client.mod import fixedclipping_mod
+
+  # Add fixedclipping_mod to the client-side mods
+  app = fl.client.ClientApp(
+      client_fn=client_fn,
+      mods=[
+          fixedclipping_mod,
+      ]
+  )
+
+
+Local Differential Privacy
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+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.
+
+.. image:: ./_static/DP/localdp.png
+  :align: center
+  :width: 700
+  :alt: local DP mod
+
+Below is a code example that shows how to use :code:`LocalDpMod`:
+
+.. code-block:: python
+
+  from flwr.client.mod.localdp_mod import LocalDpMod
+
+  # Create an instance of the mod with the required params
+  local_dp_obj = LocalDpMod(
+      cfg.clipping_norm, cfg.sensitivity, cfg.epsilon, cfg.delta
+  )
+  # Add local_dp_obj to the client-side mods
+
+  app = fl.client.ClientApp(
+    client_fn=client_fn,
+    mods=[local_dp_obj],
+  )
+
+
+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.
+
+Local Training using Privacy Engines
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+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 <https://github.com/adap/flower/tree/main/examples/opacus>`_, `Tensorflow Privacy <https://github.com/adap/flower/tree/main/examples/dp-sgd-mnist>`_).
\ No newline at end of file
diff --git a/doc/source/how-to-use-strategies.rst b/doc/source/how-to-use-strategies.rst
index 6d24f97bd7f6..d0e2cd63a091 100644
--- a/doc/source/how-to-use-strategies.rst
+++ b/doc/source/how-to-use-strategies.rst
@@ -45,7 +45,7 @@ Configuring client fit and client evaluate
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 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 arbitraty configuration values  :code:`client.fit` and :code:`client.evaluate` functions during each round of federated learning. 
+It must return a dictionary of arbitrary configuration values  :code:`client.fit` and :code:`client.evaluate` functions during each round of federated learning. 
 
 .. code-block:: python
 
diff --git a/doc/source/index.rst b/doc/source/index.rst
index c4a313414d3a..894155be03f1 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -4,7 +4,7 @@ Flower Framework Documentation
 .. meta::
    :description: Check out the documentation of the main Flower Framework enabling easy Python development for Federated Learning.
 
-Welcome to Flower's documentation. `Flower <https://flower.dev>`_ is a friendly federated learning framework.
+Welcome to Flower's documentation. `Flower <https://flower.ai>`_ is a friendly federated learning framework.
 
 
 Join the Flower Community
@@ -12,7 +12,7 @@ Join the Flower Community
 
 The Flower Community is growing quickly - we're a friendly group of researchers, engineers, students, professionals, academics, and other enthusiasts.
 
-.. button-link:: https://flower.dev/join-slack
+.. button-link:: https://flower.ai/join-slack
     :color: primary
     :shadow:
 
@@ -91,6 +91,9 @@ Problem-oriented how-to guides show step-by-step how to achieve a specific goal.
    how-to-configure-logging
    how-to-enable-ssl-connections
    how-to-upgrade-to-flower-1.0
+   how-to-use-built-in-mods
+   how-to-run-flower-using-docker
+   how-to-use-differential-privacy
 
 .. toctree::
    :maxdepth: 1
@@ -119,11 +122,17 @@ References
 
 Information-oriented API reference and other reference material.
 
+.. autosummary::
+   :toctree: ref-api
+   :template: autosummary/module.rst
+   :caption: API reference
+   :recursive:
+
+      flwr
+
 .. toctree::
    :maxdepth: 2
-   :caption: API reference
 
-   ref-api-flwr
    ref-api-cli
 
 .. toctree::
@@ -160,6 +169,7 @@ The Flower community welcomes contributions. The following docs are intended to
    contributor-how-to-write-documentation
    contributor-how-to-release-flower
    contributor-how-to-contribute-translations
+   contributor-how-to-build-docker-images
 
 .. toctree::
    :maxdepth: 1
diff --git a/doc/source/ref-api-cli.rst b/doc/source/ref-api-cli.rst
index 039a2ea27cf8..63579143755d 100644
--- a/doc/source/ref-api-cli.rst
+++ b/doc/source/ref-api-cli.rst
@@ -1,42 +1,52 @@
 Flower CLI reference
 ====================
 
-.. _flower-server-apiref:
+.. _flower-superlink-apiref:
 
-flower-server
-~~~~~~~~~~~~~
+flower-superlink
+~~~~~~~~~~~~~~~~
 
 .. argparse::
    :module: flwr.server.app
-   :func:  _parse_args_server
-   :prog: flower-server
+   :func:  _parse_args_run_superlink
+   :prog: flower-superlink
 
-.. _flower-driver-apiref:
+.. _flower-driver-api-apiref:
 
 flower-driver-api
 ~~~~~~~~~~~~~~~~~
 
 .. argparse::
    :module: flwr.server.app
-   :func: _parse_args_driver
+   :func: _parse_args_run_driver_api
    :prog: flower-driver-api
 
-.. _flower-fleet-apiref:
+.. _flower-fleet-api-apiref:
 
 flower-fleet-api
 ~~~~~~~~~~~~~~~~
 
 .. argparse::
    :module: flwr.server.app
-   :func:  _parse_args_fleet
+   :func:  _parse_args_run_fleet_api
    :prog: flower-fleet-api
 
-.. .. _flower-client-apiref:
+.. _flower-client-app-apiref:
+
+flower-client-app
+~~~~~~~~~~~~~~~~~
+
+.. argparse::
+   :module: flwr.client.app
+   :func: _parse_args_run_client_app
+   :prog: flower-client-app
+
+.. _flower-server-app-apiref:
 
-.. flower-client
-.. ~~~~~~~~~~~~~
+flower-server-app
+~~~~~~~~~~~~~~~~~
 
-    .. argparse::
-..    :filename: flwr.client
-..    :func: run_client
-..    :prog: flower-client
+.. argparse::
+   :module: flwr.server.run_serverapp
+   :func: _parse_args_run_server_app
+   :prog: flower-server-app
diff --git a/doc/source/ref-api-flwr.rst b/doc/source/ref-api-flwr.rst
deleted file mode 100644
index e1983cd92c90..000000000000
--- a/doc/source/ref-api-flwr.rst
+++ /dev/null
@@ -1,265 +0,0 @@
-flwr (Python API reference)
-===========================
-
-
-.. _flwr-client-apiref:
-
-client
-------
-
-.. automodule:: flwr.client
-
-.. _flwr-client-Client-apiref:
-
-Client
-~~~~~~
-
-.. autoclass:: flwr.client.Client
-    :members:
-
-
-.. _flwr-client-start_client-apiref:
-
-start_client
-~~~~~~~~~~~~
-
-.. autofunction:: flwr.client.start_client
-
-
-.. _flwr-client-NumPyClient-apiref:
-
-NumPyClient
-~~~~~~~~~~~
-
-.. autoclass:: flwr.client.NumPyClient
-    :members:
-
-
-.. _flwr-client-start_numpy_client-apiref:
-
-start_numpy_client
-~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: flwr.client.start_numpy_client
-
-
-.. _flwr-simulation-start_simulation-apiref:
-
-start_simulation
-~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: flwr.simulation.start_simulation
-
-
-.. _flwr-server-apiref:
-
-server
-------
-
-.. automodule:: flwr.server
-
-
-.. _flwr-server-start_server-apiref:
-
-server.start_server
-~~~~~~~~~~~~~~~~~~~
-
-.. autofunction:: flwr.server.start_server
-
-
-.. _flwr-server-strategy-apiref:
-
-server.strategy
-~~~~~~~~~~~~~~~
-
-.. automodule:: flwr.server.strategy
-
-
-.. _flwr-server-strategy-Strategy-apiref:
-
-server.strategy.Strategy
-^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.Strategy
-    :members:
-
-
-.. _flwr-server-strategy-FedAvg-apiref:
-
-server.strategy.FedAvg
-^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.FedAvg
-    :members:
-
-    .. automethod:: __init__
-
-
-.. _flwr-server-strategy-FedAvgM-apiref:
-
-server.strategy.FedAvgM
-^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.FedAvgM
-    :members:
-
-    .. automethod:: __init__
-
-
-.. _flwr-server-strategy-FedMedian-apiref:
-
-server.strategy.FedMedian
-^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.FedMedian
-    :members:
-
-    .. automethod:: __init__
-
-.. _flwr-server-strategy-QFedAvg-apiref:
-
-server.strategy.QFedAvg
-^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.QFedAvg
-    :members:
-
-    .. automethod:: __init__
-
-
-.. _flwr-server-strategy-FaultTolerantFedAvg-apiref:
-
-server.strategy.FaultTolerantFedAvg
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.FaultTolerantFedAvg
-    :members:
-
-    .. automethod:: __init__
-
-
-.. _flwr-server-strategy-FedOpt-apiref:
-
-server.strategy.FedOpt
-^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.FedOpt
-    :members:
-
-    .. automethod:: __init__
-
-
-.. _flwr-server-strategy-FedProx-apiref:
-
-server.strategy.FedProx
-^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.FedProx
-    :members:
-
-    .. automethod:: __init__
-
-
-.. _flwr-server-strategy-FedAdagrad-apiref:
-
-server.strategy.FedAdagrad
-^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.FedAdagrad
-    :members:
-
-    .. automethod:: __init__
-
-
-.. _flwr-server-strategy-FedAdam-apiref:
-
-server.strategy.FedAdam
-^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.FedAdam
-    :members:
-
-    .. automethod:: __init__
-
-
-.. _flwr-server-strategy-FedYogi-apiref:
-
-server.strategy.FedYogi
-^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.FedYogi
-    :members:
-
-    .. automethod:: __init__
-
-
-.. _flwr-server-strategy-FedTrimmedAvg-apiref:
-
-server.strategy.FedTrimmedAvg
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.FedTrimmedAvg
-    :members:
-
-    .. automethod:: __init__
-
-
-.. _flwr-server-strategy-Krum-apiref:
-
-server.strategy.Krum
-^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.Krum
-    :members:
-
-    .. automethod:: __init__
-
-.. _flwr-server-strategy-Bulyan-apiref:
-
-server.strategy.Bulyan
-^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.Bulyan
-    :members:
-
-    .. automethod:: __init__
-
-
-.. _flwr-server-strategy-FedXgbNnAvg-apiref:
-
-server.strategy.FedXgbNnAvg
-^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.FedXgbNnAvg
-    :members:
-
-    .. automethod:: __init__
-
-
-.. _flwr-server-strategy-DPFedAvgAdaptive-apiref:
-
-server.strategy.DPFedAvgAdaptive
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.DPFedAvgAdaptive
-    :members:
-
-    .. automethod:: __init__
-
-
-.. _flwr-server-strategy-DPFedAvgFixed-apiref:
-
-server.strategy.DPFedAvgFixed
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. autoclass:: flwr.server.strategy.DPFedAvgFixed
-    :members:
-
-    .. automethod:: __init__
-
-common
-------
-
-.. automodule:: flwr.common
-  :members:
-  :exclude-members: event
diff --git a/doc/source/ref-changelog.md b/doc/source/ref-changelog.md
index 030f7618f4b2..1a6524d29353 100644
--- a/doc/source/ref-changelog.md
+++ b/doc/source/ref-changelog.md
@@ -2,11 +2,102 @@
 
 ## Unreleased
 
-- **General updates to Flower Examples** ([#2381](https://github.com/adap/flower/pull/2381))
+### What's new?
+
+### Incompatible changes
+
+## v1.7.0 (2024-02-05)
+
+### 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):
+
+`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` <!---TOKEN_v1.7.0-->
+
+### What's new?
+
+- **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))
+
+  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.
+
+- **Improve performance** ([#2293](https://github.com/adap/flower/pull/2293))
+
+  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.
+
+- **Support Federated Learning with Apple MLX and Flower** ([#2693](https://github.com/adap/flower/pull/2693))
+
+  Flower has official support for federated learning using [Apple MLX](https://ml-explore.github.io/mlx) via the new `quickstart-mlx` code example.
+
+- **Introduce new XGBoost cyclic strategy** ([#2666](https://github.com/adap/flower/pull/2666), [#2668](https://github.com/adap/flower/pull/2668))
+
+  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.
+
+- **Support Python 3.11** ([#2394](https://github.com/adap/flower/pull/2394))
+
+  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.
+
+- **Update gRPC and ProtoBuf dependencies** ([#2814](https://github.com/adap/flower/pull/2814))
+
+  The `grpcio` and `protobuf` dependencies were updated to their latest versions for improved security and performance.
+
+- **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))
+
+  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.
+
+- **Introduce** `flower-via-docker-compose` **example** ([#2626](https://github.com/adap/flower/pull/2626))
+
+- **Introduce** `quickstart-sklearn-tabular` **example** ([#2719](https://github.com/adap/flower/pull/2719))
+
+- **Introduce** `custom-metrics` **example** ([#1958](https://github.com/adap/flower/pull/1958))
+
+- **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))
+
+  Several code examples were updated to use [Flower Datasets](https://flower.ai/docs/datasets/).
+
+- **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))
+
+  Many Flower code examples received substantial updates.
 
 - **Update Flower Baselines**
 
-  - HFedXGBoost [#2226](https://github.com/adap/flower/pull/2226)
+  - HFedXGBoost ([#2226](https://github.com/adap/flower/pull/2226), [#2771](https://github.com/adap/flower/pull/2771))
+  - FedVSSL ([#2412](https://github.com/adap/flower/pull/2412))
+  - FedNova ([#2179](https://github.com/adap/flower/pull/2179))
+  - HeteroFL ([#2439](https://github.com/adap/flower/pull/2439))
+  - FedAvgM ([#2246](https://github.com/adap/flower/pull/2246))
+  - FedPara ([#2722](https://github.com/adap/flower/pull/2722))
+
+- **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))
+
+- **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 Flower testing and development infrastructure has received substantial updates. This makes Flower 1.7 the most tested release ever.
+
+- **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))
+
+- **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))
+
+### Incompatible changes
+
+- **Deprecate** `start_numpy_client` ([#2563](https://github.com/adap/flower/pull/2563), [#2718](https://github.com/adap/flower/pull/2718))
+
+  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.
+
+- **Deprecate legacy DP wrappers** ([#2749](https://github.com/adap/flower/pull/2749))
+
+  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.
+
+- **Make optional arg** `--callable` **in** `flower-client` **a required positional arg** ([#2673](https://github.com/adap/flower/pull/2673))
+
+- **Rename** `certificates` **to** `root_certificates` **in** `Driver` ([#2890](https://github.com/adap/flower/pull/2890))
+
+- **Drop experimental** `Task` **fields** ([#2866](https://github.com/adap/flower/pull/2866), [#2865](https://github.com/adap/flower/pull/2865))
+
+  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.
+
+- **Retire MXNet examples** ([#2724](https://github.com/adap/flower/pull/2724))
+
+  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.
 
 ## v1.6.0 (2023-11-28)
 
@@ -86,7 +177,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
   - FedBN ([#2608](https://github.com/adap/flower/pull/2608), [#2615](https://github.com/adap/flower/pull/2615))
 
-- **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))
 
 - **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))
 
@@ -94,7 +185,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **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 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))
+- **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))
 
   Flower received many improvements under the hood, too many to list here.
 
@@ -122,11 +213,11 @@ We would like to give our special thanks to all the contributors who made the ne
 
   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.
 
-  Comprehensive documentation includes a new [how-to run simulations](https://flower.dev/docs/framework/how-to-run-simulations.html) guide, new [simulation-pytorch](https://flower.dev/docs/examples/simulation-pytorch.html) and [simulation-tensorflow](https://flower.dev/docs/examples/simulation-tensorflow.html) notebooks, and a new [YouTube tutorial series](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB).
+  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).
 
 - **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))
 
-  Much effort went into a completely restructured Flower docs experience. The documentation on [flower.dev/docs](flower.dev/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.
 
 - **Introduce Flower Swift SDK** ([#1858](https://github.com/adap/flower/pull/1858), [#1897](https://github.com/adap/flower/pull/1897))
 
@@ -204,7 +295,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **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))
 
-  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/quickstart_xgboost_horizontal) that demonstrates the usage of this new strategy in an XGBoost project.
+  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.
 
 - **Introduce iOS SDK (preview)** ([#1621](https://github.com/adap/flower/pull/1621), [#1764](https://github.com/adap/flower/pull/1764))
 
@@ -212,11 +303,11 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Introduce new "What is Federated Learning?" tutorial** ([#1657](https://github.com/adap/flower/pull/1657), [#1721](https://github.com/adap/flower/pull/1721))
 
-  A new [entry-level tutorial](https://flower.dev/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!
+  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!
 
 - **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 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 heterogenous 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.
 
 - **Introduce new Flower Baseline: FedAvg FEMNIST** ([#1655](https://github.com/adap/flower/pull/1655))
 
@@ -238,7 +329,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Add new example using** `TabNet` **and Flower** ([#1725](https://github.com/adap/flower/pull/1725))
 
-  TabNet is a powerful and flexible framework for training machine learning models on tabular data. We now have a federated example using Flower: [https://github.com/adap/flower/tree/main/examples/tabnet](https://github.com/adap/flower/tree/main/examples/quickstart_tabnet).
+  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 how-to guide for monitoring simulations** ([#1649](https://github.com/adap/flower/pull/1649))
 
@@ -280,7 +371,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Add new example of Federated Learning using fastai and Flower** ([#1598](https://github.com/adap/flower/pull/1598))
 
-  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).
+  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).
 
 - **Make Android example compatible with** `flwr >= 1.0.0` **and the latest versions of Android** ([#1603](https://github.com/adap/flower/pull/1603))
 
@@ -326,7 +417,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Introduce new Flower Baseline: FedAvg MNIST** ([#1497](https://github.com/adap/flower/pull/1497), [#1552](https://github.com/adap/flower/pull/1552))
 
-  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.dev/blog/2023-01-12-fl-starter-pack-fedavg-mnist-cnn/)
+  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/)
 
 - **Improve GPU support in simulations** ([#1555](https://github.com/adap/flower/pull/1555))
 
@@ -336,16 +427,16 @@ We would like to give our special thanks to all the contributors who made the ne
 
   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:
 
-  - [An Introduction to Federated Learning](https://flower.dev/docs/framework/tutorial-get-started-with-flower-pytorch.html)
-  - [Strategies in Federated Learning](https://flower.dev/docs/framework/tutorial-use-a-federated-learning-strategy-pytorch.html)
-  - [Building a Strategy](https://flower.dev/docs/framework/tutorial-build-a-strategy-from-scratch-pytorch.html)
-  - [Client and NumPyClient](https://flower.dev/docs/framework/tutorial-customize-the-client-pytorch.html)
+  - [An Introduction to Federated Learning](https://flower.ai/docs/framework/tutorial-get-started-with-flower-pytorch.html)
+  - [Strategies in Federated Learning](https://flower.ai/docs/framework/tutorial-use-a-federated-learning-strategy-pytorch.html)
+  - [Building a Strategy](https://flower.ai/docs/framework/tutorial-build-a-strategy-from-scratch-pytorch.html)
+  - [Client and NumPyClient](https://flower.ai/docs/framework/tutorial-customize-the-client-pytorch.html)
 
 - **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))
 
   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.
 
-  **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.dev/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).
 
 - **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))
 
@@ -359,7 +450,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
 - **Add new Federated Analytics with Pandas example** ([#1469](https://github.com/adap/flower/pull/1469), [#1535](https://github.com/adap/flower/pull/1535))
 
-  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).
 
 - **Add new strategies: Krum and MultiKrum** ([#1481](https://github.com/adap/flower/pull/1481))
 
@@ -377,7 +468,7 @@ We would like to give our special thanks to all the contributors who made the ne
 
   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!
 
-  One highlight is the new [first time contributor guide](https://flower.dev/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!
 
 ### Incompatible changes
 
@@ -458,7 +549,7 @@ None
 
 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):
 
-[@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), [@sandracl72](https://github.com/sandracl72), [@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).
+[@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).
 
 ### Incompatible changes
 
@@ -566,7 +657,7 @@ We would like to give our **special thanks** to all the contributors who made Fl
 
 - **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))
 
-  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.dev/docs/using-baselines.html). With this first preview release we're also inviting the community to [contribute their own baselines](https://flower.dev/docs/contributing-baselines.html).
+  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).
 
 - **C++ client SDK (preview) and code example** ([#1111](https://github.com/adap/flower/pull/1111))
 
@@ -612,7 +703,7 @@ We would like to give our **special thanks** to all the contributors who made Fl
 
   - New option to keep Ray running if Ray was already initialized in `start_simulation` ([#1177](https://github.com/adap/flower/pull/1177))
   - Add support for custom `ClientManager` as a `start_simulation` parameter ([#1171](https://github.com/adap/flower/pull/1171))
-  - New documentation for [implementing strategies](https://flower.dev/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))
   - New mobile-friendly documentation theme ([#1174](https://github.com/adap/flower/pull/1174))
   - 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))
 
@@ -732,7 +823,7 @@ We would like to give our **special thanks** to all the contributors who made Fl
 
 - **Renamed q-FedAvg strategy** ([#802](https://github.com/adap/flower/pull/802))
 
-  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 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).
 
 - **Deprecated and renamed code example** `simulation_pytorch` **to** `simulation_pytorch_legacy` ([#791](https://github.com/adap/flower/pull/791))
 
@@ -751,9 +842,9 @@ We would like to give our **special thanks** to all the contributors who made Fl
 
   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.
 
-  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 build-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.
 
-  Stratey 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}`.
+  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}`.
 
   Flower 0.15-style return types are deprecated (but still supported), compatibility will be removed in a future release.
 
@@ -773,7 +864,7 @@ We would like to give our **special thanks** to all the contributors who made Fl
 
   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).
 
-  Built-in strategies implement this approach by handling serialization and deserialization to/from `Weights` internally. Custom/3rd-party Strategy implementations should update to the slighly 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.
+  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.
 
 - Deprecated `flwr.server.Server.evaluate`, use `flwr.server.Server.evaluate_round` instead ([#717](https://github.com/adap/flower/pull/717))
 
@@ -794,7 +885,7 @@ What's new?
   )
   model.compile("adam", "sparse_categorical_crossentropy", metrics=["accuracy"])
 
-  # Create strategy and initilize parameters on the server-side
+  # Create strategy and initialize parameters on the server-side
   strategy = fl.server.strategy.FedAvg(
       # ... (other constructor arguments)
       initial_parameters=model.get_weights(),
diff --git a/doc/source/ref-example-projects.rst b/doc/source/ref-example-projects.rst
index b47bd8e48997..bade86dfaa54 100644
--- a/doc/source/ref-example-projects.rst
+++ b/doc/source/ref-example-projects.rst
@@ -23,8 +23,8 @@ The TensorFlow/Keras quickstart example shows CIFAR-10 image classification
 with MobileNetV2:
 
 - `Quickstart TensorFlow (Code) <https://github.com/adap/flower/tree/main/examples/quickstart-tensorflow>`_
-- `Quickstart TensorFlow (Tutorial) <https://flower.dev/docs/framework/tutorial-quickstart-tensorflow.html>`_
-- `Quickstart TensorFlow (Blog Post) <https://flower.dev/blog/2020-12-11-federated-learning-in-less-than-20-lines-of-code>`_
+- :doc:`Quickstart TensorFlow (Tutorial) <tutorial-quickstart-tensorflow>`
+- `Quickstart TensorFlow (Blog Post) <https://flower.ai/blog/2020-12-11-federated-learning-in-less-than-20-lines-of-code>`_
 
 
 Quickstart PyTorch
@@ -34,7 +34,7 @@ The PyTorch quickstart example shows CIFAR-10 image classification
 with a simple Convolutional Neural Network:
 
 - `Quickstart PyTorch (Code) <https://github.com/adap/flower/tree/main/examples/quickstart-pytorch>`_
-- `Quickstart PyTorch (Tutorial) <https://flower.dev/docs/framework/tutorial-quickstart-pytorch.html>`_
+- :doc:`Quickstart PyTorch (Tutorial) <tutorial-quickstart-pytorch>`
 
 
 PyTorch: From Centralized To Federated
@@ -43,7 +43,7 @@ PyTorch: From Centralized To Federated
 This example shows how a regular PyTorch project can be federated using Flower:
 
 - `PyTorch: From Centralized To Federated (Code) <https://github.com/adap/flower/tree/main/examples/pytorch-from-centralized-to-federated>`_
-- `PyTorch: From Centralized To Federated (Tutorial) <https://flower.dev/docs/framework/example-pytorch-from-centralized-to-federated.html>`_
+- :doc:`PyTorch: From Centralized To Federated (Tutorial) <example-pytorch-from-centralized-to-federated>`
 
 
 Federated Learning on Raspberry Pi and Nvidia Jetson
@@ -52,7 +52,7 @@ Federated Learning on Raspberry Pi and Nvidia Jetson
 This example shows how Flower can be used to build a federated learning system that run across Raspberry Pi and Nvidia Jetson:
 
 - `Federated Learning on Raspberry Pi and Nvidia Jetson (Code) <https://github.com/adap/flower/tree/main/examples/embedded-devices>`_
-- `Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) <https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_
+- `Federated Learning on Raspberry Pi and Nvidia Jetson (Blog Post) <https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_
 
 
 
@@ -60,7 +60,7 @@ Legacy Examples (`flwr_example`)
 --------------------------------
 
 .. warning::
-   The useage examples in `flwr_example` are deprecated and will be removed in
+   The usage examples in `flwr_example` are deprecated and will be removed in
    the future. New examples are provided as standalone projects in
    `examples <https://github.com/adap/flower/tree/main/examples>`_.
 
@@ -114,7 +114,7 @@ For more details, see :code:`src/py/flwr_example/pytorch_cifar`.
 ImageNet-2012 Image Classification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-`ImageNet-2012 <http://www.image-net.org/>`_ is one of the major computer
+`ImageNet-2012 <https://www.image-net.org/>`_ 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.
 
diff --git a/doc/source/ref-faq.rst b/doc/source/ref-faq.rst
index 13c44bc64b0e..26b7dca4a0a7 100644
--- a/doc/source/ref-faq.rst
+++ b/doc/source/ref-faq.rst
@@ -3,23 +3,23 @@ FAQ
 
 This page collects answers to commonly asked questions about Federated Learning with Flower.
 
-.. dropdown:: :fa:`eye,mr-1` Can Flower run on Juptyter Notebooks / Google Colab?
+.. dropdown:: :fa:`eye,mr-1` Can Flower run on Jupyter Notebooks / Google Colab?
 
     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:
-    
+
     * `Flower simulation PyTorch <https://colab.research.google.com/github/adap/flower/blob/main/examples/simulation-pytorch/sim.ipynb>`_
     * `Flower simulation TensorFlow/Keras <https://colab.research.google.com/github/adap/flower/blob/main/examples/simulation-tensorflow/sim.ipynb>`_
 
 .. dropdown:: :fa:`eye,mr-1` How can I run Federated Learning on a Raspberry Pi?
 
-    Find the `blog post about federated learning on embedded device here <https://flower.dev/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_ and the corresponding `GitHub code example <https://github.com/adap/flower/tree/main/examples/embedded-devices>`_. 
+    Find the `blog post about federated learning on embedded device here <https://flower.ai/blog/2020-12-16-running_federated_learning_applications_on_embedded_devices_with_flower>`_ and the corresponding `GitHub code example <https://github.com/adap/flower/tree/main/examples/embedded-devices>`_.
 
 .. dropdown:: :fa:`eye,mr-1` Does Flower support federated learning on Android devices?
 
-    Yes, it does. Please take a look at our `blog post <https://flower.dev/blog/2021-12-15-federated-learning-on-android-devices-with-flower>`_ or check out the code examples:
+    Yes, it does. Please take a look at our `blog post <https://flower.ai/blog/2021-12-15-federated-learning-on-android-devices-with-flower>`_ or check out the code examples:
 
-    * `Android Kotlin example <https://flower.dev/docs/examples/android-kotlin.html>`_
-    * `Android Java example <https://flower.dev/docs/examples/android.html>`_
+    * `Android Kotlin example <https://flower.ai/docs/examples/android-kotlin.html>`_
+    * `Android Java example <https://flower.ai/docs/examples/android.html>`_
 
 .. dropdown:: :fa:`eye,mr-1` Can I combine federated learning with blockchain?
 
@@ -27,6 +27,6 @@ This page collects answers to commonly asked questions about Federated Learning
 
     * `Flower meets Nevermined GitHub Repository <https://github.com/nevermined-io/fl-demo/tree/master/image-classification-flower>`_.
     * `Flower meets Nevermined YouTube video <https://www.youtube.com/watch?v=A0A9hSlPhKI>`_.
-    * `Flower meets KOSMoS <https://www.kosmos-bmbf.de/wp-content/uploads/sites/13/2021/05/Talk-Flower-Summit-2021.pdf>`_.
+    * `Flower meets KOSMoS <https://www.isw-sites.de/kosmos/wp-content/uploads/sites/13/2021/05/Talk-Flower-Summit-2021.pdf>`_.
     * `Flower meets Talan blog post <https://www.linkedin.com/pulse/federated-learning-same-mask-different-faces-imen-ayari/?trackingId=971oIlxLQ9%2BA9RB0IQ73XQ%3D%3D>`_ .
     * `Flower meets Talan GitHub Repository <https://gitlab.com/Talan_Innovation_Factory/food-waste-prevention>`_ .
diff --git a/doc/source/ref-telemetry.md b/doc/source/ref-telemetry.md
index 206e641d8b41..49efef5c8559 100644
--- a/doc/source/ref-telemetry.md
+++ b/doc/source/ref-telemetry.md
@@ -41,7 +41,7 @@ Flower telemetry collects the following metrics:
 
 **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.dev`. 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.
 
 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.
 
@@ -63,4 +63,4 @@ FLWR_TELEMETRY_ENABLED=0 FLWR_TELEMETRY_LOGGING=1 python server.py # or client.p
 
 ## 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.dev/join-slack/) (channel `#telemetry`) or email (`telemetry@flower.dev`).
+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/doc/source/tutorial-quickstart-huggingface.rst b/doc/source/tutorial-quickstart-huggingface.rst
index 7718e6558456..7d8128230901 100644
--- a/doc/source/tutorial-quickstart-huggingface.rst
+++ b/doc/source/tutorial-quickstart-huggingface.rst
@@ -9,8 +9,8 @@ Quickstart 🤗 Transformers
 
 Let's build a federated learning system using Hugging Face Transformers and Flower!
 
-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) 
+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.
 
@@ -32,8 +32,8 @@ Standard Hugging Face workflow
 Handling the data
 ^^^^^^^^^^^^^^^^^
 
-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, 
+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:
 
 .. code-block:: python
@@ -80,8 +80,8 @@ this is all done in the :code:`load_data` function:
 Training and testing the model
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-Once we have a way of creating our trainloader and testloader, 
-we can take care of the training and testing. 
+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:
 
 .. code-block:: python
@@ -120,12 +120,12 @@ This is very similar to any :code:`PyTorch` training or testing loop:
 Creating the model itself
 ^^^^^^^^^^^^^^^^^^^^^^^^^
 
-To create the model itself, 
+To create the model itself,
 we will just load the pre-trained distillBERT model using Hugging Face’s :code:`AutoModelForSequenceClassification` :
 
 .. code-block:: python
 
-    from transformers import AutoModelForSequenceClassification 
+    from transformers import AutoModelForSequenceClassification
 
     net = AutoModelForSequenceClassification.from_pretrained(
             CHECKPOINT, num_labels=2
@@ -138,8 +138,8 @@ Federating the example
 Creating the IMDBClient
 ^^^^^^^^^^^^^^^^^^^^^^^
 
-To federate our example to multiple clients, 
-we first need to write our Flower client class (inheriting from :code:`flwr.client.NumPyClient`). 
+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:
 
 .. code-block:: python
@@ -166,17 +166,17 @@ This is very easy, as our model is a standard :code:`PyTorch` model:
                 return float(loss), len(testloader), {"accuracy": float(accuracy)}
 
 
-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. 
+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.
 
 Starting the server
 ^^^^^^^^^^^^^^^^^^^
 
-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) 
+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:
 
 .. code-block:: python
@@ -186,7 +186,7 @@ and then using the :code:`flwr.server.start_server` function:
         losses = [num_examples * m["loss"] for num_examples, m in metrics]
         examples = [num_examples for num_examples, _ in metrics]
         return {"accuracy": sum(accuracies) / sum(examples), "loss": sum(losses) / sum(examples)}
-        
+
     # Define strategy
     strategy = fl.server.strategy.FedAvg(
         fraction_fit=1.0,
@@ -202,7 +202,7 @@ and then using the :code:`flwr.server.start_server` function:
     )
 
 
-The :code:`weighted_average` function is there to provide a way to aggregate the metrics distributed amongst 
+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).
 
 Putting everything together
@@ -212,19 +212,18 @@ We can now start client instances using:
 
 .. code-block:: python
 
-    fl.client.start_numpy_client(
-        server_address="127.0.0.1:8080", 
-        client=IMDBClient()
+    fl.client.start_client(
+        server_address="127.0.0.1:8080",
+        client=IMDBClient().to_client()
     )
 
 
 And they will be able to connect to the server and start the federated training.
 
-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). 
+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>`_ .
 
-Of course, this is a very basic example, and a lot can be added or modified, 
+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.
 
 Note that in this example we used :code:`PyTorch`, but we could have very well used :code:`TensorFlow`.
diff --git a/doc/source/tutorial-quickstart-ios.rst b/doc/source/tutorial-quickstart-ios.rst
index 7c8007baaa75..e4315ce569fb 100644
--- a/doc/source/tutorial-quickstart-ios.rst
+++ b/doc/source/tutorial-quickstart-ios.rst
@@ -7,14 +7,14 @@ Quickstart iOS
 .. meta::
    :description: Read this Federated Learning quickstart tutorial for creating an iOS app using Flower to train a neural network on MNIST.
 
-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.
 
-First of all, for running the Flower Python server, it is recommended to create a virtual environment and run everything within a `virtualenv <https://flower.dev/docs/recommended-env-setup.html>`_.
+First of all, for running the Flower Python server, it is recommended to create a virtual environment and run everything within a :doc:`virtualenv <contributor-how-to-set-up-a-virtual-env>`.
 For the Flower client implementation in iOS, it is recommended to use Xcode as our IDE.
 
-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.
 
-*Clients* are responsible for generating individual weight updates for the model based on their local datasets. 
+*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*.
 
@@ -44,10 +44,10 @@ For simplicity reasons we will use the complete Flower client with CoreML, that
   public func getParameters() -> GetParametersRes {
     let parameters = parameters.weightsToParameters()
     let status = Status(code: .ok, message: String())
-        
+
     return GetParametersRes(parameters: parameters, status: status)
   }
-    
+
   /// Calls the routine to fit the local model
   ///
   /// - Returns: The result from the local training, e.g., updated parameters
@@ -55,17 +55,17 @@ For simplicity reasons we will use the complete Flower client with CoreML, that
     let status = Status(code: .ok, message: String())
     let result = runMLTask(configuration: parameters.parametersToWeights(parameters: ins.parameters), task: .train)
     let parameters = parameters.weightsToParameters()
-        
+
     return FitRes(parameters: parameters, numExamples: result.numSamples, status: status)
     }
-    
+
   /// Calls the routine to evaluate the local model
   ///
   /// - Returns: The result from the evaluation, e.g., loss
   public func evaluate(ins: EvaluateIns) -> EvaluateRes {
     let status = Status(code: .ok, message: String())
     let result = runMLTask(configuration: parameters.parametersToWeights(parameters: ins.parameters), task: .test)
-        
+
     return EvaluateRes(loss: Float(result.loss), numExamples: result.numSamples, status: status)
   }
 
@@ -88,18 +88,18 @@ For the MNIST dataset, we need to preprocess it into :code:`MLBatchProvider` obj
 
   // prepare train dataset
   let trainBatchProvider = DataLoader.trainBatchProvider() { _ in }
-            
+
   // prepare test dataset
   let testBatchProvider = DataLoader.testBatchProvider() { _ in }
-            
+
   // load them together
-  let dataLoader = MLDataLoader(trainBatchProvider: trainBatchProvider, 
+  let dataLoader = MLDataLoader(trainBatchProvider: trainBatchProvider,
                                 testBatchProvider: testBatchProvider)
 
 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`.
+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`.
 
-After we have all of the necessary informations, let's create our Flower client.
+After we have all of the necessary information, let's create our Flower client.
 
 .. code-block:: swift
 
@@ -122,7 +122,7 @@ Then start the Flower gRPC client and start communicating to the server by passi
   self.flwrGRPC.startFlwrGRPC(client: self.mlFlwrClient)
 
 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. 
+: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.
 
 Flower Server
diff --git a/doc/source/tutorial-quickstart-jax.rst b/doc/source/tutorial-quickstart-jax.rst
index 945f231e112e..d2b9243e2bb3 100644
--- a/doc/source/tutorial-quickstart-jax.rst
+++ b/doc/source/tutorial-quickstart-jax.rst
@@ -265,7 +265,7 @@ Having defined the federation process, we can run it.
 
         # Start Flower client
         client = FlowerClient(params, grad_fn, train_x, train_y, test_x, test_y)
-        fl.client.start_numpy_client(server_address="0.0.0.0:8080", client)
+        fl.client.start_client(server_address="0.0.0.0:8080", client=client.to_client())
 
     if __name__ == "__main__":
         main()
diff --git a/doc/source/tutorial-quickstart-mxnet.rst b/doc/source/tutorial-quickstart-mxnet.rst
index 149d060e4c00..fe582f793280 100644
--- a/doc/source/tutorial-quickstart-mxnet.rst
+++ b/doc/source/tutorial-quickstart-mxnet.rst
@@ -4,16 +4,18 @@
 Quickstart MXNet
 ================
 
+.. warning:: MXNet is no longer maintained and has been moved into `Attic <https://attic.apache.org/projects/mxnet.html>`_. 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.
+
 .. meta::
    :description: Check out this Federated Learning quickstart tutorial for using Flower with MXNet to train a Sequential model on MNIST.
 
-In this tutorial, we will learn how to train a :code:`Sequential` model on MNIST using Flower and MXNet. 
+In this tutorial, we will learn how to train a :code:`Sequential` model on MNIST using Flower and MXNet.
 
-It is recommended to create a virtual environment and run everything within this `virtualenv <https://flower.dev/docs/recommended-env-setup.html>`_. 
+It is recommended to create a virtual environment and run everything within this :doc:`virtualenv <contributor-how-to-set-up-a-virtual-env>`.
 
-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.
 
-*Clients* are responsible for generating individual model parameter updates for the model based on their local datasets. 
+*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*.
 
@@ -33,12 +35,12 @@ Since we want to use MXNet, let's go ahead and install it:
 Flower 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 MXNet´s `Hand-written Digit Recognition tutorial <https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html>`_. 
+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 <https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html>`_.
 
 In a file called :code:`client.py`, import Flower and MXNet related packages:
 
 .. code-block:: python
-      
+
     import flwr as fl
 
     import numpy as np
@@ -56,7 +58,7 @@ In addition, define the device allocation in MXNet with:
 
     DEVICE = [mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()]
 
-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. 
+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.
 
 .. code-block:: python
 
@@ -70,7 +72,7 @@ We use MXNet to load MNIST, a popular image classification dataset of handwritte
         val_data = mx.io.NDArrayIter(mnist["test_data"], mnist["test_label"], batch_size)
         return train_data, val_data
 
-Define the training and loss with MXNet. We train the model by looping over the dataset, measure the corresponding loss, and optimize it. 
+Define the training and loss with MXNet. We train the model by looping over the dataset, measure the corresponding loss, and optimize it.
 
 .. code-block:: python
 
@@ -108,7 +110,7 @@ Define the training and loss with MXNet. We train the model by looping over the
         return trainings_metric, num_examples
 
 
-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. 
+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.
 
 .. code-block:: python
 
@@ -153,7 +155,7 @@ Our Flower clients will use a simple :code:`Sequential` model:
         init = nd.random.uniform(shape=(2, 784))
         model(init)
 
-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. 
+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.
 
 The Flower server interacts with clients through an interface called
 :code:`Client`. When the server selects a particular client for training, it
@@ -205,7 +207,7 @@ They can be implemented in the following way:
             [accuracy, loss], num_examples = test(model, val_data)
             print("Evaluation accuracy & loss", accuracy, loss)
             return float(loss[1]), val_data.batch_size, {"accuracy": float(accuracy[1])}
-    
+
 
 We can now create an instance of our class :code:`MNISTClient` and add one line
 to actually run this client:
diff --git a/doc/source/tutorial-quickstart-pytorch.rst b/doc/source/tutorial-quickstart-pytorch.rst
index fb77d107b63f..895590808a2b 100644
--- a/doc/source/tutorial-quickstart-pytorch.rst
+++ b/doc/source/tutorial-quickstart-pytorch.rst
@@ -10,13 +10,13 @@ Quickstart PyTorch
 ..  youtube:: jOmmuzMIQ4c
    :width: 100%
 
-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.
 
-First of all, it is recommended to create a virtual environment and run everything within a `virtualenv <https://flower.dev/docs/recommended-env-setup.html>`_. 
+First of all, it is recommended to create a virtual environment and run everything within a :doc:`virtualenv <contributor-how-to-set-up-a-virtual-env>`.
 
-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.
 
-*Clients* are responsible for generating individual weight-updates for the model based on their local datasets. 
+*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*.
 
@@ -26,7 +26,7 @@ Now that we have a rough idea of what is going on, let's get started. We first n
 
   $ pip install flwr
 
-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:
 
 .. code-block:: shell
 
@@ -36,12 +36,12 @@ Since we want to use PyTorch to solve a computer vision task, let's go ahead and
 Flower 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 <https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_. 
+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 <https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html>`_.
 
 In a file called :code:`client.py`, import Flower and PyTorch related packages:
 
 .. code-block:: python
-      
+
     from collections import OrderedDict
 
     import torch
@@ -59,7 +59,7 @@ In addition, we define the device allocation in PyTorch with:
 
     DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
-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. 
+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.
 
 .. code-block:: python
 
@@ -75,7 +75,7 @@ We use PyTorch to load CIFAR10, a popular colored image classification dataset f
         num_examples = {"trainset" : len(trainset), "testset" : len(testset)}
         return trainloader, testloader, num_examples
 
-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. 
+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.
 
 .. code-block:: python
 
@@ -91,7 +91,7 @@ Define the loss and optimizer with PyTorch. The training of the dataset is done
                 loss.backward()
                 optimizer.step()
 
-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.
 
 .. code-block:: python
 
@@ -139,7 +139,7 @@ The Flower clients will use a simple CNN adapted from 'PyTorch: A 60 Minute Blit
     net = Net().to(DEVICE)
     trainloader, testloader, num_examples = load_data()
 
-After loading the data set with :code:`load_data()` we define the Flower interface. 
+After loading the data set with :code:`load_data()` we define the Flower interface.
 
 The Flower server interacts with clients through an interface called
 :code:`Client`. When the server selects a particular client for training, it
@@ -191,10 +191,10 @@ to actually run this client:
 
 .. code-block:: python
 
-     fl.client.start_numpy_client(server_address="[::]:8080", client=CifarClient())
+     fl.client.start_client(server_address="[::]:8080", client=CifarClient().to_client())
 
 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:`"[::]: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:`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.
diff --git a/doc/source/tutorial-quickstart-scikitlearn.rst b/doc/source/tutorial-quickstart-scikitlearn.rst
index b33068e975fa..d1d47dc37f19 100644
--- a/doc/source/tutorial-quickstart-scikitlearn.rst
+++ b/doc/source/tutorial-quickstart-scikitlearn.rst
@@ -7,13 +7,13 @@ Quickstart scikit-learn
 .. meta::
    :description: Check out this Federated Learning quickstart tutorial for using Flower with scikit-learn to train a linear regression model.
 
-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.
 
-It is recommended to create a virtual environment and run everything within this `virtualenv <https://flower.dev/docs/recommended-env-setup.html>`_. 
+It is recommended to create a virtual environment and run everything within this :doc:`virtualenv <contributor-how-to-set-up-a-virtual-env>`.
 
-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.
 
-*Clients* are responsible for generating individual model parameter updates for the model based on their local datasets. 
+*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*.
 
@@ -23,7 +23,7 @@ Now that we have a rough idea of what is going on, let's get started. We first n
 
   $ pip install flwr
 
-Since we want to use scikt-learn, let's go ahead and install it:
+Since we want to use scikit-learn, let's go ahead and install it:
 
 .. code-block:: shell
 
@@ -43,7 +43,7 @@ Now that we have all our dependencies installed, let's run a simple distributed
 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:
 
 * :code:`get_model_parameters()`
-    * Returns the paramters of a :code:`sklearn` LogisticRegression model
+    * Returns the parameters of a :code:`sklearn` LogisticRegression model
 * :code:`set_model_params()`
     * Sets the parameters of a :code:`sklean` LogisticRegression model
 * :code:`set_initial_params()`
@@ -59,7 +59,7 @@ Please check out :code:`utils.py` `here <https://github.com/adap/flower/blob/mai
 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:
 
 .. code-block:: python
-      
+
   import warnings
   import flwr as fl
   import numpy as np
@@ -70,7 +70,7 @@ The pre-defined functions are used in the :code:`client.py` and imported. The :c
   import utils
 
 
-We load the MNIST dataset from `OpenML <https://www.openml.org/d/554>`_, 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()`. 
+We load the MNIST dataset from `OpenML <https://www.openml.org/search?type=data&sort=runs&id=554>`_, 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()`.
 
 .. code-block:: python
 
@@ -145,10 +145,10 @@ to actually run this client:
 
 .. code-block:: python
 
-    fl.client.start_numpy_client("0.0.0.0:8080", client=MnistClient())
+    fl.client.start_client("0.0.0.0:8080", client=MnistClient().to_client())
 
 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:`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.
diff --git a/doc/source/tutorial-quickstart-tensorflow.rst b/doc/source/tutorial-quickstart-tensorflow.rst
index 64b2255a9ac6..bd63eb461d21 100644
--- a/doc/source/tutorial-quickstart-tensorflow.rst
+++ b/doc/source/tutorial-quickstart-tensorflow.rst
@@ -84,11 +84,11 @@ to actually run this client:
 
 .. code-block:: python
 
-     fl.client.start_numpy_client(server_address="[::]:8080", client=CifarClient())
+     fl.client.start_client(server_address="[::]:8080", client=CifarClient().to_client())
 
 
 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:`"[::]: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:`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.
diff --git a/doc/source/tutorial-quickstart-xgboost.rst b/doc/source/tutorial-quickstart-xgboost.rst
index 7eb58da7f2f6..7ac055138814 100644
--- a/doc/source/tutorial-quickstart-xgboost.rst
+++ b/doc/source/tutorial-quickstart-xgboost.rst
@@ -36,7 +36,7 @@ and then we dive into a more complex example (`full code xgboost-comprehensive <
 Environment Setup
 --------------------
 
-First of all, it is recommended to create a virtual environment and run everything within a `virtualenv <https://flower.dev/docs/recommended-env-setup.html>`_.
+First of all, it is recommended to create a virtual environment and run everything within a :doc:`virtualenv <contributor-how-to-set-up-a-virtual-env>`.
 
 We first need to install Flower and Flower Datasets. You can do this by running :
 
@@ -595,9 +595,164 @@ Comprehensive Federated XGBoost
 
 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 <https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive>`_),
-we provide more options to define various experimental setups, including data partitioning and centralised/distributed evaluation.
+we provide more options to define various experimental setups, including aggregation strategies, data partitioning and centralised/distributed evaluation.
+We also support :doc:`Flower simulation <how-to-run-simulations>` making it easy to simulate large client cohorts in a resource-aware manner.
 Let's take a look!
 
+Cyclic training
+~~~~~~~~~~~~~~~~~~
+
+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.
+
+To do this, we first customise a :code:`ClientManager` in :code:`server_utils.py`:
+
+.. code-block:: python
+
+  class CyclicClientManager(SimpleClientManager):
+      """Provides a cyclic client selection rule."""
+
+      def sample(
+          self,
+          num_clients: int,
+          min_num_clients: Optional[int] = None,
+          criterion: Optional[Criterion] = None,
+      ) -> List[ClientProxy]:
+          """Sample a number of Flower ClientProxy instances."""
+
+          # Block until at least num_clients are connected.
+          if min_num_clients is None:
+              min_num_clients = num_clients
+          self.wait_for(min_num_clients)
+
+          # Sample clients which meet the criterion
+          available_cids = list(self.clients)
+          if criterion is not None:
+              available_cids = [
+                  cid for cid in available_cids if criterion.select(self.clients[cid])
+              ]
+
+          if num_clients > len(available_cids):
+              log(
+                  INFO,
+                  "Sampling failed: number of available clients"
+                  " (%s) is less than number of requested clients (%s).",
+                  len(available_cids),
+                  num_clients,
+              )
+              return []
+
+          # Return all available clients
+          return [self.clients[cid] for cid in available_cids]
+
+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.
+
+.. code-block:: python
+
+  class FedXgbCyclic(FedAvg):
+      """Configurable FedXgbCyclic strategy implementation."""
+
+      # pylint: disable=too-many-arguments,too-many-instance-attributes, line-too-long
+      def __init__(
+          self,
+          **kwargs: Any,
+      ):
+          self.global_model: Optional[bytes] = None
+          super().__init__(**kwargs)
+
+      def aggregate_fit(
+          self,
+          server_round: int,
+          results: List[Tuple[ClientProxy, FitRes]],
+          failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+      ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+          """Aggregate fit results using bagging."""
+          if not results:
+              return None, {}
+          # Do not aggregate if there are failures and failures are not accepted
+          if not self.accept_failures and failures:
+              return None, {}
+
+          # Fetch the client model from last round as global model
+          for _, fit_res in results:
+              update = fit_res.parameters.tensors
+              for bst in update:
+                  self.global_model = bst
+
+          return (
+              Parameters(tensor_type="", tensors=[cast(bytes, self.global_model)]),
+              {},
+          )
+
+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`.
+
+Also, the customised :code:`configure_fit` and :code:`configure_evaluate` methods ensure the clients to be sequentially selected given FL round:
+
+.. code-block:: python
+
+      def configure_fit(
+          self, server_round: int, parameters: Parameters, client_manager: ClientManager
+      ) -> List[Tuple[ClientProxy, FitIns]]:
+          """Configure the next round of training."""
+          config = {}
+          if self.on_fit_config_fn is not None:
+              # Custom fit config function provided
+              config = self.on_fit_config_fn(server_round)
+          fit_ins = FitIns(parameters, config)
+
+          # Sample clients
+          sample_size, min_num_clients = self.num_fit_clients(
+              client_manager.num_available()
+          )
+          clients = client_manager.sample(
+              num_clients=sample_size,
+              min_num_clients=min_num_clients,
+          )
+
+          # Sample the clients sequentially given server_round
+          sampled_idx = (server_round - 1) % len(clients)
+          sampled_clients = [clients[sampled_idx]]
+
+          # Return client/config pairs
+          return [(client, fit_ins) for client in sampled_clients]
+
+      def configure_evaluate(
+          self, server_round: int, parameters: Parameters, client_manager: ClientManager
+      ) -> List[Tuple[ClientProxy, EvaluateIns]]:
+          """Configure the next round of evaluation."""
+          # Do not configure federated evaluation if fraction eval is 0.
+          if self.fraction_evaluate == 0.0:
+              return []
+
+          # Parameters and config
+          config = {}
+          if self.on_evaluate_config_fn is not None:
+              # Custom evaluation config function provided
+              config = self.on_evaluate_config_fn(server_round)
+          evaluate_ins = EvaluateIns(parameters, config)
+
+          # Sample clients
+          sample_size, min_num_clients = self.num_evaluation_clients(
+              client_manager.num_available()
+          )
+          clients = client_manager.sample(
+              num_clients=sample_size,
+              min_num_clients=min_num_clients,
+          )
+
+          # Sample the clients sequentially given server_round
+          sampled_idx = (server_round - 1) % len(clients)
+          sampled_clients = [clients[sampled_idx]]
+
+          # Return client/config pairs
+          return [(client, evaluate_ins) for client in sampled_clients]
+
+
+
 Customised data partitioning
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -634,7 +789,7 @@ Currently, we provide four supported partitioner type to simulate the uniformity
 Customised centralised/distributed evaluation
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-To facilitate centralised evaluation, we define a function in :code:`server.py`:
+To facilitate centralised evaluation, we define a function in :code:`server_utils.py`:
 
 .. code-block:: python
 
@@ -670,51 +825,265 @@ This function returns a evaluation function which instantiates a :code:`Booster`
 The evaluation is conducted by calling :code:`eval_set()` method, and the tested AUC value is reported.
 
 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.py`.
+overriding the :code:`evaluate()` method insides the :code:`XgbClient` class in :code:`client_utils.py`.
 
-Arguments parser
-~~~~~~~~~~~~~~~~~~~~~~
+Flower simulation
+~~~~~~~~~~~~~~~~~~~~
+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.
 
-In :code:`utils.py`, we define the arguments parsers for clients and server, allowing users to specify different experimental settings.
-Let's first see the sever side:
+.. code-block:: python
+
+  from logging import INFO
+  import xgboost as xgb
+  from tqdm import tqdm
+
+  import flwr as fl
+  from flwr_datasets import FederatedDataset
+  from flwr.common.logger import log
+  from flwr.server.strategy import FedXgbBagging, FedXgbCyclic
+
+  from dataset import (
+      instantiate_partitioner,
+      train_test_split,
+      transform_dataset_to_dmatrix,
+      separate_xy,
+      resplit,
+  )
+  from utils import (
+      sim_args_parser,
+      NUM_LOCAL_ROUND,
+      BST_PARAMS,
+  )
+  from server_utils import (
+      eval_config,
+      fit_config,
+      evaluate_metrics_aggregation,
+      get_evaluate_fn,
+      CyclicClientManager,
+  )
+  from client_utils import XgbClient
+
+After importing all required packages, we define a :code:`main()` function to perform the simulation process:
 
 .. code-block:: python
 
-    import argparse
+  def main():
+    # Parse arguments for experimental settings
+    args = sim_args_parser()
 
+    # Load (HIGGS) dataset and conduct partitioning
+    partitioner = instantiate_partitioner(
+        partitioner_type=args.partitioner_type, num_partitions=args.pool_size
+    )
+    fds = FederatedDataset(
+        dataset="jxie/higgs",
+        partitioners={"train": partitioner},
+        resplitter=resplit,
+    )
 
-    def server_args_parser():
-        """Parse arguments to define experimental settings on server side."""
-        parser = argparse.ArgumentParser()
+    # Load centralised test set
+    if args.centralised_eval or args.centralised_eval_client:
+        log(INFO, "Loading centralised test set...")
+        test_data = fds.load_split("test")
+        test_data.set_format("numpy")
+        num_test = test_data.shape[0]
+        test_dmatrix = transform_dataset_to_dmatrix(test_data)
+
+    # Load partitions and reformat data to DMatrix for xgboost
+    log(INFO, "Loading client local partitions...")
+    train_data_list = []
+    valid_data_list = []
+
+    # Load and process all client partitions. This upfront cost is amortized soon
+    # after the simulation begins since clients wont need to preprocess their partition.
+    for node_id in tqdm(range(args.pool_size), desc="Extracting client partition"):
+        # Extract partition for client with node_id
+        partition = fds.load_partition(node_id=node_id, split="train")
+        partition.set_format("numpy")
+
+        if args.centralised_eval_client:
+            # Use centralised test set for evaluation
+            train_data = partition
+            num_train = train_data.shape[0]
+            x_test, y_test = separate_xy(test_data)
+            valid_data_list.append(((x_test, y_test), num_test))
+        else:
+            # Train/test splitting
+            train_data, valid_data, num_train, num_val = train_test_split(
+                partition, test_fraction=args.test_fraction, seed=args.seed
+            )
+            x_valid, y_valid = separate_xy(valid_data)
+            valid_data_list.append(((x_valid, y_valid), num_val))
 
-        parser.add_argument(
-            "--pool-size", default=2, type=int, help="Number of total clients."
-        )
-        parser.add_argument(
-            "--num-rounds", default=5, type=int, help="Number of FL rounds."
-        )
-        parser.add_argument(
-            "--num-clients-per-round",
-            default=2,
-            type=int,
-            help="Number of clients participate in training each round.",
-        )
-        parser.add_argument(
-            "--num-evaluate-clients",
-            default=2,
-            type=int,
-            help="Number of clients selected for evaluation.",
+        x_train, y_train = separate_xy(train_data)
+        train_data_list.append(((x_train, y_train), num_train))
+
+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.
+
+Then, we define the strategies and other hyper-parameters:
+
+.. code-block:: python
+
+    # Define strategy
+    if args.train_method == "bagging":
+        # Bagging training
+        strategy = FedXgbBagging(
+            evaluate_function=get_evaluate_fn(test_dmatrix)
+            if args.centralised_eval
+            else None,
+            fraction_fit=(float(args.num_clients_per_round) / args.pool_size),
+            min_fit_clients=args.num_clients_per_round,
+            min_available_clients=args.pool_size,
+            min_evaluate_clients=args.num_evaluate_clients
+            if not args.centralised_eval
+            else 0,
+            fraction_evaluate=1.0 if not args.centralised_eval else 0.0,
+            on_evaluate_config_fn=eval_config,
+            on_fit_config_fn=fit_config,
+            evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation
+            if not args.centralised_eval
+            else None,
         )
-        parser.add_argument(
-            "--centralised-eval",
-            action="store_true",
-            help="Conduct centralised evaluation (True), or client evaluation on hold-out data (False).",
+    else:
+        # Cyclic training
+        strategy = FedXgbCyclic(
+            fraction_fit=1.0,
+            min_available_clients=args.pool_size,
+            fraction_evaluate=1.0,
+            evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation,
+            on_evaluate_config_fn=eval_config,
+            on_fit_config_fn=fit_config,
         )
 
-        args = parser.parse_args()
-        return args
+    # Resources to be assigned to each virtual client
+    # In this example we use CPU by default
+    client_resources = {
+        "num_cpus": args.num_cpus_per_client,
+        "num_gpus": 0.0,
+    }
+
+    # Hyper-parameters for xgboost training
+    num_local_round = NUM_LOCAL_ROUND
+    params = BST_PARAMS
+
+    # Setup learning rate
+    if args.train_method == "bagging" and args.scaled_lr:
+        new_lr = params["eta"] / args.pool_size
+        params.update({"eta": new_lr})
+
+After that, we start the simulation by calling :code:`fl.simulation.start_simulation`:
+
+.. code-block:: python
 
-This allows user to specify the number of total clients / FL rounds / participating clients / clients for evaluation,
+    # Start simulation
+    fl.simulation.start_simulation(
+        client_fn=get_client_fn(
+            train_data_list,
+            valid_data_list,
+            args.train_method,
+            params,
+            num_local_round,
+        ),
+        num_clients=args.pool_size,
+        client_resources=client_resources,
+        config=fl.server.ServerConfig(num_rounds=args.num_rounds),
+        strategy=strategy,
+        client_manager=CyclicClientManager() if args.train_method == "cyclic" else None,
+    )
+
+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:
+
+.. code-block:: python
+
+  def get_client_fn(
+      train_data_list, valid_data_list, train_method, params, num_local_round
+  ):
+      """Return a function to construct a client.
+
+      The VirtualClientEngine will execute this function whenever a client is sampled by
+      the strategy to participate.
+      """
+
+      def client_fn(cid: str) -> fl.client.Client:
+          """Construct a FlowerClient with its own dataset partition."""
+          x_train, y_train = train_data_list[int(cid)][0]
+          x_valid, y_valid = valid_data_list[int(cid)][0]
+
+          # Reformat data to DMatrix
+          train_dmatrix = xgb.DMatrix(x_train, label=y_train)
+          valid_dmatrix = xgb.DMatrix(x_valid, label=y_valid)
+
+          # Fetch the number of examples
+          num_train = train_data_list[int(cid)][1]
+          num_val = valid_data_list[int(cid)][1]
+
+          # Create and return client
+          return XgbClient(
+              train_dmatrix,
+              valid_dmatrix,
+              num_train,
+              num_val,
+              num_local_round,
+              params,
+              train_method,
+          )
+
+      return client_fn
+
+
+
+Arguments parser
+~~~~~~~~~~~~~~~~~~~~~~
+
+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:
+
+.. code-block:: python
+
+    import argparse
+
+
+    def server_args_parser():
+      """Parse arguments to define experimental settings on server side."""
+      parser = argparse.ArgumentParser()
+
+      parser.add_argument(
+          "--train-method",
+          default="bagging",
+          type=str,
+          choices=["bagging", "cyclic"],
+          help="Training methods selected from bagging aggregation or cyclic training.",
+      )
+      parser.add_argument(
+          "--pool-size", default=2, type=int, help="Number of total clients."
+      )
+      parser.add_argument(
+          "--num-rounds", default=5, type=int, help="Number of FL rounds."
+      )
+      parser.add_argument(
+          "--num-clients-per-round",
+          default=2,
+          type=int,
+          help="Number of clients participate in training each round.",
+      )
+      parser.add_argument(
+          "--num-evaluate-clients",
+          default=2,
+          type=int,
+          help="Number of clients selected for evaluation.",
+      )
+      parser.add_argument(
+          "--centralised-eval",
+          action="store_true",
+          help="Conduct centralised evaluation (True), or client evaluation on hold-out data (False).",
+      )
+
+      args = parser.parse_args()
+      return args
+
+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.
 
@@ -723,60 +1092,159 @@ Then, the argument parser on client side:
 .. code-block:: python
 
     def client_args_parser():
-        """Parse arguments to define experimental settings on client side."""
-        parser = argparse.ArgumentParser()
+      """Parse arguments to define experimental settings on client side."""
+      parser = argparse.ArgumentParser()
+
+      parser.add_argument(
+          "--train-method",
+          default="bagging",
+          type=str,
+          choices=["bagging", "cyclic"],
+          help="Training methods selected from bagging aggregation or cyclic training.",
+      )
+      parser.add_argument(
+          "--num-partitions", default=10, type=int, help="Number of partitions."
+      )
+      parser.add_argument(
+          "--partitioner-type",
+          default="uniform",
+          type=str,
+          choices=["uniform", "linear", "square", "exponential"],
+          help="Partitioner types.",
+      )
+      parser.add_argument(
+          "--node-id",
+          default=0,
+          type=int,
+          help="Node ID used for the current client.",
+      )
+      parser.add_argument(
+          "--seed", default=42, type=int, help="Seed used for train/test splitting."
+      )
+      parser.add_argument(
+          "--test-fraction",
+          default=0.2,
+          type=float,
+          help="Test fraction for train/test splitting.",
+      )
+      parser.add_argument(
+          "--centralised-eval",
+          action="store_true",
+          help="Conduct evaluation on centralised test set (True), or on hold-out data (False).",
+      )
+      parser.add_argument(
+          "--scaled-lr",
+          action="store_true",
+          help="Perform scaled learning rate based on the number of clients (True).",
+      )
+
+      args = parser.parse_args()
+      return args
 
-        parser.add_argument(
-            "--num-partitions", default=10, type=int, help="Number of partitions."
-        )
-        parser.add_argument(
-            "--partitioner-type",
-            default="uniform",
-            type=str,
-            choices=["uniform", "linear", "square", "exponential"],
-            help="Partitioner types.",
-        )
-        parser.add_argument(
-            "--node-id",
-            default=0,
-            type=int,
-            help="Node ID used for the current client.",
-        )
-        parser.add_argument(
-            "--seed", default=42, type=int, help="Seed used for train/test splitting."
-        )
-        parser.add_argument(
-            "--test-fraction",
-            default=0.2,
-            type=float,
-            help="Test fraction for train/test splitting.",
-        )
-        parser.add_argument(
-            "--centralised-eval",
-            action="store_true",
-            help="Conduct centralised evaluation (True), or client evaluation on hold-out data (False).",
-        )
+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`.
 
-        args = parser.parse_args()
-        return args
+We also have an argument parser for simulation:
 
-This defines various options for client data partitioning.
-Besides, clients also have a option to conduct evaluation on centralised test set by setting :code:`--centralised-eval`.
+.. code-block:: python
+
+  def sim_args_parser():
+      """Parse arguments to define experimental settings on server side."""
+      parser = argparse.ArgumentParser()
+
+      parser.add_argument(
+          "--train-method",
+          default="bagging",
+          type=str,
+          choices=["bagging", "cyclic"],
+          help="Training methods selected from bagging aggregation or cyclic training.",
+      )
+
+      # Server side
+      parser.add_argument(
+          "--pool-size", default=5, type=int, help="Number of total clients."
+      )
+      parser.add_argument(
+          "--num-rounds", default=30, type=int, help="Number of FL rounds."
+      )
+      parser.add_argument(
+          "--num-clients-per-round",
+          default=5,
+          type=int,
+          help="Number of clients participate in training each round.",
+      )
+      parser.add_argument(
+          "--num-evaluate-clients",
+          default=5,
+          type=int,
+          help="Number of clients selected for evaluation.",
+      )
+      parser.add_argument(
+          "--centralised-eval",
+          action="store_true",
+          help="Conduct centralised evaluation (True), or client evaluation on hold-out data (False).",
+      )
+      parser.add_argument(
+          "--num-cpus-per-client",
+          default=2,
+          type=int,
+          help="Number of CPUs used for per client.",
+      )
+
+      # Client side
+      parser.add_argument(
+          "--partitioner-type",
+          default="uniform",
+          type=str,
+          choices=["uniform", "linear", "square", "exponential"],
+          help="Partitioner types.",
+      )
+      parser.add_argument(
+          "--seed", default=42, type=int, help="Seed used for train/test splitting."
+      )
+      parser.add_argument(
+          "--test-fraction",
+          default=0.2,
+          type=float,
+          help="Test fraction for train/test splitting.",
+      )
+      parser.add_argument(
+          "--centralised-eval-client",
+          action="store_true",
+          help="Conduct evaluation on centralised test set (True), or on hold-out data (False).",
+      )
+      parser.add_argument(
+          "--scaled-lr",
+          action="store_true",
+          help="Perform scaled learning rate based on the number of clients (True).",
+      )
+
+      args = parser.parse_args()
+      return args
+
+This integrates all arguments for both client and server sides.
 
 Example commands
 ~~~~~~~~~~~~~~~~~~~~~
 
-To run a centralised evaluated experiment on 5 clients with exponential distribution for 50 rounds,
+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:
 
 .. code-block:: shell
 
-    $ python3 server.py --pool-size=5 --num-rounds=50 --num-clients-per-round=5 --centralised-eval
+    $ python3 server.py --train-method=bagging --pool-size=5 --num-rounds=50 --num-clients-per-round=5 --centralised-eval
 
 Then, on each client terminal, we start the clients:
 
 .. code-block:: shell
 
-    $ python3 clients.py --num-partitions=5 --partitioner-type=exponential --node-id=NODE_ID
+    $ python3 clients.py --train-method=bagging --num-partitions=5 --partitioner-type=exponential --node-id=NODE_ID
+
+To run the same experiment with Flower simulation:
+
+.. code-block:: shell
+
+    $ python3 sim.py --train-method=bagging --pool-size=5 --num-rounds=50 --num-clients-per-round=5 --partitioner-type=exponential --centralised-eval
 
 The full `code <https://github.com/adap/flower/blob/main/examples/xgboost-comprehensive/>`_ for this comprehensive example can be found in :code:`examples/xgboost-comprehensive`.
diff --git a/doc/source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb b/doc/source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb
index 5b2236468909..c5fc777e7f26 100644
--- a/doc/source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb
+++ b/doc/source/tutorial-series-build-a-strategy-from-scratch-pytorch.ipynb
@@ -7,11 +7,11 @@
    "source": [
     "# Build a strategy from scratch\n",
     "\n",
-    "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](https://flower.dev/docs/framework/tutorial-get-started-with-flower-pytorch.html)) and we learned how strategies can be used to customize the execution on both the server and the clients ([part 2](https://flower.dev/docs/framework/tutorial-use-a-federated-learning-strategy-pytorch.html)).\n",
+    "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](https://flower.ai/docs/framework/tutorial-get-started-with-flower-pytorch.html)) and we learned how strategies can be used to customize the execution on both the server and the clients ([part 2](https://flower.ai/docs/framework/tutorial-use-a-federated-learning-strategy-pytorch.html)).\n",
     "\n",
-    "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](https://flower.dev/) and [PyTorch](https://pytorch.org/)).\n",
+    "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](https://flower.ai/) and [PyTorch](https://pytorch.org/)).\n",
     "\n",
-    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.dev/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
+    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.ai/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
     "\n",
     "Let's build a new `Strategy` from scratch!"
    ]
@@ -489,11 +489,11 @@
    "source": [
     "## Next steps\n",
     "\n",
-    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.dev/join-slack/)\n",
+    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.ai/join-slack/)\n",
     "\n",
     "There's a dedicated `#questions` channel if you need help, but we'd also love to hear who you are in `#introductions`!\n",
     "\n",
-    "The [Flower Federated Learning Tutorial - Part 4](https://flower.dev/docs/framework/tutorial-customize-the-client-pytorch.html) introduces `Client`, the flexible API underlying `NumPyClient`."
+    "The [Flower Federated Learning Tutorial - Part 4](https://flower.ai/docs/framework/tutorial-customize-the-client-pytorch.html) introduces `Client`, the flexible API underlying `NumPyClient`."
    ]
   }
  ],
diff --git a/doc/source/tutorial-series-customize-the-client-pytorch.ipynb b/doc/source/tutorial-series-customize-the-client-pytorch.ipynb
index 0ff67de6f51d..dbdd1094173c 100644
--- a/doc/source/tutorial-series-customize-the-client-pytorch.ipynb
+++ b/doc/source/tutorial-series-customize-the-client-pytorch.ipynb
@@ -7,11 +7,11 @@
    "source": [
     "# Customize the client\n",
     "\n",
-    "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](https://flower.dev/docs/framework/tutorial-get-started-with-flower-pytorch.html)), we learned how strategies can be used to customize the execution on both the server and the clients ([part 2](https://flower.dev/docs/framework/tutorial-use-a-federated-learning-strategy-pytorch.html)), and we built our own custom strategy from scratch ([part 3](https://flower.dev/docs/framework/tutorial-build-a-strategy-from-scratch-pytorch.html)).\n",
+    "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](https://flower.ai/docs/framework/tutorial-get-started-with-flower-pytorch.html)), we learned how strategies can be used to customize the execution on both the server and the clients ([part 2](https://flower.ai/docs/framework/tutorial-use-a-federated-learning-strategy-pytorch.html)), and we built our own custom strategy from scratch ([part 3](https://flower.ai/docs/framework/tutorial-build-a-strategy-from-scratch-pytorch.html)).\n",
     "\n",
     "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.\n",
     "\n",
-    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.dev/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
+    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.ai/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
     "\n",
     "Let's go deeper and see what it takes to move from `NumPyClient` to `Client`!"
    ]
@@ -500,7 +500,7 @@
     "        # We convert our ndarray into a sparse matrix\n",
     "        ndarray = torch.tensor(ndarray).to_sparse_csr()\n",
     "\n",
-    "        # And send it by utilizng the sparse matrix attributes\n",
+    "        # And send it byutilizing the sparse matrix attributes\n",
     "        # WARNING: NEVER set allow_pickle to true.\n",
     "        # Reason: loading pickled data can execute arbitrary code\n",
     "        # Source: https://numpy.org/doc/stable/reference/generated/numpy.save.html\n",
@@ -550,7 +550,7 @@
    "source": [
     "### Client-side\n",
     "\n",
-    "To be able to able to serialize our `ndarray`s into sparse parameters, we will just have to call our custom functions in our `flwr.client.Client`.\n",
+    "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`.\n",
     "\n",
     "Indeed, in `get_parameters` we need to serialize the parameters we got from our network using our custom `ndarrays_to_sparse_parameters` defined above.\n",
     "\n",
@@ -813,7 +813,7 @@
     "            for _, fit_res in results\n",
     "        ]\n",
     "\n",
-    "        # We serialize the aggregated result using our cutom method\n",
+    "        # We serialize the aggregated result using our custom method\n",
     "        parameters_aggregated = ndarrays_to_sparse_parameters(\n",
     "            aggregate(weights_results)\n",
     "        )\n",
@@ -869,16 +869,16 @@
    "source": [
     "## Next steps\n",
     "\n",
-    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.dev/join-slack/)\n",
+    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.ai/join-slack/)\n",
     "\n",
     "There's a dedicated `#questions` channel if you need help, but we'd also love to hear who you are in `#introductions`!\n",
     "\n",
     "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:\n",
     "\n",
-    "- [Read Flower Docs](https://flower.dev/docs/)\n",
+    "- [Read Flower Docs](https://flower.ai/docs/)\n",
     "- [Check out Flower Code Examples](https://github.com/adap/flower/tree/main/examples)\n",
-    "- [Use Flower Baselines for your research](https://flower.dev/docs/baselines/)\n",
-    "- [Watch Flower Summit 2023 videos](https://flower.dev/conf/flower-summit-2023/)\n"
+    "- [Use Flower Baselines for your research](https://flower.ai/docs/baselines/)\n",
+    "- [Watch Flower Summit 2023 videos](https://flower.ai/conf/flower-summit-2023/)\n"
    ]
   }
  ],
diff --git a/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb b/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb
old mode 100755
new mode 100644
index 41c9254e9d69..2b8dd382bb79
--- a/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb
+++ b/doc/source/tutorial-series-get-started-with-flower-pytorch.ipynb
@@ -9,9 +9,9 @@
     "\n",
     "Welcome to the Flower federated learning tutorial!\n",
     "\n",
-    "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.\n",
+    "In this notebook, we'll build a federated learning system using Flower, [Flower Datasets](https://flower.ai/docs/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.\n",
     "\n",
-    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.dev/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
+    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.ai/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
     "\n",
     "Let's get stated!"
    ]
@@ -31,7 +31,7 @@
    "source": [
     "### Installing dependencies\n",
     "\n",
-    "Next, we install the necessary packages for PyTorch (`torch` and `torchvision`) and Flower (`flwr`):"
+    "Next, we install the necessary packages for PyTorch (`torch` and `torchvision`), Flower Datasets (`flwr-datasets`) and Flower (`flwr`):"
    ]
   },
   {
@@ -40,7 +40,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -q flwr[simulation] torch torchvision matplotlib"
+    "!pip install -q flwr[simulation] flwr_datasets[vision] torch torchvision matplotlib"
    ]
   },
   {
@@ -64,25 +64,26 @@
     "import torch\n",
     "import torch.nn as nn\n",
     "import torch.nn.functional as F\n",
-    "import torchvision\n",
     "import torchvision.transforms as transforms\n",
-    "from torch.utils.data import DataLoader, random_split\n",
-    "from torchvision.datasets import CIFAR10\n",
+    "from datasets.utils.logging import disable_progress_bar\n",
+    "from torch.utils.data import DataLoader\n",
     "\n",
     "import flwr as fl\n",
     "from flwr.common import Metrics\n",
+    "from flwr_datasets import FederatedDataset\n",
     "\n",
     "DEVICE = torch.device(\"cpu\")  # Try \"cuda\" to train on GPU\n",
     "print(\n",
     "    f\"Training on {DEVICE} using PyTorch {torch.__version__} and Flower {fl.__version__}\"\n",
-    ")"
+    ")\n",
+    "disable_progress_bar()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "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 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`."
    ]
   },
   {
@@ -92,27 +93,7 @@
     "\n",
     "### Loading the data\n",
     "\n",
-    "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:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "CLASSES = (\n",
-    "    \"plane\",\n",
-    "    \"car\",\n",
-    "    \"bird\",\n",
-    "    \"cat\",\n",
-    "    \"deer\",\n",
-    "    \"dog\",\n",
-    "    \"frog\",\n",
-    "    \"horse\",\n",
-    "    \"ship\",\n",
-    "    \"truck\",\n",
-    ")"
+    "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'."
    ]
   },
   {
@@ -121,16 +102,7 @@
    "source": [
     "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).\n",
     "\n",
-    "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:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "NUM_CLIENTS = 10"
+    "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.\n"
    ]
   },
   {
@@ -138,7 +110,7 @@
    "metadata": {},
    "source": [
     "\n",
-    "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:"
+    "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`:"
    ]
   },
   {
@@ -147,32 +119,36 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "NUM_CLIENTS = 10\n",
     "BATCH_SIZE = 32\n",
     "\n",
     "\n",
     "def load_datasets():\n",
-    "    # Download and transform CIFAR-10 (train and test)\n",
-    "    transform = transforms.Compose(\n",
-    "        [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]\n",
-    "    )\n",
-    "    trainset = CIFAR10(\"./dataset\", train=True, download=True, transform=transform)\n",
-    "    testset = CIFAR10(\"./dataset\", train=False, download=True, transform=transform)\n",
-    "\n",
-    "    # Split training set into 10 partitions to simulate the individual dataset\n",
-    "    partition_size = len(trainset) // NUM_CLIENTS\n",
-    "    lengths = [partition_size] * NUM_CLIENTS\n",
-    "    datasets = random_split(trainset, lengths, torch.Generator().manual_seed(42))\n",
-    "\n",
-    "    # Split each partition into train/val and create DataLoader\n",
+    "    fds = FederatedDataset(dataset=\"cifar10\", partitioners={\"train\": NUM_CLIENTS})\n",
+    "\n",
+    "    def apply_transforms(batch):\n",
+    "        # Instead of passing transforms to CIFAR10(..., transform=transform)\n",
+    "        # we will use this function to dataset.with_transform(apply_transforms)\n",
+    "        # The transforms object is exactly the same\n",
+    "        transform = transforms.Compose(\n",
+    "            [\n",
+    "                transforms.ToTensor(),\n",
+    "                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
+    "            ]\n",
+    "        )\n",
+    "        batch[\"img\"] = [transform(img) for img in batch[\"img\"]]\n",
+    "        return batch\n",
+    "\n",
+    "    # Create train/val for each partition and wrap it into DataLoader\n",
     "    trainloaders = []\n",
     "    valloaders = []\n",
-    "    for ds in datasets:\n",
-    "        len_val = len(ds) // 10  # 10 % validation set\n",
-    "        len_train = len(ds) - len_val\n",
-    "        lengths = [len_train, len_val]\n",
-    "        ds_train, ds_val = random_split(ds, lengths, torch.Generator().manual_seed(42))\n",
-    "        trainloaders.append(DataLoader(ds_train, batch_size=BATCH_SIZE, shuffle=True))\n",
-    "        valloaders.append(DataLoader(ds_val, batch_size=BATCH_SIZE))\n",
+    "    for partition_id in range(NUM_CLIENTS):\n",
+    "        partition = fds.load_partition(partition_id, \"train\")\n",
+    "        partition = partition.with_transform(apply_transforms)\n",
+    "        partition = partition.train_test_split(train_size=0.8)\n",
+    "        trainloaders.append(DataLoader(partition[\"train\"], batch_size=BATCH_SIZE))\n",
+    "        valloaders.append(DataLoader(partition[\"test\"], batch_size=BATCH_SIZE))\n",
+    "    testset = fds.load_split(\"test\").with_transform(apply_transforms)\n",
     "    testloader = DataLoader(testset, batch_size=BATCH_SIZE)\n",
     "    return trainloaders, valloaders, testloader\n",
     "\n",
@@ -195,8 +171,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "images, labels = next(iter(trainloaders[0]))\n",
-    "\n",
+    "batch = next(iter(trainloaders[0]))\n",
+    "images, labels = batch[\"img\"], batch[\"label\"]\n",
     "# Reshape and convert images to a NumPy array\n",
     "# matplotlib requires images with the shape (height, width, 3)\n",
     "images = images.permute(0, 2, 3, 1).numpy()\n",
@@ -209,7 +185,7 @@
     "# Loop over the images and plot them\n",
     "for i, ax in enumerate(axs.flat):\n",
     "    ax.imshow(images[i])\n",
-    "    ax.set_title(CLASSES[labels[i]])\n",
+    "    ax.set_title(trainloaders[0].dataset.features[\"label\"].int2str([labels[i]])[0])\n",
     "    ax.axis(\"off\")\n",
     "\n",
     "# Show the plot\n",
@@ -294,8 +270,8 @@
     "    net.train()\n",
     "    for epoch in range(epochs):\n",
     "        correct, total, epoch_loss = 0, 0, 0.0\n",
-    "        for images, labels in trainloader:\n",
-    "            images, labels = images.to(DEVICE), labels.to(DEVICE)\n",
+    "        for batch in trainloader:\n",
+    "            images, labels = batch[\"img\"].to(DEVICE), batch[\"label\"].to(DEVICE)\n",
     "            optimizer.zero_grad()\n",
     "            outputs = net(images)\n",
     "            loss = criterion(outputs, labels)\n",
@@ -317,8 +293,8 @@
     "    correct, total, loss = 0, 0, 0.0\n",
     "    net.eval()\n",
     "    with torch.no_grad():\n",
-    "        for images, labels in testloader:\n",
-    "            images, labels = images.to(DEVICE), labels.to(DEVICE)\n",
+    "        for batch in testloader:\n",
+    "            images, labels = batch[\"img\"].to(DEVICE), batch[\"label\"].to(DEVICE)\n",
     "            outputs = net(images)\n",
     "            loss += criterion(outputs, labels).item()\n",
     "            _, predicted = torch.max(outputs.data, 1)\n",
@@ -392,14 +368,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_parameters(net) -> List[np.ndarray]:\n",
-    "    return [val.cpu().numpy() for _, val in net.state_dict().items()]\n",
-    "\n",
-    "\n",
     "def set_parameters(net, parameters: List[np.ndarray]):\n",
     "    params_dict = zip(net.state_dict().keys(), parameters)\n",
     "    state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})\n",
-    "    net.load_state_dict(state_dict, strict=True)"
+    "    net.load_state_dict(state_dict, strict=True)\n",
+    "\n",
+    "\n",
+    "def get_parameters(net) -> List[np.ndarray]:\n",
+    "    return [val.cpu().numpy() for _, val in net.state_dict().items()]"
    ]
   },
   {
@@ -477,7 +453,7 @@
     "    valloader = valloaders[int(cid)]\n",
     "\n",
     "    # Create a  single Flower client representing a single organization\n",
-    "    return FlowerClient(net, trainloader, valloader)"
+    "    return FlowerClient(net, trainloader, valloader).to_client()"
    ]
   },
   {
@@ -508,10 +484,14 @@
     "    min_available_clients=10,  # Wait until all 10 clients are available\n",
     ")\n",
     "\n",
-    "# Specify client resources if you need GPU (defaults to 1 CPU and 0 GPU)\n",
-    "client_resources = None\n",
+    "# Specify the resources each of your clients need. By default, each\n",
+    "# client will be allocated 1x CPU and 0x GPUs\n",
+    "client_resources = {\"num_cpus\": 1, \"num_gpus\": 0.0}\n",
     "if DEVICE.type == \"cuda\":\n",
-    "    client_resources = {\"num_gpus\": 1}\n",
+    "    # here we are assigning an entire GPU for each client.\n",
+    "    client_resources = {\"num_cpus\": 1, \"num_gpus\": 1.0}\n",
+    "    # Refer to our documentation for more details about Flower Simulations\n",
+    "    # and how to setup these `client_resources`.\n",
     "\n",
     "# Start simulation\n",
     "fl.simulation.start_simulation(\n",
@@ -625,11 +605,11 @@
    "source": [
     "## Next steps\n",
     "\n",
-    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.dev/join-slack/)\n",
+    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.ai/join-slack/)\n",
     "\n",
     "There's a dedicated `#questions` channel if you need help, but we'd also love to hear who you are in `#introductions`!\n",
     "\n",
-    "The [Flower Federated Learning Tutorial - Part 2](https://flower.dev/docs/framework/tutorial-use-a-federated-learning-strategy-pytorch.html) goes into more depth about strategies and all the advanced things you can build with them."
+    "The [Flower Federated Learning Tutorial - Part 2](https://flower.ai/docs/framework/tutorial-use-a-federated-learning-strategy-pytorch.html) goes into more depth about strategies and all the advanced things you can build with them.\n"
    ]
   }
  ],
@@ -640,7 +620,7 @@
    "toc_visible": true
   },
   "kernelspec": {
-   "display_name": "flower-3.7.12",
+   "display_name": "flwr",
    "language": "python",
    "name": "python3"
   }
diff --git a/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb b/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb
index 06f53cd8e1b1..e20a8d83f674 100644
--- a/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb
+++ b/doc/source/tutorial-series-use-a-federated-learning-strategy-pytorch.ipynb
@@ -7,11 +7,11 @@
    "source": [
     "# Use a federated learning strategy\n",
     "\n",
-    "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](https://flower.dev/docs/framework/tutorial-get-started-with-flower-pytorch.html)).\n",
+    "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](https://flower.ai/docs/framework/tutorial-get-started-with-flower-pytorch.html)).\n",
     "\n",
-    "In this notebook, we'll begin to customize the federated learning system we built in the introductory notebook (again, using [Flower](https://flower.dev/) and [PyTorch](https://pytorch.org/)).\n",
+    "In this notebook, we'll begin to customize the federated learning system we built in the introductory notebook (again, using [Flower](https://flower.ai/) and [PyTorch](https://pytorch.org/)).\n",
     "\n",
-    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.dev/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
+    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.ai/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
     "\n",
     "Let's move beyond FedAvg with Flower strategies!"
    ]
@@ -614,11 +614,11 @@
    "source": [
     "## Next steps\n",
     "\n",
-    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.dev/join-slack/)\n",
+    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.ai/join-slack/)\n",
     "\n",
     "There's a dedicated `#questions` channel if you need help, but we'd also love to hear who you are in `#introductions`!\n",
     "\n",
-    "The [Flower Federated Learning Tutorial - Part 3](https://flower.dev/docs/framework/tutorial-build-a-strategy-from-scratch-pytorch.html) shows how to build a fully custom `Strategy` from scratch."
+    "The [Flower Federated Learning Tutorial - Part 3](https://flower.ai/docs/framework/tutorial-build-a-strategy-from-scratch-pytorch.html) shows how to build a fully custom `Strategy` from scratch."
    ]
   }
  ],
diff --git a/doc/source/tutorial-series-what-is-federated-learning.ipynb b/doc/source/tutorial-series-what-is-federated-learning.ipynb
index 3f7e383b9fbc..7d77d1770457 100755
--- a/doc/source/tutorial-series-what-is-federated-learning.ipynb
+++ b/doc/source/tutorial-series-what-is-federated-learning.ipynb
@@ -13,7 +13,7 @@
     "\n",
     "🧑‍🏫 This tutorial starts at zero and expects no familiarity with federated learning. Only a basic understanding of data science and Python programming is assumed.\n",
     "\n",
-    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the open-source Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.dev/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
+    "> [Star Flower on GitHub](https://github.com/adap/flower) ⭐️ and join the open-source Flower community on Slack to connect, ask questions, and get help: [Join Slack](https://flower.ai/join-slack) 🌼 We'd love to hear from you in the `#introductions` channel! And if anything is unclear, head over to the `#questions` channel.\n",
     "\n",
     "Let's get started!"
    ]
@@ -98,7 +98,7 @@
     "- Location data from your electric car to make better range prediction\n",
     "- End-to-end encrypted messages to train better auto-complete models\n",
     "\n",
-    "The popularity of privacy-enhancing systems like the [Brave](https://brave.com/) browser or the [Signal](https://signal.org/) messenger shows that users care about privacy. In fact, they choose the privacy-enhancing version over other alternatives, if such an alernative 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](https://brave.com/) browser or the [Signal](https://signal.org/) 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."
    ]
   },
   {
@@ -217,11 +217,11 @@
    "source": [
     "## Next steps\n",
     "\n",
-    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.dev/join-slack/)\n",
+    "Before you continue, make sure to join the Flower community on Slack: [Join Slack](https://flower.ai/join-slack/)\n",
     "\n",
     "There's a dedicated `#questions` channel if you need help, but we'd also love to hear who you are in `#introductions`!\n",
     "\n",
-    "The [Flower Federated Learning Tutorial - Part 1](https://flower.dev/docs/framework/tutorial-get-started-with-flower-pytorch.html) shows how to build a simple federated learning system with PyTorch and Flower."
+    "The [Flower Federated Learning Tutorial - Part 1](https://flower.ai/docs/framework/tutorial-get-started-with-flower-pytorch.html) shows how to build a simple federated learning system with PyTorch and Flower."
    ]
   }
  ],
diff --git a/e2e/bare-https/client.py b/e2e/bare-https/client.py
index 20a5b4875ddf..b4570b36512d 100644
--- a/e2e/bare-https/client.py
+++ b/e2e/bare-https/client.py
@@ -25,15 +25,15 @@ def evaluate(self, parameters, config):
 def client_fn(cid):
     return FlowerClient().to_client()
 
-flower = fl.flower.Flower(
+app = fl.client.ClientApp(
     client_fn=client_fn,
 )
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_numpy_client(
+    fl.client.start_client(
         server_address="127.0.0.1:8080", 
-        client=FlowerClient(),
+        client=FlowerClient().to_client(),
         root_certificates=Path("certificates/ca.crt").read_bytes(),
         insecure=False,
     )
diff --git a/e2e/bare-https/driver.py b/e2e/bare-https/driver.py
index 5c44e4c641ae..f7bfeb613f6a 100644
--- a/e2e/bare-https/driver.py
+++ b/e2e/bare-https/driver.py
@@ -3,7 +3,7 @@
 
 
 # Start Flower server
-hist = fl.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="127.0.0.1:9091",
     config=fl.server.ServerConfig(num_rounds=3),
     root_certificates=Path("certificates/ca.crt").read_bytes(),
diff --git a/e2e/bare-https/pyproject.toml b/e2e/bare-https/pyproject.toml
index 9489a43195f9..3afb7b57a084 100644
--- a/e2e/bare-https/pyproject.toml
+++ b/e2e/bare-https/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "bare_https_test"
 version = "0.1.0"
 description = "HTTPS-enabled bare Federated Learning test with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/e2e/bare/client.py b/e2e/bare/client.py
index 8e5c3adff5e6..c291fb0963e4 100644
--- a/e2e/bare/client.py
+++ b/e2e/bare/client.py
@@ -3,6 +3,8 @@
 import flwr as fl
 import numpy as np
 
+from flwr.common import ConfigsRecord
+
 SUBSET_SIZE = 1000
 STATE_VAR = 'timestamp'
 
@@ -18,13 +20,15 @@ def get_parameters(self, config):
     def _record_timestamp_to_state(self):
         """Record timestamp to client's state."""
         t_stamp = datetime.now().timestamp()
-        if STATE_VAR in self.state.state:
-            self.state.state[STATE_VAR] += f",{t_stamp}"
-        else:
-            self.state.state[STATE_VAR] = str(t_stamp)
+        value = str(t_stamp)
+        if STATE_VAR in self.context.state.configs_records.keys():
+            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})
     
     def _retrieve_timestamp_from_state(self):
-        return self.state.state[STATE_VAR]
+        return self.context.state.configs_records[STATE_VAR][STATE_VAR]
     
     def fit(self, parameters, config):
         model_params = parameters
@@ -42,10 +46,10 @@ def evaluate(self, parameters, config):
 def client_fn(cid):
     return FlowerClient().to_client()
 
-flower = fl.flower.Flower(
+app = fl.client.ClientApp(
     client_fn=client_fn,
 )
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=FlowerClient())
+    fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client())
diff --git a/e2e/bare/driver.py b/e2e/bare/driver.py
index 6bd61e344ad1..defc2ad56213 100644
--- a/e2e/bare/driver.py
+++ b/e2e/bare/driver.py
@@ -2,7 +2,7 @@
 
 
 # Start Flower server
-hist = fl.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=3),
 )
diff --git a/e2e/bare/pyproject.toml b/e2e/bare/pyproject.toml
index b9a4028806c3..45ce7ea333af 100644
--- a/e2e/bare/pyproject.toml
+++ b/e2e/bare/pyproject.toml
@@ -6,8 +6,8 @@ build-backend = "poetry.core.masonry.api"
 name = "bare_test"
 version = "0.1.0"
 description = "Bare Federated Learning test with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
-flwr = { path = "../../", develop = true, extras = ["simulation"] }
+flwr = { path = "../../", develop = true, extras = ["simulation", "rest"] }
diff --git a/e2e/fastai/client.py b/e2e/fastai/client.py
index 4425fed25277..c4bfb89c2dde 100644
--- a/e2e/fastai/client.py
+++ b/e2e/fastai/client.py
@@ -53,14 +53,14 @@ def client_fn(cid):
     return FlowerClient().to_client()
 
 
-flower = fl.flower.Flower(
+app = fl.client.ClientApp(
     client_fn=client_fn,
 )
 
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_numpy_client(
+    fl.client.start_client(
         server_address="127.0.0.1:8080",
-        client=FlowerClient(),
+        client=FlowerClient().to_client(),
     )
diff --git a/e2e/fastai/driver.py b/e2e/fastai/driver.py
index 2b1b35d9e89c..cc452ea523ca 100644
--- a/e2e/fastai/driver.py
+++ b/e2e/fastai/driver.py
@@ -1,6 +1,6 @@
 import flwr as fl
 
-hist = fl.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=3),
 )
diff --git a/e2e/fastai/pyproject.toml b/e2e/fastai/pyproject.toml
index 66fcb393d988..feed31f6d202 100644
--- a/e2e/fastai/pyproject.toml
+++ b/e2e/fastai/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-fastai"
 version = "0.1.0"
 description = "Fastai Federated Learning E2E test with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.10"
diff --git a/e2e/jax/client.py b/e2e/jax/client.py
index 495d6a671981..a4e4d1f55117 100644
--- a/e2e/jax/client.py
+++ b/e2e/jax/client.py
@@ -53,10 +53,10 @@ def evaluate(
 def client_fn(cid):
     return FlowerClient().to_client()
 
-flower = fl.flower.Flower(
+app = fl.client.ClientApp(
     client_fn=client_fn,
 )
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=FlowerClient())
+    fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client())
diff --git a/e2e/jax/driver.py b/e2e/jax/driver.py
index 2b1b35d9e89c..cc452ea523ca 100644
--- a/e2e/jax/driver.py
+++ b/e2e/jax/driver.py
@@ -1,6 +1,6 @@
 import flwr as fl
 
-hist = fl.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=3),
 )
diff --git a/e2e/jax/pyproject.toml b/e2e/jax/pyproject.toml
index fde3c32608ca..9a4af5dee59a 100644
--- a/e2e/jax/pyproject.toml
+++ b/e2e/jax/pyproject.toml
@@ -2,13 +2,13 @@
 name = "jax_example"
 version = "0.1.0"
 description = "JAX example training a linear regression model with federated learning"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
 flwr = { path = "../../", develop = true, extras = ["simulation"] }
-jax = "^0.4.0"
-jaxlib = "^0.4.0"
+jax = "==0.4.13"
+jaxlib = "==0.4.13"
 scikit-learn = "^1.1.1"
 numpy = "^1.21.4"
 
diff --git a/e2e/mxnet/.gitignore b/e2e/mxnet/.gitignore
deleted file mode 100644
index 10d00b5797e2..000000000000
--- a/e2e/mxnet/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-*.gz
diff --git a/e2e/mxnet/README.md b/e2e/mxnet/README.md
deleted file mode 100644
index 3fa76bac5ce0..000000000000
--- a/e2e/mxnet/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Flower with MXNet testing
-
-This directory is used for testing Flower with MXNet by using a simple NN with MNIST data.
-
-It uses the `FedAvg` strategy.
\ No newline at end of file
diff --git a/e2e/mxnet/client.py b/e2e/mxnet/client.py
deleted file mode 100644
index 2f0b714e708c..000000000000
--- a/e2e/mxnet/client.py
+++ /dev/null
@@ -1,141 +0,0 @@
-"""Flower client example using MXNet for MNIST classification.
-
-The code is generally adapted from:
-
-https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html
-"""
-
-
-import flwr as fl
-import numpy as np
-import mxnet as mx
-from mxnet import nd
-from mxnet import gluon
-from mxnet.gluon import nn
-from mxnet import autograd as ag
-import mxnet.ndarray as F
-
-SUBSET_SIZE = 50
-
-# Fixing the random seed
-mx.random.seed(42)
-
-# Setup context to GPU or CPU
-DEVICE = [mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()]
-
-def model():
-    net = nn.Sequential()
-    net.add(nn.Dense(256, activation="relu"))
-    net.add(nn.Dense(64, activation="relu"))
-    net.add(nn.Dense(10))
-    net.collect_params().initialize()
-    return net
-
-def load_data():
-    print("Download Dataset")
-    mnist = mx.test_utils.get_mnist()
-    batch_size = 100
-    train_data = mx.io.NDArrayIter(
-            mnist["train_data"][:SUBSET_SIZE], mnist["train_label"][:SUBSET_SIZE], batch_size, shuffle=True
-    )
-    val_data = mx.io.NDArrayIter(mnist["test_data"][:10], mnist["test_label"][:10], batch_size)
-    return train_data, val_data
-
-
-def train(net, train_data, epoch):
-    trainer = gluon.Trainer(net.collect_params(), "sgd", {"learning_rate": 0.01})
-    accuracy_metric = mx.metric.Accuracy()
-    loss_metric = mx.metric.CrossEntropy()
-    metrics = mx.metric.CompositeEvalMetric()
-    for child_metric in [accuracy_metric, loss_metric]:
-        metrics.add(child_metric)
-    softmax_cross_entropy_loss = gluon.loss.SoftmaxCrossEntropyLoss()
-    for i in range(epoch):
-        train_data.reset()
-        num_examples = 0
-        for batch in train_data:
-            data = gluon.utils.split_and_load(
-                batch.data[0], ctx_list=DEVICE, batch_axis=0
-            )
-            label = gluon.utils.split_and_load(
-                batch.label[0], ctx_list=DEVICE, batch_axis=0
-            )
-            outputs = []
-            with ag.record():
-                for x, y in zip(data, label):
-                    z = net(x)
-                    loss = softmax_cross_entropy_loss(z, y)
-                    loss.backward()
-                    outputs.append(z.softmax())
-                    num_examples += len(x)
-            metrics.update(label, outputs)
-            trainer.step(batch.data[0].shape[0])
-        trainings_metric = metrics.get_name_value()
-        print("Accuracy & loss at epoch %d: %s" % (i, trainings_metric))
-    return trainings_metric, num_examples
-
-
-def test(net, val_data):
-    accuracy_metric = mx.metric.Accuracy()
-    loss_metric = mx.metric.CrossEntropy()
-    metrics = mx.metric.CompositeEvalMetric()
-    for child_metric in [accuracy_metric, loss_metric]:
-        metrics.add(child_metric)
-    val_data.reset()
-    num_examples = 0
-    for batch in val_data:
-        data = gluon.utils.split_and_load(batch.data[0], ctx_list=DEVICE, batch_axis=0)
-        label = gluon.utils.split_and_load(
-            batch.label[0], ctx_list=DEVICE, batch_axis=0
-        )
-        outputs = []
-        for x in data:
-            outputs.append(net(x).softmax())
-            num_examples += len(x)
-        metrics.update(label, outputs)
-    metrics.update(label, outputs)
-    return metrics.get_name_value(), num_examples
-
-train_data, val_data = load_data()
-
-model = model()
-init = nd.random.uniform(shape=(2, 784))
-model(init)
-
-# Flower Client
-class FlowerClient(fl.client.NumPyClient):
-    def get_parameters(self, config):
-        param = []
-        for val in model.collect_params(".*weight").values():
-            p = val.data()
-            param.append(p.asnumpy())
-        return param
-
-    def set_parameters(self, parameters):
-        params = zip(model.collect_params(".*weight").keys(), parameters)
-        for key, value in params:
-            model.collect_params().setattr(key, value)
-
-    def fit(self, parameters, config):
-        self.set_parameters(parameters)
-        [accuracy, loss], num_examples = train(model, train_data, epoch=2)
-        results = {"accuracy": float(accuracy[1]), "loss": float(loss[1])}
-        return self.get_parameters(config={}), num_examples, results
-
-    def evaluate(self, parameters, config):
-        self.set_parameters(parameters)
-        [accuracy, loss], num_examples = test(model, val_data)
-        print("Evaluation accuracy & loss", accuracy, loss)
-        return float(loss[1]), num_examples, {"accuracy": float(accuracy[1])}
-
-
-def client_fn(cid):
-    return FlowerClient().to_client()
-
-flower = fl.flower.Flower(
-    client_fn=client_fn,
-)
-
-if __name__ == "__main__":
-    # Start Flower client
-    fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=FlowerClient())
diff --git a/e2e/mxnet/driver.py b/e2e/mxnet/driver.py
deleted file mode 100644
index 2b1b35d9e89c..000000000000
--- a/e2e/mxnet/driver.py
+++ /dev/null
@@ -1,7 +0,0 @@
-import flwr as fl
-
-hist = fl.driver.start_driver(
-    server_address="0.0.0.0:9091",
-    config=fl.server.ServerConfig(num_rounds=3),
-)
-assert (hist.losses_distributed[0][1] / hist.losses_distributed[-1][1]) >= 1
diff --git a/e2e/mxnet/pyproject.toml b/e2e/mxnet/pyproject.toml
deleted file mode 100644
index 71bd0e6374bd..000000000000
--- a/e2e/mxnet/pyproject.toml
+++ /dev/null
@@ -1,15 +0,0 @@
-[build-system]
-requires = ["poetry-core>=1.4.0"]
-build-backend = "poetry.core.masonry.api"
-
-[tool.poetry]
-name = "mxnet_example"
-version = "0.1.0"
-description = "MXNet example with MNIST and CNN"
-authors = ["The Flower Authors <hello@flower.dev>"]
-
-[tool.poetry.dependencies]
-python = "^3.8"
-flwr = { path = "../../", develop = true, extras = ["simulation"] }
-mxnet = "^1.7.0"
-numpy = "1.23.1"
diff --git a/e2e/mxnet/simulation.py b/e2e/mxnet/simulation.py
deleted file mode 100644
index 5f0e5334bd08..000000000000
--- a/e2e/mxnet/simulation.py
+++ /dev/null
@@ -1,11 +0,0 @@
-import flwr as fl
-
-from client import client_fn
-
-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
diff --git a/e2e/opacus/client.py b/e2e/opacus/client.py
index 2e5c363381fa..00437a31233c 100644
--- a/e2e/opacus/client.py
+++ b/e2e/opacus/client.py
@@ -137,12 +137,12 @@ def client_fn(cid):
     model = Net()
     return FlowerClient(model).to_client()
 
-flower = fl.flower.Flower(
+app = fl.client.ClientApp(
     client_fn=client_fn,
 )
 
 if __name__ == "__main__":
-    fl.client.start_numpy_client(
+    fl.client.start_client(
         server_address="127.0.0.1:8080",
-        client=FlowerClient(model)
+        client=FlowerClient(model).to_client()
     )
diff --git a/e2e/opacus/driver.py b/e2e/opacus/driver.py
index 5a0309914ee0..75acd9ccea24 100644
--- a/e2e/opacus/driver.py
+++ b/e2e/opacus/driver.py
@@ -1,6 +1,6 @@
 import flwr as fl
 
-hist = fl.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=3),
 )
diff --git a/e2e/opacus/pyproject.toml b/e2e/opacus/pyproject.toml
index 1aee7c6ec6d9..ab4a727cc00b 100644
--- a/e2e/opacus/pyproject.toml
+++ b/e2e/opacus/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "opacus_e2e"
 version = "0.1.0"
 description = "Opacus E2E testing"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/e2e/pandas/client.py b/e2e/pandas/client.py
index 5b8670091cb3..0ecd75df3ae8 100644
--- a/e2e/pandas/client.py
+++ b/e2e/pandas/client.py
@@ -1,4 +1,3 @@
-import warnings
 from typing import Dict, List, Tuple
 
 import numpy as np
@@ -36,13 +35,13 @@ def fit(
 def client_fn(cid):
     return FlowerClient().to_client()
 
-flower = fl.flower.Flower(
+app = fl.client.ClientApp(
     client_fn=client_fn,
 )
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_numpy_client(
+    fl.client.start_client(
         server_address="127.0.0.1:8080",
-        client=FlowerClient(),
+        client=FlowerClient().to_client(),
     )
diff --git a/e2e/pandas/driver.py b/e2e/pandas/driver.py
index b33e1e54f4a0..f5dc74c9f3f8 100644
--- a/e2e/pandas/driver.py
+++ b/e2e/pandas/driver.py
@@ -3,7 +3,7 @@
 from strategy import FedAnalytics
 
 # Start Flower server
-hist = fl.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=1),
     strategy=FedAnalytics(),
diff --git a/e2e/pandas/pyproject.toml b/e2e/pandas/pyproject.toml
index b90037a31068..416dfeec3460 100644
--- a/e2e/pandas/pyproject.toml
+++ b/e2e/pandas/pyproject.toml
@@ -7,7 +7,7 @@ name = "quickstart-pandas"
 version = "0.1.0"
 description = "Pandas Federated Analytics Quickstart with Flower"
 authors = ["Ragy Haddad <ragy202@gmail.com>"]
-maintainers = ["The Flower Authors <hello@flower.dev>"]
+maintainers = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/e2e/pandas/simulation.py b/e2e/pandas/simulation.py
index 91af84062712..b548b5ebb760 100644
--- a/e2e/pandas/simulation.py
+++ b/e2e/pandas/simulation.py
@@ -1,12 +1,8 @@
 import flwr as fl
 
-from client import FlowerClient
+from client import client_fn
 from strategy import FedAnalytics
 
-def client_fn(cid):
-    _ = cid
-    return FlowerClient()
-
 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 71b178eca8c3..fde550e31c08 100644
--- a/e2e/pytorch-lightning/client.py
+++ b/e2e/pytorch-lightning/client.py
@@ -55,7 +55,7 @@ def client_fn(cid):
     # Flower client
     return FlowerClient(model, train_loader, val_loader, test_loader).to_client()
 
-flower = fl.flower.Flower(
+app = fl.client.ClientApp(
     client_fn=client_fn,
 )
 
@@ -65,8 +65,8 @@ def main() -> None:
     train_loader, val_loader, test_loader = mnist.load_data()
 
     # Flower client
-    client = FlowerClient(model, train_loader, val_loader, test_loader)
-    fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=client)
+    client = FlowerClient(model, train_loader, val_loader, test_loader).to_client()
+    fl.client.start_client(server_address="127.0.0.1:8080", client=client)
 
 
 if __name__ == "__main__":
diff --git a/e2e/pytorch-lightning/driver.py b/e2e/pytorch-lightning/driver.py
index 2b1b35d9e89c..cc452ea523ca 100644
--- a/e2e/pytorch-lightning/driver.py
+++ b/e2e/pytorch-lightning/driver.py
@@ -1,6 +1,6 @@
 import flwr as fl
 
-hist = fl.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=3),
 )
diff --git a/e2e/pytorch-lightning/pyproject.toml b/e2e/pytorch-lightning/pyproject.toml
index e79eb72a56df..88cddddf500f 100644
--- a/e2e/pytorch-lightning/pyproject.toml
+++ b/e2e/pytorch-lightning/pyproject.toml
@@ -6,10 +6,10 @@ build-backend = "poetry.masonry.api"
 name = "quickstart-pytorch-lightning"
 version = "0.1.0"
 description = "Federated Learning E2E test with Flower and PyTorch Lightning"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
 flwr = { path = "../../", develop = true, extras = ["simulation"] }
-pytorch-lightning = "1.6.0"
+pytorch-lightning = "2.1.3"
 torchvision = "0.14.1"
diff --git a/e2e/pytorch/client.py b/e2e/pytorch/client.py
index d180ad5d4eca..1fd07763148e 100644
--- a/e2e/pytorch/client.py
+++ b/e2e/pytorch/client.py
@@ -11,6 +11,7 @@
 from tqdm import tqdm
 
 import flwr as fl
+from flwr.common import ConfigsRecord
 
 # #############################################################################
 # 1. Regular PyTorch pipeline: nn.Module, train, test, and DataLoader
@@ -95,14 +96,15 @@ def get_parameters(self, config):
     def _record_timestamp_to_state(self):
         """Record timestamp to client's state."""
         t_stamp = datetime.now().timestamp()
-        if STATE_VAR in self.state.state:
-            self.state.state[STATE_VAR] += f",{t_stamp}"
-        else:
-            self.state.state[STATE_VAR] = str(t_stamp)
+        value = str(t_stamp)
+        if STATE_VAR in self.context.state.configs_records.keys():
+            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})
     
     def _retrieve_timestamp_from_state(self):
-        return self.state.state[STATE_VAR]
-
+        return self.context.state.configs_records[STATE_VAR][STATE_VAR]
     def fit(self, parameters, config):
         set_parameters(net, parameters)
         train(net, trainloader, epochs=1)
@@ -124,14 +126,14 @@ def set_parameters(model, parameters):
 def client_fn(cid):
     return FlowerClient().to_client()
 
-flower = fl.flower.Flower(
+app = fl.client.ClientApp(
     client_fn=client_fn,
 )
 
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_numpy_client(
+    fl.client.start_client(
         server_address="127.0.0.1:8080",
-        client=FlowerClient(),
+        client=FlowerClient().to_client(),
     )
diff --git a/e2e/pytorch/driver.py b/e2e/pytorch/driver.py
index ca860ea47b2d..2ea4de69a62b 100644
--- a/e2e/pytorch/driver.py
+++ b/e2e/pytorch/driver.py
@@ -18,7 +18,7 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 strategy = fl.server.strategy.FedAvg(evaluate_metrics_aggregation_fn=weighted_average)
 
 # Start Flower server
-hist = fl.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=3),
     strategy=strategy,
diff --git a/e2e/pytorch/pyproject.toml b/e2e/pytorch/pyproject.toml
index 4aa326330c62..e538f1437df6 100644
--- a/e2e/pytorch/pyproject.toml
+++ b/e2e/pytorch/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-pytorch"
 version = "0.1.0"
 description = "PyTorch Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
diff --git a/e2e/scikit-learn/client.py b/e2e/scikit-learn/client.py
index fdca96c1697a..e073d3cb2748 100644
--- a/e2e/scikit-learn/client.py
+++ b/e2e/scikit-learn/client.py
@@ -46,10 +46,10 @@ def evaluate(self, parameters, config):  # type: ignore
 def client_fn(cid):
     return FlowerClient().to_client()
 
-flower = fl.flower.Flower(
+app = fl.client.ClientApp(
     client_fn=client_fn,
 )
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_numpy_client(server_address="0.0.0.0:8080", client=FlowerClient())
+    fl.client.start_client(server_address="0.0.0.0:8080", client=FlowerClient().to_client())
diff --git a/e2e/scikit-learn/driver.py b/e2e/scikit-learn/driver.py
index 032a2f7a0dc6..29051d02c6b6 100644
--- a/e2e/scikit-learn/driver.py
+++ b/e2e/scikit-learn/driver.py
@@ -36,7 +36,7 @@ def evaluate(server_round, parameters: fl.common.NDArrays, config):
         evaluate_fn=get_evaluate_fn(model),
         on_fit_config_fn=fit_round,
     )
-    hist = fl.driver.start_driver(
+    hist = fl.server.start_driver(
         server_address="0.0.0.0:9091",
         strategy=strategy,
         config=fl.server.ServerConfig(num_rounds=3),
diff --git a/e2e/scikit-learn/pyproject.toml b/e2e/scikit-learn/pyproject.toml
index 372ae1218bbe..50c07d31add7 100644
--- a/e2e/scikit-learn/pyproject.toml
+++ b/e2e/scikit-learn/pyproject.toml
@@ -7,8 +7,8 @@ name = "sklearn-mnist"
 version = "0.1.0"
 description = "Federated learning with scikit-learn and Flower"
 authors = [
-    "The Flower Authors <hello@flower.dev>",
-    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>"
+    "The Flower Authors <hello@flower.ai>",
+    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>",
 ]
 
 [tool.poetry.dependencies]
diff --git a/e2e/scikit-learn/utils.py b/e2e/scikit-learn/utils.py
index d6804fdcce12..2b8dcf8655ee 100644
--- a/e2e/scikit-learn/utils.py
+++ b/e2e/scikit-learn/utils.py
@@ -10,7 +10,7 @@
 
 
 def get_model_parameters(model: LogisticRegression) -> LogRegParams:
-    """Returns the paramters of a sklearn LogisticRegression model."""
+    """Returns the parameters of a sklearn LogisticRegression model."""
     if model.fit_intercept:
         params = [
             model.coef_,
diff --git a/e2e/strategies/client.py b/e2e/strategies/client.py
index eb4598cb5439..3b49f770dc6b 100644
--- a/e2e/strategies/client.py
+++ b/e2e/strategies/client.py
@@ -47,11 +47,11 @@ def client_fn(cid):
     return FlowerClient().to_client()
 
 
-flower = fl.flower.Flower(
+app = fl.client.ClientApp(
     client_fn=client_fn,
 )
 
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=FlowerClient())
+    fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client())
diff --git a/e2e/strategies/pyproject.toml b/e2e/strategies/pyproject.toml
index 611325ce721b..5cc74b20fa24 100644
--- a/e2e/strategies/pyproject.toml
+++ b/e2e/strategies/pyproject.toml
@@ -6,9 +6,10 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart_tensorflow"
 version = "0.1.0"
 description = "Keras Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [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"
diff --git a/e2e/tabnet/client.py b/e2e/tabnet/client.py
index 3c10df0c79f1..0290ba4629de 100644
--- a/e2e/tabnet/client.py
+++ b/e2e/tabnet/client.py
@@ -81,10 +81,10 @@ def evaluate(self, parameters, config):
 def client_fn(cid):
     return FlowerClient().to_client()
 
-flower = fl.flower.Flower(
+app = fl.client.ClientApp(
     client_fn=client_fn,
 )
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=FlowerClient())
+    fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client())
diff --git a/e2e/tabnet/driver.py b/e2e/tabnet/driver.py
index 2b1b35d9e89c..cc452ea523ca 100644
--- a/e2e/tabnet/driver.py
+++ b/e2e/tabnet/driver.py
@@ -1,6 +1,6 @@
 import flwr as fl
 
-hist = fl.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=3),
 )
diff --git a/e2e/tabnet/pyproject.toml b/e2e/tabnet/pyproject.toml
index 644f422d6762..b1abf382a24a 100644
--- a/e2e/tabnet/pyproject.toml
+++ b/e2e/tabnet/pyproject.toml
@@ -6,12 +6,13 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-tabnet"
 version = "0.1.0"
 description = "Tabnet Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [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-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"
diff --git a/e2e/tensorflow/client.py b/e2e/tensorflow/client.py
index 4ad2d5ebda57..10ee91136241 100644
--- a/e2e/tensorflow/client.py
+++ b/e2e/tensorflow/client.py
@@ -34,10 +34,10 @@ def evaluate(self, parameters, config):
 def client_fn(cid):
     return FlowerClient().to_client()
 
-flower = fl.flower.Flower(
+app = fl.client.ClientApp(
     client_fn=client_fn,
 )
 
 if __name__ == "__main__":
     # Start Flower client
-    fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=FlowerClient())
+    fl.client.start_client(server_address="127.0.0.1:8080", client=FlowerClient().to_client())
diff --git a/e2e/tensorflow/driver.py b/e2e/tensorflow/driver.py
index ca860ea47b2d..2ea4de69a62b 100644
--- a/e2e/tensorflow/driver.py
+++ b/e2e/tensorflow/driver.py
@@ -18,7 +18,7 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
 strategy = fl.server.strategy.FedAvg(evaluate_metrics_aggregation_fn=weighted_average)
 
 # Start Flower server
-hist = fl.driver.start_driver(
+hist = fl.server.start_driver(
     server_address="0.0.0.0:9091",
     config=fl.server.ServerConfig(num_rounds=3),
     strategy=strategy,
diff --git a/e2e/tensorflow/pyproject.toml b/e2e/tensorflow/pyproject.toml
index c66ffc30fdf0..a7dbfe2305db 100644
--- a/e2e/tensorflow/pyproject.toml
+++ b/e2e/tensorflow/pyproject.toml
@@ -6,9 +6,10 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-tensorflow"
 version = "0.1.0"
 description = "Keras Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [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"
diff --git a/e2e/test_driver.sh b/e2e/test_driver.sh
index ca54dbf4852f..3d4864a1b0fb 100755
--- a/e2e/test_driver.sh
+++ b/e2e/test_driver.sh
@@ -13,13 +13,34 @@ case "$1" in
     ;;
 esac
 
-timeout 2m flower-server $server_arg &
+case "$2" in
+  rest)
+    rest_arg="--rest"
+    server_address="http://localhost:9093"
+    db_arg="--database :flwr-in-memory-state:"
+    ;;
+  sqlite)
+    rest_arg=""
+    server_address="127.0.0.1:9092"
+    db_arg="--database $(date +%s).db"
+    ;;
+  *)
+    rest_arg=""
+    server_address="127.0.0.1:9092"
+    db_arg="--database :flwr-in-memory-state:"
+    ;;
+esac
+
+timeout 2m flower-superlink $server_arg $db_arg $rest_arg &
+sl_pid=$!
 sleep 3
 
-timeout 2m flower-client $client_arg --callable client:flower --server 127.0.0.1:9092 &
+timeout 2m flower-client-app client:app $client_arg $rest_arg --server $server_address &
+cl1_pid=$!
 sleep 3
 
-timeout 2m flower-client $client_arg --callable client:flower --server 127.0.0.1:9092 &
+timeout 2m flower-client-app client:app $client_arg $rest_arg --server $server_address &
+cl2_pid=$!
 sleep 3
 
 timeout 2m python driver.py &
@@ -29,7 +50,7 @@ wait $pid
 res=$?
 
 if [[ "$res" = "0" ]];
-  then echo "Training worked correctly" && pkill flower-client && pkill flower-server;
+  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/advanced-pytorch/README.md b/examples/advanced-pytorch/README.md
index db0245e41453..c1ba85b95879 100644
--- a/examples/advanced-pytorch/README.md
+++ b/examples/advanced-pytorch/README.md
@@ -1,6 +1,6 @@
 # Advanced Flower Example (PyTorch)
 
-This example demonstrates an advanced federated learning setup using Flower with PyTorch. It differs from the quickstart example in the following ways:
+This example demonstrates an advanced federated learning setup using Flower with PyTorch. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) and it differs from the quickstart example in the following ways:
 
 - 10 clients (instead of just 2)
 - Each client holds a local dataset of 5000 training examples and 1000 test examples (note that using the `run.sh` script will only select 10 data samples by default, as the `--toy` argument is set).
@@ -59,12 +59,13 @@ pip install -r requirements.txt
 
 The included `run.sh` will start the Flower server (using `server.py`),
 sleep for 2 seconds to ensure that the server is up, and then start 10 Flower clients (using `client.py`) with only a small subset of the data (in order to run on any machine),
-but this can be changed by removing the `--toy True` argument in the script. You can simply start everything in a terminal as follows:
+but this can be changed by removing the `--toy` argument in the script. You can simply start everything in a terminal as follows:
 
 ```shell
-poetry run ./run.sh
+# After activating your environment
+./run.sh
 ```
 
 The `run.sh` script starts processes in the background so that you don't have to open eleven terminal windows. If you experiment with the code example and something goes wrong, simply using `CTRL + C` on Linux (or `CMD + C` on macOS) wouldn't normally kill all these processes, which is why the script ends with `trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM EXIT` and `wait`. This simply allows you to stop the experiment using `CTRL + C` (or `CMD + C`). If you change the script and anything goes wrong you can still use `killall python` (or `killall python3`) to kill all background processes (or a more specific command if you have other Python processes running that you don't want to kill).
 
-You can also manually run `poetry run python3 server.py` and `poetry run python3 client.py` for as many clients as you want but you have to make sure that each command is ran in a different terminal window (or a different computer on the network).
+You can also manually run `python3 server.py` and `python3 client.py --client-id <ID>` for as many clients as you want but you have to make sure that each command is run in a different terminal window (or a different computer on the network). In addition, you can make your clients use either `EfficienNet` (default) or `AlexNet` (but all clients in the experiment should use the same). Switch between models using the `--model` flag when launching `client.py` and `server.py`.
diff --git a/examples/advanced-pytorch/client.py b/examples/advanced-pytorch/client.py
index f9ffb6181fd8..d4c8abe3d404 100644
--- a/examples/advanced-pytorch/client.py
+++ b/examples/advanced-pytorch/client.py
@@ -1,11 +1,11 @@
 import utils
 from torch.utils.data import DataLoader
-import torchvision.datasets
 import torch
 import flwr as fl
 import argparse
 from collections import OrderedDict
 import warnings
+import datasets
 
 warnings.filterwarnings("ignore")
 
@@ -13,47 +13,49 @@
 class CifarClient(fl.client.NumPyClient):
     def __init__(
         self,
-        trainset: torchvision.datasets,
-        testset: torchvision.datasets,
-        device: str,
+        trainset: datasets.Dataset,
+        testset: datasets.Dataset,
+        device: torch.device,
+        model_str: str,
         validation_split: int = 0.1,
     ):
         self.device = device
         self.trainset = trainset
         self.testset = testset
         self.validation_split = validation_split
+        if model_str == "alexnet":
+            self.model = utils.load_alexnet(classes=10)
+        else:
+            self.model = utils.load_efficientnet(classes=10)
 
     def set_parameters(self, parameters):
-        """Loads a efficientnet model and replaces it parameters with the ones given."""
-        model = utils.load_efficientnet(classes=10)
-        params_dict = zip(model.state_dict().keys(), parameters)
+        """Loads a alexnet or efficientnet model and replaces it parameters with the
+        ones given."""
+
+        params_dict = zip(self.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 model
+        self.model.load_state_dict(state_dict, strict=True)
 
     def fit(self, parameters, config):
         """Train parameters on the locally held training set."""
 
         # Update local model parameters
-        model = self.set_parameters(parameters)
+        self.set_parameters(parameters)
 
         # Get hyperparameters for this round
         batch_size: int = config["batch_size"]
         epochs: int = config["local_epochs"]
 
-        n_valset = int(len(self.trainset) * self.validation_split)
-
-        valset = torch.utils.data.Subset(self.trainset, range(0, n_valset))
-        trainset = torch.utils.data.Subset(
-            self.trainset, range(n_valset, len(self.trainset))
-        )
+        train_valid = self.trainset.train_test_split(self.validation_split)
+        trainset = train_valid["train"]
+        valset = train_valid["test"]
 
-        trainLoader = DataLoader(trainset, batch_size=batch_size, shuffle=True)
-        valLoader = DataLoader(valset, batch_size=batch_size)
+        train_loader = DataLoader(trainset, batch_size=batch_size, shuffle=True)
+        val_loader = DataLoader(valset, batch_size=batch_size)
 
-        results = utils.train(model, trainLoader, valLoader, epochs, self.device)
+        results = utils.train(self.model, train_loader, val_loader, epochs, self.device)
 
-        parameters_prime = utils.get_model_params(model)
+        parameters_prime = utils.get_model_params(self.model)
         num_examples_train = len(trainset)
 
         return parameters_prime, num_examples_train, results
@@ -61,7 +63,7 @@ def fit(self, parameters, config):
     def evaluate(self, parameters, config):
         """Evaluate parameters on the locally held test set."""
         # Update local model parameters
-        model = self.set_parameters(parameters)
+        self.set_parameters(parameters)
 
         # Get config values
         steps: int = config["val_steps"]
@@ -69,17 +71,17 @@ def evaluate(self, parameters, config):
         # Evaluate global model parameters on the local test data and return results
         testloader = DataLoader(self.testset, batch_size=16)
 
-        loss, accuracy = utils.test(model, testloader, steps, self.device)
+        loss, accuracy = utils.test(self.model, testloader, steps, self.device)
         return float(loss), len(self.testset), {"accuracy": float(accuracy)}
 
 
-def client_dry_run(device: str = "cpu"):
+def client_dry_run(device: torch.device = "cpu"):
     """Weak tests to check whether all client methods are working as expected."""
 
     model = utils.load_efficientnet(classes=10)
     trainset, testset = utils.load_partition(0)
-    trainset = torch.utils.data.Subset(trainset, range(10))
-    testset = torch.utils.data.Subset(testset, range(10))
+    trainset = trainset.select(range(10))
+    testset = testset.select(range(10))
     client = CifarClient(trainset, testset, device)
     client.fit(
         utils.get_model_params(model),
@@ -102,7 +104,7 @@ def main() -> None:
         help="Do a dry-run to check the client",
     )
     parser.add_argument(
-        "--partition",
+        "--client-id",
         type=int,
         default=0,
         choices=range(0, 10),
@@ -112,9 +114,7 @@ def main() -> None:
     )
     parser.add_argument(
         "--toy",
-        type=bool,
-        default=False,
-        required=False,
+        action="store_true",
         help="Set to true to quicky run the client using only 10 datasamples. \
         Useful for testing purposes. Default: False",
     )
@@ -125,6 +125,14 @@ def main() -> None:
         required=False,
         help="Set to true to use GPU. Default: False",
     )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="efficientnet",
+        choices=["efficientnet", "alexnet"],
+        help="Use either Efficientnet or Alexnet models. \
+             If you want to achieve differential privacy, please use the Alexnet model",
+    )
 
     args = parser.parse_args()
 
@@ -136,16 +144,14 @@ def main() -> None:
         client_dry_run(device)
     else:
         # Load a subset of CIFAR-10 to simulate the local data partition
-        trainset, testset = utils.load_partition(args.partition)
+        trainset, testset = utils.load_partition(args.client_id)
 
         if args.toy:
-            trainset = torch.utils.data.Subset(trainset, range(10))
-            testset = torch.utils.data.Subset(testset, range(10))
-
+            trainset = trainset.select(range(10))
+            testset = testset.select(range(10))
         # Start Flower client
-        client = CifarClient(trainset, testset, device)
-
-        fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=client)
+        client = CifarClient(trainset, testset, device, args.model).to_client()
+        fl.client.start_client(server_address="127.0.0.1:8080", client=client)
 
 
 if __name__ == "__main__":
diff --git a/examples/advanced-pytorch/pyproject.toml b/examples/advanced-pytorch/pyproject.toml
index a12f3c47de70..b846a6054cc8 100644
--- a/examples/advanced-pytorch/pyproject.toml
+++ b/examples/advanced-pytorch/pyproject.toml
@@ -7,13 +7,14 @@ name = "advanced-pytorch"
 version = "0.1.0"
 description = "Advanced Flower/PyTorch Example"
 authors = [
-    "The Flower Authors <hello@flower.dev>",
-    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>"
+    "The Flower Authors <hello@flower.ai>",
+    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>",
 ]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = ">=1.0,<2.0"
+flwr-datasets = { extras = ["vision"], version = ">=0.0.2,<1.0.0" }
 torch = "1.13.1"
 torchvision = "0.14.1"
 validators = "0.18.2"
diff --git a/examples/advanced-pytorch/requirements.txt b/examples/advanced-pytorch/requirements.txt
index ba7b284df90e..f4d6a0774162 100644
--- a/examples/advanced-pytorch/requirements.txt
+++ b/examples/advanced-pytorch/requirements.txt
@@ -1,4 +1,5 @@
 flwr>=1.0, <2.0
+flwr-datasets[vision]>=0.0.2, <1.0.0
 torch==1.13.1
 torchvision==0.14.1
 validators==0.18.2
diff --git a/examples/advanced-pytorch/run.sh b/examples/advanced-pytorch/run.sh
index 212285f504f9..c3d52491b987 100755
--- a/examples/advanced-pytorch/run.sh
+++ b/examples/advanced-pytorch/run.sh
@@ -2,20 +2,12 @@
 set -e
 cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/
 
-# Download the CIFAR-10 dataset
-python -c "from torchvision.datasets import CIFAR10; CIFAR10('./dataset', download=True)"
-
-# Download the EfficientNetB0 model
-python -c "import torch; torch.hub.load( \
-        'NVIDIA/DeepLearningExamples:torchhub', \
-        'nvidia_efficientnet_b0', pretrained=True)"
-
-python server.py &
-sleep 3  # Sleep for 3s to give the server enough time to start
+python server.py --toy  &
+sleep 10  # Sleep for 10s to give the server enough time to start and dowload the dataset
 
 for i in `seq 0 9`; do
     echo "Starting client $i"
-    python client.py --partition=${i} --toy True &
+    python client.py --client-id=${i} --toy &
 done
 
 # Enable CTRL+C to stop all background processes
diff --git a/examples/advanced-pytorch/server.py b/examples/advanced-pytorch/server.py
index 8343e62da69f..489694ab1ea1 100644
--- a/examples/advanced-pytorch/server.py
+++ b/examples/advanced-pytorch/server.py
@@ -10,6 +10,8 @@
 
 import warnings
 
+from flwr_datasets import FederatedDataset
+
 warnings.filterwarnings("ignore")
 
 
@@ -39,18 +41,13 @@ def evaluate_config(server_round: int):
 def get_evaluate_fn(model: torch.nn.Module, toy: bool):
     """Return an evaluation function for server-side evaluation."""
 
-    # Load data and model here to avoid the overhead of doing it in `evaluate` itself
-    trainset, _, _ = utils.load_data()
-
-    n_train = len(trainset)
+    # Load data here to avoid the overhead of doing it in `evaluate` itself
+    centralized_data = utils.load_centralized_data()
     if toy:
         # use only 10 samples as validation set
-        valset = torch.utils.data.Subset(trainset, range(n_train - 10, n_train))
-    else:
-        # Use the last 5k training examples as a validation set
-        valset = torch.utils.data.Subset(trainset, range(n_train - 5000, n_train))
+        centralized_data = centralized_data.select(range(10))
 
-    valLoader = DataLoader(valset, batch_size=16)
+    val_loader = DataLoader(centralized_data, batch_size=16)
 
     # The `evaluate` function will be called after every round
     def evaluate(
@@ -63,7 +60,7 @@ def evaluate(
         state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
         model.load_state_dict(state_dict, strict=True)
 
-        loss, accuracy = utils.test(model, valLoader)
+        loss, accuracy = utils.test(model, val_loader)
         return loss, {"accuracy": accuracy}
 
     return evaluate
@@ -79,23 +76,32 @@ def main():
     parser = argparse.ArgumentParser(description="Flower")
     parser.add_argument(
         "--toy",
-        type=bool,
-        default=False,
-        required=False,
+        action="store_true",
         help="Set to true to use only 10 datasamples for validation. \
             Useful for testing purposes. Default: False",
     )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="efficientnet",
+        choices=["efficientnet", "alexnet"],
+        help="Use either Efficientnet or Alexnet models. \
+             If you want to achieve differential privacy, please use the Alexnet model",
+    )
 
     args = parser.parse_args()
 
-    model = utils.load_efficientnet(classes=10)
+    if args.model == "alexnet":
+        model = utils.load_alexnet(classes=10)
+    else:
+        model = utils.load_efficientnet(classes=10)
 
     model_parameters = [val.cpu().numpy() for _, val in model.state_dict().items()]
 
     # Create strategy
     strategy = fl.server.strategy.FedAvg(
-        fraction_fit=0.2,
-        fraction_evaluate=0.2,
+        fraction_fit=1.0,
+        fraction_evaluate=1.0,
         min_fit_clients=2,
         min_evaluate_clients=2,
         min_available_clients=10,
diff --git a/examples/advanced-pytorch/utils.py b/examples/advanced-pytorch/utils.py
index 8788ead90dee..fd9dab19a70d 100644
--- a/examples/advanced-pytorch/utils.py
+++ b/examples/advanced-pytorch/utils.py
@@ -1,59 +1,59 @@
 import torch
-import torchvision.transforms as transforms
-from torchvision.datasets import CIFAR10
-
+from torchvision.transforms import Compose, ToTensor, Normalize, Resize, CenterCrop
+from torchvision.models import efficientnet_b0, AlexNet
 import warnings
 
-warnings.filterwarnings("ignore")
+from flwr_datasets import FederatedDataset
 
-# DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
+warnings.filterwarnings("ignore")
 
-def load_data():
-    """Load CIFAR-10 (training and test set)."""
-    transform = transforms.Compose(
-        [
-            transforms.Resize(256),
-            transforms.CenterCrop(224),
-            transforms.ToTensor(),
-            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-        ]
-    )
 
-    trainset = CIFAR10("./dataset", train=True, download=True, transform=transform)
-    testset = CIFAR10("./dataset", train=False, download=True, transform=transform)
+def load_partition(partition_id, toy: bool = False):
+    """Load partition CIFAR10 data."""
+    fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
+    partition = fds.load_partition(partition_id)
+    # Divide data on each node: 80% train, 20% test
+    partition_train_test = partition.train_test_split(test_size=0.2)
+    partition_train_test = partition_train_test.with_transform(apply_transforms)
+    return partition_train_test["train"], partition_train_test["test"]
 
-    num_examples = {"trainset": len(trainset), "testset": len(testset)}
-    return trainset, testset, num_examples
 
+def load_centralized_data():
+    fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
+    centralized_data = fds.load_split("test")
+    centralized_data = centralized_data.with_transform(apply_transforms)
+    return centralized_data
 
-def load_partition(idx: int):
-    """Load 1/10th of the training and test data to simulate a partition."""
-    assert idx in range(10)
-    trainset, testset, num_examples = load_data()
-    n_train = int(num_examples["trainset"] / 10)
-    n_test = int(num_examples["testset"] / 10)
 
-    train_parition = torch.utils.data.Subset(
-        trainset, range(idx * n_train, (idx + 1) * n_train)
-    )
-    test_parition = torch.utils.data.Subset(
-        testset, range(idx * n_test, (idx + 1) * n_test)
+def apply_transforms(batch):
+    """Apply transforms to the partition from FederatedDataset."""
+    pytorch_transforms = Compose(
+        [
+            Resize(256),
+            CenterCrop(224),
+            ToTensor(),
+            Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ]
     )
-    return (train_parition, test_parition)
+    batch["img"] = [pytorch_transforms(img) for img in batch["img"]]
+    return batch
 
 
-def train(net, trainloader, valloader, epochs, device: str = "cpu"):
+def train(
+    net, trainloader, valloader, epochs, device: torch.device = torch.device("cpu")
+):
     """Train the network on the training set."""
     print("Starting training...")
     net.to(device)  # move model to GPU if available
     criterion = torch.nn.CrossEntropyLoss().to(device)
     optimizer = torch.optim.SGD(
-        net.parameters(), lr=0.1, momentum=0.9, weight_decay=1e-4
+        net.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4
     )
     net.train()
     for _ in range(epochs):
-        for images, labels in trainloader:
+        for batch in trainloader:
+            images, labels = batch["img"], batch["label"]
             images, labels = images.to(device), labels.to(device)
             optimizer.zero_grad()
             loss = criterion(net(images), labels)
@@ -74,7 +74,9 @@ def train(net, trainloader, valloader, epochs, device: str = "cpu"):
     return results
 
 
-def test(net, testloader, steps: int = None, device: str = "cpu"):
+def test(
+    net, testloader, steps: int = None, device: torch.device = torch.device("cpu")
+):
     """Validate the network on the entire test set."""
     print("Starting evalutation...")
     net.to(device)  # move model to GPU if available
@@ -82,7 +84,8 @@ def test(net, testloader, steps: int = None, device: str = "cpu"):
     correct, loss = 0, 0.0
     net.eval()
     with torch.no_grad():
-        for batch_idx, (images, labels) in enumerate(testloader):
+        for batch_idx, batch in enumerate(testloader):
+            images, labels = batch["img"], batch["label"]
             images, labels = images.to(device), labels.to(device)
             outputs = net(images)
             loss += criterion(outputs, labels).item()
@@ -95,36 +98,21 @@ def test(net, testloader, steps: int = None, device: str = "cpu"):
     return loss, accuracy
 
 
-def replace_classifying_layer(efficientnet_model, num_classes: int = 10):
-    """Replaces the final layer of the classifier."""
-    num_features = efficientnet_model.classifier.fc.in_features
-    efficientnet_model.classifier.fc = torch.nn.Linear(num_features, num_classes)
-
-
-def load_efficientnet(entrypoint: str = "nvidia_efficientnet_b0", classes: int = None):
-    """Loads pretrained efficientnet model from torch hub. Replaces final classifying
-    layer if classes is specified.
-
-    Args:
-        entrypoint: EfficientNet model to download.
-                    For supported entrypoints, please refer
-                    https://pytorch.org/hub/nvidia_deeplearningexamples_efficientnet/
-        classes: Number of classes in final classifying layer. Leave as None to get the downloaded
-                 model untouched.
-    Returns:
-        EfficientNet Model
-
-    Note: One alternative implementation can be found at https://github.com/lukemelas/EfficientNet-PyTorch
-    """
-    efficientnet = torch.hub.load(
-        "NVIDIA/DeepLearningExamples:torchhub", entrypoint, pretrained=True
-    )
-
-    if classes is not None:
-        replace_classifying_layer(efficientnet, classes)
+def load_efficientnet(classes: int = 10):
+    """Loads EfficienNetB0 from TorchVision."""
+    efficientnet = efficientnet_b0(pretrained=True)
+    # Re-init output linear layer with the right number of classes
+    model_classes = efficientnet.classifier[1].in_features
+    if classes != model_classes:
+        efficientnet.classifier[1] = torch.nn.Linear(model_classes, classes)
     return efficientnet
 
 
 def get_model_params(model):
     """Returns a model's parameters."""
     return [val.cpu().numpy() for _, val in model.state_dict().items()]
+
+
+def load_alexnet(classes):
+    """Load AlexNet model from TorchVision."""
+    return AlexNet(num_classes=classes)
diff --git a/examples/advanced-tensorflow/README.md b/examples/advanced-tensorflow/README.md
index 31bf5edb64c6..94707b5cbc98 100644
--- a/examples/advanced-tensorflow/README.md
+++ b/examples/advanced-tensorflow/README.md
@@ -1,9 +1,9 @@
 # Advanced Flower Example (TensorFlow/Keras)
 
-This example demonstrates an advanced federated learning setup using Flower with TensorFlow/Keras. It differs from the quickstart example in the following ways:
+This example demonstrates an advanced federated learning setup using Flower with TensorFlow/Keras. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) and it differs from the quickstart example in the following ways:
 
 - 10 clients (instead of just 2)
-- Each client holds a local dataset of 5000 training examples and 1000 test examples (note that by default only a small subset of this data is used when running the `run.sh` script)
+- Each client holds a local dataset of 1/10 of the train datasets and 80% is training examples and 20% as test examples (note that by default only a small subset of this data is used when running the `run.sh` script)
 - Server-side model evaluation after parameter aggregation
 - Hyperparameter schedule using config functions
 - Custom return values
@@ -57,10 +57,11 @@ pip install -r requirements.txt
 
 ## Run Federated Learning with TensorFlow/Keras and Flower
 
-The included `run.sh` will call a script to generate certificates (which will be used by server and clients), start the Flower server (using `server.py`), sleep for 2 seconds to ensure the the server is up, and then start 10 Flower clients (using `client.py`). You can simply start everything in a terminal as follows:
+The included `run.sh` will call a script to generate certificates (which will be used by server and clients), start the Flower server (using `server.py`), sleep for 10 seconds to ensure the server is up, and then start 10 Flower clients (using `client.py`). You can simply start everything in a terminal as follows:
 
 ```shell
-poetry run ./run.sh
+# Once you have activated your environment
+./run.sh
 ```
 
 The `run.sh` script starts processes in the background so that you don't have to open eleven terminal windows. If you experiment with the code example and something goes wrong, simply using `CTRL + C` on Linux (or `CMD + C` on macOS) wouldn't normally kill all these processes, which is why the script ends with `trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM EXIT` and `wait`. This simply allows you to stop the experiment using `CTRL + C` (or `CMD + C`). If you change the script and anything goes wrong you can still use `killall python` (or `killall python3`) to kill all background processes (or a more specific command if you have other Python processes running that you don't want to kill).
diff --git a/examples/advanced-tensorflow/client.py b/examples/advanced-tensorflow/client.py
index 1c0b61575635..17d1d2306270 100644
--- a/examples/advanced-tensorflow/client.py
+++ b/examples/advanced-tensorflow/client.py
@@ -6,6 +6,8 @@
 
 import flwr as fl
 
+from flwr_datasets import FederatedDataset
+
 # Make TensorFlow logs less verbose
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 
@@ -74,7 +76,7 @@ def main() -> None:
     # Parse command line argument `partition`
     parser = argparse.ArgumentParser(description="Flower")
     parser.add_argument(
-        "--partition",
+        "--client-id",
         type=int,
         default=0,
         choices=range(0, 10),
@@ -84,9 +86,7 @@ def main() -> None:
     )
     parser.add_argument(
         "--toy",
-        type=bool,
-        default=False,
-        required=False,
+        action="store_true",
         help="Set to true to quicky run the client using only 10 datasamples. "
         "Useful for testing purposes. Default: False",
     )
@@ -99,16 +99,16 @@ def main() -> None:
     model.compile("adam", "sparse_categorical_crossentropy", metrics=["accuracy"])
 
     # Load a subset of CIFAR-10 to simulate the local data partition
-    (x_train, y_train), (x_test, y_test) = load_partition(args.partition)
+    x_train, y_train, x_test, y_test = load_partition(args.client_id)
 
     if args.toy:
         x_train, y_train = x_train[:10], y_train[:10]
         x_test, y_test = x_test[:10], y_test[:10]
 
     # Start Flower client
-    client = CifarClient(model, x_train, y_train, x_test, y_test)
+    client = CifarClient(model, x_train, y_train, x_test, y_test).to_client()
 
-    fl.client.start_numpy_client(
+    fl.client.start_client(
         server_address="127.0.0.1:8080",
         client=client,
         root_certificates=Path(".cache/certificates/ca.crt").read_bytes(),
@@ -117,15 +117,16 @@ def main() -> None:
 
 def load_partition(idx: int):
     """Load 1/10th of the training and test data to simulate a partition."""
-    assert idx in range(10)
-    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
-    return (
-        x_train[idx * 5000 : (idx + 1) * 5000],
-        y_train[idx * 5000 : (idx + 1) * 5000],
-    ), (
-        x_test[idx * 1000 : (idx + 1) * 1000],
-        y_test[idx * 1000 : (idx + 1) * 1000],
-    )
+    # Download and partition dataset
+    fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
+    partition = fds.load_partition(idx)
+    partition.set_format("numpy")
+
+    # Divide data on each node: 80% train, 20% test
+    partition = partition.train_test_split(test_size=0.2)
+    x_train, y_train = partition["train"]["img"] / 255.0, partition["train"]["label"]
+    x_test, y_test = partition["test"]["img"] / 255.0, partition["test"]["label"]
+    return x_train, y_train, x_test, y_test
 
 
 if __name__ == "__main__":
diff --git a/examples/advanced-tensorflow/pyproject.toml b/examples/advanced-tensorflow/pyproject.toml
index 293ba64b3f43..02bd923129a4 100644
--- a/examples/advanced-tensorflow/pyproject.toml
+++ b/examples/advanced-tensorflow/pyproject.toml
@@ -6,10 +6,11 @@ build-backend = "poetry.core.masonry.api"
 name = "advanced-tensorflow"
 version = "0.1.0"
 description = "Advanced Flower/TensorFlow Example"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 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\""}
+flwr-datasets = { extras = ["vision"], version = ">=0.0.2,<1.0.0" }
+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\"" }
diff --git a/examples/advanced-tensorflow/requirements.txt b/examples/advanced-tensorflow/requirements.txt
index 7a70c46a8128..0cb5fe8c07af 100644
--- a/examples/advanced-tensorflow/requirements.txt
+++ b/examples/advanced-tensorflow/requirements.txt
@@ -1,3 +1,4 @@
 flwr>=1.0, <2.0
+flwr-datasets = { extras = ["vision"], version = ">=0.0.2,<1.0.0" }
 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"
diff --git a/examples/advanced-tensorflow/run.sh b/examples/advanced-tensorflow/run.sh
index 8ddb6a252b52..4acef1371571 100755
--- a/examples/advanced-tensorflow/run.sh
+++ b/examples/advanced-tensorflow/run.sh
@@ -5,14 +5,11 @@
 echo "Starting server"
 
 python server.py &
-sleep 3  # Sleep for 3s to give the server enough time to start
+sleep 10  # Sleep for 10s to give the server enough time to start and download the dataset
 
-# Ensure that the Keras dataset used in client.py is already cached.
-python -c "import tensorflow as tf; tf.keras.datasets.cifar10.load_data()"
-
-for i in `seq 0 9`; do
+for i in $(seq 0 9); do
     echo "Starting client $i"
-    python client.py --partition=${i} --toy True &
+    python client.py --client-id=${i} --toy &
 done
 
 # This will allow you to use CTRL+C to stop all background processes
diff --git a/examples/advanced-tensorflow/server.py b/examples/advanced-tensorflow/server.py
index e1eb3d4fd8f7..e159a096dc83 100644
--- a/examples/advanced-tensorflow/server.py
+++ b/examples/advanced-tensorflow/server.py
@@ -4,6 +4,8 @@
 import flwr as fl
 import tensorflow as tf
 
+from flwr_datasets import FederatedDataset
+
 
 def main() -> None:
     # Load and compile model for
@@ -43,11 +45,11 @@ def main() -> None:
 def get_evaluate_fn(model):
     """Return an evaluation function for server-side evaluation."""
 
-    # Load data and model here to avoid the overhead of doing it in `evaluate` itself
-    (x_train, y_train), _ = tf.keras.datasets.cifar10.load_data()
-
-    # Use the last 5k training examples as a validation set
-    x_val, y_val = x_train[45000:50000], y_train[45000:50000]
+    # Load data here to avoid the overhead of doing it in `evaluate` itself
+    fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
+    test = fds.load_split("test")
+    test.set_format("numpy")
+    x_test, y_test = test["img"] / 255.0, test["label"]
 
     # The `evaluate` function will be called after every round
     def evaluate(
@@ -56,7 +58,7 @@ def evaluate(
         config: Dict[str, fl.common.Scalar],
     ) -> Optional[Tuple[float, Dict[str, fl.common.Scalar]]]:
         model.set_weights(parameters)  # Update model with the latest parameters
-        loss, accuracy = model.evaluate(x_val, y_val)
+        loss, accuracy = model.evaluate(x_test, y_test)
         return loss, {"accuracy": accuracy}
 
     return evaluate
diff --git a/examples/android/README.md b/examples/android/README.md
index 7931aa96b0c5..f9f2bb93b8dc 100644
--- a/examples/android/README.md
+++ b/examples/android/README.md
@@ -54,4 +54,4 @@ poetry run ./run.sh
 
 Download and install the `flwr_android_client.apk` on each Android device/emulator. The server currently expects a minimum of 4 Android clients, but it can be changed in the `server.py`.
 
-When the Android app runs, add the client ID (between 1-10), the IP and port of your server, and press `Load Dataset`. This will load the local CIFAR10 dataset in memory. Then press `Setup Connection Channel` which will establish connection with the server. Finally, press `Train Federated!` which will start the federated training.
+When the Android app runs, add the client ID (between 1-10), the IP and port of your server, and press `Start`. This will load the local CIFAR10 dataset in memory, establish connection with the server, and start the federated training. To abort the federated learning process, press `Stop`. You can clear and refresh the log messages by pressing `Clear` and `Refresh` buttons respectively.
diff --git a/examples/android/client/app/src/main/java/flwr/android_client/MainActivity.java b/examples/android/client/app/src/main/java/flwr/android_client/MainActivity.java
index cbf804140954..4de369a7e684 100644
--- a/examples/android/client/app/src/main/java/flwr/android_client/MainActivity.java
+++ b/examples/android/client/app/src/main/java/flwr/android_client/MainActivity.java
@@ -60,7 +60,7 @@ public void onChanged(List<WorkInfo> workInfos) {
                 if (workInfos.size() > 0) {
                     WorkInfo info = workInfos.get(0);
                     int progress = info.getProgress().getInt("progress", -1);
-                    // You can recieve any message from the Worker Thread
+                    // You can receive any message from the Worker Thread
                     refreshRecyclerView();
                 }
             }
diff --git a/examples/android/pyproject.toml b/examples/android/pyproject.toml
index 2b9cd8c978a7..0371f7208292 100644
--- a/examples/android/pyproject.toml
+++ b/examples/android/pyproject.toml
@@ -6,10 +6,10 @@ build-backend = "poetry.masonry.api"
 name = "android_flwr_tensorflow"
 version = "0.1.0"
 description = "Android Example"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 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-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\"" }
diff --git a/examples/app-pytorch/README.md b/examples/app-pytorch/README.md
new file mode 100644
index 000000000000..14de3c7d632e
--- /dev/null
+++ b/examples/app-pytorch/README.md
@@ -0,0 +1,75 @@
+# Flower App (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 then run a Flower App (consisting of a `ClientApp` and a `ServerApp`).
+
+## Preconditions
+
+Let's assume the following project structure:
+
+```bash
+$ tree .
+.
+├── client.py           # <-- contains `ClientApp`
+├── server.py           # <-- contains `ServerApp`
+├── server_workflow.py  # <-- contains `ServerApp` with workflow
+├── server_custom.py    # <-- contains `ServerApp` with custom main function
+├── task.py             # <-- task-specific code (model, data)
+└── requirements.txt    # <-- dependencies
+```
+
+## Install dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+## Run a simulation
+
+```bash
+flower-simulation --server-app server:app --client-app client:app --num-supernodes 2
+```
+
+## Run a deployment
+
+### Start the long-running Flower server (SuperLink)
+
+```bash
+flower-superlink --insecure
+```
+
+### Start the long-running Flower client (SuperNode)
+
+In a new terminal window, start the first long-running Flower client:
+
+```bash
+flower-client-app client:app --insecure
+```
+
+In yet another new terminal window, start the second long-running Flower client:
+
+```bash
+flower-client-app client:app --insecure
+```
+
+### 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
+```
+
+Or, to try the workflow example, run:
+
+```bash
+flower-server-app server_workflow:app --insecure
+```
+
+Or, to try the custom server function example, run:
+
+```bash
+flower-server-app server_custom:app --insecure
+```
diff --git a/examples/app-pytorch/client.py b/examples/app-pytorch/client.py
new file mode 100644
index 000000000000..ebbe977ecab1
--- /dev/null
+++ b/examples/app-pytorch/client.py
@@ -0,0 +1,51 @@
+from flwr.client import ClientApp, NumPyClient
+
+from task import (
+    Net,
+    DEVICE,
+    load_data,
+    get_weights,
+    set_weights,
+    train,
+    test,
+)
+
+
+# Load model and data (simple CNN, CIFAR-10)
+net = Net().to(DEVICE)
+trainloader, testloader = load_data()
+
+
+# Define FlowerClient and client_fn
+class FlowerClient(NumPyClient):
+
+    def fit(self, parameters, config):
+        set_weights(net, parameters)
+        results = train(net, trainloader, testloader, epochs=1, device=DEVICE)
+        return get_weights(net), len(trainloader.dataset), results
+
+    def evaluate(self, parameters, config):
+        set_weights(net, parameters)
+        loss, accuracy = test(net, testloader)
+        return loss, len(testloader.dataset), {"accuracy": accuracy}
+
+
+def client_fn(cid: str):
+    """Create and return an instance of Flower `Client`."""
+    return FlowerClient().to_client()
+
+
+# Flower ClientApp
+app = ClientApp(
+    client_fn=client_fn,
+)
+
+
+# Legacy mode
+if __name__ == "__main__":
+    from flwr.client import start_client
+
+    start_client(
+        server_address="127.0.0.1:8080",
+        client=FlowerClient().to_client(),
+    )
diff --git a/examples/app-pytorch/client_low_level.py b/examples/app-pytorch/client_low_level.py
new file mode 100644
index 000000000000..feea1ee658fe
--- /dev/null
+++ b/examples/app-pytorch/client_low_level.py
@@ -0,0 +1,35 @@
+from flwr.client import ClientApp
+from flwr.common import Message, Context
+
+
+def hello_world_mod(msg, ctx, call_next) -> Message:
+    print("Hello, ...[pause for dramatic effect]...")
+    out = call_next(msg, ctx)
+    print("...[pause was long enough]... World!")
+    return out
+
+
+# Flower ClientApp
+app = ClientApp(
+    mods=[
+        hello_world_mod,
+    ],
+)
+
+
+@app.train()
+def train(msg: Message, ctx: Context):
+    print("`train` is not implemented, echoing original message")
+    return msg.create_reply(msg.content, ttl="")
+
+
+@app.evaluate()
+def eval(msg: Message, ctx: Context):
+    print("`evaluate` is not implemented, echoing original message")
+    return msg.create_reply(msg.content, ttl="")
+
+
+@app.query()
+def query(msg: Message, ctx: Context):
+    print("`query` is not implemented, echoing original message")
+    return msg.create_reply(msg.content, ttl="")
diff --git a/examples/app-pytorch/pyproject.toml b/examples/app-pytorch/pyproject.toml
new file mode 100644
index 000000000000..e47dd2db949d
--- /dev/null
+++ b/examples/app-pytorch/pyproject.toml
@@ -0,0 +1,17 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "app-pytorch"
+version = "0.1.0"
+description = "Multi-Tenant Federated Learning with Flower and PyTorch"
+authors = ["The Flower Authors <hello@flower.ai>"]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+# Mandatory dependencies
+flwr-nightly = { version = "1.8.0.dev20240309", extras = ["simulation"] }
+flwr-datasets = { version = "0.0.2", extras = ["vision"] }
+torch = "2.2.1"
+torchvision = "0.17.1"
diff --git a/examples/app-pytorch/requirements.txt b/examples/app-pytorch/requirements.txt
new file mode 100644
index 000000000000..016a84043cbe
--- /dev/null
+++ b/examples/app-pytorch/requirements.txt
@@ -0,0 +1,4 @@
+flwr-nightly[simulation]==1.8.0.dev20240309
+flwr-datasets[vision]==0.0.2
+torch==2.2.1
+torchvision==0.17.1
diff --git a/examples/mt-pytorch/start_server.py b/examples/app-pytorch/server.py
similarity index 64%
rename from examples/mt-pytorch/start_server.py
rename to examples/app-pytorch/server.py
index d96edd7d45ad..0b4ad1ddba46 100644
--- a/examples/mt-pytorch/start_server.py
+++ b/examples/app-pytorch/server.py
@@ -1,7 +1,10 @@
 from typing import List, Tuple
 
-import flwr as fl
-from flwr.common import Metrics
+from flwr.server import ServerApp, ServerConfig
+from flwr.server.strategy import FedAvg
+from flwr.common import Metrics, ndarrays_to_parameters
+
+from task import Net, get_weights
 
 
 # Define metric aggregation function
@@ -25,17 +28,38 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
     }
 
 
+# Initialize model parameters
+ndarrays = get_weights(Net())
+parameters = ndarrays_to_parameters(ndarrays)
+
+
 # Define strategy
-strategy = fl.server.strategy.FedAvg(
+strategy = FedAvg(
     fraction_fit=1.0,  # Select all available clients
     fraction_evaluate=0.0,  # Disable evaluation
     min_available_clients=2,
     fit_metrics_aggregation_fn=weighted_average,
+    initial_parameters=parameters,
 )
 
-# Start Flower server
-fl.server.start_server(
-    server_address="0.0.0.0:9092",
-    config=fl.server.ServerConfig(num_rounds=3),
+
+# Define config
+config = ServerConfig(num_rounds=3)
+
+
+# Flower ServerApp
+app = ServerApp(
+    config=config,
     strategy=strategy,
 )
+
+
+# Legacy mode
+if __name__ == "__main__":
+    from flwr.server import start_server
+
+    start_server(
+        server_address="0.0.0.0:8080",
+        config=config,
+        strategy=strategy,
+    )
diff --git a/examples/app-pytorch/server_custom.py b/examples/app-pytorch/server_custom.py
new file mode 100644
index 000000000000..0c2851e2afee
--- /dev/null
+++ b/examples/app-pytorch/server_custom.py
@@ -0,0 +1,148 @@
+from typing import List, Tuple, Dict
+import random
+import time
+
+import flwr as fl
+from flwr.common import (
+    Context,
+    FitIns,
+    ndarrays_to_parameters,
+    parameters_to_ndarrays,
+    NDArrays,
+    Code,
+    Message,
+    MessageType,
+    Metrics,
+)
+from flwr.common.recordset_compat import fitins_to_recordset, recordset_to_fitres
+from flwr.server import Driver, History
+from flwr.server.strategy.aggregate import aggregate
+
+from task import Net, get_weights
+
+
+# Define metric aggregation function
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+    examples = [num_examples for num_examples, _ in metrics]
+
+    # Multiply accuracy of each client by number of examples used
+    train_losses = [num_examples * m["train_loss"] for num_examples, m in metrics]
+    train_accuracies = [
+        num_examples * m["train_accuracy"] for num_examples, m in metrics
+    ]
+    val_losses = [num_examples * m["val_loss"] for num_examples, m in metrics]
+    val_accuracies = [num_examples * m["val_accuracy"] for num_examples, m in metrics]
+
+    # Aggregate and return custom metric (weighted average)
+    return {
+        "train_loss": sum(train_losses) / sum(examples),
+        "train_accuracy": sum(train_accuracies) / sum(examples),
+        "val_loss": sum(val_losses) / sum(examples),
+        "val_accuracy": sum(val_accuracies) / sum(examples),
+    }
+
+
+# Run via `flower-server-app server:app`
+app = fl.server.ServerApp()
+
+
+@app.main()
+def main(driver: Driver, context: Context) -> None:
+    """."""
+    print("RUNNING!!!!!")
+
+    num_client_nodes_per_round = 2
+    sleep_time = 1
+    num_rounds = 3
+    parameters = ndarrays_to_parameters(get_weights(net=Net()))
+
+    history = History()
+    for server_round in range(num_rounds):
+        print(f"Commencing server round {server_round + 1}")
+
+        # List of sampled node IDs in this round
+        sampled_nodes: List[int] = []
+
+        # The Driver API might not immediately return enough client node IDs, so we
+        # loop and wait until enough client nodes are available.
+        while True:
+            all_node_ids = driver.get_node_ids()
+
+            print(f"Got {len(all_node_ids)} client nodes: {all_node_ids}")
+            if len(all_node_ids) >= num_client_nodes_per_round:
+                # Sample client nodes
+                sampled_nodes = random.sample(all_node_ids, num_client_nodes_per_round)
+                break
+            time.sleep(3)
+
+        # Log sampled node IDs
+        print(f"Sampled {len(sampled_nodes)} node IDs: {sampled_nodes}")
+
+        # Schedule a task for all sampled nodes
+        fit_ins: FitIns = FitIns(parameters=parameters, config={})
+        recordset = fitins_to_recordset(fitins=fit_ins, keep_input=True)
+
+        messages = []
+        for node_id in sampled_nodes:
+            message = driver.create_message(
+                content=recordset,
+                message_type=MessageType.TRAIN,
+                dst_node_id=node_id,
+                group_id=str(server_round),
+                ttl="",
+            )
+            messages.append(message)
+
+        message_ids = driver.push_messages(messages)
+        print(f"Pushed {len(message_ids)} messages: {message_ids}")
+
+        # Wait for results, ignore empty message_ids
+        message_ids = [message_id for message_id in message_ids if message_id != ""]
+
+        all_replies: List[Message] = []
+        while True:
+            replies = driver.pull_messages(message_ids=message_ids)
+            print(f"Got {len(replies)} results")
+            all_replies += replies
+            if len(all_replies) == len(message_ids):
+                break
+            time.sleep(3)
+
+        # Collect correct results
+        all_fitres = [
+            recordset_to_fitres(msg.content, keep_input=True) for msg in all_replies
+        ]
+        print(f"Received {len(all_fitres)} results")
+
+        weights_results: List[Tuple[NDArrays, int]] = []
+        metrics_results: List[Tuple[int, Dict]] = []
+        for fitres in all_fitres:
+            print(f"num_examples: {fitres.num_examples}, status: {fitres.status.code}")
+
+            # Aggregate only if the status is OK
+            if fitres.status.code != Code.OK:
+                continue
+            weights_results.append(
+                (parameters_to_ndarrays(fitres.parameters), fitres.num_examples)
+            )
+            metrics_results.append((fitres.num_examples, fitres.metrics))
+
+        # Aggregate parameters (FedAvg)
+        parameters_aggregated = ndarrays_to_parameters(aggregate(weights_results))
+        parameters = parameters_aggregated
+
+        # Aggregate metrics
+        metrics_aggregated = weighted_average(metrics_results)
+        history.add_metrics_distributed_fit(
+            server_round=server_round, metrics=metrics_aggregated
+        )
+        print("Round ", server_round, " metrics: ", metrics_aggregated)
+
+        # Slow down the start of the next round
+        time.sleep(sleep_time)
+
+    print("app_fit: losses_distributed %s", str(history.losses_distributed))
+    print("app_fit: metrics_distributed_fit %s", str(history.metrics_distributed_fit))
+    print("app_fit: metrics_distributed %s", str(history.metrics_distributed))
+    print("app_fit: losses_centralized %s", str(history.losses_centralized))
+    print("app_fit: metrics_centralized %s", str(history.metrics_centralized))
diff --git a/examples/app-pytorch/server_low_level.py b/examples/app-pytorch/server_low_level.py
new file mode 100644
index 000000000000..560babac1b95
--- /dev/null
+++ b/examples/app-pytorch/server_low_level.py
@@ -0,0 +1,52 @@
+from typing import List, Tuple, Dict
+import random
+import time
+
+import flwr as fl
+from flwr.common import Context, NDArrays, Message, MessageType, Metrics, RecordSet
+from flwr.server import Driver
+
+
+# Run via `flower-server-app server:app`
+app = fl.server.ServerApp()
+
+
+@app.main()
+def main(driver: Driver, context: Context) -> None:
+    """This is a stub example that simply sends and receives messages."""
+    print("Starting test run")
+    for server_round in range(3):
+        print(f"Commencing server round {server_round + 1}")
+
+        # Get node IDs
+        node_ids = driver.get_node_ids()
+
+        # Create messages
+        recordset = RecordSet()
+        messages = []
+        for node_id in node_ids:
+            message = driver.create_message(
+                content=recordset,
+                message_type=MessageType.TRAIN,
+                dst_node_id=node_id,
+                group_id=str(server_round),
+                ttl="",
+            )
+            messages.append(message)
+
+        # Send messages
+        message_ids = driver.push_messages(messages)
+        print(f"Pushed {len(message_ids)} messages: {message_ids}")
+
+        # Wait for results, ignore empty message_ids
+        message_ids = [message_id for message_id in message_ids if message_id != ""]
+        all_replies: List[Message] = []
+        while True:
+            replies = driver.pull_messages(message_ids=message_ids)
+            print(f"Got {len(replies)} results")
+            all_replies += replies
+            if len(all_replies) == len(message_ids):
+                break
+            time.sleep(3)
+
+        print(f"Received {len(all_replies)} results")
diff --git a/examples/app-pytorch/server_workflow.py b/examples/app-pytorch/server_workflow.py
new file mode 100644
index 000000000000..6923010ecf7b
--- /dev/null
+++ b/examples/app-pytorch/server_workflow.py
@@ -0,0 +1,63 @@
+from typing import List, Tuple
+
+from task import Net, get_weights
+
+import flwr as fl
+from flwr.common import Context, Metrics, ndarrays_to_parameters
+from flwr.server import Driver, LegacyContext
+
+
+# Define metric aggregation function
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+    examples = [num_examples for num_examples, _ in metrics]
+
+    # Multiply accuracy of each client by number of examples used
+    train_losses = [num_examples * m["train_loss"] for num_examples, m in metrics]
+    train_accuracies = [
+        num_examples * m["train_accuracy"] for num_examples, m in metrics
+    ]
+    val_losses = [num_examples * m["val_loss"] for num_examples, m in metrics]
+    val_accuracies = [num_examples * m["val_accuracy"] for num_examples, m in metrics]
+
+    # Aggregate and return custom metric (weighted average)
+    return {
+        "train_loss": sum(train_losses) / sum(examples),
+        "train_accuracy": sum(train_accuracies) / sum(examples),
+        "val_loss": sum(val_losses) / sum(examples),
+        "val_accuracy": sum(val_accuracies) / sum(examples),
+    }
+
+
+# Initialize model parameters
+ndarrays = get_weights(Net())
+parameters = ndarrays_to_parameters(ndarrays)
+
+
+# Define strategy
+strategy = fl.server.strategy.FedAvg(
+    fraction_fit=1.0,  # Select all available clients
+    fraction_evaluate=0.0,  # Disable evaluation
+    min_available_clients=2,
+    fit_metrics_aggregation_fn=weighted_average,
+    initial_parameters=parameters,
+)
+
+
+# Run via `flower-server-app server_workflow:app`
+app = fl.server.ServerApp()
+
+
+@app.main()
+def main(driver: Driver, context: Context) -> None:
+    # Construct the LegacyContext
+    context = LegacyContext(
+        state=context.state,
+        config=fl.server.ServerConfig(num_rounds=3),
+        strategy=strategy,
+    )
+
+    # Create the workflow
+    workflow = fl.server.workflow.DefaultWorkflow()
+
+    # Execute
+    workflow(driver, context)
diff --git a/examples/app-pytorch/task.py b/examples/app-pytorch/task.py
new file mode 100644
index 000000000000..240f290df320
--- /dev/null
+++ b/examples/app-pytorch/task.py
@@ -0,0 +1,94 @@
+from collections import OrderedDict
+from logging import INFO
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from flwr.common.logger import log
+from torch.utils.data import DataLoader
+from torchvision.datasets import CIFAR10
+from torchvision.transforms import Compose, Normalize, ToTensor
+
+
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+class Net(nn.Module):
+    """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz')"""
+
+    def __init__(self) -> None:
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        return self.fc3(x)
+
+
+def load_data():
+    """Load CIFAR-10 (training and test set)."""
+    trf = Compose([ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
+    trainset = CIFAR10("./data", train=True, download=True, transform=trf)
+    testset = CIFAR10("./data", train=False, download=True, transform=trf)
+    return DataLoader(trainset, batch_size=32, shuffle=True), DataLoader(testset)
+
+
+def train(net, trainloader, valloader, epochs, device):
+    """Train the model on the training set."""
+    log(INFO, "Starting training...")
+    net.to(device)  # move model to GPU if available
+    criterion = torch.nn.CrossEntropyLoss().to(device)
+    optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
+    net.train()
+    for _ in range(epochs):
+        for images, labels in trainloader:
+            images, labels = images.to(device), labels.to(device)
+            optimizer.zero_grad()
+            loss = criterion(net(images), labels)
+            loss.backward()
+            optimizer.step()
+
+    train_loss, train_acc = test(net, trainloader)
+    val_loss, val_acc = test(net, valloader)
+
+    results = {
+        "train_loss": train_loss,
+        "train_accuracy": train_acc,
+        "val_loss": val_loss,
+        "val_accuracy": val_acc,
+    }
+    return results
+
+
+def test(net, testloader):
+    """Validate the model on the test set."""
+    net.to(DEVICE)
+    criterion = torch.nn.CrossEntropyLoss()
+    correct, loss = 0, 0.0
+    with torch.no_grad():
+        for images, labels in testloader:
+            outputs = net(images.to(DEVICE))
+            labels = labels.to(DEVICE)
+            loss += criterion(outputs, labels).item()
+            correct += (torch.max(outputs.data, 1)[1] == labels).sum().item()
+    accuracy = correct / len(testloader.dataset)
+    return loss, accuracy
+
+
+def get_weights(net):
+    return [val.cpu().numpy() for _, val in net.state_dict().items()]
+
+
+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)
diff --git a/examples/app-secure-aggregation/README.md b/examples/app-secure-aggregation/README.md
new file mode 100644
index 000000000000..d1ea7bdc893f
--- /dev/null
+++ b/examples/app-secure-aggregation/README.md
@@ -0,0 +1,93 @@
+# Secure aggregation with Flower (the SecAgg+ protocol) 🧪
+
+> 🧪 = 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 use Secure Aggregation in flower, with `ClientApp` using `secaggplus_mod` and `ServerApp` using `SecAggPlusWorkflow`.
+
+## Preconditions
+
+Let's assume the following project structure:
+
+```bash
+$ tree .
+.
+├── client.py               # Client application using `secaggplus_mod`
+├── server.py               # Server application using `SecAggPlusWorkflow`
+├── workflow_with_log.py    # Augmented `SecAggPlusWorkflow`
+├── run.sh                  # Quick start script
+├── pyproject.toml          # Project dependencies (poetry)
+└── requirements.txt        # Project dependencies (pip)
+```
+
+## Installing dependencies
+
+Project dependencies (such as and `flwr`) are defined in `pyproject.toml`. 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)), 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"
+```
+
+### 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
+```
+
+If you don't see any errors you're good to go!
+
+## Run the example with one command (recommended)
+
+```bash
+./run.sh
+```
+
+## Run the example with the simulation engine
+
+```bash
+flower-simulation --server-app server:app --client-app client:app --num-supernodes 5
+```
+
+## Alternatively, run the example (in 7 terminal windows)
+
+Start the Flower Superlink in one terminal window:
+
+```bash
+flower-superlink --insecure
+```
+
+Start 5 Flower `ClientApp` in 5 separate terminal windows:
+
+```bash
+flower-client-app client:app --insecure
+```
+
+Start the Flower `ServerApp`:
+
+```bash
+flower-server-app server:app --insecure --verbose
+```
+
+## Amend the example for practical usage
+
+For real-world applications, modify the `workflow` in `server.py` as follows:
+
+```python
+workflow = fl.server.workflow.DefaultWorkflow(
+    fit_workflow=SecAggPlusWorkflow(
+        num_shares=<number of shares>,
+        reconstruction_threshold=<reconstruction threshold>,
+    )
+)
+```
diff --git a/examples/app-secure-aggregation/client.py b/examples/app-secure-aggregation/client.py
new file mode 100644
index 000000000000..b2fd02ec00d4
--- /dev/null
+++ b/examples/app-secure-aggregation/client.py
@@ -0,0 +1,34 @@
+import time
+
+from flwr.client import ClientApp, NumPyClient
+from flwr.client.mod import secaggplus_mod
+import numpy as np
+
+
+# Define FlowerClient and client_fn
+class FlowerClient(NumPyClient):
+    def fit(self, parameters, config):
+        # Instead of training and returning model parameters,
+        # the client directly returns [1.0, 1.0, 1.0] for demonstration purposes.
+        ret_vec = [np.ones(3)]
+        # Force a significant delay for testing purposes
+        if "drop" in config and config["drop"]:
+            print(f"Client dropped for testing purposes.")
+            time.sleep(8)
+        else:
+            print(f"Client uploading {ret_vec[0]}...")
+        return ret_vec, 1, {}
+
+
+def client_fn(cid: str):
+    """Create and return an instance of Flower `Client`."""
+    return FlowerClient().to_client()
+
+
+# Flower ClientApp
+app = ClientApp(
+    client_fn=client_fn,
+    mods=[
+        secaggplus_mod,
+    ],
+)
diff --git a/examples/app-secure-aggregation/pyproject.toml b/examples/app-secure-aggregation/pyproject.toml
new file mode 100644
index 000000000000..84b6502064c8
--- /dev/null
+++ b/examples/app-secure-aggregation/pyproject.toml
@@ -0,0 +1,14 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "app-secure-aggregation"
+version = "0.1.0"
+description = "Flower Secure Aggregation example."
+authors = ["The Flower Authors <hello@flower.ai>"]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+# Mandatory dependencies
+flwr-nightly = { version = "1.8.0.dev20240309", extras = ["simulation"] }
diff --git a/examples/app-secure-aggregation/requirements.txt b/examples/app-secure-aggregation/requirements.txt
new file mode 100644
index 000000000000..5bac63a0d44c
--- /dev/null
+++ b/examples/app-secure-aggregation/requirements.txt
@@ -0,0 +1 @@
+flwr-nightly[simulation]==1.8.0.dev20240309
diff --git a/examples/app-secure-aggregation/run.sh b/examples/app-secure-aggregation/run.sh
new file mode 100755
index 000000000000..fa8dc47f26ef
--- /dev/null
+++ b/examples/app-secure-aggregation/run.sh
@@ -0,0 +1,34 @@
+#!/bin/bash
+# Kill any currently running client.py processes
+pkill -f 'flower-client-app'
+
+# Kill any currently running flower-superlink processes
+pkill -f 'flower-superlink'
+
+# Start the flower server
+echo "Starting flower server in background..."
+flower-superlink --insecure > /dev/null 2>&1 &
+sleep 2
+
+# Number of client processes to start
+N=5 # Replace with your desired value
+
+echo "Starting $N ClientApps in background..."
+
+# Start N client processes
+for i in $(seq 1 $N)
+do
+  flower-client-app --insecure client:app > /dev/null 2>&1 &
+  sleep 0.1
+done
+
+echo "Starting ServerApp..."
+flower-server-app --insecure server:app --verbose
+
+echo "Clearing background processes..."
+
+# Kill any currently running client.py processes
+pkill -f 'flower-client-app'
+
+# Kill any currently running flower-superlink processes
+pkill -f 'flower-superlink'
diff --git a/examples/app-secure-aggregation/server.py b/examples/app-secure-aggregation/server.py
new file mode 100644
index 000000000000..e9737a5a3c7f
--- /dev/null
+++ b/examples/app-secure-aggregation/server.py
@@ -0,0 +1,45 @@
+from flwr.common import Context
+from flwr.server import Driver, LegacyContext, ServerApp, ServerConfig
+from flwr.server.strategy import FedAvg
+from flwr.server.workflow import DefaultWorkflow, SecAggPlusWorkflow
+
+from workflow_with_log import SecAggPlusWorkflowWithLogs
+
+
+# Define strategy
+strategy = FedAvg(
+    fraction_fit=1.0,  # Select all available clients
+    fraction_evaluate=0.0,  # Disable evaluation
+    min_available_clients=5,
+)
+
+
+# Flower ServerApp
+app = ServerApp()
+
+
+@app.main()
+def main(driver: Driver, context: Context) -> None:
+    # Construct the LegacyContext
+    context = LegacyContext(
+        state=context.state,
+        config=ServerConfig(num_rounds=3),
+        strategy=strategy,
+    )
+
+    # Create the workflow
+    workflow = DefaultWorkflow(
+        fit_workflow=SecAggPlusWorkflowWithLogs(
+            num_shares=3,
+            reconstruction_threshold=2,
+            timeout=5,
+        )
+        # # For real-world applications, use the following code instead
+        # fit_workflow=SecAggPlusWorkflow(
+        #     num_shares=<number of shares>,
+        #     reconstruction_threshold=<reconstruction threshold>,
+        # )
+    )
+
+    # Execute
+    workflow(driver, context)
diff --git a/examples/app-secure-aggregation/workflow_with_log.py b/examples/app-secure-aggregation/workflow_with_log.py
new file mode 100644
index 000000000000..a03ff8c13b6c
--- /dev/null
+++ b/examples/app-secure-aggregation/workflow_with_log.py
@@ -0,0 +1,92 @@
+from flwr.common import Context, log, parameters_to_ndarrays
+from logging import INFO
+from flwr.server import Driver, LegacyContext
+from flwr.server.workflow.secure_aggregation.secaggplus_workflow import (
+    SecAggPlusWorkflow,
+    WorkflowState,
+)
+import numpy as np
+from flwr.common.secure_aggregation.quantization import quantize
+from flwr.server.workflow.constant import MAIN_PARAMS_RECORD
+import flwr.common.recordset_compat as compat
+
+
+class SecAggPlusWorkflowWithLogs(SecAggPlusWorkflow):
+    """The SecAggPlusWorkflow augmented for this example.
+
+    This class includes additional logging and modifies one of the FitIns to instruct
+    the target client to simulate a dropout.
+    """
+
+    node_ids = []
+
+    def __call__(self, driver: Driver, context: Context) -> None:
+        _quantized = quantize(
+            [np.ones(3) for _ in range(5)], self.clipping_range, self.quantization_range
+        )
+        log(INFO, "")
+        log(
+            INFO,
+            "################################ Introduction ################################",
+        )
+        log(
+            INFO,
+            "In the example, each client will upload a vector [1.0, 1.0, 1.0] instead of",
+        )
+        log(INFO, "model updates for demonstration purposes.")
+        log(
+            INFO,
+            "Client 0 is configured to drop out before uploading the masked vector.",
+        )
+        log(INFO, "After quantization, the raw vectors will look like:")
+        for i in range(1, 5):
+            log(INFO, "\t%s from Client %s", _quantized[i], i)
+        log(
+            INFO,
+            "Numbers are rounded to integers stochastically during the quantization",
+        )
+        log(INFO, ", and thus entries may not be identical.")
+        log(
+            INFO,
+            "The above raw vectors are hidden from the driver through adding masks.",
+        )
+        log(INFO, "")
+        log(
+            INFO,
+            "########################## Secure Aggregation Start ##########################",
+        )
+
+        super().__call__(driver, context)
+
+        paramsrecord = context.state.parameters_records[MAIN_PARAMS_RECORD]
+        parameters = compat.parametersrecord_to_parameters(paramsrecord, True)
+        ndarrays = parameters_to_ndarrays(parameters)
+        log(
+            INFO,
+            "Weighted average of vectors (dequantized): %s",
+            ndarrays[0],
+        )
+        log(
+            INFO,
+            "########################### Secure Aggregation End ###########################",
+        )
+        log(INFO, "")
+
+    def setup_stage(
+        self, driver: Driver, context: LegacyContext, state: WorkflowState
+    ) -> bool:
+        ret = super().setup_stage(driver, context, state)
+        self.node_ids = list(state.active_node_ids)
+        state.nid_to_fitins[self.node_ids[0]].configs_records["fitins.config"][
+            "drop"
+        ] = True
+        return ret
+
+    def collect_masked_vectors_stage(
+        self, driver: Driver, context: LegacyContext, state: WorkflowState
+    ) -> bool:
+        ret = super().collect_masked_vectors_stage(driver, context, state)
+        for node_id in state.sampled_node_ids - state.active_node_ids:
+            log(INFO, "Client %s dropped out.", self.node_ids.index(node_id))
+        log(INFO, "Obtained sum of masked vectors: %s", state.aggregate_ndarrays[1])
+        return ret
diff --git a/examples/custom-metrics/README.md b/examples/custom-metrics/README.md
new file mode 100644
index 000000000000..317fb6336106
--- /dev/null
+++ b/examples/custom-metrics/README.md
@@ -0,0 +1,106 @@
+# Flower Example using Custom Metrics
+
+This simple example demonstrates how to calculate custom metrics over multiple clients beyond the traditional ones available in the ML frameworks. In this case, it demonstrates the use of ready-available `scikit-learn` metrics: accuracy, recall, precision, and f1-score.
+
+Once both the test values (`y_test`) and the predictions (`y_pred`) are available on the client side (`client.py`), other metrics or custom ones are possible to be calculated.
+
+The main takeaways of this implementation are:
+
+- the use of the `output_dict` on the client side - inside `evaluate` method on `client.py`
+- the use of the `evaluate_metrics_aggregation_fn` - to aggregate the metrics on the server side, part of the `strategy` on `server.py`
+
+This example is based on the `quickstart-tensorflow` with CIFAR-10, source [here](https://flower.ai/docs/quickstart-tensorflow.html), with the addition of [Flower Datasets](https://flower.ai/docs/datasets/index.html) to retrieve the CIFAR-10.
+
+Using the CIFAR-10 dataset for classification, this is a multi-class classification problem, thus some changes on how to calculate the metrics using `average='micro'` and `np.argmax` is required. For binary classification, this is not required. Also, for unsupervised learning tasks, such as using a deep autoencoder, a custom metric based on reconstruction error could be implemented on client side.
+
+## 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/custom-metrics . && rm -rf flower && cd custom-metrics
+```
+
+This will create a new directory called `custom-metrics` containing the following files:
+
+```shell
+-- pyproject.toml
+-- requirements.txt
+-- client.py
+-- server.py
+-- run.sh
+-- README.md
+```
+
+### Installing Dependencies
+
+Project dependencies (such as `scikit-learn`, `tensorflow` 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
+
+```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
+python -m venv venv
+source venv/bin/activate
+pip install -r requirements.txt
+```
+
+## Run Federated Learning with Custom Metrics
+
+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
+python 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
+python client.py
+```
+
+Alternatively you can run all of it in one shell as follows:
+
+```shell
+python server.py &
+# Wait for a few seconds to give the server enough time to start, then:
+python client.py &
+python client.py
+```
+
+or
+
+```shell
+chmod +x run.sh
+./run.sh
+```
+
+You will see that Keras is starting a federated training. Have a look to the [Flower Quickstarter documentation](https://flower.ai/docs/quickstart-tensorflow.html) for a detailed explanation. You can add `steps_per_epoch=3` to `model.fit()` if you just want to evaluate that everything works without having to wait for the client-side training to finish (this will save you a lot of time during development).
+
+Running `run.sh` will result in the following output (after 3 rounds):
+
+```shell
+INFO flwr 2024-01-17 17:45:23,794 | app.py:228 | app_fit: metrics_distributed {
+    'accuracy': [(1, 0.10000000149011612), (2, 0.10000000149011612), (3, 0.3393000066280365)],
+    'acc': [(1, 0.1), (2, 0.1), (3, 0.3393)],
+    'rec': [(1, 0.1), (2, 0.1), (3, 0.3393)],
+    'prec': [(1, 0.1), (2, 0.1), (3, 0.3393)],
+    'f1': [(1, 0.10000000000000002), (2, 0.10000000000000002), (3, 0.3393)]
+}
+```
diff --git a/examples/custom-metrics/client.py b/examples/custom-metrics/client.py
new file mode 100644
index 000000000000..6a194e92cdce
--- /dev/null
+++ b/examples/custom-metrics/client.py
@@ -0,0 +1,73 @@
+import os
+
+import flwr as fl
+import numpy as np
+import tensorflow as tf
+from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score
+from flwr_datasets import FederatedDataset
+
+
+# Make TensorFlow log less verbose
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+
+# Load model (MobileNetV2)
+model = tf.keras.applications.MobileNetV2((32, 32, 3), classes=10, weights=None)
+model.compile("adam", "sparse_categorical_crossentropy", metrics=["accuracy"])
+
+# Load data with Flower Datasets (CIFAR-10)
+fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
+train = fds.load_split("train")
+test = fds.load_split("test")
+
+# Using Numpy format
+train_np = train.with_format("numpy")
+test_np = test.with_format("numpy")
+x_train, y_train = train_np["img"], train_np["label"]
+x_test, y_test = test_np["img"], test_np["label"]
+
+
+# Method for extra learning metrics calculation
+def eval_learning(y_test, y_pred):
+    acc = accuracy_score(y_test, y_pred)
+    rec = recall_score(
+        y_test, y_pred, average="micro"
+    )  # average argument required for multi-class
+    prec = precision_score(y_test, y_pred, average="micro")
+    f1 = f1_score(y_test, y_pred, average="micro")
+    return acc, rec, prec, f1
+
+
+# Define Flower client
+class FlowerClient(fl.client.NumPyClient):
+    def get_parameters(self, config):
+        return model.get_weights()
+
+    def fit(self, parameters, config):
+        model.set_weights(parameters)
+        model.fit(x_train, y_train, epochs=1, batch_size=32)
+        return model.get_weights(), len(x_train), {}
+
+    def evaluate(self, parameters, config):
+        model.set_weights(parameters)
+        loss, accuracy = model.evaluate(x_test, y_test)
+        y_pred = model.predict(x_test)
+        y_pred = np.argmax(y_pred, axis=1).reshape(
+            -1, 1
+        )  # MobileNetV2 outputs 10 possible classes, argmax returns just the most probable
+
+        acc, rec, prec, f1 = eval_learning(y_test, y_pred)
+        output_dict = {
+            "accuracy": accuracy,  # accuracy from tensorflow model.evaluate
+            "acc": acc,
+            "rec": rec,
+            "prec": prec,
+            "f1": f1,
+        }
+        return loss, len(x_test), output_dict
+
+
+# Start Flower client
+fl.client.start_client(
+    server_address="127.0.0.1:8080", client=FlowerClient().to_client()
+)
diff --git a/examples/custom-metrics/pyproject.toml b/examples/custom-metrics/pyproject.toml
new file mode 100644
index 000000000000..51c29e213d81
--- /dev/null
+++ b/examples/custom-metrics/pyproject.toml
@@ -0,0 +1,19 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "custom-metrics"
+version = "0.1.0"
+description = "Federated Learning with Flower and Custom Metrics"
+authors = [
+	"The Flower Authors <hello@flower.ai>",
+	"Gustavo Bertoli <gubertoli -at- gmail.com>",
+]
+
+[tool.poetry.dependencies]
+python = ">=3.8,<3.11"
+flwr = ">=1.0,<2.0"
+flwr-datasets = { version = "*", extras = ["vision"] }
+scikit-learn = "^1.2.2"
+tensorflow = "==2.12.0"
diff --git a/examples/custom-metrics/requirements.txt b/examples/custom-metrics/requirements.txt
new file mode 100644
index 000000000000..69d867c5f287
--- /dev/null
+++ b/examples/custom-metrics/requirements.txt
@@ -0,0 +1,4 @@
+flwr>=1.0,<2.0
+flwr-datasets[vision]
+scikit-learn>=1.2.2
+tensorflow==2.12.0
diff --git a/examples/custom-metrics/run.sh b/examples/custom-metrics/run.sh
new file mode 100755
index 000000000000..c64f362086aa
--- /dev/null
+++ b/examples/custom-metrics/run.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+
+echo "Starting server"
+python server.py &
+sleep 3  # Sleep for 3s to give the server enough time to start
+
+for i in `seq 0 1`; do
+    echo "Starting client $i"
+    python client.py &
+done
+
+# This will allow you to use CTRL+C to stop all background processes
+trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM
+# Wait for all background processes to complete
+wait
diff --git a/examples/custom-metrics/server.py b/examples/custom-metrics/server.py
new file mode 100644
index 000000000000..f8420bf51f16
--- /dev/null
+++ b/examples/custom-metrics/server.py
@@ -0,0 +1,58 @@
+import flwr as fl
+import numpy as np
+
+
+# Define metrics aggregation function
+def average_metrics(metrics):
+    """Aggregate metrics from multiple clients by calculating mean averages.
+
+    Parameters:
+    - metrics (list): A list containing tuples, where each tuple represents metrics for a client.
+                    Each tuple is structured as (num_examples, metric), where:
+                    - num_examples (int): The number of examples used to compute the metrics.
+                    - metric (dict): A dictionary containing custom metrics provided as `output_dict`
+                                    in the `evaluate` method from `client.py`.
+
+    Returns:
+    A dictionary with the aggregated metrics, calculating mean averages. The keys of the
+    dictionary represent different metrics, including:
+    - 'accuracy': Mean accuracy calculated by TensorFlow.
+    - 'acc': Mean accuracy from scikit-learn.
+    - 'rec': Mean recall from scikit-learn.
+    - 'prec': Mean precision from scikit-learn.
+    - 'f1': Mean F1 score from scikit-learn.
+
+    Note: If a weighted average is required, the `num_examples` parameter can be leveraged.
+
+    Example:
+        Example `metrics` list for two clients after the last round:
+        [(10000, {'prec': 0.108, 'acc': 0.108, 'f1': 0.108, 'accuracy': 0.1080000028014183, 'rec': 0.108}),
+        (10000, {'f1': 0.108, 'rec': 0.108, 'accuracy': 0.1080000028014183, 'prec': 0.108, 'acc': 0.108})]
+    """
+
+    # Here num_examples are not taken into account by using _
+    accuracies_tf = np.mean([metric["accuracy"] for _, metric in metrics])
+    accuracies = np.mean([metric["acc"] for _, metric in metrics])
+    recalls = np.mean([metric["rec"] for _, metric in metrics])
+    precisions = np.mean([metric["prec"] for _, metric in metrics])
+    f1s = np.mean([metric["f1"] for _, metric in metrics])
+
+    return {
+        "accuracy": accuracies_tf,
+        "acc": accuracies,
+        "rec": recalls,
+        "prec": precisions,
+        "f1": f1s,
+    }
+
+
+# Define strategy and the custom aggregation function for the evaluation metrics
+strategy = fl.server.strategy.FedAvg(evaluate_metrics_aggregation_fn=average_metrics)
+
+
+# Start Flower server
+fl.server.start_server(
+    server_address="0.0.0.0:8080",
+    config=fl.server.ServerConfig(num_rounds=3),
+    strategy=strategy,
+)
diff --git a/examples/custom-mods/.gitignore b/examples/custom-mods/.gitignore
new file mode 100644
index 000000000000..260d28a67c6f
--- /dev/null
+++ b/examples/custom-mods/.gitignore
@@ -0,0 +1,2 @@
+wandb/
+.runs_history/
diff --git a/examples/custom-mods/README.md b/examples/custom-mods/README.md
new file mode 100644
index 000000000000..b0ad668c2dec
--- /dev/null
+++ b/examples/custom-mods/README.md
@@ -0,0 +1,339 @@
+# Using custom mods 🧪
+
+> 🧪 = 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 write custom Flower Mods and use them in a simple example.
+
+## Writing custom Flower Mods
+
+### Flower Mods basics
+
+As described [here](https://flower.ai/docs/framework/how-to-use-built-in-mods.html#what-are-mods), Flower Mods in their simplest form can be described as:
+
+```python
+def basic_mod(msg: Message, context: Context, app: ClientApp) -> Message:
+    # Do something with incoming Message (or Context)
+    # before passing to the inner ``ClientApp``
+    reply = app(msg, context)
+    # Do something with outgoing Message (or Context)
+    # before returning
+    return reply
+```
+
+and used when defining the `ClientApp`:
+
+```python
+app = fl.client.ClientApp(
+    client_fn=client_fn,
+    mods=[basic_mod],
+)
+```
+
+Note that in this specific case, this mod won't modify anything, and perform FL as usual.
+
+### WandB Flower Mod
+
+If we want to write a mod to monitor our client-side training using [Weights & Biases](https://github.com/wandb/wandb), we can follow the steps below.
+
+First, we need to initialize our W&B project with the correct parameters:
+
+```python
+wandb.init(
+    project=...,
+    group=...,
+    name=...,
+    id=...,
+    resume="allow",
+    reinit=True,
+)
+```
+
+In our case, the group should be the `run_id`, specific to a `ServerApp` run, and the `name` should be the `node_id`. This will make it easy to navigate our W&B project, as for each run we will be able to see the computed results as a whole or for each individual client.
+
+The `id` needs to be unique, so it will be a combination of `run_id` and `node_id`.
+
+In the end we have:
+
+```python
+def wandb_mod(msg: Message, context: Context, app: ClientAppCallable) -> Message:
+    run_id = msg.metadata.run_id
+    group_name = f"Run ID: {run_id}"
+
+    node_id = str(msg.metadata.dst_node_id)
+    run_name = f"Node ID: {node_id}"
+
+    wandb.init(
+        project="Mod Name",
+        group=group_name,
+        name=run_name,
+        id=f"{run_id}_{node_id}",
+        resume="allow",
+        reinit=True,
+    )
+```
+
+Now, before the message is processed by the server, we will store the starting time and the round number, in order to compute the time it took the client to perform its fit step.
+
+```python
+server_round = int(msg.metadata.group_id)
+start_time = time.time()
+```
+
+And then, we can send the message to the client:
+
+```python
+reply = app(msg, context)
+```
+
+And now, with the message we got back, we can gather our metrics:
+
+```python
+if reply.metadata.message_type == MessageType.TRAIN and reply.has_content():
+
+    time_diff = time.time() - start_time
+
+    metrics = reply.content.configs_records
+
+    results_to_log = dict(metrics.get("fitres.metrics", ConfigsRecord()))
+    results_to_log["fit_time"] = time_diff
+```
+
+Note that we store our metrics in the `results_to_log` variable and that we only initialize this variable when our client is sending back fit results (with content in it).
+
+Finally, we can send our results to W&B using:
+
+```python
+wandb.log(results_to_log, step=int(server_round), commit=True)
+```
+
+The complete mod becomes:
+
+```python
+def wandb_mod(msg: Message, context: Context, app: ClientAppCallable) -> Message:
+    server_round = int(msg.metadata.group_id)
+
+    if reply.metadata.message_type == MessageType.TRAIN and server_round == 1:
+        run_id = msg.metadata.run_id
+        group_name = f"Run ID: {run_id}"
+
+        node_id = str(msg.metadata.dst_node_id)
+        run_name = f"Node ID: {node_id}"
+
+        wandb.init(
+            project="Mod Name",
+            group=group_name,
+            name=run_name,
+            id=f"{run_id}_{node_id}",
+            resume="allow",
+            reinit=True,
+        )
+
+    start_time = time.time()
+
+    reply = app(msg, context)
+
+    if reply.metadata.message_type == MessageType.TRAIN and reply.has_content():
+
+        time_diff = time.time() - start_time
+
+        metrics = reply.content.configs_records
+
+        results_to_log = dict(metrics.get("fitres.metrics", ConfigsRecord()))
+
+        results_to_log["fit_time"] = time_diff
+
+        wandb.log(results_to_log, step=int(server_round), commit=True)
+
+    return reply
+```
+
+And it can be used like:
+
+```python
+app = fl.client.ClientApp(
+    client_fn=client_fn,
+    mods=[wandb_mod],
+)
+```
+
+If we want to pass an argument to our mod, we can use a wrapper function:
+
+```python
+def get_wandb_mod(name: str) -> Mod:
+    def wandb_mod(msg: Message, context: Context, app: ClientAppCallable) -> Message:
+        server_round = int(msg.metadata.group_id)
+
+        run_id = msg.metadata.run_id
+        group_name = f"Run ID: {run_id}"
+
+        node_id = str(msg.metadata.dst_node_id)
+        run_name = f"Node ID: {node_id}"
+
+        wandb.init(
+            project=name,
+            group=group_name,
+            name=run_name,
+            id=f"{run_id}_{node_id}",
+            resume="allow",
+            reinit=True,
+        )
+
+        start_time = time.time()
+
+        reply = app(msg, context)
+
+        if reply.metadata.message_type == MessageType.TRAIN and reply.has_content():
+
+            time_diff = time.time() - start_time
+
+            metrics = reply.content.configs_records
+
+            results_to_log = dict(metrics.get("fitres.metrics", ConfigsRecord()))
+
+            results_to_log["fit_time"] = time_diff
+
+            wandb.log(results_to_log, step=int(server_round), commit=True)
+
+        return reply
+
+    return wandb_mod
+```
+
+And use it like:
+
+```python
+app = fl.client.ClientApp(
+    client_fn=client_fn,
+    mods=[
+        get_wandb_mod("Custom mods example"),
+     ],
+)
+```
+
+### TensorBoard Flower Mod
+
+The [TensorBoard](https://www.tensorflow.org/tensorboard) Mod will only differ in the initialization and how the data is sent to TensorBoard:
+
+```python
+def get_tensorboard_mod(logdir) -> Mod:
+    os.makedirs(logdir, exist_ok=True)
+
+    def tensorboard_mod(
+        msg: Message, context: Context, app: ClientAppCallable
+    ) -> Message:
+        logdir_run = os.path.join(logdir, str(msg.metadata.run_id))
+
+        node_id = str(msg.metadata.dst_node_id)
+
+        server_round = int(msg.metadata.group_id)
+
+        start_time = time.time()
+
+        reply = app(msg, context)
+
+        time_diff = time.time() - start_time
+
+        if reply.metadata.message_type == MessageType.TRAIN and reply.has_content():
+            writer = tf.summary.create_file_writer(os.path.join(logdir_run, node_id))
+
+            metrics = dict(
+                reply.content.configs_records.get("fitres.metrics", ConfigsRecord())
+            )
+
+            with writer.as_default(step=server_round):
+                tf.summary.scalar(f"fit_time", time_diff, step=server_round)
+                for metric in metrics:
+                    tf.summary.scalar(
+                        f"{metric}",
+                        metrics[metric],
+                        step=server_round,
+                    )
+                writer.flush()
+
+        return reply
+
+    return tensorboard_mod
+```
+
+For the initialization, TensorBoard uses a custom directory path, which can, in this case, be passed as an argument to the wrapper function.
+
+It can be used in the following way:
+
+```python
+app = fl.client.ClientApp(
+    client_fn=client_fn,
+    mods=[get_tensorboard_mod(".runs_history/")],
+)
+```
+
+## Running the example
+
+### Preconditions
+
+Let's assume the following project structure:
+
+```bash
+$ tree .
+.
+├── client.py           # <-- contains `ClientApp`
+├── server.py           # <-- contains `ServerApp`
+├── task.py             # <-- task-specific code (model, data)
+└── requirements.txt    # <-- dependencies
+```
+
+### Install dependencies
+
+```bash
+pip install -r requirements.txt
+```
+
+For [W&B](wandb.ai) you will also need a valid account.
+
+### Start the long-running Flower server (SuperLink)
+
+```bash
+flower-superlink --insecure
+```
+
+### Start the long-running Flower client (SuperNode)
+
+In a new terminal window, start the first long-running Flower client using:
+
+```bash
+flower-client-app client:wandb_app --insecure
+```
+
+for W&B monitoring, or:
+
+```bash
+flower-client-app client:tb_app --insecure
+```
+
+for TensorBoard.
+
+In yet another new terminal window, start the second long-running Flower client (with the mod of your choice):
+
+```bash
+flower-client-app client:{wandb,tb}_app --insecure
+```
+
+### 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
+```
+
+### Check the results
+
+For W&B, you will need to login to the [website](wandb.ai).
+
+For TensorBoard, you will need to run the following command in your terminal:
+
+```sh
+tensorboard --logdir <LOG_DIR>
+```
+
+Where `<LOG_DIR>` needs to be replaced by the directory passed as an argument to the wrapper function (`.runs_history/` by default).
diff --git a/examples/custom-mods/client.py b/examples/custom-mods/client.py
new file mode 100644
index 000000000000..2b87a24da19d
--- /dev/null
+++ b/examples/custom-mods/client.py
@@ -0,0 +1,160 @@
+import logging
+import os
+import time
+
+import flwr as fl
+import tensorflow as tf
+import wandb
+from flwr.common import ConfigsRecord
+from flwr.client.typing import ClientAppCallable, Mod
+from flwr.common.context import Context
+from flwr.common.message import Message
+from flwr.common.constant import MessageType
+
+from task import (
+    Net,
+    DEVICE,
+    load_data,
+    get_parameters,
+    set_parameters,
+    train,
+    test,
+)
+
+
+class WBLoggingFilter(logging.Filter):
+    def filter(self, record):
+        return (
+            "login" in record.getMessage()
+            or "View project at" in record.getMessage()
+            or "View run at" in record.getMessage()
+        )
+
+
+# Load model and data (simple CNN, CIFAR-10)
+net = Net().to(DEVICE)
+trainloader, testloader = load_data()
+
+
+# Define Flower client
+class FlowerClient(fl.client.NumPyClient):
+    def get_parameters(self, config):
+        return get_parameters(net)
+
+    def fit(self, parameters, config):
+        set_parameters(net, parameters)
+        results = train(net, trainloader, testloader, epochs=1, device=DEVICE)
+        return get_parameters(net), len(trainloader.dataset), results
+
+    def evaluate(self, parameters, config):
+        set_parameters(net, parameters)
+        loss, accuracy = test(net, testloader)
+        return loss, len(testloader.dataset), {"accuracy": accuracy}
+
+
+def client_fn(cid: str):
+    return FlowerClient().to_client()
+
+
+def get_wandb_mod(name: str) -> Mod:
+    def wandb_mod(msg: Message, context: Context, app: ClientAppCallable) -> Message:
+        """Flower Mod that logs the metrics dictionary returned by the client's fit
+        function to Weights & Biases."""
+        server_round = int(msg.metadata.group_id)
+
+        if server_round == 1 and msg.metadata.message_type == MessageType.TRAIN:
+            run_id = msg.metadata.run_id
+            group_name = f"Run ID: {run_id}"
+
+            node_id = str(msg.metadata.dst_node_id)
+            run_name = f"Node ID: {node_id}"
+
+            wandb.init(
+                project=name,
+                group=group_name,
+                name=run_name,
+                id=f"{run_id}_{node_id}",
+                resume="allow",
+                reinit=True,
+            )
+
+        start_time = time.time()
+
+        reply = app(msg, context)
+
+        time_diff = time.time() - start_time
+
+        # if the `ClientApp` just processed a "fit" message, let's log some metrics to W&B
+        if reply.metadata.message_type == MessageType.TRAIN and reply.has_content():
+
+            metrics = reply.content.configs_records
+
+            results_to_log = dict(metrics.get("fitres.metrics", ConfigsRecord()))
+
+            results_to_log["fit_time"] = time_diff
+
+            wandb.log(results_to_log, step=int(server_round), commit=True)
+
+        return reply
+
+    return wandb_mod
+
+
+def get_tensorboard_mod(logdir) -> Mod:
+    os.makedirs(logdir, exist_ok=True)
+
+    def tensorboard_mod(
+        msg: Message, context: Context, app: ClientAppCallable
+    ) -> Message:
+        """Flower Mod that logs the metrics dictionary returned by the client's fit
+        function to TensorBoard."""
+        logdir_run = os.path.join(logdir, str(msg.metadata.run_id))
+
+        node_id = str(msg.metadata.dst_node_id)
+
+        server_round = int(msg.metadata.group_id)
+
+        start_time = time.time()
+
+        reply = app(msg, context)
+
+        time_diff = time.time() - start_time
+
+        # if the `ClientApp` just processed a "fit" message, let's log some metrics to TensorBoard
+        if reply.metadata.message_type == MessageType.TRAIN and reply.has_content():
+            writer = tf.summary.create_file_writer(os.path.join(logdir_run, node_id))
+
+            metrics = dict(
+                reply.content.configs_records.get("fitres.metrics", ConfigsRecord())
+            )
+
+            with writer.as_default(step=server_round):
+                tf.summary.scalar(f"fit_time", time_diff, step=server_round)
+                for metric in metrics:
+                    tf.summary.scalar(
+                        f"{metric}",
+                        metrics[metric],
+                        step=server_round,
+                    )
+                writer.flush()
+
+        return reply
+
+    return tensorboard_mod
+
+
+# Run via `flower-client-app client:wandb_app`
+wandb_app = fl.client.ClientApp(
+    client_fn=client_fn,
+    mods=[
+        get_wandb_mod("Custom mods example"),
+    ],
+)
+
+# Run via `flower-client-app client:tb_app`
+tb_app = fl.client.ClientApp(
+    client_fn=client_fn,
+    mods=[
+        get_tensorboard_mod(".runs_history/"),
+    ],
+)
diff --git a/examples/mt-pytorch/pyproject.toml b/examples/custom-mods/pyproject.toml
similarity index 62%
rename from examples/mt-pytorch/pyproject.toml
rename to examples/custom-mods/pyproject.toml
index 4978035495ea..e690e05bab8f 100644
--- a/examples/mt-pytorch/pyproject.toml
+++ b/examples/custom-mods/pyproject.toml
@@ -3,14 +3,16 @@ requires = ["poetry-core>=1.4.0"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
-name = "mt-pytorch"
+name = "app-pytorch"
 version = "0.1.0"
 description = "Multi-Tenant Federated Learning with Flower and PyTorch"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
-flwr-nightly = {version = ">=1.0,<2.0", extras = ["rest", "simulation"]}
+flwr = { path = "../../", develop = true, extras = ["simulation"] }
+tensorboard = "2.16.2"
 torch = "1.13.1"
 torchvision = "0.14.1"
 tqdm = "4.65.0"
+wandb = "0.16.3"
diff --git a/examples/mt-pytorch/requirements.txt b/examples/custom-mods/requirements.txt
similarity index 72%
rename from examples/mt-pytorch/requirements.txt
rename to examples/custom-mods/requirements.txt
index ae0a65386f2b..75b2c1135f11 100644
--- a/examples/mt-pytorch/requirements.txt
+++ b/examples/custom-mods/requirements.txt
@@ -1,4 +1,6 @@
 flwr-nightly[rest,simulation]>=1.0, <2.0
+tensorboard==2.16.2
 torch==1.13.1
 torchvision==0.14.1
 tqdm==4.65.0
+wandb==0.16.3
diff --git a/examples/mt-pytorch/start_driver.py b/examples/custom-mods/server.py
similarity index 68%
rename from examples/mt-pytorch/start_driver.py
rename to examples/custom-mods/server.py
index 3241a548950a..c2d8a4fe5ee7 100644
--- a/examples/mt-pytorch/start_driver.py
+++ b/examples/custom-mods/server.py
@@ -9,12 +9,16 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
     examples = [num_examples for num_examples, _ in metrics]
 
     # Multiply accuracy of each client by number of examples used
-    train_losses = [num_examples * m["train_loss"] for num_examples, m in metrics]
+    train_losses = [
+        num_examples * float(m["train_loss"]) for num_examples, m in metrics
+    ]
     train_accuracies = [
-        num_examples * m["train_accuracy"] for num_examples, m in metrics
+        num_examples * float(m["train_accuracy"]) for num_examples, m in metrics
+    ]
+    val_losses = [num_examples * float(m["val_loss"]) for num_examples, m in metrics]
+    val_accuracies = [
+        num_examples * float(m["val_accuracy"]) for num_examples, m in metrics
     ]
-    val_losses = [num_examples * m["val_loss"] for num_examples, m in metrics]
-    val_accuracies = [num_examples * m["val_accuracy"] for num_examples, m in metrics]
 
     # Aggregate and return custom metric (weighted average)
     return {
@@ -33,9 +37,9 @@ def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
     fit_metrics_aggregation_fn=weighted_average,
 )
 
-# Start Flower server
-fl.driver.start_driver(
-    server_address="0.0.0.0:9091",
+
+# Run via `flower-server-app server:app`
+app = fl.server.ServerApp(
     config=fl.server.ServerConfig(num_rounds=3),
     strategy=strategy,
 )
diff --git a/examples/mt-pytorch/task.py b/examples/custom-mods/task.py
similarity index 100%
rename from examples/mt-pytorch/task.py
rename to examples/custom-mods/task.py
diff --git a/examples/doc/source/_templates/base.html b/examples/doc/source/_templates/base.html
index e4fe80720b74..08030fb08c15 100644
--- a/examples/doc/source/_templates/base.html
+++ b/examples/doc/source/_templates/base.html
@@ -5,7 +5,7 @@
     <meta charset="utf-8"/>
     <meta name="viewport" content="width=device-width,initial-scale=1"/>
     <meta name="color-scheme" content="light dark">
-    <link rel="canonical" href="https://flower.dev/docs/examples/{{ pagename }}.html">
+    <link rel="canonical" href="https://flower.ai/docs/examples/{{ pagename }}.html">
 
     {%- if metatags %}{{ metatags }}{% endif -%}
 
@@ -99,6 +99,6 @@
     {%- endblock -%}
 
     {%- endblock scripts -%}
-    <script defer data-domain="flower.dev" data-api="/static/pla/api/event" src="/static/pla/js/script.js"></script>
+    <script defer data-domain="flower.ai" data-api="/static/pla/api/event" src="/static/pla/js/script.js"></script>
   </body>
 </html>
diff --git a/examples/doc/source/conf.py b/examples/doc/source/conf.py
index 01cbb48c1587..bf177aa5ae24 100644
--- a/examples/doc/source/conf.py
+++ b/examples/doc/source/conf.py
@@ -22,12 +22,15 @@
 
 # -- Project information -----------------------------------------------------
 
+import datetime
+
+
 project = "Flower"
-copyright = "2022 Flower Labs GmbH"
+copyright = f"{datetime.date.today().year} Flower Labs GmbH"
 author = "The Flower Authors"
 
 # The full version, including alpha/beta/rc tags
-release = "1.7.0"
+release = "1.8.0"
 
 
 # -- General configuration ---------------------------------------------------
@@ -73,7 +76,7 @@
 html_title = f"Flower Examples {release}"
 html_logo = "_static/flower-logo.png"
 html_favicon = "_static/favicon.ico"
-html_baseurl = "https://flower.dev/docs/examples/"
+html_baseurl = "https://flower.ai/docs/examples/"
 
 html_theme_options = {
     #
diff --git a/examples/dp-sgd-mnist/pyproject.toml b/examples/dp-sgd-mnist/pyproject.toml
index 3158ab3d52f6..161952fd2aa4 100644
--- a/examples/dp-sgd-mnist/pyproject.toml
+++ b/examples/dp-sgd-mnist/pyproject.toml
@@ -7,14 +7,14 @@ name = "dp-sgd-mnist"
 version = "0.1.0"
 description = "Federated training with Tensorflow Privacy"
 authors = [
-    "The Flower Authors <hello@flower.dev>",
-    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>"
+    "The Flower Authors <hello@flower.ai>",
+    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>",
 ]
 
 [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-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/embedded-devices/Dockerfile b/examples/embedded-devices/Dockerfile
index ea63839bc9d6..a85c05c4bb7a 100644
--- a/examples/embedded-devices/Dockerfile
+++ b/examples/embedded-devices/Dockerfile
@@ -8,6 +8,7 @@ RUN pip3 install --upgrade pip
 
 # Install flower
 RUN pip3 install flwr>=1.0
+RUN pip3 install flwr-datsets>=0.2
 RUN pip3 install tqdm==4.65.0
 
 WORKDIR /client
diff --git a/examples/embedded-devices/README.md b/examples/embedded-devices/README.md
index 4c79eafbbf84..f1c5931b823a 100644
--- a/examples/embedded-devices/README.md
+++ b/examples/embedded-devices/README.md
@@ -192,7 +192,8 @@ On the machine of your choice, launch the server:
 # Launch your server.
 # Will wait for at least 2 clients to be connected, then will train for 3 FL rounds
 # The command below will sample all clients connected (since sample_fraction=1.0)
-python server.py --rounds 3 --min_num_clients 2 --sample_fraction 1.0 # append `--mnist` if you want to use that dataset/model setting
+# The server is dataset agnostic (use the same command for MNIST and CIFAR10)
+python server.py --rounds 3 --min_num_clients 2 --sample_fraction 1.0
 ```
 
 > If you are on macOS with Apple Silicon (i.e. M1, M2 chips), you might encounter a `grpcio`-related issue when launching your server. If you are in a conda environment you can solve this easily by doing: `pip uninstall grpcio` and then `conda install grpcio`.
diff --git a/examples/embedded-devices/client_pytorch.py b/examples/embedded-devices/client_pytorch.py
index 5d236c9e9389..6bd69c16567e 100644
--- a/examples/embedded-devices/client_pytorch.py
+++ b/examples/embedded-devices/client_pytorch.py
@@ -6,18 +6,19 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from torch.utils.data import DataLoader, random_split
-from torchvision.datasets import CIFAR10, MNIST
+from torch.utils.data import DataLoader
 from torchvision.transforms import Compose, Normalize, ToTensor
 from torchvision.models import mobilenet_v3_small
 from tqdm import tqdm
 
+from flwr_datasets import FederatedDataset
+
 parser = argparse.ArgumentParser(description="Flower Embedded devices")
 parser.add_argument(
     "--server_address",
     type=str,
     default="0.0.0.0:8080",
-    help=f"gRPC server address (deafault '0.0.0.0:8080')",
+    help=f"gRPC server address (default '0.0.0.0:8080')",
 )
 parser.add_argument(
     "--cid",
@@ -28,25 +29,13 @@
 parser.add_argument(
     "--mnist",
     action="store_true",
-    help="If you use Raspberry Pi Zero clients (which just have 512MB or RAM) use MNIST",
+    help="If you use Raspberry Pi Zero clients (which just have 512MB or RAM) use "
+    "MNIST",
 )
 
-
 warnings.filterwarnings("ignore", category=UserWarning)
 NUM_CLIENTS = 50
 
-# a config for mobilenetv2 that works for
-# small input sizes (i.e. 32x32 as in CIFAR)
-mb2_cfg = [
-    (1, 16, 1, 1),
-    (6, 24, 2, 1),
-    (6, 32, 3, 2),
-    (6, 64, 4, 2),
-    (6, 96, 3, 1),
-    (6, 160, 3, 2),
-    (6, 320, 1, 1),
-]
-
 
 class Net(nn.Module):
     """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz')."""
@@ -73,7 +62,9 @@ def train(net, trainloader, optimizer, epochs, device):
     """Train the model on the training set."""
     criterion = torch.nn.CrossEntropyLoss()
     for _ in range(epochs):
-        for images, labels in tqdm(trainloader):
+        for batch in tqdm(trainloader):
+            batch = list(batch.values())
+            images, labels = batch[0], batch[1]
             optimizer.zero_grad()
             criterion(net(images.to(device)), labels.to(device)).backward()
             optimizer.step()
@@ -84,7 +75,9 @@ def test(net, testloader, device):
     criterion = torch.nn.CrossEntropyLoss()
     correct, loss = 0, 0.0
     with torch.no_grad():
-        for images, labels in tqdm(testloader):
+        for batch in tqdm(testloader):
+            batch = list(batch.values())
+            images, labels = batch[0], batch[1]
             outputs = net(images.to(device))
             labels = labels.to(device)
             loss += criterion(outputs, labels).item()
@@ -95,44 +88,33 @@ def test(net, testloader, device):
 
 def prepare_dataset(use_mnist: bool):
     """Get MNIST/CIFAR-10 and return client partitions and global testset."""
-    dataset = MNIST if use_mnist else CIFAR10
     if use_mnist:
+        fds = FederatedDataset(dataset="mnist", partitioners={"train": NUM_CLIENTS})
+        img_key = "image"
         norm = Normalize((0.1307,), (0.3081,))
     else:
+        fds = FederatedDataset(dataset="cifar10", partitioners={"train": NUM_CLIENTS})
+        img_key = "img"
         norm = Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-
-    trf = Compose([ToTensor(), norm])
-    trainset = dataset("./data", train=True, download=True, transform=trf)
-    testset = dataset("./data", train=False, download=True, transform=trf)
-
-    print("Partitioning dataset (IID)...")
-
-    # Split trainset into `num_partitions` trainsets
-    num_images = len(trainset) // NUM_CLIENTS
-    partition_len = [num_images] * NUM_CLIENTS
-
-    trainsets = random_split(
-        trainset, partition_len, torch.Generator().manual_seed(2023)
-    )
-
-    val_ratio = 0.1
-
-    # Create dataloaders with train+val support
-    train_partitions = []
-    val_partitions = []
-    for trainset_ in trainsets:
-        num_total = len(trainset_)
-        num_val = int(val_ratio * num_total)
-        num_train = num_total - num_val
-
-        for_train, for_val = random_split(
-            trainset_, [num_train, num_val], torch.Generator().manual_seed(2023)
-        )
-
-        train_partitions.append(for_train)
-        val_partitions.append(for_val)
-
-    return train_partitions, val_partitions, testset
+    pytorch_transforms = Compose([ToTensor(), norm])
+
+    def apply_transforms(batch):
+        """Apply transforms to the partition from FederatedDataset."""
+        batch[img_key] = [pytorch_transforms(img) for img in batch[img_key]]
+        return batch
+
+    trainsets = []
+    validsets = []
+    for partition_id in range(NUM_CLIENTS):
+        partition = fds.load_partition(partition_id, "train")
+        # Divide data on each node: 90% train, 10% test
+        partition = partition.train_test_split(test_size=0.1)
+        partition = partition.with_transform(apply_transforms)
+        trainsets.append(partition["train"])
+        validsets.append(partition["test"])
+    testset = fds.load_split("test")
+    testset = testset.with_transform(apply_transforms)
+    return trainsets, validsets, testset
 
 
 # Flower client, adapted from Pytorch quickstart/simulation example
@@ -148,8 +130,6 @@ def __init__(self, trainset, valset, use_mnist):
             self.model = Net()
         else:
             self.model = mobilenet_v3_small(num_classes=10)
-            # let's not reduce spatial resolution too early
-            self.model.features[0][0].stride = (1, 1)
         # Determine device
         self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
         self.model.to(self.device)  # send model to device
@@ -200,15 +180,15 @@ def main():
     assert args.cid < NUM_CLIENTS
 
     use_mnist = args.mnist
-    # Download CIFAR-10 dataset and partition it
+    # Download dataset and partition it
     trainsets, valsets, _ = prepare_dataset(use_mnist)
 
     # Start Flower client setting its associated data partition
-    fl.client.start_numpy_client(
+    fl.client.start_client(
         server_address=args.server_address,
         client=FlowerClient(
             trainset=trainsets[args.cid], valset=valsets[args.cid], use_mnist=use_mnist
-        ),
+        ).to_client(),
     )
 
 
diff --git a/examples/embedded-devices/client_tf.py b/examples/embedded-devices/client_tf.py
index 3457af1c7a66..49c63ce5d9dc 100644
--- a/examples/embedded-devices/client_tf.py
+++ b/examples/embedded-devices/client_tf.py
@@ -6,6 +6,8 @@
 import tensorflow as tf
 from tensorflow import keras as keras
 
+from flwr_datasets import FederatedDataset
+
 parser = argparse.ArgumentParser(description="Flower Embedded devices")
 parser.add_argument(
     "--server_address",
@@ -32,30 +34,28 @@
 def prepare_dataset(use_mnist: bool):
     """Download and partitions the CIFAR-10/MNIST dataset."""
     if use_mnist:
-        (x_train, y_train), testset = tf.keras.datasets.mnist.load_data()
+        fds = FederatedDataset(dataset="mnist", partitioners={"train": NUM_CLIENTS})
+        img_key = "image"
     else:
-        (x_train, y_train), testset = tf.keras.datasets.cifar10.load_data()
+        fds = FederatedDataset(dataset="cifar10", partitioners={"train": NUM_CLIENTS})
+        img_key = "img"
     partitions = []
-    # We keep all partitions equal-sized in this example
-    partition_size = math.floor(len(x_train) / NUM_CLIENTS)
-    for cid in range(NUM_CLIENTS):
-        # Split dataset into non-overlapping NUM_CLIENT partitions
-        idx_from, idx_to = int(cid) * partition_size, (int(cid) + 1) * partition_size
-
-        x_train_cid, y_train_cid = (
-            x_train[idx_from:idx_to] / 255.0,
-            y_train[idx_from:idx_to],
+    for partition_id in range(NUM_CLIENTS):
+        partition = fds.load_partition(partition_id, "train")
+        partition.set_format("numpy")
+        # Divide data on each node: 90% train, 10% test
+        partition = partition.train_test_split(test_size=0.1)
+        x_train, y_train = (
+            partition["train"][img_key] / 255.0,
+            partition["train"]["label"],
         )
-
-        # now partition into train/validation
-        # Use 10% of the client's training data for validation
-        split_idx = math.floor(len(x_train_cid) * 0.9)
-
-        client_train = (x_train_cid[:split_idx], y_train_cid[:split_idx])
-        client_val = (x_train_cid[split_idx:], y_train_cid[split_idx:])
-        partitions.append((client_train, client_val))
-
-    return partitions, testset
+        x_test, y_test = partition["test"][img_key] / 255.0, partition["test"]["label"]
+        partitions.append(((x_train, y_train), (x_test, y_test)))
+    data_centralized = fds.load_split("test")
+    data_centralized.set_format("numpy")
+    x_centralized = data_centralized[img_key] / 255.0
+    y_centralized = data_centralized["label"]
+    return partitions, (x_centralized, y_centralized)
 
 
 class FlowerClient(fl.client.NumPyClient):
@@ -68,7 +68,7 @@ def __init__(self, trainset, valset, use_mnist: bool):
         # Instantiate model
         if use_mnist:
             # small model for MNIST
-            self.model = model = keras.Sequential(
+            self.model = keras.Sequential(
                 [
                     keras.Input(shape=(28, 28, 1)),
                     keras.layers.Conv2D(32, kernel_size=(5, 5), activation="relu"),
@@ -118,14 +118,16 @@ def main():
     assert args.cid < NUM_CLIENTS
 
     use_mnist = args.mnist
-    # Download CIFAR-10 dataset and partition it
+    # Download dataset and partition it
     partitions, _ = prepare_dataset(use_mnist)
     trainset, valset = partitions[args.cid]
 
     # Start Flower client setting its associated data partition
-    fl.client.start_numpy_client(
+    fl.client.start_client(
         server_address=args.server_address,
-        client=FlowerClient(trainset=trainset, valset=valset, use_mnist=use_mnist),
+        client=FlowerClient(
+            trainset=trainset, valset=valset, use_mnist=use_mnist
+        ).to_client(),
     )
 
 
diff --git a/examples/embedded-devices/requirements_pytorch.txt b/examples/embedded-devices/requirements_pytorch.txt
index 797ca6db6244..f859c4efef17 100644
--- a/examples/embedded-devices/requirements_pytorch.txt
+++ b/examples/embedded-devices/requirements_pytorch.txt
@@ -1,4 +1,5 @@
 flwr>=1.0, <2.0
+flwr-datasets[vision]>=0.0.2, <1.0.0
 torch==1.13.1
 torchvision==0.14.1
 tqdm==4.65.0
diff --git a/examples/embedded-devices/requirements_tf.txt b/examples/embedded-devices/requirements_tf.txt
index c7068d40b9c2..ff65b9c31648 100644
--- a/examples/embedded-devices/requirements_tf.txt
+++ b/examples/embedded-devices/requirements_tf.txt
@@ -1,2 +1,3 @@
 flwr>=1.0, <2.0
+flwr-datasets[vision]>=0.0.2, <1.0.0
 tensorflow >=2.9.1, != 2.11.1
diff --git a/examples/embedded-devices/server.py b/examples/embedded-devices/server.py
index 2a15f792297e..2a6194aa5088 100644
--- a/examples/embedded-devices/server.py
+++ b/examples/embedded-devices/server.py
@@ -30,16 +30,11 @@
     default=2,
     help="Minimum number of available clients required for sampling (default: 2)",
 )
-parser.add_argument(
-    "--mnist",
-    action="store_true",
-    help="If you use Raspberry Pi Zero clients (which just have 512MB or RAM) use MNIST",
-)
 
 
 # Define metric aggregation function
 def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
-    """Thist function averages teh `accuracy` metric sent by the clients in a `evaluate`
+    """This function averages teh `accuracy` metric sent by the clients in a `evaluate`
     stage (i.e. clients received the global model and evaluate it on their local
     validation sets)."""
     # Multiply accuracy of each client by number of examples used
diff --git a/examples/federated-kaplan-meier-fitter/README.md b/examples/federated-kaplan-meier-fitter/README.md
new file mode 100644
index 000000000000..1569467d6f82
--- /dev/null
+++ b/examples/federated-kaplan-meier-fitter/README.md
@@ -0,0 +1,104 @@
+# Flower Example using KaplanMeierFitter
+
+This is an introductory example on **federated survival analysis** using [Flower](https://flower.ai/)
+and [lifelines](https://lifelines.readthedocs.io/en/stable/index.html) library.
+
+The aim of this example is to estimate the survival function using the
+[Kaplan-Meier Estimate](https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator) implemented in
+lifelines library (see [KaplanMeierFitter](https://lifelines.readthedocs.io/en/stable/fitters/univariate/KaplanMeierFitter.html#lifelines.fitters.kaplan_meier_fitter.KaplanMeierFitter)). The distributed/federated aspect of this example
+is the data sending to the server. You can think of it as a federated analytics example. However, it's worth noting that this procedure violates privacy since the raw data is exchanged.
+
+Finally, many other estimators beyond KaplanMeierFitter can be used with the provided strategy:
+AalenJohansenFitter, GeneralizedGammaFitter, LogLogisticFitter,
+SplineFitter, and WeibullFitter.
+
+We also use the [NatualPartitioner](https://flower.ai/docs/datasets/ref-api/flwr_datasets.partitioner.NaturalIdPartitioner.html#flwr_datasets.partitioner.NaturalIdPartitioner) from [Flower Datasets](https://flower.ai/docs/datasets/) to divide the data according to
+the group it comes from therefore to simulate the division that might occur.
+
+<p style="text-align:center;">
+<img src="_static/survival_function_federated.png" alt="Survival Function" width="600"/>
+</p>
+
+## 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/federated-kaplan-meier-fitter . && rm -rf _tmp && cd federated-kaplan-meier-fitter
+```
+
+This will create a new directory called `federated-kaplan-meier-fitter` containing the following files:
+
+```shell
+-- pyproject.toml
+-- requirements.txt
+-- client.py
+-- server.py
+-- centralized.py
+-- README.md
+```
+
+### Installing Dependencies
+
+Project dependencies (such as `lifelines` 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
+
+```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 Survival Analysis with Flower and lifelines's KaplanMeierFitter
+
+### Start the long-running Flower server (SuperLink)
+
+```bash
+flower-superlink --insecure
+```
+
+### Start the long-running Flower client (SuperNode)
+
+In a new terminal window, start the first long-running Flower client:
+
+```bash
+flower-client-app client:node_1_app --insecure
+```
+
+In yet another new terminal window, start the second long-running Flower client:
+
+```bash
+flower-client-app client:node_2_app --insecure
+```
+
+### 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
+```
+
+You will see that the server is printing survival function, median survival time and saves the plot with the survival function.
+
+You can also check that the results match the centralized version.
+
+```shell
+$ python3 centralized.py
+```
diff --git a/examples/federated-kaplan-meier-fitter/_static/survival_function_centralized.png b/examples/federated-kaplan-meier-fitter/_static/survival_function_centralized.png
new file mode 100644
index 000000000000..b7797f0879f2
Binary files /dev/null and b/examples/federated-kaplan-meier-fitter/_static/survival_function_centralized.png differ
diff --git a/examples/federated-kaplan-meier-fitter/_static/survival_function_federated.png b/examples/federated-kaplan-meier-fitter/_static/survival_function_federated.png
new file mode 100644
index 000000000000..b7797f0879f2
Binary files /dev/null and b/examples/federated-kaplan-meier-fitter/_static/survival_function_federated.png differ
diff --git a/examples/federated-kaplan-meier-fitter/centralized.py b/examples/federated-kaplan-meier-fitter/centralized.py
new file mode 100644
index 000000000000..c94edfef9a18
--- /dev/null
+++ b/examples/federated-kaplan-meier-fitter/centralized.py
@@ -0,0 +1,16 @@
+import matplotlib.pyplot as plt
+from lifelines import KaplanMeierFitter
+from lifelines.datasets import load_waltons
+
+if __name__ == "__main__":
+    X = load_waltons()
+    fitter = KaplanMeierFitter()
+    fitter.fit(X["T"], X["E"])
+    print("Survival function")
+    print(fitter.survival_function_)
+    print("Mean survival time:")
+    print(fitter.median_survival_time_)
+    fitter.plot_survival_function()
+    plt.title("Survival function of fruit flies (Walton's data)", fontsize=16)
+    plt.savefig("./_static/survival_function_centralized.png", dpi=200)
+    print("Centralized survival function saved.")
diff --git a/examples/federated-kaplan-meier-fitter/client.py b/examples/federated-kaplan-meier-fitter/client.py
new file mode 100644
index 000000000000..948492efc575
--- /dev/null
+++ b/examples/federated-kaplan-meier-fitter/client.py
@@ -0,0 +1,65 @@
+from typing import Dict, List, Tuple
+
+import flwr as fl
+import numpy as np
+from datasets import Dataset
+from flwr.common import NDArray, NDArrays
+from flwr_datasets.partitioner import NaturalIdPartitioner
+from lifelines.datasets import load_waltons
+
+
+class FlowerClient(fl.client.NumPyClient):
+    """Flower client that holds and sends the events and times data.
+
+    Parameters
+    ----------
+    times: NDArray
+        Times of the `events`.
+    events: NDArray
+        Events represented by 0 - no event, 1 - event occurred.
+
+    Raises
+    ------
+    ValueError
+        If the `times` and `events` are not the same shape.
+    """
+
+    def __init__(self, times: NDArray, events: NDArray):
+        if len(times) != len(events):
+            raise ValueError("The times and events arrays have to be same shape.")
+        self._times = times
+        self._events = events
+
+    def fit(
+        self, parameters: List[np.ndarray], config: Dict[str, str]
+    ) -> Tuple[NDArrays, int, Dict]:
+        return (
+            [self._times, self._events],
+            len(self._times),
+            {},
+        )
+
+
+# Prepare data
+X = load_waltons()
+partitioner = NaturalIdPartitioner(partition_by="group")
+partitioner.dataset = Dataset.from_pandas(X)
+
+
+def get_client_fn(partition_id: int):
+    def client_fn(cid: str):
+        partition = partitioner.load_partition(partition_id).to_pandas()
+        events = partition["E"].values
+        times = partition["T"].values
+        return FlowerClient(times=times, events=events).to_client()
+
+    return client_fn
+
+
+# Run via `flower-client-app client:app`
+node_1_app = fl.client.ClientApp(
+    client_fn=get_client_fn(0),
+)
+node_2_app = fl.client.ClientApp(
+    client_fn=get_client_fn(1),
+)
diff --git a/examples/federated-kaplan-meier-fitter/pyproject.toml b/examples/federated-kaplan-meier-fitter/pyproject.toml
new file mode 100644
index 000000000000..8fe354ffb750
--- /dev/null
+++ b/examples/federated-kaplan-meier-fitter/pyproject.toml
@@ -0,0 +1,18 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "federated-kaplan-meier-fitter"
+version = "0.1.0"
+description = "Federated Kaplan Meier Fitter with Flower"
+authors = ["The Flower Authors <hello@flower.ai>"]
+maintainers = ["The Flower Authors <hello@flower.ai>"]
+
+[tool.poetry.dependencies]
+python = ">=3.9,<3.11"
+flwr-nightly = "*"
+flwr-datasets = ">=0.0.2,<1.0.0"
+numpy = ">=1.23.2"
+pandas = ">=2.0.0"
+lifelines = ">=0.28.0"
diff --git a/examples/federated-kaplan-meier-fitter/requirements.txt b/examples/federated-kaplan-meier-fitter/requirements.txt
new file mode 100644
index 000000000000..cc8146545c7b
--- /dev/null
+++ b/examples/federated-kaplan-meier-fitter/requirements.txt
@@ -0,0 +1,5 @@
+flwr-nightly
+flwr-datasets>=0.0.2, <1.0.0
+numpy>=1.23.2
+pandas>=2.0.0
+lifelines>=0.28.0
diff --git a/examples/federated-kaplan-meier-fitter/server.py b/examples/federated-kaplan-meier-fitter/server.py
new file mode 100644
index 000000000000..141504ab59c0
--- /dev/null
+++ b/examples/federated-kaplan-meier-fitter/server.py
@@ -0,0 +1,145 @@
+# 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.
+# ==============================================================================
+"""Strategy that supports many univariate fitters from lifelines library."""
+
+from typing import Dict, List, Optional, Tuple, Union, Any
+
+import numpy as np
+import flwr as fl
+import matplotlib.pyplot as plt
+from flwr.common import (
+    FitIns,
+    Parameters,
+    Scalar,
+    EvaluateRes,
+    EvaluateIns,
+    FitRes,
+    parameters_to_ndarrays,
+)
+from flwr.server.client_manager import ClientManager
+from flwr.server.client_proxy import ClientProxy
+from flwr.server.strategy import Strategy
+from lifelines import KaplanMeierFitter
+
+
+class EventTimeFitterStrategy(Strategy):
+    """Federated strategy to aggregate the data that consist of events and times.
+
+    It works with the following uni-variate fitters from the lifelines library:
+    AalenJohansenFitter, GeneralizedGammaFitter, KaplanMeierFitter, LogLogisticFitter,
+    SplineFitter, WeibullFitter. Note that each of them might require slightly different
+    initialization but constructed fitter object are required to be passed.
+
+    This strategy recreates the event and time data based on the data received from the
+    nodes.
+
+    Parameters
+    ----------
+    min_num_clients: int
+        pass
+    fitter: Any
+        uni-variate fitter from lifelines library that works with event, time data e.g.
+        KaplanMeierFitter
+    """
+
+    def __init__(self, min_num_clients: int, fitter: Any):
+        # Fitter can be access after the federated training ends
+        self._min_num_clients = min_num_clients
+        self.fitter = fitter
+
+    def configure_fit(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, FitIns]]:
+        """Configure the fit method."""
+        config = {}
+        fit_ins = FitIns(parameters, config)
+        clients = client_manager.sample(
+            num_clients=client_manager.num_available(),
+            min_num_clients=self._min_num_clients,
+        )
+        return [(client, fit_ins) for client in clients]
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        """Merge data and perform the fitting of the fitter from lifelines library.
+
+        Assume just a single federated learning round. Assume the data comes as a list
+        with two elements of 1-dim numpy arrays of events and times.
+        """
+        remote_data = [
+            (parameters_to_ndarrays(fit_res.parameters)) for _, fit_res in results
+        ]
+
+        combined_times = remote_data[0][0]
+        combined_events = remote_data[0][1]
+
+        for te_list in remote_data[1:]:
+            combined_times = np.concatenate((combined_times, te_list[0]))
+            combined_events = np.concatenate((combined_events, te_list[1]))
+
+        args_sorted = np.argsort(combined_times)
+        sorted_times = combined_times[args_sorted]
+        sorted_events = combined_events[args_sorted]
+        self.fitter.fit(sorted_times, sorted_events)
+        print("Survival function:")
+        print(self.fitter.survival_function_)
+        self.fitter.plot_survival_function()
+        plt.title("Survival function of fruit flies (Walton's data)", fontsize=16)
+        plt.savefig("./_static/survival_function_federated.png", dpi=200)
+        print("Mean survival time:")
+        print(self.fitter.median_survival_time_)
+        return None, {}
+
+    # The methods below return None or empty results.
+    # They need to be implemented to since the methods are abstract in the parent class
+    def initialize_parameters(
+        self, client_manager: Optional[ClientManager] = None
+    ) -> Optional[Parameters]:
+        """No parameter initialization is needed."""
+        return None
+
+    def evaluate(
+        self, server_round: int, parameters: Parameters
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """No centralized evaluation."""
+        return None
+
+    def aggregate_evaluate(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, EvaluateRes]],
+        failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
+    ) -> Tuple[Optional[float], Dict[str, Scalar]]:
+        """No federated evaluation."""
+        return None, {}
+
+    def configure_evaluate(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, EvaluateIns]]:
+        """No federated evaluation."""
+        return []
+
+
+fitter = KaplanMeierFitter()  # You can choose other method that work on E, T data
+strategy = EventTimeFitterStrategy(min_num_clients=2, fitter=fitter)
+
+app = fl.server.ServerApp(
+    config=fl.server.ServerConfig(num_rounds=1),
+    strategy=strategy,
+)
diff --git a/examples/fl-dp-sa/README.md b/examples/fl-dp-sa/README.md
new file mode 100644
index 000000000000..99a0a7e50980
--- /dev/null
+++ b/examples/fl-dp-sa/README.md
@@ -0,0 +1,22 @@
+# fl_dp_sa
+
+This is a simple example that utilizes central differential privacy with client-side fixed clipping and secure aggregation.
+Note: This example is designed for a small number of rounds and is intended for demonstration purposes.
+
+## Install dependencies
+
+```bash
+# Using pip
+pip install .
+
+# Or using Poetry
+poetry install
+```
+
+## Run
+
+The example uses the CIFAR-10 dataset with a total of 100 clients, with 20 clients sampled in each round. The hyperparameters for DP and SecAgg are specified in `server.py`.
+
+```shell
+flower-simulation --server-app fl_dp_sa.server:app --client-app fl_dp_sa.client:app --num-supernodes 100
+```
diff --git a/examples/fl-dp-sa/fl_dp_sa/__init__.py b/examples/fl-dp-sa/fl_dp_sa/__init__.py
new file mode 100644
index 000000000000..741260348ab8
--- /dev/null
+++ b/examples/fl-dp-sa/fl_dp_sa/__init__.py
@@ -0,0 +1 @@
+"""fl_dp_sa: A Flower / PyTorch app."""
diff --git a/examples/fl-dp-sa/fl_dp_sa/client.py b/examples/fl-dp-sa/fl_dp_sa/client.py
new file mode 100644
index 000000000000..104264158833
--- /dev/null
+++ b/examples/fl-dp-sa/fl_dp_sa/client.py
@@ -0,0 +1,43 @@
+"""fl_dp_sa: A Flower / PyTorch app."""
+
+from flwr.client import ClientApp, NumPyClient
+from flwr.client.mod import fixedclipping_mod, secaggplus_mod
+
+from fl_dp_sa.task import DEVICE, Net, get_weights, load_data, set_weights, test, train
+
+
+# Load model and data (simple CNN, CIFAR-10)
+net = Net().to(DEVICE)
+
+
+# Define FlowerClient and client_fn
+class FlowerClient(NumPyClient):
+    def __init__(self, trainloader, testloader) -> None:
+        self.trainloader = trainloader
+        self.testloader = testloader
+
+    def fit(self, parameters, config):
+        set_weights(net, parameters)
+        results = train(net, self.trainloader, self.testloader, epochs=1, device=DEVICE)
+        return get_weights(net), len(self.trainloader.dataset), results
+
+    def evaluate(self, parameters, config):
+        set_weights(net, parameters)
+        loss, accuracy = test(net, self.testloader)
+        return loss, len(self.testloader.dataset), {"accuracy": accuracy}
+
+
+def client_fn(cid: str):
+    """Create and return an instance of Flower `Client`."""
+    trainloader, testloader = load_data(partition_id=int(cid))
+    return FlowerClient(trainloader, testloader).to_client()
+
+
+# Flower ClientApp
+app = ClientApp(
+    client_fn=client_fn,
+    mods=[
+        secaggplus_mod,
+        fixedclipping_mod,
+    ],
+)
diff --git a/examples/fl-dp-sa/fl_dp_sa/server.py b/examples/fl-dp-sa/fl_dp_sa/server.py
new file mode 100644
index 000000000000..f7da75997e98
--- /dev/null
+++ b/examples/fl-dp-sa/fl_dp_sa/server.py
@@ -0,0 +1,77 @@
+"""fl_dp_sa: A Flower / PyTorch app."""
+
+from typing import List, Tuple
+
+from flwr.server import Driver, LegacyContext, ServerApp, ServerConfig
+from flwr.common import Context, Metrics, ndarrays_to_parameters
+from flwr.server.strategy import (
+    DifferentialPrivacyClientSideFixedClipping,
+    FedAvg,
+)
+from flwr.server.workflow import DefaultWorkflow, SecAggPlusWorkflow
+
+from fl_dp_sa.task import Net, get_weights
+
+
+# Define metric aggregation function
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+    examples = [num_examples for num_examples, _ in metrics]
+
+    # Multiply accuracy of each client by number of examples used
+    train_losses = [num_examples * m["train_loss"] for num_examples, m in metrics]
+    train_accuracies = [
+        num_examples * m["train_accuracy"] for num_examples, m in metrics
+    ]
+    val_losses = [num_examples * m["val_loss"] for num_examples, m in metrics]
+    val_accuracies = [num_examples * m["val_accuracy"] for num_examples, m in metrics]
+
+    # Aggregate and return custom metric (weighted average)
+    return {
+        "train_loss": sum(train_losses) / sum(examples),
+        "train_accuracy": sum(train_accuracies) / sum(examples),
+        "val_loss": sum(val_losses) / sum(examples),
+        "val_accuracy": sum(val_accuracies) / sum(examples),
+    }
+
+
+# Initialize model parameters
+ndarrays = get_weights(Net())
+parameters = ndarrays_to_parameters(ndarrays)
+
+
+# Define strategy
+strategy = FedAvg(
+    fraction_fit=0.2,
+    fraction_evaluate=0.0,  # Disable evaluation for demo purpose
+    min_fit_clients=20,
+    min_available_clients=20,
+    fit_metrics_aggregation_fn=weighted_average,
+    initial_parameters=parameters,
+)
+strategy = DifferentialPrivacyClientSideFixedClipping(
+    strategy, noise_multiplier=0.2, clipping_norm=10, num_sampled_clients=20
+)
+
+
+app = ServerApp()
+
+
+@app.main()
+def main(driver: Driver, context: Context) -> None:
+    # Construct the LegacyContext
+    context = LegacyContext(
+        state=context.state,
+        config=ServerConfig(num_rounds=3),
+        strategy=strategy,
+    )
+
+    # Create the train/evaluate workflow
+    workflow = DefaultWorkflow(
+        fit_workflow=SecAggPlusWorkflow(
+            num_shares=7,
+            reconstruction_threshold=4,
+        )
+    )
+
+    # Execute
+    workflow(driver, context)
diff --git a/examples/fl-dp-sa/fl_dp_sa/task.py b/examples/fl-dp-sa/fl_dp_sa/task.py
new file mode 100644
index 000000000000..3d506263d5a3
--- /dev/null
+++ b/examples/fl-dp-sa/fl_dp_sa/task.py
@@ -0,0 +1,110 @@
+"""fl_dp_sa: A Flower / PyTorch app."""
+
+from collections import OrderedDict
+from logging import INFO
+from flwr_datasets import FederatedDataset
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from flwr.common.logger import log
+from torch.utils.data import DataLoader
+from torchvision.transforms import Compose, Normalize, ToTensor
+
+
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+class Net(nn.Module):
+    """Model."""
+
+    def __init__(self) -> None:
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 6, 3, padding=1)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        batch_size = x.size(0)
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(batch_size, -1)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        return self.fc3(x)
+
+
+def load_data(partition_id):
+    """Load partition CIFAR10 data."""
+    fds = FederatedDataset(dataset="mnist", partitioners={"train": 100})
+    partition = fds.load_partition(partition_id)
+    # Divide data on each node: 80% train, 20% test
+    partition_train_test = partition.train_test_split(test_size=0.2)
+    pytorch_transforms = Compose([ToTensor(), Normalize((0.5,), (0.5,))])
+
+    def apply_transforms(batch):
+        """Apply transforms to the partition from FederatedDataset."""
+        batch["image"] = [pytorch_transforms(img) for img in batch["image"]]
+        return batch
+
+    partition_train_test = partition_train_test.with_transform(apply_transforms)
+    trainloader = DataLoader(partition_train_test["train"], batch_size=32, shuffle=True)
+    testloader = DataLoader(partition_train_test["test"], batch_size=32)
+    return trainloader, testloader
+
+
+def train(net, trainloader, valloader, epochs, device):
+    """Train the model on the training set."""
+    net.to(device)  # move model to GPU if available
+    criterion = torch.nn.CrossEntropyLoss().to(device)
+    optimizer = torch.optim.Adam(net.parameters())
+    net.train()
+    for _ in range(epochs):
+        for batch in trainloader:
+            images = batch["image"].to(device)
+            labels = batch["label"].to(device)
+            optimizer.zero_grad()
+            loss = criterion(net(images), labels)
+            loss.backward()
+            optimizer.step()
+
+    train_loss, train_acc = test(net, trainloader)
+    val_loss, val_acc = test(net, valloader)
+
+    results = {
+        "train_loss": train_loss,
+        "train_accuracy": train_acc,
+        "val_loss": val_loss,
+        "val_accuracy": val_acc,
+    }
+    return results
+
+
+def test(net, testloader):
+    """Validate the model on the test set."""
+    net.to(DEVICE)
+    criterion = torch.nn.CrossEntropyLoss()
+    correct, loss = 0, 0.0
+    with torch.no_grad():
+        for batch in testloader:
+            images = batch["image"].to(DEVICE)
+            labels = batch["label"].to(DEVICE)
+            outputs = net(images.to(DEVICE))
+            labels = labels.to(DEVICE)
+            loss += criterion(outputs, labels).item()
+            correct += (torch.max(outputs.data, 1)[1] == labels).sum().item()
+    accuracy = correct / len(testloader.dataset)
+    return loss, accuracy
+
+
+def get_weights(net):
+    return [val.cpu().numpy() for _, val in net.state_dict().items()]
+
+
+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)
diff --git a/examples/fl-dp-sa/flower.toml b/examples/fl-dp-sa/flower.toml
new file mode 100644
index 000000000000..ea2e98206791
--- /dev/null
+++ b/examples/fl-dp-sa/flower.toml
@@ -0,0 +1,13 @@
+[project]
+name = "fl_dp_sa"
+version = "1.0.0"
+description = ""
+license = "Apache-2.0"
+authors = [
+    "The Flower Authors <hello@flower.ai>",
+]
+readme = "README.md"
+
+[flower.components]
+serverapp = "fl_dp_sa.server:app"
+clientapp = "fl_dp_sa.client:app"
diff --git a/examples/fl-dp-sa/pyproject.toml b/examples/fl-dp-sa/pyproject.toml
new file mode 100644
index 000000000000..d30fa4675e34
--- /dev/null
+++ b/examples/fl-dp-sa/pyproject.toml
@@ -0,0 +1,21 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "fl-dp-sa"
+version = "0.1.0"
+description = ""
+license = "Apache-2.0"
+authors = [
+    "The Flower Authors <hello@flower.ai>",
+]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.9"
+# Mandatory dependencies
+flwr-nightly = { version = "1.8.0.dev20240313", extras = ["simulation"] }
+flwr-datasets = { version = "0.0.2", extras = ["vision"] }
+torch = "2.2.1"
+torchvision = "0.17.1"
diff --git a/examples/fl-dp-sa/requirements.txt b/examples/fl-dp-sa/requirements.txt
new file mode 100644
index 000000000000..ddb8a814447b
--- /dev/null
+++ b/examples/fl-dp-sa/requirements.txt
@@ -0,0 +1,4 @@
+flwr-nightly[simulation]==1.8.0.dev20240313
+flwr-datasets[vision]==0.0.2
+torch==2.2.1
+torchvision==0.17.1
diff --git a/examples/flower-in-30-minutes/tutorial.ipynb b/examples/flower-in-30-minutes/tutorial.ipynb
index 336ec4c19644..0e42cff924e8 100644
--- a/examples/flower-in-30-minutes/tutorial.ipynb
+++ b/examples/flower-in-30-minutes/tutorial.ipynb
@@ -7,11 +7,11 @@
    "source": [
     "Welcome to the 30 minutes Flower federated learning tutorial!\n",
     "\n",
-    "In this tutorial you will implement your first Federated Learning project using [Flower](https://flower.dev/).\n",
+    "In this tutorial you will implement your first Federated Learning project using [Flower](https://flower.ai/).\n",
     "\n",
     "🧑‍🏫 This tutorial starts at zero and expects no familiarity with federated learning. Only a basic understanding of data science and Python programming is assumed. A minimal understanding of ML is not required but if you already know about it, nothing is stopping your from modifying this code as you see fit!\n",
     "\n",
-    "> Star Flower on [GitHub ⭐️](https://github.com/adap/flower) and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack 🌼](https://flower.dev/join-slack/). We'd love to hear from you in the #introductions channel! And if anything is unclear, head over to the #questions channel.\n",
+    "> Star Flower on [GitHub ⭐️](https://github.com/adap/flower) and join the Flower community on Slack to connect, ask questions, and get help: [Join Slack 🌼](https://flower.ai/join-slack/). We'd love to hear from you in the #introductions channel! And if anything is unclear, head over to the #questions channel.\n",
     "\n",
     "Let's get stated!"
    ]
@@ -59,7 +59,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will be using the _simulation_ model in Flower, which allows you to run a large number of clients without the overheads of manually managing devices. This is achieved via the `Virtual Client Engine`, the core component that runs [FL Simulations](https://flower.dev/docs/framework/how-to-run-simulations.html) with Flower. With simulation, you can dynamically scale your experiments whether you run the code on your laptop, a machine with a single GPU, a server with multiple GPUs os even on a cluster with multiple servers. The `Virtual Client Engine` handles everything transparently and it allows you to specify how many resources (e.g. CPU cores, GPU VRAM) should be assigned to each virtual client."
+    "We will be using the _simulation_ model in Flower, which allows you to run a large number of clients without the overheads of manually managing devices. This is achieved via the `Virtual Client Engine`, the core component that runs [FL Simulations](https://flower.ai/docs/framework/how-to-run-simulations.html) with Flower. With simulation, you can dynamically scale your experiments whether you run the code on your laptop, a machine with a single GPU, a server with multiple GPUs os even on a cluster with multiple servers. The `Virtual Client Engine` handles everything transparently and it allows you to specify how many resources (e.g. CPU cores, GPU VRAM) should be assigned to each virtual client."
    ]
   },
   {
@@ -69,7 +69,7 @@
    "source": [
     "## Install your ML framework\n",
     "\n",
-    "Flower is agnostic to your choice of ML Framework. Flower works with `PyTorch`, `Tensorflow`, `NumPy`, `🤗 Transformers`, `MXNet`, `JAX`, `scikit-learn`, `fastai`, `Pandas`. Flower also supports all major platforms: `iOS`, `Android` and plain `C++`. You can find a _quickstart- example for each of the above in the [Flower Repository](https://github.com/adap/flower/tree/main/examples) inside the `examples/` directory. And check the [Flower Documentation](https://flower.dev/docs/) for even more learning materials.\n",
+    "Flower is agnostic to your choice of ML Framework. Flower works with `PyTorch`, `Tensorflow`, `NumPy`, `🤗 Transformers`, `MXNet`, `JAX`, `scikit-learn`, `fastai`, `Pandas`. Flower also supports all major platforms: `iOS`, `Android` and plain `C++`. You can find a _quickstart- example for each of the above in the [Flower Repository](https://github.com/adap/flower/tree/main/examples) inside the `examples/` directory. And check the [Flower Documentation](https://flower.ai/docs/) for even more learning materials.\n",
     "\n",
     "In this tutorial we are going to use PyTorch, so let's install a recent version. In this tutorial we'll use a small model so using CPU only training will suffice (this will also prevent Colab from abruptly terminating your experiment if resource limits are exceeded)"
    ]
@@ -631,11 +631,11 @@
     "\n",
     "\n",
     "class FlowerClient(fl.client.NumPyClient):\n",
-    "    def __init__(self, trainloader, vallodaer) -> None:\n",
+    "    def __init__(self, trainloader, valloader) -> None:\n",
     "        super().__init__()\n",
     "\n",
     "        self.trainloader = trainloader\n",
-    "        self.valloader = vallodaer\n",
+    "        self.valloader = valloader\n",
     "        self.model = Net(num_classes=10)\n",
     "\n",
     "    def set_parameters(self, parameters):\n",
@@ -714,7 +714,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def get_evalulate_fn(testloader):\n",
+    "def get_evaluate_fn(testloader):\n",
     "    \"\"\"This is a function that returns a function. The returned\n",
     "    function (i.e. `evaluate_fn`) will be executed by the strategy\n",
     "    at the end of each round to evaluate the stat of the global\n",
@@ -747,7 +747,7 @@
     "    fraction_fit=0.1,  # let's sample 10% of the client each round to do local training\n",
     "    fraction_evaluate=0.1,  # after each round, let's sample 20% of the clients to asses how well the global model is doing\n",
     "    min_available_clients=100,  # total number of clients available in the experiment\n",
-    "    evaluate_fn=get_evalulate_fn(testloader),\n",
+    "    evaluate_fn=get_evaluate_fn(testloader),\n",
     ")  # a callback to a function that the strategy can execute to evaluate the state of the global model on a centralised dataset"
    ]
   },
@@ -775,8 +775,8 @@
     "        \"\"\"Returns a FlowerClient containing the cid-th data partition\"\"\"\n",
     "\n",
     "        return FlowerClient(\n",
-    "            trainloader=trainloaders[int(cid)], vallodaer=valloaders[int(cid)]\n",
-    "        )\n",
+    "            trainloader=trainloaders[int(cid)], valloader=valloaders[int(cid)]\n",
+    "        ).to_client()\n",
     "\n",
     "    return client_fn\n",
     "\n",
@@ -853,21 +853,21 @@
     "\n",
     "Well, if you enjoyed this content, consider giving us a ⭐️ on GitHub -> https://github.com/adap/flower\n",
     "\n",
-    "* **[DOCS]** How about running your Flower clients on the GPU? find out how to do it in the [Flower Simulation Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html)\n",
+    "* **[DOCS]** How about running your Flower clients on the GPU? find out how to do it in the [Flower Simulation Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html)\n",
     "\n",
     "* **[VIDEO]** You can follow our [detailed line-by-line 9-videos tutorial](https://www.youtube.com/watch?v=cRebUIGB5RU&list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB) about everything you need to know to design your own Flower Simulation pipelines\n",
     "\n",
     "* Check more advanced simulation examples the Flower GitHub:\n",
     "\n",
     "    * Flower simulation with Tensorflow/Keras: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/adap/flower/tree/main/examples/simulation-tensorflow)\n",
-    "    \n",
+    "\n",
     "    * Flower simulation with Pytorch: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/adap/flower/tree/main/examples/simulation-pytorch)\n",
     "\n",
-    "* **[DOCS]** All Flower examples: https://flower.dev/docs/examples/\n",
+    "* **[DOCS]** All Flower examples: https://flower.ai/docs/examples/\n",
     "\n",
     "* **[VIDEO]** Our Youtube channel: https://www.youtube.com/@flowerlabs\n",
     "\n",
-    "Don't forget to join our Slack channel: https://flower.dev/join-slack/\n"
+    "Don't forget to join our Slack channel: https://flower.ai/join-slack/\n"
    ]
   },
   {
diff --git a/examples/flower-simulation-step-by-step-pytorch/Part-I/client.py b/examples/flower-simulation-step-by-step-pytorch/Part-I/client.py
index b10c32d36c42..eac831ad1932 100644
--- a/examples/flower-simulation-step-by-step-pytorch/Part-I/client.py
+++ b/examples/flower-simulation-step-by-step-pytorch/Part-I/client.py
@@ -98,7 +98,7 @@ def client_fn(cid: str):
             trainloader=trainloaders[int(cid)],
             vallodaer=valloaders[int(cid)],
             num_classes=num_classes,
-        )
+        ).to_client()
 
     # return the function to spawn client
     return client_fn
diff --git a/examples/flower-simulation-step-by-step-pytorch/Part-I/main.py b/examples/flower-simulation-step-by-step-pytorch/Part-I/main.py
index f5c76ab6dc99..f8124b9353f7 100644
--- a/examples/flower-simulation-step-by-step-pytorch/Part-I/main.py
+++ b/examples/flower-simulation-step-by-step-pytorch/Part-I/main.py
@@ -24,7 +24,7 @@ def main(cfg: DictConfig):
     save_path = HydraConfig.get().runtime.output_dir
 
     ## 2. Prepare your dataset
-    # When simulating FL workloads we have a lot of freedom on how the FL clients behave,
+    # When simulating FL runs we have a lot of freedom on how the FL clients behave,
     # what data they have, how much data, etc. This is not possible in real FL settings.
     # In simulation you'd often encounter two types of dataset:
     #       * naturally partitioned, that come pre-partitioned by user id (e.g. FEMNIST,
@@ -91,7 +91,7 @@ def main(cfg: DictConfig):
             "num_gpus": 0.0,
         },  # (optional) controls the degree of parallelism of your simulation.
         # Lower resources per client allow for more clients to run concurrently
-        # (but need to be set taking into account the compute/memory footprint of your workload)
+        # (but need to be set taking into account the compute/memory footprint of your run)
         # `num_cpus` is an absolute number (integer) indicating the number of threads a client should be allocated
         # `num_gpus` is a ratio indicating the portion of gpu memory that a client needs.
     )
diff --git a/examples/flower-simulation-step-by-step-pytorch/Part-II/client.py b/examples/flower-simulation-step-by-step-pytorch/Part-II/client.py
index d269d4892a0e..7da9547d7362 100644
--- a/examples/flower-simulation-step-by-step-pytorch/Part-II/client.py
+++ b/examples/flower-simulation-step-by-step-pytorch/Part-II/client.py
@@ -75,6 +75,6 @@ def client_fn(cid: str):
             trainloader=trainloaders[int(cid)],
             vallodaer=valloaders[int(cid)],
             model_cfg=model_cfg,
-        )
+        ).to_client()
 
     return client_fn
diff --git a/examples/flower-simulation-step-by-step-pytorch/README.md b/examples/flower-simulation-step-by-step-pytorch/README.md
index 55b8d837b090..beb8dd7f6f95 100644
--- a/examples/flower-simulation-step-by-step-pytorch/README.md
+++ b/examples/flower-simulation-step-by-step-pytorch/README.md
@@ -1,5 +1,7 @@
 # Flower Simulation Step-by-Step
 
+> Since this tutorial (and its video series) was put together, Flower has been updated a few times. As a result, some of the steps to construct the environment (see below) have been updated. Some parts of the code have also been updated. Overall, the content of this tutorial and how things work remains the same as in the video tutorials.
+
 This directory contains the code developed in the `Flower Simulation` tutorial series on Youtube. You can find all the videos [here](https://www.youtube.com/playlist?list=PLNG4feLHqCWlnj8a_E1A_n5zr2-8pafTB) or clicking on the video preview below.
 
 - In `Part-I` (7 videos) we developed from scratch a complete Federated Learning pipeline for simulation using PyTorch.
@@ -19,20 +21,17 @@ As presented in the video, we first need to create a Python environment. You are
 # I'm assuming you are running this on an Ubuntu 22.04 machine (GPU is not required)
 
 # create the environment
-conda create -n flower_tutorial python=3.8 -y
+conda create -n flower_tutorial python=3.9 -y
 
 # activate your environment (depending on how you installed conda you might need to use `conda activate ...` instead)
 source activate flower_tutorial
 
 # install PyToch (other versions would likely work)
 conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia -y
-# conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 -c pytorch # If you don't have a GPU
+# conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 -c pytorch -y # If you don't have a GPU
 
-# install flower (for FL) and hydra (for configs)
-pip install flwr==1.4.0 hydra-core==1.3.2
-# install ray
-# you might see some warning messages after installing it (you can ignore them)
-pip install ray==1.11.1
+# Install Flower and other dependencies
+pip install -r requirements.txt
 ```
 
 If you are running this on macOS with Apple Silicon (i.e. M1, M2), you'll need a different `grpcio` package if you see an error when running the code. To fix this do:
diff --git a/examples/flower-simulation-step-by-step-pytorch/requirements.txt b/examples/flower-simulation-step-by-step-pytorch/requirements.txt
new file mode 100644
index 000000000000..a322192ca711
--- /dev/null
+++ b/examples/flower-simulation-step-by-step-pytorch/requirements.txt
@@ -0,0 +1,2 @@
+flwr[simulation]>=1.0, <2.0
+hydra-core==1.3.2
\ No newline at end of file
diff --git a/examples/flower-via-docker-compose/.gitignore b/examples/flower-via-docker-compose/.gitignore
new file mode 100644
index 000000000000..de5b2e7692bf
--- /dev/null
+++ b/examples/flower-via-docker-compose/.gitignore
@@ -0,0 +1,17 @@
+# ignore __pycache__ directories
+__pycache__/
+
+# ignore .pyc files
+*.pyc
+
+# ignore .vscode directory
+.vscode/
+
+# ignore .npz files
+*.npz
+
+# ignore .csv files
+*.csv
+
+# ignore docker-compose.yaml file
+docker-compose.yml
\ No newline at end of file
diff --git a/examples/flower-via-docker-compose/Dockerfile b/examples/flower-via-docker-compose/Dockerfile
new file mode 100644
index 000000000000..ee6fee3103a5
--- /dev/null
+++ b/examples/flower-via-docker-compose/Dockerfile
@@ -0,0 +1,19 @@
+# Use an official Python runtime as a parent image
+FROM python:3.10-slim-buster
+
+# Set the working directory in the container to /app
+WORKDIR /app
+
+# Copy the requirements file into the container
+COPY ./requirements.txt /app/requirements.txt
+
+# Install gcc and other dependencies
+RUN apt-get update && apt-get install -y \
+    gcc \
+    python3-dev && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install any needed packages specified in requirements.txt
+RUN pip install -r requirements.txt
+
+
diff --git a/examples/flower-via-docker-compose/README.md b/examples/flower-via-docker-compose/README.md
new file mode 100644
index 000000000000..3ef1ac37bcda
--- /dev/null
+++ b/examples/flower-via-docker-compose/README.md
@@ -0,0 +1,254 @@
+# Leveraging Flower and Docker for Device Heterogeneity Management in Federated Learning
+
+<p align="center">
+  <img src="https://flower.ai/_next/image/?url=%2F_next%2Fstatic%2Fmedia%2Fflower_white_border.c2012e70.png&w=640&q=75" width="140px" alt="Flower Website" />
+  <img src="https://github.com/ChoosyDevs/Choosy/assets/59146613/73d15990-453b-4da6-b8d6-df0f956a127c" width="140px" alt="Docker Logo" />
+</p>
+
+## Introduction
+
+In this example, we tackle device heterogeneity in federated learning, arising from differences in memory and CPU capabilities across devices. This diversity affects training efficiency and inclusivity. Our strategy includes simulating this heterogeneity by setting CPU and memory limits in a Docker setup, using a custom Docker compose generator script. This approach creates a varied training environment and enables us to develop strategies to manage these disparities effectively.
+
+## Handling Device Heterogeneity
+
+1. **System Metrics Access**:
+
+   - Effective management of device heterogeneity begins with monitoring system metrics of each container. We integrate the following services to achieve this:
+     - **Cadvisor**: Collects comprehensive metrics from each Docker container.
+     - **Prometheus**: Using `prometheus.yaml` for configuration, it scrapes data from Cadvisor at scheduled intervals, serving as a robust time-series database. Users can access the Prometheus UI at `http://localhost:9090` to create and run queries using PromQL, allowing for detailed insight into container performance.
+
+2. **Mitigating Heterogeneity**:
+
+   - In this basic use case, we address device heterogeneity by establishing rules tailored to each container's system capabilities. This involves modifying training parameters, such as batch sizes and learning rates, based on each device's memory capacity and CPU availability. These settings are specified in the `client_configs` array in the `create_docker_compose` script. For example:
+
+     ```python
+     client_configs = [
+           {"mem_limit": "3g", "batch_size": 32, "cpus": 4, "learning_rate": 0.001},
+           {"mem_limit": "6g", "batch_size": 256, "cpus": 1, "learning_rate": 0.05},
+           {"mem_limit": "4g", "batch_size": 64, "cpus": 3, "learning_rate": 0.02},
+           {"mem_limit": "5g", "batch_size": 128, "cpus": 2.5, "learning_rate": 0.09},
+     ]
+     ```
+
+## Prerequisites
+
+Docker must be installed and the Docker daemon running on your server. If you don't already have Docker installed, you can get [installation instructions for your specific Linux distribution or macOS from Docker](https://docs.docker.com/engine/install/). Besides Docker, the only extra requirement is having Python installed. You don't need to create a new environment for this example since all dependencies will be installed inside Docker containers automatically.
+
+## Running the Example
+
+Running this example is easy. For a more detailed step-by-step guide, including more useful material, refer to the detailed guide in the following section.
+
+```bash
+
+# Generate docker compose file
+python helpers/generate_docker_compose.py # by default will configure to use 2 clients for 100 rounds
+
+# Build docker images
+docker-compose build
+
+# Launch everything
+docker-compose up
+```
+
+On your favourite browser, go to `http://localhost:3000` to see the Graphana dashboard showing system-level and application-level metrics.
+
+To stop all containers, open a new terminal and `cd` into this directory, then run `docker-compose down`. Alternatively, you can do `ctrl+c` on the same terminal and then run `docker-compose down` to ensure everything is terminated.
+
+## Running the Example (detailed)
+
+### Step 1: Configure Docker Compose
+
+Execute the following command to run the `helpers/generate_docker_compose.py` script. This script creates the docker-compose configuration needed to set up the environment.
+
+```bash
+python helpers/generate_docker_compose.py
+```
+
+Within the script, specify the number of clients (`total_clients`) and resource limitations for each client in the `client_configs` array. You can adjust the number of rounds by passing `--num_rounds` to the above command.
+
+### Step 2: Build and Launch Containers
+
+1. **Execute Initialization Script**:
+
+   - To build the Docker images and start the containers, use the following command:
+
+     ```bash
+     # this is the only command you need to execute to run the entire example
+     docker-compose up
+     ```
+
+   - If you make any changes to the Dockerfile or other configuration files, you should rebuild the images to reflect these changes. This can be done by adding the `--build` flag to the command:
+
+     ```bash
+     docker-compose up --build
+     ```
+
+   - The `--build` flag instructs Docker Compose to rebuild the images before starting the containers, ensuring that any code or configuration changes are included.
+
+   - To stop all services, you have two options:
+
+     - Run `docker-compose down` in another terminal if you are in the same directory. This command will stop and remove the containers, networks, and volumes created by `docker-compose up`.
+     - Press `Ctrl+C` once in the terminal where `docker-compose up` is running. This will stop the containers but won't remove them or the networks and volumes they use.
+
+2. **Services Startup**:
+
+   - Several services will automatically launch as defined in your `docker-compose.yml` file:
+
+     - **Monitoring Services**: Prometheus for metrics collection, Cadvisor for container monitoring, and Grafana for data visualization.
+     - **Flower Federated Learning Environment**: The Flower server and client containers are initialized and start running.
+
+   - After launching the services, verify that all Docker containers are running correctly by executing the `docker ps` command. Here's an example output:
+
+     ```bash
+     ➜  ~ docker ps
+     CONTAINER ID   IMAGE                               COMMAND                  CREATED          STATUS                 PORTS                                                              NAMES
+     9f05820eba45   flower-via-docker-compose-client2   "python client.py --…"   50 seconds ago   Up 48 seconds   0.0.0.0:6002->6002/tcp                                                   client2
+     a0333715d504   flower-via-docker-compose-client1   "python client.py --…"   50 seconds ago   Up 48 seconds   0.0.0.0:6001->6001/tcp                                                   client1
+     0da2bf735965   flower-via-docker-compose-server    "python server.py --…"   50 seconds ago   Up 48 seconds   0.0.0.0:6000->6000/tcp, 0.0.0.0:8000->8000/tcp, 0.0.0.0:8265->8265/tcp   server
+     c57ef50657ae   grafana/grafana:latest              "/run.sh --config=/e…"   50 seconds ago   Up 49 seconds   0.0.0.0:3000->3000/tcp                                                   grafana
+     4f274c2083dc   prom/prometheus:latest              "/bin/prometheus --c…"   50 seconds ago   Up 49 seconds   0.0.0.0:9090->9090/tcp                                                   prometheus
+     e9f4c9644a1c   gcr.io/cadvisor/cadvisor:v0.47.0    "/usr/bin/cadvisor -…"   50 seconds ago   Up 49 seconds   0.0.0.0:8080->8080/tcp                                                   cadvisor
+     ```
+
+   - To monitor the resource utilization of your containers in real-time and see the limits imposed in the Docker Compose file, you can use the `docker stats` command. This command provides a live stream of container CPU, memory, and network usage statistics.
+
+     ```bash
+     ➜  ~ docker stats
+     CONTAINER ID   NAME         CPU %     MEM USAGE / LIMIT     MEM %     NET I/O           BLOCK I/O         PIDS
+     9f05820eba45   client2      104.44%   1.968GiB / 6GiB       32.80%    148MB / 3.22MB    0B / 284MB        82
+     a0333715d504   client1      184.69%   1.498GiB / 3GiB       49.92%    149MB / 2.81MB    1.37MB / 284MB    82
+     0da2bf735965   server       0.12%     218.5MiB / 15.61GiB   1.37%     1.47MB / 2.89MB   2.56MB / 2.81MB   45
+     c57ef50657ae   grafana      0.24%     96.19MiB / 400MiB     24.05%    18.9kB / 3.79kB   77.8kB / 152kB    20
+     4f274c2083dc   prometheus   1.14%     52.73MiB / 500MiB     10.55%    6.79MB / 211kB    1.02MB / 1.31MB   15
+     e9f4c9644a1c   cadvisor     7.31%     32.14MiB / 500MiB     6.43%     139kB / 6.66MB    500kB / 0B        18
+     ```
+
+3. **Automated Grafana Configuration**:
+
+   - Grafana is configured to load pre-defined data sources and dashboards for immediate monitoring, facilitated by provisioning files. The provisioning files include `prometheus-datasource.yml` for data sources, located in the `./config/provisioning/datasources` directory, and `dashboard_index.json` for dashboards, in the `./config/provisioning/dashboards` directory. The `grafana.ini` file is also tailored to enhance user experience:
+     - **Admin Credentials**: We provide default admin credentials in the `grafana.ini` configuration, which simplifies access by eliminating the need for users to go through the initial login process.
+     - **Default Dashboard Path**: A default dashboard path is set in `grafana.ini` to ensure that the dashboard with all the necessary panels is rendered when Grafana is accessed.
+
+   These files and settings are directly mounted into the Grafana container via Docker Compose volume mappings. This setup guarantees that upon startup, Grafana is pre-configured for monitoring, requiring no additional manual setup.
+
+4. **Begin Training Process**:
+
+   - The federated learning training automatically begins once all client containers are successfully connected to the Flower server. This synchronizes the learning process across all participating clients.
+
+By following these steps, you will have a fully functional federated learning environment with device heterogeneity and monitoring capabilities.
+
+## Model Training and Dataset Integration
+
+### Data Pipeline with FLWR-Datasets
+
+We have integrated [`flwr-datasets`](https://flower.ai/docs/datasets/) into our data pipeline, which is managed within the `load_data.py` file in the `helpers/` directory. This script facilitates standardized access to datasets across the federated network and incorporates a `data_sampling_percentage` argument. This argument allows users to specify the percentage of the dataset to be used for training and evaluation, accommodating devices with lower memory capabilities to prevent Out-of-Memory (OOM) errors.
+
+### Model Selection and Dataset
+
+For the federated learning system, we have selected the MobileNet model due to its efficiency in image classification tasks. The model is trained and evaluated on the CIFAR-10 dataset. The combination of MobileNet and CIFAR-10 is ideal for demonstrating the capabilities of our federated learning solution in a heterogeneous device environment.
+
+- **MobileNet**: A streamlined architecture for mobile and embedded devices that balances performance and computational cost.
+- **CIFAR-10 Dataset**: A standard benchmark dataset for image classification, containing various object classes that pose a comprehensive challenge for the learning model.
+
+By integrating these components, our framework is well-prepared to handle the intricacies of training over a distributed network with varying device capabilities and data availability.
+
+## Visualizing with Grafana
+
+### Access Grafana Dashboard
+
+Visit `http://localhost:3000` to enter Grafana. The automated setup ensures that you're greeted with a series of pre-configured dashboards, including the default screen with a comprehensive set of graphs. These dashboards are ready for immediate monitoring and can be customized to suit your specific requirements.
+
+### Dashboard Configuration
+
+The `dashboard_index.json` file, located in the `./config/provisioning/dashboards` directory, serves as the backbone of our Grafana dashboard's configuration. It defines the structure and settings of the dashboard panels, which are rendered when you access Grafana. This JSON file contains the specifications for various panels such as model accuracy, CPU usage, memory utilization, and network traffic. Each panel's configuration includes the data source, queries, visualization type, and other display settings like thresholds and colors.
+
+For instance, in our project setup, the `dashboard_index.json` configures a panel to display the model's accuracy over time using a time-series graph, and another panel to show the CPU usage across clients using a graph that plots data points as they are received. This file is fundamental for creating a customized and informative dashboard that provides a snapshot of the federated learning system's health and performance metrics.
+
+By modifying the `dashboard_index.json` file, users can tailor the Grafana dashboard to include additional metrics or change the appearance and behavior of existing panels to better fit their monitoring requirements.
+
+### Grafana Default Dashboard
+
+Below is the default Grafana dashboard that users will see upon accessing Grafana:
+
+<img width="1440" alt="grafana_home_screen" src="https://github.com/ChoosyDevs/Choosy/assets/59146613/46c1016d-2376-4fdc-ae5f-68c550fc8e46">
+
+This comprehensive dashboard provides insights into various system metrics across client-server containers. It includes visualizations such as:
+
+- **Application Metrics**: The "Model Accuracy" graph shows an upward trend as rounds of training progress, which is a positive indicator of the model learning and improving over time. Conversely, the "Model Loss" graph trends downward, suggesting that the model is becoming more precise and making fewer mistakes as it trains.
+
+- **CPU Usage**: The sharp spikes in the red graph, representing "client1", indicate peak CPU usage, which is considerably higher than that of "client2" (blue graph). This difference is due to "client1" being allocated more computing resources (up to 4 CPU cores) compared to "client2", which is limited to just 1 CPU core, hence the more subdued CPU usage pattern.
+
+- **Memory Utilization**: Both clients are allocated a similar amount of memory, reflected in the nearly same lines for memory usage. This uniform allocation allows for a straightforward comparison of how each client manages memory under similar conditions.
+
+- **Network Traffic**: Monitor incoming and outgoing network traffic to each client, which is crucial for understanding data exchange volumes during federated learning cycles.
+
+Together, these metrics paint a detailed picture of the federated learning operation, showcasing resource usage and model performance. Such insights are invaluable for system optimization, ensuring balanced load distribution and efficient model training.
+
+## Comprehensive Monitoring System Integration
+
+### Capturing Container Metrics with cAdvisor
+
+cAdvisor is seamlessly integrated into our monitoring setup to capture a variety of system and container metrics, such as CPU, memory, and network usage. These metrics are vital for analyzing the performance and resource consumption of the containers in the federated learning environment.
+
+### Custom Metrics: Setup and Monitoring via Prometheus
+
+In addition to the standard metrics captured by cAdvisor, we have implemented a process to track custom metrics like model's accuracy and loss within Grafana, using Prometheus as the backbone for metric collection.
+
+1. **Prometheus Client Installation**:
+
+   - We began by installing the `prometheus_client` library in our Python environment, enabling us to define and expose custom metrics that Prometheus can scrape.
+
+2. **Defining Metrics in Server Script**:
+
+   - Within our `server.py` script, we have established two key Prometheus Gauge metrics, specifically tailored for monitoring our federated learning model: `model_accuracy` and `model_loss`. These custom gauges are instrumental in capturing the most recent values of the model's accuracy and loss, which are essential metrics for evaluating the model's performance. The gauges are defined as follows:
+
+     ```python
+     from prometheus_client import Gauge
+
+     accuracy_gauge = Gauge('model_accuracy', 'Current accuracy of the global model')
+     loss_gauge = Gauge('model_loss', 'Current loss of the global model')
+     ```
+
+3. **Exposing Metrics via HTTP Endpoint**:
+
+   - We leveraged the `start_http_server` function from the `prometheus_client` library to launch an HTTP server on port 8000. This server provides the `/metrics` endpoint, where the custom metrics are accessible for Prometheus scraping. The function is called at the end of the `main` method in `server.py`:
+
+     ```python
+     start_http_server(8000)
+     ```
+
+4. **Updating Metrics Recording Strategy**:
+
+   - The core of our metrics tracking lies in the `strategy.py` file, particularly within the `aggregate_evaluate` method. This method is crucial as it's where the federated learning model's accuracy and loss values are computed after each round of training with the aggregated data from all clients.
+
+     ```python
+        self.accuracy_gauge.set(accuracy_aggregated)
+        self.loss_gauge.set(loss_aggregated)
+     ```
+
+5. **Configuring Prometheus Scraping**:
+
+   - In the `prometheus.yml` file, under `scrape_configs`, we configured a new job to scrape the custom metrics from the HTTP server. This setup includes the job's name, the scraping interval, and the target server's URL.
+
+### Visualizing the Monitoring Architecture
+
+The image below depicts the Prometheus scraping process as it is configured in our monitoring setup. Within this architecture:
+
+- The "Prometheus server" is the central component that retrieves and stores metrics.
+- "cAdvisor" and the "HTTP server" we set up to expose our custom metrics are represented as "Prometheus targets" in the diagram. cAdvisor captures container metrics, while the HTTP server serves our custom `model_accuracy` and `model_loss` metrics at the `/metrics` endpoint.
+- These targets are periodically scraped by the Prometheus server, aggregating data from both system-level and custom performance metrics.
+- The aggregated data is then made available to the "Prometheus web UI" and "Grafana," as shown, enabling detailed visualization and analysis through the Grafana dashboard.
+
+<img width="791" alt="prometheus-architecture" src="https://github.com/ChoosyDevs/Choosy/assets/59146613/3b915e04-f12c-4aef-99ff-d75853234728">
+
+By incorporating these steps, we have enriched our monitoring capabilities to not only include system-level metrics but also critical performance indicators of our federated learning model. This approach is pivotal for understanding and improving the learning process. Similarly, you can apply this methodology to track any other metric that you find interesting or relevant to your specific needs. This flexibility allows for a comprehensive and customized monitoring environment, tailored to the unique aspects and requirements of your federated learning system.
+
+## Additional Resources
+
+- **Grafana Tutorials**: Explore a variety of tutorials on Grafana at [Grafana Tutorials](https://grafana.com/tutorials/).
+- **Prometheus Overview**: Learn more about Prometheus at their [official documentation](https://prometheus.io/docs/introduction/overview/).
+- **cAdvisor Guide**: For information on monitoring Docker containers with cAdvisor, see this [Prometheus guide](https://prometheus.io/docs/guides/cadvisor/).
+
+## Conclusion
+
+This project serves as a foundational example of managing device heterogeneity within the federated learning context, employing the Flower framework alongside Docker, Prometheus, and Grafana. It's designed to be a starting point for users to explore and further adapt to the complexities of device heterogeneity in federated learning environments.
diff --git a/examples/flower-via-docker-compose/client.py b/examples/flower-via-docker-compose/client.py
new file mode 100644
index 000000000000..c894143532a1
--- /dev/null
+++ b/examples/flower-via-docker-compose/client.py
@@ -0,0 +1,110 @@
+import os
+import argparse
+import flwr as fl
+import tensorflow as tf
+import logging
+from helpers.load_data import load_data
+import os
+from model.model import Model
+
+logging.basicConfig(level=logging.INFO)  # Configure logging
+logger = logging.getLogger(__name__)  # Create logger for the module
+
+# Make TensorFlow log less verbose
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+# Parse command line arguments
+parser = argparse.ArgumentParser(description="Flower client")
+
+parser.add_argument(
+    "--server_address", type=str, default="server:8080", help="Address of the server"
+)
+parser.add_argument(
+    "--batch_size", type=int, default=32, help="Batch size for training"
+)
+parser.add_argument(
+    "--learning_rate", type=float, default=0.1, help="Learning rate for the optimizer"
+)
+parser.add_argument("--client_id", type=int, default=1, help="Unique ID for the client")
+parser.add_argument(
+    "--total_clients", type=int, default=2, help="Total number of clients"
+)
+parser.add_argument(
+    "--data_percentage", type=float, default=0.5, help="Portion of client data to use"
+)
+
+args = parser.parse_args()
+
+# Create an instance of the model and pass the learning rate as an argument
+model = Model(learning_rate=args.learning_rate)
+
+# Compile the model
+model.compile()
+
+
+class Client(fl.client.NumPyClient):
+    def __init__(self, args):
+        self.args = args
+
+        logger.info("Preparing data...")
+        (x_train, y_train), (x_test, y_test) = load_data(
+            data_sampling_percentage=self.args.data_percentage,
+            client_id=self.args.client_id,
+            total_clients=self.args.total_clients,
+        )
+
+        self.x_train = x_train
+        self.y_train = y_train
+        self.x_test = x_test
+        self.y_test = y_test
+
+    def get_parameters(self, config):
+        # Return the parameters of the model
+        return model.get_model().get_weights()
+
+    def fit(self, parameters, config):
+        # Set the weights of the model
+        model.get_model().set_weights(parameters)
+
+        # Train the model
+        history = model.get_model().fit(
+            self.x_train, self.y_train, batch_size=self.args.batch_size
+        )
+
+        # Calculate evaluation metric
+        results = {
+            "accuracy": float(history.history["accuracy"][-1]),
+        }
+
+        # Get the parameters after training
+        parameters_prime = model.get_model().get_weights()
+
+        # Directly return the parameters and the number of examples trained on
+        return parameters_prime, len(self.x_train), results
+
+    def evaluate(self, parameters, config):
+        # Set the weights of the model
+        model.get_model().set_weights(parameters)
+
+        # Evaluate the model and get the loss and accuracy
+        loss, accuracy = model.get_model().evaluate(
+            self.x_test, self.y_test, batch_size=self.args.batch_size
+        )
+
+        # Return the loss, the number of examples evaluated on and the accuracy
+        return float(loss), len(self.x_test), {"accuracy": float(accuracy)}
+
+
+# Function to Start the Client
+def start_fl_client():
+    try:
+        client = Client(args).to_client()
+        fl.client.start_client(server_address=args.server_address, client=client)
+    except Exception as e:
+        logger.error("Error starting FL client: %s", e)
+        return {"status": "error", "message": str(e)}
+
+
+if __name__ == "__main__":
+    # Call the function to start the client
+    start_fl_client()
diff --git a/examples/flower-via-docker-compose/config/grafana.ini b/examples/flower-via-docker-compose/config/grafana.ini
new file mode 100644
index 000000000000..775f39d7ec22
--- /dev/null
+++ b/examples/flower-via-docker-compose/config/grafana.ini
@@ -0,0 +1,12 @@
+[security]
+allow_embedding = true
+admin_user = admin
+admin_password = admin
+
+[dashboards]
+default_home_dashboard_path = /etc/grafana/provisioning/dashboards/dashboard_index.json
+
+[auth.anonymous]
+enabled = true
+org_name = Main Org.
+org_role = Admin
diff --git a/examples/flower-via-docker-compose/config/prometheus.yml b/examples/flower-via-docker-compose/config/prometheus.yml
new file mode 100644
index 000000000000..46cf07b9dcee
--- /dev/null
+++ b/examples/flower-via-docker-compose/config/prometheus.yml
@@ -0,0 +1,19 @@
+
+global:
+  scrape_interval:     1s
+  evaluation_interval: 1s
+
+rule_files:
+scrape_configs:
+  - job_name: 'cadvisor'
+    scrape_interval: 1s 
+    metrics_path: '/metrics'
+    static_configs:
+      - targets: ['cadvisor:8080']
+        labels:
+          group: 'cadvisor'
+  - job_name: 'server_metrics'
+    scrape_interval: 1s
+    metrics_path: '/metrics'
+    static_configs:
+      - targets: ['server:8000']
\ No newline at end of file
diff --git a/examples/flower-via-docker-compose/config/provisioning/dashboards/dashboard_index.json b/examples/flower-via-docker-compose/config/provisioning/dashboards/dashboard_index.json
new file mode 100644
index 000000000000..b52f19c57508
--- /dev/null
+++ b/examples/flower-via-docker-compose/config/provisioning/dashboards/dashboard_index.json
@@ -0,0 +1,1051 @@
+{
+  "annotations": {
+    "list": [
+      {
+        "builtIn": 1,
+        "datasource": {
+          "type": "datasource",
+          "uid": "grafana"
+        },
+        "enable": true,
+        "hide": true,
+        "iconColor": "rgba(0, 211, 255, 1)",
+        "name": "Annotations & Alerts",
+        "type": "dashboard"
+      }
+    ]
+  },
+  "description": "Simple exporter for cadvisor only",
+  "editable": true,
+  "fiscalYearStartMonth": 0,
+  "gnetId": 14282,
+  "graphTooltip": 0,
+  "id": 12,
+  "links": [],
+  "liveNow": false,
+  "panels": [
+    {
+      "gridPos": {
+        "h": 1,
+        "w": 24,
+        "x": 0,
+        "y": 0
+      },
+      "id": 22,
+      "title": "Application metrics",
+      "type": "row"
+    },
+    {
+      "datasource": {
+        "type": "prometheus",
+        "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+      },
+      "description": "Averaged federated accuracy across clients",
+      "fieldConfig": {
+        "defaults": {
+          "color": {
+            "mode": "palette-classic"
+          },
+          "custom": {
+            "axisBorderShow": false,
+            "axisCenteredZero": false,
+            "axisColorMode": "text",
+            "axisLabel": "",
+            "axisPlacement": "auto",
+            "barAlignment": 0,
+            "drawStyle": "line",
+            "fillOpacity": 0,
+            "gradientMode": "none",
+            "hideFrom": {
+              "legend": false,
+              "tooltip": false,
+              "viz": false
+            },
+            "insertNulls": false,
+            "lineInterpolation": "smooth",
+            "lineStyle": {
+              "fill": "solid"
+            },
+            "lineWidth": 1,
+            "pointSize": 5,
+            "scaleDistribution": {
+              "type": "linear"
+            },
+            "showPoints": "auto",
+            "spanNulls": false,
+            "stacking": {
+              "group": "A",
+              "mode": "none"
+            },
+            "thresholdsStyle": {
+              "mode": "off"
+            }
+          },
+          "mappings": [],
+          "thresholds": {
+            "mode": "absolute",
+            "steps": [
+              {
+                "color": "green",
+                "value": null
+              },
+              {
+                "color": "red",
+                "value": 80
+              }
+            ]
+          }
+        },
+        "overrides": []
+      },
+      "gridPos": {
+        "h": 8,
+        "w": 12,
+        "x": 0,
+        "y": 1
+      },
+      "id": 23,
+      "options": {
+        "legend": {
+          "calcs": [],
+          "displayMode": "list",
+          "placement": "bottom",
+          "showLegend": false
+        },
+        "tooltip": {
+          "mode": "single",
+          "sort": "none"
+        }
+      },
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+          },
+          "disableTextWrap": false,
+          "editorMode": "builder",
+          "expr": "model_accuracy",
+          "fullMetaSearch": false,
+          "includeNullMetadata": true,
+          "instant": false,
+          "legendFormat": "__auto",
+          "range": true,
+          "refId": "A",
+          "useBackend": false
+        }
+      ],
+      "title": "Model Accuracy",
+      "type": "timeseries"
+    },
+    {
+      "datasource": {
+        "type": "prometheus",
+        "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+      },
+      "description": "Averaged Federated Loss across clients",
+      "fieldConfig": {
+        "defaults": {
+          "color": {
+            "mode": "palette-classic"
+          },
+          "custom": {
+            "axisBorderShow": false,
+            "axisCenteredZero": false,
+            "axisColorMode": "text",
+            "axisLabel": "",
+            "axisPlacement": "auto",
+            "barAlignment": 0,
+            "drawStyle": "line",
+            "fillOpacity": 0,
+            "gradientMode": "none",
+            "hideFrom": {
+              "legend": false,
+              "tooltip": false,
+              "viz": false
+            },
+            "insertNulls": false,
+            "lineInterpolation": "smooth",
+            "lineWidth": 1,
+            "pointSize": 5,
+            "scaleDistribution": {
+              "type": "linear"
+            },
+            "showPoints": "auto",
+            "spanNulls": false,
+            "stacking": {
+              "group": "A",
+              "mode": "none"
+            },
+            "thresholdsStyle": {
+              "mode": "off"
+            }
+          },
+          "mappings": [],
+          "thresholds": {
+            "mode": "absolute",
+            "steps": [
+              {
+                "color": "green",
+                "value": null
+              },
+              {
+                "color": "red",
+                "value": 80
+              }
+            ]
+          }
+        },
+        "overrides": []
+      },
+      "gridPos": {
+        "h": 8,
+        "w": 12,
+        "x": 12,
+        "y": 1
+      },
+      "id": 21,
+      "options": {
+        "legend": {
+          "calcs": [],
+          "displayMode": "list",
+          "placement": "bottom",
+          "showLegend": false
+        },
+        "tooltip": {
+          "mode": "single",
+          "sort": "none"
+        }
+      },
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+          },
+          "disableTextWrap": false,
+          "editorMode": "builder",
+          "expr": "model_loss",
+          "fullMetaSearch": false,
+          "includeNullMetadata": true,
+          "instant": false,
+          "legendFormat": "__auto",
+          "range": true,
+          "refId": "A",
+          "useBackend": false
+        }
+      ],
+      "title": "Model Loss",
+      "type": "timeseries"
+    },
+    {
+      "collapsed": false,
+      "datasource": {
+        "type": "prometheus",
+        "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+      },
+      "gridPos": {
+        "h": 1,
+        "w": 24,
+        "x": 0,
+        "y": 9
+      },
+      "id": 8,
+      "panels": [],
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+          },
+          "refId": "A"
+        }
+      ],
+      "title": "CPU",
+      "type": "row"
+    },
+    {
+      "aliasColors": {
+        "client1": "red",
+        "client2": "blue",
+        "server": "yellow"
+      },
+      "bars": false,
+      "dashLength": 10,
+      "dashes": false,
+      "datasource": {
+        "type": "prometheus",
+        "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+      },
+      "fill": 1,
+      "fillGradient": 0,
+      "gridPos": {
+        "h": 7,
+        "w": 24,
+        "x": 0,
+        "y": 10
+      },
+      "hiddenSeries": false,
+      "id": 15,
+      "legend": {
+        "alignAsTable": true,
+        "avg": true,
+        "current": false,
+        "max": true,
+        "min": false,
+        "rightSide": true,
+        "show": true,
+        "total": false,
+        "values": true
+      },
+      "lines": true,
+      "linewidth": 1,
+      "nullPointMode": "null as zero",
+      "options": {
+        "alertThreshold": true
+      },
+      "percentage": false,
+      "pluginVersion": "10.2.2",
+      "pointradius": 2,
+      "points": false,
+      "renderer": "flot",
+      "seriesOverrides": [],
+      "spaceLength": 10,
+      "stack": false,
+      "steppedLine": false,
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+          },
+          "editorMode": "code",
+          "expr": "sum(rate(container_cpu_usage_seconds_total{instance=~\"$host\",name=~\"$container\",name=~\".+\", name !~ \"(prometheus|cadvisor|grafana)\"}[10s])) by (name) *100",
+          "hide": false,
+          "interval": "",
+          "legendFormat": "{{name}}",
+          "range": true,
+          "refId": "A"
+        }
+      ],
+      "thresholds": [],
+      "timeRegions": [],
+      "title": "CPU Usage",
+      "tooltip": {
+        "shared": true,
+        "sort": 0,
+        "value_type": "individual"
+      },
+      "type": "graph",
+      "xaxis": {
+        "mode": "time",
+        "show": true,
+        "values": []
+      },
+      "yaxes": [
+        {
+          "$$hashKey": "object:606",
+          "format": "percent",
+          "logBase": 1,
+          "show": true
+        },
+        {
+          "$$hashKey": "object:607",
+          "format": "short",
+          "logBase": 1,
+          "show": true
+        }
+      ],
+      "yaxis": {
+        "align": false
+      }
+    },
+    {
+      "collapsed": false,
+      "datasource": {
+        "type": "prometheus",
+        "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+      },
+      "gridPos": {
+        "h": 1,
+        "w": 24,
+        "x": 0,
+        "y": 17
+      },
+      "id": 11,
+      "panels": [],
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+          },
+          "refId": "A"
+        }
+      ],
+      "title": "Memory",
+      "type": "row"
+    },
+    {
+      "aliasColors": {
+        "client1": "red",
+        "client2": "blue",
+        "server": "yellow"
+      },
+      "bars": false,
+      "dashLength": 10,
+      "dashes": false,
+      "datasource": {
+        "type": "prometheus",
+        "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+      },
+      "fill": 1,
+      "fillGradient": 0,
+      "gridPos": {
+        "h": 8,
+        "w": 12,
+        "x": 0,
+        "y": 18
+      },
+      "hiddenSeries": false,
+      "id": 9,
+      "legend": {
+        "alignAsTable": true,
+        "avg": true,
+        "current": false,
+        "max": true,
+        "min": false,
+        "rightSide": true,
+        "show": true,
+        "total": false,
+        "values": true
+      },
+      "lines": true,
+      "linewidth": 1,
+      "nullPointMode": "null as zero",
+      "options": {
+        "alertThreshold": true
+      },
+      "percentage": false,
+      "pluginVersion": "10.2.2",
+      "pointradius": 2,
+      "points": false,
+      "renderer": "flot",
+      "seriesOverrides": [],
+      "spaceLength": 10,
+      "stack": false,
+      "steppedLine": false,
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+          },
+          "editorMode": "code",
+          "expr": "sum(container_memory_rss{instance=~\"$host\",name=~\"$container\",name=~\".+\", name !~ \"(prometheus|cadvisor|grafana)\"}) by (name)",
+          "hide": false,
+          "interval": "",
+          "legendFormat": "{{name}}",
+          "range": true,
+          "refId": "A"
+        }
+      ],
+      "thresholds": [],
+      "timeRegions": [],
+      "title": "Memory Usage",
+      "tooltip": {
+        "shared": true,
+        "sort": 0,
+        "value_type": "individual"
+      },
+      "type": "graph",
+      "xaxis": {
+        "mode": "time",
+        "show": true,
+        "values": []
+      },
+      "yaxes": [
+        {
+          "$$hashKey": "object:606",
+          "format": "bytes",
+          "logBase": 1,
+          "show": true
+        },
+        {
+          "$$hashKey": "object:607",
+          "format": "short",
+          "logBase": 1,
+          "show": true
+        }
+      ],
+      "yaxis": {
+        "align": false
+      }
+    },
+    {
+      "aliasColors": {
+        "client1": "red",
+        "client2": "blue",
+        "server": "yellow"
+      },
+      "bars": false,
+      "dashLength": 10,
+      "dashes": false,
+      "datasource": {
+        "type": "prometheus",
+        "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+      },
+      "fill": 1,
+      "fillGradient": 0,
+      "gridPos": {
+        "h": 8,
+        "w": 12,
+        "x": 12,
+        "y": 18
+      },
+      "hiddenSeries": false,
+      "id": 14,
+      "legend": {
+        "alignAsTable": true,
+        "avg": true,
+        "current": false,
+        "max": true,
+        "min": false,
+        "rightSide": true,
+        "show": true,
+        "total": false,
+        "values": true
+      },
+      "lines": true,
+      "linewidth": 1,
+      "nullPointMode": "null as zero",
+      "options": {
+        "alertThreshold": true
+      },
+      "percentage": false,
+      "pluginVersion": "10.2.2",
+      "pointradius": 2,
+      "points": false,
+      "renderer": "flot",
+      "seriesOverrides": [],
+      "spaceLength": 10,
+      "stack": false,
+      "steppedLine": false,
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+          },
+          "editorMode": "code",
+          "expr": "sum(container_memory_cache{instance=~\"$host\",name=~\"$container\",name=~\".+\", name !~ \"(prometheus|cadvisor|grafana)\"}) by (name)",
+          "hide": false,
+          "interval": "",
+          "legendFormat": "{{name}}",
+          "range": true,
+          "refId": "A"
+        }
+      ],
+      "thresholds": [],
+      "timeRegions": [],
+      "title": "Memory Cached",
+      "tooltip": {
+        "shared": true,
+        "sort": 0,
+        "value_type": "individual"
+      },
+      "type": "graph",
+      "xaxis": {
+        "mode": "time",
+        "show": true,
+        "values": []
+      },
+      "yaxes": [
+        {
+          "$$hashKey": "object:606",
+          "format": "bytes",
+          "logBase": 1,
+          "show": true
+        },
+        {
+          "$$hashKey": "object:607",
+          "format": "short",
+          "logBase": 1,
+          "show": true
+        }
+      ],
+      "yaxis": {
+        "align": false
+      }
+    },
+    {
+      "collapsed": false,
+      "datasource": {
+        "type": "prometheus",
+        "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+      },
+      "gridPos": {
+        "h": 1,
+        "w": 24,
+        "x": 0,
+        "y": 26
+      },
+      "id": 2,
+      "panels": [],
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+          },
+          "refId": "A"
+        }
+      ],
+      "title": "Network",
+      "type": "row"
+    },
+    {
+      "aliasColors": {
+        "client1": "red",
+        "client2": "blue",
+        "server": "yellow"
+      },
+      "bars": false,
+      "dashLength": 10,
+      "dashes": false,
+      "datasource": {
+        "type": "prometheus",
+        "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+      },
+      "fill": 1,
+      "fillGradient": 0,
+      "gridPos": {
+        "h": 8,
+        "w": 12,
+        "x": 0,
+        "y": 27
+      },
+      "hiddenSeries": false,
+      "id": 4,
+      "legend": {
+        "alignAsTable": true,
+        "avg": true,
+        "current": false,
+        "hideEmpty": false,
+        "hideZero": false,
+        "max": true,
+        "min": false,
+        "rightSide": true,
+        "show": true,
+        "total": false,
+        "values": true
+      },
+      "lines": true,
+      "linewidth": 1,
+      "nullPointMode": "null",
+      "options": {
+        "alertThreshold": true
+      },
+      "percentage": false,
+      "pluginVersion": "10.2.2",
+      "pointradius": 2,
+      "points": false,
+      "renderer": "flot",
+      "seriesOverrides": [],
+      "spaceLength": 10,
+      "stack": false,
+      "steppedLine": false,
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+          },
+          "editorMode": "code",
+          "expr": "sum(rate(container_network_receive_bytes_total{instance=~\"$host\",name=~\"$container\",name=~\".+\", name !~ \"(prometheus|cadvisor|grafana)\"}[10s])) by (name)",
+          "hide": false,
+          "interval": "",
+          "legendFormat": "{{name}}",
+          "range": true,
+          "refId": "A"
+        }
+      ],
+      "thresholds": [],
+      "timeRegions": [],
+      "title": "Received Network Traffic",
+      "tooltip": {
+        "shared": true,
+        "sort": 0,
+        "value_type": "individual"
+      },
+      "type": "graph",
+      "xaxis": {
+        "mode": "time",
+        "show": true,
+        "values": []
+      },
+      "yaxes": [
+        {
+          "$$hashKey": "object:674",
+          "format": "Bps",
+          "logBase": 1,
+          "show": true
+        },
+        {
+          "$$hashKey": "object:675",
+          "format": "short",
+          "logBase": 1,
+          "show": true
+        }
+      ],
+      "yaxis": {
+        "align": false
+      }
+    },
+    {
+      "aliasColors": {
+        "client1": "red",
+        "client2": "blue",
+        "server": "yellow"
+      },
+      "bars": false,
+      "dashLength": 10,
+      "dashes": false,
+      "datasource": {
+        "type": "prometheus",
+        "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+      },
+      "fill": 1,
+      "fillGradient": 0,
+      "gridPos": {
+        "h": 8,
+        "w": 12,
+        "x": 12,
+        "y": 27
+      },
+      "hiddenSeries": false,
+      "id": 6,
+      "legend": {
+        "alignAsTable": true,
+        "avg": true,
+        "current": false,
+        "max": true,
+        "min": false,
+        "rightSide": true,
+        "show": true,
+        "total": false,
+        "values": true
+      },
+      "lines": true,
+      "linewidth": 1,
+      "nullPointMode": "null",
+      "options": {
+        "alertThreshold": true
+      },
+      "percentage": false,
+      "pluginVersion": "10.2.2",
+      "pointradius": 2,
+      "points": false,
+      "renderer": "flot",
+      "seriesOverrides": [],
+      "spaceLength": 10,
+      "stack": false,
+      "steppedLine": false,
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+          },
+          "editorMode": "code",
+          "expr": "sum(rate(container_network_transmit_bytes_total{instance=~\"$host\",name=~\"$container\",name=~\".+\", name !~ \"(prometheus|cadvisor|grafana)\"}[10s])) by (name)",
+          "interval": "",
+          "legendFormat": "{{name}}",
+          "range": true,
+          "refId": "A"
+        }
+      ],
+      "thresholds": [],
+      "timeRegions": [],
+      "title": "Sent Network Traffic",
+      "tooltip": {
+        "shared": true,
+        "sort": 0,
+        "value_type": "individual"
+      },
+      "type": "graph",
+      "xaxis": {
+        "mode": "time",
+        "show": true,
+        "values": []
+      },
+      "yaxes": [
+        {
+          "$$hashKey": "object:832",
+          "format": "Bps",
+          "logBase": 1,
+          "show": true
+        },
+        {
+          "$$hashKey": "object:833",
+          "format": "short",
+          "logBase": 1,
+          "show": true
+        }
+      ],
+      "yaxis": {
+        "align": false
+      }
+    },
+    {
+      "collapsed": false,
+      "datasource": {
+        "type": "prometheus",
+        "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+      },
+      "gridPos": {
+        "h": 1,
+        "w": 24,
+        "x": 0,
+        "y": 35
+      },
+      "id": 19,
+      "panels": [],
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+          },
+          "refId": "A"
+        }
+      ],
+      "title": "Misc",
+      "type": "row"
+    },
+    {
+      "datasource": {
+        "type": "prometheus",
+        "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+      },
+      "fieldConfig": {
+        "defaults": {
+          "custom": {
+            "align": "auto",
+            "cellOptions": {
+              "type": "auto"
+            },
+            "filterable": false,
+            "inspect": false
+          },
+          "mappings": [],
+          "thresholds": {
+            "mode": "absolute",
+            "steps": [
+              {
+                "color": "green"
+              },
+              {
+                "color": "red",
+                "value": 80
+              }
+            ]
+          }
+        },
+        "overrides": [
+          {
+            "matcher": {
+              "id": "byName",
+              "options": "id"
+            },
+            "properties": [
+              {
+                "id": "custom.width",
+                "value": 260
+              }
+            ]
+          },
+          {
+            "matcher": {
+              "id": "byName",
+              "options": "Running"
+            },
+            "properties": [
+              {
+                "id": "unit",
+                "value": "d"
+              },
+              {
+                "id": "decimals",
+                "value": 1
+              },
+              {
+                "id": "custom.cellOptions",
+                "value": {
+                  "type": "color-text"
+                }
+              },
+              {
+                "id": "color",
+                "value": {
+                  "fixedColor": "dark-green",
+                  "mode": "fixed"
+                }
+              }
+            ]
+          }
+        ]
+      },
+      "gridPos": {
+        "h": 10,
+        "w": 24,
+        "x": 0,
+        "y": 36
+      },
+      "id": 17,
+      "options": {
+        "cellHeight": "sm",
+        "footer": {
+          "countRows": false,
+          "fields": "",
+          "reducer": [
+            "sum"
+          ],
+          "show": false
+        },
+        "showHeader": true,
+        "sortBy": []
+      },
+      "pluginVersion": "10.2.2",
+      "targets": [
+        {
+          "datasource": {
+            "type": "prometheus",
+            "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+          },
+          "expr": "(time() - container_start_time_seconds{instance=~\"$host\",name=~\"$container\",name=~\".+\"})/86400",
+          "format": "table",
+          "instant": true,
+          "interval": "",
+          "legendFormat": "{{name}}",
+          "refId": "A"
+        }
+      ],
+      "title": "Containers Info",
+      "transformations": [
+        {
+          "id": "filterFieldsByName",
+          "options": {
+            "include": {
+              "names": [
+                "container_label_com_docker_compose_project",
+                "container_label_com_docker_compose_project_working_dir",
+                "image",
+                "instance",
+                "name",
+                "Value",
+                "container_label_com_docker_compose_service"
+              ]
+            }
+          }
+        },
+        {
+          "id": "organize",
+          "options": {
+            "excludeByName": {},
+            "indexByName": {},
+            "renameByName": {
+              "Value": "Running",
+              "container_label_com_docker_compose_project": "Label",
+              "container_label_com_docker_compose_project_working_dir": "Working dir",
+              "container_label_com_docker_compose_service": "Service",
+              "image": "Registry Image",
+              "instance": "Instance",
+              "name": "Name"
+            }
+          }
+        }
+      ],
+      "type": "table"
+    }
+  ],
+  "refresh": "auto",
+  "schemaVersion": 38,
+  "tags": [],
+  "templating": {
+    "list": [
+      {
+        "allValue": ".*",
+        "current": {
+          "selected": false,
+          "text": "All",
+          "value": "$__all"
+        },
+        "datasource": {
+          "type": "prometheus",
+          "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+        },
+        "definition": "label_values({__name__=~\"container.*\"},instance)",
+        "hide": 0,
+        "includeAll": true,
+        "label": "Host",
+        "multi": false,
+        "name": "host",
+        "options": [],
+        "query": {
+          "query": "label_values({__name__=~\"container.*\"},instance)",
+          "refId": "Prometheus-host-Variable-Query"
+        },
+        "refresh": 1,
+        "regex": "",
+        "skipUrlSync": false,
+        "sort": 5,
+        "tagValuesQuery": "",
+        "tagsQuery": "",
+        "type": "query",
+        "useTags": false
+      },
+      {
+        "allValue": ".*",
+        "current": {
+          "selected": false,
+          "text": "All",
+          "value": "$__all"
+        },
+        "datasource": {
+          "type": "prometheus",
+          "uid": "db69454e-e558-479e-b4fc-80db52bf91da"
+        },
+        "definition": "label_values({__name__=~\"container.*\", instance=~\"$host\"},name)",
+        "hide": 0,
+        "includeAll": true,
+        "label": "Container",
+        "multi": false,
+        "name": "container",
+        "options": [],
+        "query": {
+          "query": "label_values({__name__=~\"container.*\", instance=~\"$host\"},name)",
+          "refId": "Prometheus-container-Variable-Query"
+        },
+        "refresh": 1,
+        "regex": "",
+        "skipUrlSync": false,
+        "sort": 0,
+        "tagValuesQuery": "",
+        "tagsQuery": "",
+        "type": "query",
+        "useTags": false
+      }
+    ]
+  },
+  "time": {
+    "from": "now-15m",
+    "to": "now"
+  },
+  "timepicker": {},
+  "timezone": "",
+  "title": "Cadvisor exporter Copy",
+  "uid": "fcf2a8da-792c-4b9f-a22f-876820b53c2f",
+  "version": 2,
+  "weekStart": ""
+}
\ No newline at end of file
diff --git a/examples/flower-via-docker-compose/config/provisioning/dashboards/dashboards.yml b/examples/flower-via-docker-compose/config/provisioning/dashboards/dashboards.yml
new file mode 100644
index 000000000000..e0d542f58f2b
--- /dev/null
+++ b/examples/flower-via-docker-compose/config/provisioning/dashboards/dashboards.yml
@@ -0,0 +1,12 @@
+apiVersion: 1
+
+providers:
+- name: 'default'
+  orgId: 1
+  folder: ''
+  type: file
+  disableDeletion: false
+  editable: true
+  updateIntervalSeconds: 5 # Optional: How often Grafana will scan for changed dashboards
+  options:
+    path: /etc/grafana/provisioning/dashboards
diff --git a/examples/flower-via-docker-compose/config/provisioning/datasources/prometheus-datasource.yml b/examples/flower-via-docker-compose/config/provisioning/datasources/prometheus-datasource.yml
new file mode 100644
index 000000000000..7c8ce00fdcdc
--- /dev/null
+++ b/examples/flower-via-docker-compose/config/provisioning/datasources/prometheus-datasource.yml
@@ -0,0 +1,9 @@
+apiVersion: 1
+
+datasources:
+- name: Prometheus
+  type: prometheus
+  access: proxy  
+  uid: db69454e-e558-479e-b4fc-80db52bf91da 
+  url: http://host.docker.internal:9090
+  isDefault: true
diff --git a/examples/flower-via-docker-compose/helpers/generate_docker_compose.py b/examples/flower-via-docker-compose/helpers/generate_docker_compose.py
new file mode 100644
index 000000000000..cde553a95e68
--- /dev/null
+++ b/examples/flower-via-docker-compose/helpers/generate_docker_compose.py
@@ -0,0 +1,147 @@
+import random
+import argparse
+
+parser = argparse.ArgumentParser(description="Generated Docker Compose")
+parser.add_argument(
+    "--total_clients", type=int, default=2, help="Total clients to spawn (default: 2)"
+)
+parser.add_argument(
+    "--num_rounds", type=int, default=100, help="Number of FL rounds (default: 100)"
+)
+parser.add_argument(
+    "--data_percentage",
+    type=float,
+    default=0.6,
+    help="Portion of client data to use (default: 0.6)",
+)
+parser.add_argument(
+    "--random", action="store_true", help="Randomize client configurations"
+)
+
+
+def create_docker_compose(args):
+    # cpus is used to set the number of CPUs available to the container as a fraction of the total number of CPUs on the host machine.
+    # mem_limit is used to set the memory limit for the container.
+    client_configs = [
+        {"mem_limit": "3g", "batch_size": 32, "cpus": 4, "learning_rate": 0.001},
+        {"mem_limit": "6g", "batch_size": 256, "cpus": 1, "learning_rate": 0.05},
+        {"mem_limit": "4g", "batch_size": 64, "cpus": 3, "learning_rate": 0.02},
+        {"mem_limit": "5g", "batch_size": 128, "cpus": 2.5, "learning_rate": 0.09},
+        # Add or modify the configurations depending on your host machine
+    ]
+
+    docker_compose_content = f"""
+version: '3'
+services:
+  prometheus:
+    image: prom/prometheus:latest
+    container_name: prometheus
+    ports:
+      - 9090:9090
+    deploy:
+      restart_policy:
+        condition: on-failure
+    command:
+      - --config.file=/etc/prometheus/prometheus.yml
+    volumes:
+      - ./config/prometheus.yml:/etc/prometheus/prometheus.yml:ro
+    depends_on:
+      - cadvisor
+
+  cadvisor:
+    image: gcr.io/cadvisor/cadvisor:v0.47.0
+    container_name: cadvisor
+    privileged: true
+    deploy:
+      restart_policy:
+        condition: on-failure
+    ports:
+      - "8080:8080"
+    volumes:
+      - /:/rootfs:ro
+      - /var/run:/var/run:ro
+      - /sys:/sys:ro
+      - /var/lib/docker/:/var/lib/docker:ro
+      - /dev/disk/:/dev/disk:ro
+      - /var/run/docker.sock:/var/run/docker.sock  
+
+  grafana:
+    image: grafana/grafana:latest
+    container_name: grafana
+    ports:
+      - 3000:3000
+    deploy:
+      restart_policy:
+        condition: on-failure
+    volumes:
+      - grafana-storage:/var/lib/grafana
+      - ./config/grafana.ini:/etc/grafana/grafana.ini
+      - ./config/provisioning/datasources:/etc/grafana/provisioning/datasources
+      - ./config/provisioning/dashboards:/etc/grafana/provisioning/dashboards
+    depends_on:
+      - prometheus
+      - cadvisor
+    command:
+      - --config=/etc/grafana/grafana.ini
+
+
+  server:
+    container_name: server
+    build:
+      context: .
+      dockerfile: Dockerfile
+    command: python server.py --number_of_rounds={args.num_rounds}
+    environment:
+      FLASK_RUN_PORT: 6000
+      DOCKER_HOST_IP: host.docker.internal
+    volumes:
+      - .:/app
+      - /var/run/docker.sock:/var/run/docker.sock      
+    ports:
+      - "6000:6000"
+      - "8265:8265"
+      - "8000:8000"
+    depends_on:
+      - prometheus
+      - grafana
+"""
+    # Add client services
+    for i in range(1, args.total_clients + 1):
+        if args.random:
+            config = random.choice(client_configs)
+        else:
+            config = client_configs[(i - 1) % len(client_configs)]
+        docker_compose_content += f"""
+  client{i}:
+    container_name: client{i}
+    build:
+      context: .
+      dockerfile: Dockerfile
+    command: python client.py --server_address=server:8080 --data_percentage={args.data_percentage}  --client_id={i} --total_clients={args.total_clients} --batch_size={config["batch_size"]} --learning_rate={config["learning_rate"]}
+    deploy:
+      resources:
+        limits:
+          cpus: "{(config['cpus'])}"
+          memory: "{config['mem_limit']}"
+    volumes:
+      - .:/app
+      - /var/run/docker.sock:/var/run/docker.sock
+    ports:
+      - "{6000 + i}:{6000 + i}"
+    depends_on:
+      - server
+    environment:
+      FLASK_RUN_PORT: {6000 + i}
+      container_name: client{i}
+      DOCKER_HOST_IP: host.docker.internal
+"""
+
+    docker_compose_content += "volumes:\n  grafana-storage:\n"
+
+    with open("docker-compose.yml", "w") as file:
+        file.write(docker_compose_content)
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    create_docker_compose(args)
diff --git a/examples/flower-via-docker-compose/helpers/load_data.py b/examples/flower-via-docker-compose/helpers/load_data.py
new file mode 100644
index 000000000000..1f2784946868
--- /dev/null
+++ b/examples/flower-via-docker-compose/helpers/load_data.py
@@ -0,0 +1,37 @@
+import numpy as np
+import tensorflow as tf
+from flwr_datasets import FederatedDataset
+import logging
+
+logging.basicConfig(level=logging.INFO)  # Configure logging
+logger = logging.getLogger(__name__)  # Create logger for the module
+
+
+def load_data(data_sampling_percentage=0.5, client_id=1, total_clients=2):
+    """Load federated dataset partition based on client ID.
+
+    Args:
+        data_sampling_percentage (float): Percentage of the dataset to use for training.
+        client_id (int): Unique ID for the client.
+        total_clients (int): Total number of clients.
+
+    Returns:
+        Tuple of arrays: `(x_train, y_train), (x_test, y_test)`.
+    """
+
+    # Download and partition dataset
+    fds = FederatedDataset(dataset="cifar10", partitioners={"train": total_clients})
+    partition = fds.load_partition(client_id - 1, "train")
+    partition.set_format("numpy")
+
+    # Divide data on each client: 80% train, 20% test
+    partition = partition.train_test_split(test_size=0.2)
+    x_train, y_train = partition["train"]["img"] / 255.0, partition["train"]["label"]
+    x_test, y_test = partition["test"]["img"] / 255.0, partition["test"]["label"]
+
+    # Apply data sampling
+    num_samples = int(data_sampling_percentage * len(x_train))
+    indices = np.random.choice(len(x_train), num_samples, replace=False)
+    x_train, y_train = x_train[indices], y_train[indices]
+
+    return (x_train, y_train), (x_test, y_test)
diff --git a/examples/flower-via-docker-compose/model/model.py b/examples/flower-via-docker-compose/model/model.py
new file mode 100644
index 000000000000..ab26d089b858
--- /dev/null
+++ b/examples/flower-via-docker-compose/model/model.py
@@ -0,0 +1,18 @@
+import tensorflow as tf
+
+
+# Class for the model. In this case, we are using the MobileNetV2 model from Keras
+class Model:
+    def __init__(self, learning_rate):
+        self.learning_rate = learning_rate
+        self.loss_function = tf.keras.losses.SparseCategoricalCrossentropy()
+        self.model = tf.keras.applications.MobileNetV2(
+            (32, 32, 3), alpha=0.1, classes=10, weights=None
+        )
+        self.optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate)
+
+    def compile(self):
+        self.model.compile(self.optimizer, self.loss_function, metrics=["accuracy"])
+
+    def get_model(self):
+        return self.model
diff --git a/examples/flower-via-docker-compose/requirements.txt b/examples/flower-via-docker-compose/requirements.txt
new file mode 100644
index 000000000000..b93e5b1d9f2b
--- /dev/null
+++ b/examples/flower-via-docker-compose/requirements.txt
@@ -0,0 +1,5 @@
+flwr==1.7.0
+tensorflow==2.13.1
+numpy==1.24.3
+prometheus_client == 0.19.0
+flwr_datasets[vision] == 0.0.2
diff --git a/examples/flower-via-docker-compose/server.py b/examples/flower-via-docker-compose/server.py
new file mode 100644
index 000000000000..99d1a7ef7399
--- /dev/null
+++ b/examples/flower-via-docker-compose/server.py
@@ -0,0 +1,47 @@
+import argparse
+import flwr as fl
+import logging
+from strategy.strategy import FedCustom
+from prometheus_client import start_http_server, Gauge
+
+# Initialize Logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Define a gauge to track the global model accuracy
+accuracy_gauge = Gauge("model_accuracy", "Current accuracy of the global model")
+
+# Define a gauge to track the global model loss
+loss_gauge = Gauge("model_loss", "Current loss of the global model")
+
+# Parse command line arguments
+parser = argparse.ArgumentParser(description="Flower Server")
+parser.add_argument(
+    "--number_of_rounds",
+    type=int,
+    default=100,
+    help="Number of FL rounds (default: 100)",
+)
+args = parser.parse_args()
+
+
+# Function to Start Federated Learning Server
+def start_fl_server(strategy, rounds):
+    try:
+        fl.server.start_server(
+            server_address="0.0.0.0:8080",
+            config=fl.server.ServerConfig(num_rounds=rounds),
+            strategy=strategy,
+        )
+    except Exception as e:
+        logger.error(f"FL Server error: {e}", exc_info=True)
+
+
+# Main Function
+if __name__ == "__main__":
+    # Start Prometheus Metrics Server
+    start_http_server(8000)
+
+    # Initialize Strategy Instance and Start FL Server
+    strategy_instance = FedCustom(accuracy_gauge=accuracy_gauge, loss_gauge=loss_gauge)
+    start_fl_server(strategy=strategy_instance, rounds=args.number_of_rounds)
diff --git a/examples/flower-via-docker-compose/strategy/strategy.py b/examples/flower-via-docker-compose/strategy/strategy.py
new file mode 100644
index 000000000000..9471a99f037f
--- /dev/null
+++ b/examples/flower-via-docker-compose/strategy/strategy.py
@@ -0,0 +1,60 @@
+from typing import Dict, List, Optional, Tuple, Union
+from flwr.common import Scalar, EvaluateRes
+from flwr.server.client_proxy import ClientProxy
+from flwr.server.strategy.aggregate import aggregate, weighted_loss_avg
+import flwr as fl
+import logging
+from prometheus_client import Gauge
+
+logging.basicConfig(level=logging.INFO)  # Configure logging
+logger = logging.getLogger(__name__)  # Create logger for the module
+
+
+class FedCustom(fl.server.strategy.FedAvg):
+    def __init__(
+        self, accuracy_gauge: Gauge = None, loss_gauge: Gauge = None, *args, **kwargs
+    ):
+        super().__init__(*args, **kwargs)
+
+        self.accuracy_gauge = accuracy_gauge
+        self.loss_gauge = loss_gauge
+
+    def __repr__(self) -> str:
+        return "FedCustom"
+
+    def aggregate_evaluate(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, EvaluateRes]],
+        failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
+    ) -> Tuple[Optional[float], Dict[str, Scalar]]:
+        """Aggregate evaluation losses and accuracy using weighted average."""
+
+        if not results:
+            return None, {}
+
+        # Calculate weighted average for loss using the provided function
+        loss_aggregated = weighted_loss_avg(
+            [
+                (evaluate_res.num_examples, evaluate_res.loss)
+                for _, evaluate_res in results
+            ]
+        )
+
+        # Calculate weighted average for accuracy
+        accuracies = [
+            evaluate_res.metrics["accuracy"] * evaluate_res.num_examples
+            for _, evaluate_res in results
+        ]
+        examples = [evaluate_res.num_examples for _, evaluate_res in results]
+        accuracy_aggregated = (
+            sum(accuracies) / sum(examples) if sum(examples) != 0 else 0
+        )
+
+        # Update the Prometheus gauges with the latest aggregated accuracy and loss values
+        self.accuracy_gauge.set(accuracy_aggregated)
+        self.loss_gauge.set(loss_aggregated)
+
+        metrics_aggregated = {"loss": loss_aggregated, "accuracy": accuracy_aggregated}
+
+        return loss_aggregated, metrics_aggregated
diff --git a/examples/ios/FLiOS/CoreMLProto/NeuralNetwork.pb.swift b/examples/ios/FLiOS/CoreMLProto/NeuralNetwork.pb.swift
index 6075e689cb1e..3348ceae9e0e 100644
--- a/examples/ios/FLiOS/CoreMLProto/NeuralNetwork.pb.swift
+++ b/examples/ios/FLiOS/CoreMLProto/NeuralNetwork.pb.swift
@@ -4308,7 +4308,7 @@ struct CoreML_Specification_ConvolutionLayerParams {
 ///   The output blob's shape is ``[batch, channelsOut, depthOut, heightOut, widthOut]``.
 ///
 /// Type of padding can be ``custom``, ``valid``, or ``same``. Padded values are all zeros.
-/// Output spatial dimensions depend on the the type of padding. For details, refer to the
+/// Output spatial dimensions depend on the type of padding. For details, refer to the
 /// descriptions of the ``PaddingType`` field of this ``Convolution3DLayerParams`` message.
 ///
 /// Example
@@ -5219,7 +5219,7 @@ struct CoreML_Specification_Pooling3DLayerParams {
   /// SAME padding adds enough padding to each dimension such that the output
   /// has the same spatial dimensions as the input. Padding is added evenly to both
   /// sides of each dimension unless the total padding to add is odd, in which case the extra padding
-  /// is added to the back/bottom/right side of the respective dimension.  For example, if the the
+  /// is added to the back/bottom/right side of the respective dimension.  For example, if the
   /// total horizontal padding is 3, then there will be 1 padding on the left, and 2 padding on the right.
   enum Pooling3DPaddingType: SwiftProtobuf.Enum {
     typealias RawValue = Int
@@ -9493,7 +9493,7 @@ struct CoreML_Specification_ConstantPaddingLayerParams {
 
   ///*
   /// Length of this repeated field must be twice the rank of the first input.
-  /// 2*i-th and (2*i+1)-th values represent the amount of padding to be applied to the the i-th input
+  /// 2*i-th and (2*i+1)-th values represent the amount of padding to be applied to the i-th input
   /// dimension, "before" and "after" the input values, respectively.
   var padAmounts: [UInt64] = []
 
diff --git a/examples/ios/FLiOS/CoreMLProto/NeuralNetwork.proto b/examples/ios/FLiOS/CoreMLProto/NeuralNetwork.proto
index 6b2ebb1c8ba1..e80a3566c2c3 100644
--- a/examples/ios/FLiOS/CoreMLProto/NeuralNetwork.proto
+++ b/examples/ios/FLiOS/CoreMLProto/NeuralNetwork.proto
@@ -1554,7 +1554,7 @@ message ConvolutionLayerParams {
  *   The output blob's shape is ``[batch, channelsOut, depthOut, heightOut, widthOut]``.
  *
  * Type of padding can be ``custom``, ``valid``, or ``same``. Padded values are all zeros.
- * Output spatial dimensions depend on the the type of padding. For details, refer to the
+ * Output spatial dimensions depend on the type of padding. For details, refer to the
  * descriptions of the ``PaddingType`` field of this ``Convolution3DLayerParams`` message.
  *
  * Example
@@ -2056,7 +2056,7 @@ message Pooling3DLayerParams {
      * SAME padding adds enough padding to each dimension such that the output
      * has the same spatial dimensions as the input. Padding is added evenly to both
      * sides of each dimension unless the total padding to add is odd, in which case the extra padding
-     * is added to the back/bottom/right side of the respective dimension.  For example, if the the
+     * is added to the back/bottom/right side of the respective dimension.  For example, if the
      * total horizontal padding is 3, then there will be 1 padding on the left, and 2 padding on the right.
      */
     enum Pooling3DPaddingType {
@@ -4779,7 +4779,7 @@ enum ScatterMode {
  *
  * The output has the same shape as the first input.
  * "indices" input must have rank less than or equal to the "updates" input and its shape
- * must be a subset of the the shape of the "updates" input.
+ * must be a subset of the shape of the "updates" input.
  *
  * e.g:
  *
@@ -5064,7 +5064,7 @@ message ConstantPaddingLayerParams {
 
     /**
      * Length of this repeated field must be twice the rank of the first input.
-     * 2*i-th and (2*i+1)-th values represent the amount of padding to be applied to the the i-th input
+     * 2*i-th and (2*i+1)-th values represent the amount of padding to be applied to the i-th input
      * dimension, "before" and "after" the input values, respectively.
      */
     repeated uint64 padAmounts = 2;
diff --git a/examples/ios/pyproject.toml b/examples/ios/pyproject.toml
index c1bdbb815bd5..2e55b14cf761 100644
--- a/examples/ios/pyproject.toml
+++ b/examples/ios/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "flwr_ios_coreml"
 version = "0.1.0"
 description = "Example Server for Flower iOS/CoreML"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/llm-flowertune/README.md b/examples/llm-flowertune/README.md
new file mode 100644
index 000000000000..60e183d2a9c0
--- /dev/null
+++ b/examples/llm-flowertune/README.md
@@ -0,0 +1,139 @@
+# Federated Large Language Model (LLM) Fine-tuning with Flower
+
+Large language models (LLMs), which have been trained on vast amounts of publicly accessible data, have shown remarkable effectiveness in a wide range of areas.
+However, despite the fact that more data typically leads to improved performance, there is a concerning prospect that the supply of high-quality public data will deplete within a few years.
+Federated LLM training could unlock access to an endless pool of distributed private data by allowing multiple data owners to collaboratively train a shared model without the need to exchange raw data.
+
+This introductory example conducts federated instruction tuning with pretrained [LLama2](https://huggingface.co/openlm-research) models on [Alpaca-GPT4](https://huggingface.co/datasets/vicgalle/alpaca-gpt4) dataset.
+We use [Flower Datasets](https://flower.dev/docs/datasets/) to download, partition and preprocess the dataset.
+The fine-tuning is done using the [🤗PEFT](https://huggingface.co/docs/peft/en/index) library.
+We use Flower's Simulation Engine to simulate the LLM fine-tuning process in federated way,
+which allows users to perform the training on a single GPU.
+
+## Environments Setup
+
+Start by cloning the code 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/llm-flowertune . && rm -rf flower && cd llm-flowertune
+```
+
+This will create a new directory called `llm-flowertune` containing the following files:
+
+```
+-- README.md           <- Your're reading this right now
+-- main.py             <- Start fed-LLM simulation
+-- client.py           <- Flower client constructor
+-- model.py            <- Model build
+-- dataset.py          <- Dataset and tokenizer build
+-- utils.py            <- Utility functions
+-- test.py             <- Test pre-trained model
+-- app.py              <- ServerApp/ClientApp for Flower-Next
+-- conf/config.yaml    <- Configuration file
+-- requirements.txt    <- Example dependencies
+```
+
+### Installing dependencies
+
+Project dependencies are defined in `requirements.txt`. Install them with:
+
+```shell
+pip install -r requirements.txt
+```
+
+## Run LLM Fine-tuning
+
+With an activated Python environment, run the example with default config values. The config is in `conf/config.yaml` and is loaded automatically.
+
+```bash
+# Run with default config
+python main.py
+```
+
+This command will run FL simulations with a 4-bit [OpenLLaMA 7Bv2](https://huggingface.co/openlm-research/open_llama_7b_v2) model involving 2 clients per rounds for 100 FL rounds. You can override configuration parameters directly from the command line. Below are a few settings you might want to test:
+
+```bash
+# Use OpenLLaMA-3B instead of 7B and 8-bits quantization
+python main.py model.name="openlm-research/open_llama_3b_v2" model.quantization=8
+
+# Run for 50 rounds but increasing the fraction of clients that participate per round to 25%
+python main.py num_rounds=50 fraction_fit.fraction_fit=0.25
+```
+
+## Expected Results
+
+![](_static/train_loss_smooth.png)
+
+As expected, LLama2-7B model works better than its 3B version with lower training loss. With the hyperparameters tested, the 8-bit model seems to deliver lower training loss for the smaller 3B model compared to its 4-bit version.
+
+You can run all 8 experiments with a single command as:
+
+```bash
+python main.py --multirun model.name="openlm-research/open_llama_7b_v2","openlm-research/open_llama_3b_v2" model.quantization=8,4 strategy.fraction_fit=0.1,0.2
+```
+
+## VRAM Consumption
+
+| Models | 7-billion (8-bit) | 7-billion (4-bit) | 3-billion (8-bit) | 3-billion (4-bit) |
+| :----: | :---------------: | :---------------: | :---------------: | :---------------: |
+|  VRAM  |     ~22.00 GB     |     ~16.50 GB     |     ~13.50 GB     |     ~10.60 GB     |
+
+We make use of the [bitsandbytes](https://huggingface.co/docs/bitsandbytes/main/en/index) library in conjunction with [PEFT](https://huggingface.co/docs/peft/en/index) to derive LLMs that can be fine-tuned efficiently.
+The above table shows the VRAM consumption per client for the different models considered in this example.
+You can adjust the CPU/GPU resources you assign to each of the clients based on your device.
+For example, it is easy to train 2 concurrent clients on each GPU (24 GB VRAM) if you choose 3-billion (4-bit) model.
+
+```bash
+# This will assign 50% of the GPU's VRAM to each client.
+python main.py model.name="openlm-research/open_llama_3b_v2" model.quantization=4 client_resources.num_gpus=0.5
+```
+
+## Test with your Questions
+
+We provide a script to test your trained model by passing your specified questions. For example:
+
+```bash
+python test.py --peft-path=/path/to/trained-model-dir/ \
+    --question="What is the ideal 1-day plan in London?"
+```
+
+An answer generated from federated trained 7-billion (8-bit) LLama2 model:
+
+```
+Great choice. 
+London has so much to offer, and you can really soak up all the sights and sounds in just a single day. 
+Here's a suggested itinerary for you. 
+Start your day off with a hearty breakfast at an authentic British diner. 
+Then head to the iconic Big Ben and the Houses of Parliament to learn about the history of the city. 
+Next, make your way to Westminster Abbey to see the many historical monuments and memorials. 
+From there, cross the river Thames to the Tower of London, which is home to the Crown Jewels of England and Scotland. 
+Finally, end your day with a relaxing visit to the London Eye, the tallest Ferris wheel in Europe, for a beautiful view of the city.
+```
+
+The [`Vicuna`](https://huggingface.co/lmsys/vicuna-13b-v1.1) template we used in this example is for a chat assistant.
+The generated answer is expected to be a multi-turn conversations. Feel free to try more interesting questions!
+
+## Run with Flower Next (preview)
+
+We conduct a 2-client setting to demonstrate how to run federated LLM fine-tuning with Flower Next.
+Please follow the steps below:
+
+1. Start the long-running Flower server (SuperLink)
+   ```bash
+   flower-superlink --insecure
+   ```
+2. Start the long-running Flower client (SuperNode)
+   ```bash
+   # In a new terminal window, start the first long-running Flower client:
+   flower-client-app app:client1 --insecure
+   ```
+   ```bash
+   # In another new terminal window, start the second long-running Flower client:
+   flower-client-app app:client2 --insecure
+   ```
+3. Run the Flower App
+   ```bash
+   # With both the long-running server (SuperLink) and two clients (SuperNode) up and running,
+   # we can now run the actual Flower App:
+   flower-server-app app:server --insecure
+   ```
diff --git a/examples/llm-flowertune/_static/train_loss_smooth.png b/examples/llm-flowertune/_static/train_loss_smooth.png
new file mode 100644
index 000000000000..02034e6e9eb5
Binary files /dev/null and b/examples/llm-flowertune/_static/train_loss_smooth.png differ
diff --git a/examples/llm-flowertune/app.py b/examples/llm-flowertune/app.py
new file mode 100644
index 000000000000..e04ad8715de6
--- /dev/null
+++ b/examples/llm-flowertune/app.py
@@ -0,0 +1,86 @@
+import os
+import warnings
+from hydra import compose, initialize
+
+import flwr as fl
+from flwr_datasets import FederatedDataset
+
+from dataset import get_tokenizer_and_data_collator_and_propt_formatting
+from client import gen_client_fn
+from utils import get_on_fit_config, fit_weighted_average
+
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+NUM_ROUNDS = 100
+save_path = "./results/"
+
+with initialize(config_path="conf"):
+    cfg = compose(config_name="config")
+
+# Reset the number of number
+cfg.num_rounds = NUM_ROUNDS
+cfg.train.num_rounds = NUM_ROUNDS
+
+# Create output directory
+if not os.path.exists(save_path):
+    os.mkdir(save_path)
+
+# Partition dataset and get dataloaders
+# We set the number of partitions to 20 for fast processing.
+fds = FederatedDataset(
+    dataset=cfg.dataset.name, partitioners={"train": cfg.num_clients}
+)
+(
+    tokenizer,
+    data_collator,
+    formatting_prompts_func,
+) = get_tokenizer_and_data_collator_and_propt_formatting(cfg.model.name)
+
+
+# ClientApp for client #1 (Flower Next)
+client1 = fl.client.ClientApp(
+    client_fn=gen_client_fn(
+        fds,
+        tokenizer,
+        formatting_prompts_func,
+        data_collator,
+        cfg.model,
+        cfg.train,
+        save_path,
+        partition_id=0,
+        api=True,
+    ),
+)
+
+
+# ClientApp for client #2 (Flower Next)
+client2 = fl.client.ClientApp(
+    client_fn=gen_client_fn(
+        fds,
+        tokenizer,
+        formatting_prompts_func,
+        data_collator,
+        cfg.model,
+        cfg.train,
+        save_path,
+        partition_id=1,
+        api=True,
+    ),
+)
+
+
+# Instantiate strategy.
+strategy = fl.server.strategy.FedAvg(
+    min_available_clients=2,  # Simulate a 2-client setting
+    fraction_fit=1.0,
+    fraction_evaluate=0.0,  # no client evaluation
+    on_fit_config_fn=get_on_fit_config(),
+    fit_metrics_aggregation_fn=fit_weighted_average,
+)
+
+# ServerApp for Flower-Next
+server = fl.server.ServerApp(
+    config=fl.server.ServerConfig(num_rounds=NUM_ROUNDS),
+    strategy=strategy,
+)
diff --git a/examples/llm-flowertune/client.py b/examples/llm-flowertune/client.py
new file mode 100644
index 000000000000..28b324ba5bf1
--- /dev/null
+++ b/examples/llm-flowertune/client.py
@@ -0,0 +1,129 @@
+from collections import OrderedDict
+from typing import Callable, Dict, Tuple
+
+import flwr as fl
+import torch
+from flwr.common.typing import NDArrays, Scalar
+from omegaconf import DictConfig
+from trl import SFTTrainer
+from transformers import TrainingArguments
+from peft import get_peft_model_state_dict, set_peft_model_state_dict
+
+from models import get_model, cosine_annealing
+
+
+# pylint: disable=too-many-arguments
+class FlowerClient(
+    fl.client.NumPyClient
+):  # pylint: disable=too-many-instance-attributes
+    """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 get_parameters(self, config: Dict[str, Scalar]) -> NDArrays:
+        """Return the parameters of the current net."""
+
+        state_dict = get_peft_model_state_dict(self.model)
+        return [val.cpu().numpy() for _, val in state_dict.items()]
+
+    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 (
+            self.get_parameters({}),
+            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 gen_client_fn(
+    fds,
+    tokenizer,
+    formatting_prompts_func,
+    data_collator,
+    model_cfg: DictConfig,
+    train_cfg: DictConfig,
+    save_path: str,
+    partition_id: int = 0,
+    api: bool = False,
+) -> 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(partition_id, "train")
+            if api
+            else fds.load_partition(int(cid), "train")
+        )
+        client_trainset = client_trainset.rename_column("output", "response")
+
+        return FlowerClient(
+            model_cfg,
+            train_cfg,
+            client_trainset,
+            tokenizer,
+            formatting_prompts_func,
+            data_collator,
+            save_path,
+        ).to_client()
+
+    return client_fn
diff --git a/examples/llm-flowertune/conf/config.yaml b/examples/llm-flowertune/conf/config.yaml
new file mode 100644
index 000000000000..0b769d351479
--- /dev/null
+++ b/examples/llm-flowertune/conf/config.yaml
@@ -0,0 +1,45 @@
+# Federated Instruction Tuning on General Dataset
+---
+
+num_clients: 20 # total number of clients
+num_rounds: 100
+
+dataset:
+  name: "vicgalle/alpaca-gpt4"
+
+model:
+  name: "openlm-research/open_llama_7b_v2"
+  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: ${num_rounds}
+  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: ${model.gradient_checkpointing}
+    lr_scheduler_type: "constant"
+
+strategy:
+  _target_: flwr.server.strategy.FedAvg
+  fraction_fit: 0.1 # sample 10% of clients (i.e. 2 per round)
+  fraction_evaluate: 0.0 # no client evaluation
+
+client_resources:
+  num_cpus: 8
+  num_gpus: 1.0
diff --git a/examples/llm-flowertune/dataset.py b/examples/llm-flowertune/dataset.py
new file mode 100644
index 000000000000..571be31f7fba
--- /dev/null
+++ b/examples/llm-flowertune/dataset.py
@@ -0,0 +1,29 @@
+from transformers import AutoTokenizer
+from trl import DataCollatorForCompletionOnlyLM
+
+
+def formatting_prompts_func(example):
+    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### Response: {example['response'][i]}"
+        output_texts.append(text)
+    return output_texts
+
+
+def get_tokenizer_and_data_collator_and_propt_formatting(model_name: str):
+    # 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
diff --git a/examples/llm-flowertune/main.py b/examples/llm-flowertune/main.py
new file mode 100644
index 000000000000..2d03e9cbcae5
--- /dev/null
+++ b/examples/llm-flowertune/main.py
@@ -0,0 +1,94 @@
+import warnings
+import pickle
+
+import flwr as fl
+from flwr_datasets import FederatedDataset
+
+import hydra
+from hydra.core.hydra_config import HydraConfig
+from hydra.utils import instantiate
+from omegaconf import DictConfig, OmegaConf
+
+from dataset import get_tokenizer_and_data_collator_and_propt_formatting
+from utils import get_on_fit_config, fit_weighted_average, get_evaluate_fn
+from client import gen_client_fn
+
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+@hydra.main(config_path="conf", config_name="config", version_base=None)
+def main(cfg: DictConfig) -> None:
+    """Run federated LLM fine-tuning.
+
+    Parameters
+    ----------
+    cfg : DictConfig
+        An omegaconf object that stores the hydra config.
+    """
+    # Print config structured as YAML
+    print(OmegaConf.to_yaml(cfg))
+
+    # Partition dataset and get dataloaders
+    fds = FederatedDataset(
+        dataset=cfg.dataset.name, partitioners={"train": cfg.num_clients}
+    )
+    (
+        tokenizer,
+        data_collator,
+        formatting_prompts_func,
+    ) = get_tokenizer_and_data_collator_and_propt_formatting(
+        cfg.model.name,
+    )
+
+    # Hydra automatically creates an output directory
+    # Let's retrieve it and save some results there
+    save_path = HydraConfig.get().runtime.output_dir
+
+    # Prepare function that will be used to spawn each client
+    client_fn = gen_client_fn(
+        fds,
+        tokenizer,
+        formatting_prompts_func,
+        data_collator,
+        cfg.model,
+        cfg.train,
+        save_path,
+    )
+
+    # Instantiate strategy according to config. Here we pass other arguments
+    # that are only defined at run time.
+    strategy = instantiate(
+        cfg.strategy,
+        on_fit_config_fn=get_on_fit_config(),
+        fit_metrics_aggregation_fn=fit_weighted_average,
+        evaluate_fn=get_evaluate_fn(
+            cfg.model, cfg.train.save_every_round, cfg.num_rounds, save_path
+        ),
+    )
+
+    # Start simulation
+    history = fl.simulation.start_simulation(
+        client_fn=client_fn,
+        num_clients=cfg.num_clients,
+        config=fl.server.ServerConfig(num_rounds=cfg.num_rounds),
+        client_resources={
+            "num_cpus": cfg.client_resources.num_cpus,
+            "num_gpus": cfg.client_resources.num_gpus,
+        },
+        strategy=strategy,
+    )
+
+    # Experiment completed. Now we save the results and
+    # generate plots using the `history`
+    print("................")
+    print(history)
+
+    # Save results as a Python pickle using a file_path
+    # the directory created by Hydra for each run
+    with open(f"{save_path}/results.pkl", "wb") as f:
+        pickle.dump(history, f)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/llm-flowertune/models.py b/examples/llm-flowertune/models.py
new file mode 100644
index 000000000000..78eef75d10d2
--- /dev/null
+++ b/examples/llm-flowertune/models.py
@@ -0,0 +1,56 @@
+import torch
+from omegaconf import DictConfig
+from transformers import AutoModelForCausalLM
+from transformers import BitsAndBytesConfig
+from peft import get_peft_model, LoraConfig
+from peft.utils import prepare_model_for_kbit_training
+
+import math
+
+
+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,
+    )
+
+    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",
+    )
+
+    return get_peft_model(model, peft_config)
diff --git a/examples/llm-flowertune/requirements.txt b/examples/llm-flowertune/requirements.txt
new file mode 100644
index 000000000000..c7ff57b403f7
--- /dev/null
+++ b/examples/llm-flowertune/requirements.txt
@@ -0,0 +1,8 @@
+flwr-nightly[rest,simulation]
+flwr_datasets==0.0.2
+hydra-core==1.3.2
+trl==0.7.2
+bitsandbytes==0.41.3
+scipy==1.11.2
+peft==0.4.0
+fschat[model_worker,webui]==0.2.35
diff --git a/examples/llm-flowertune/test.py b/examples/llm-flowertune/test.py
new file mode 100644
index 000000000000..652bb9aafcf5
--- /dev/null
+++ b/examples/llm-flowertune/test.py
@@ -0,0 +1,73 @@
+# This python file is adapted from https://github.com/lm-sys/FastChat/blob/main/fastchat/llm_judge/gen_model_answer.py
+
+import argparse
+import torch
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer
+from fastchat.conversation import get_conv_template
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--peft-path", type=str, default=None)
+parser.add_argument("--question", type=str, default="How are you")
+parser.add_argument("--template", type=str, default="vicuna_v1.1")
+args = parser.parse_args()
+
+# Load model and tokenizer
+model = AutoPeftModelForCausalLM.from_pretrained(
+    args.peft_path, torch_dtype=torch.float16
+).to("cuda")
+base_model = model.peft_config["default"].base_model_name_or_path
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+
+# Generate answers
+temperature = 0.7
+choices = []
+conv = get_conv_template(args.template)
+
+conv.append_message(conv.roles[0], args.question)
+conv.append_message(conv.roles[1], None)
+prompt = conv.get_prompt()
+input_ids = tokenizer([prompt]).input_ids
+
+output_ids = model.generate(
+    input_ids=torch.as_tensor(input_ids).cuda(),
+    do_sample=True,
+    temperature=temperature,
+    max_new_tokens=1024,
+)
+
+output_ids = (
+    output_ids[0]
+    if model.config.is_encoder_decoder
+    else output_ids[0][len(input_ids[0]) :]
+)
+
+# Be consistent with the template's stop_token_ids
+if conv.stop_token_ids:
+    stop_token_ids_index = [
+        i for i, id in enumerate(output_ids) if id in conv.stop_token_ids
+    ]
+    if len(stop_token_ids_index) > 0:
+        output_ids = output_ids[: stop_token_ids_index[0]]
+
+output = tokenizer.decode(
+    output_ids,
+    spaces_between_special_tokens=False,
+)
+
+if conv.stop_str and output.find(conv.stop_str) > 0:
+    output = output[: output.find(conv.stop_str)]
+
+for special_token in tokenizer.special_tokens_map.values():
+    if isinstance(special_token, list):
+        for special_tok in special_token:
+            output = output.replace(special_tok, "")
+    else:
+        output = output.replace(special_token, "")
+output = output.strip()
+
+conv.update_last_message(output)
+
+print(f">> prompt: {prompt}")
+print(f">> Generated: {output}")
diff --git a/examples/llm-flowertune/utils.py b/examples/llm-flowertune/utils.py
new file mode 100644
index 000000000000..bbb607810537
--- /dev/null
+++ b/examples/llm-flowertune/utils.py
@@ -0,0 +1,43 @@
+from client import set_parameters
+from 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
+
+
+# Get a function that will be used to construct the config that the client's
+# fit() method will receive
+def get_on_fit_config():
+    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):
+    """Aggregation function for (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/examples/mt-pytorch-callable/README.md b/examples/mt-pytorch-callable/README.md
deleted file mode 100644
index 65ef000c26f2..000000000000
--- a/examples/mt-pytorch-callable/README.md
+++ /dev/null
@@ -1,49 +0,0 @@
-# Deploy 🧪
-
-🧪 = this page covers experimental features that might change in future versions of Flower
-
-This how-to guide describes the deployment of a long-running Flower server.
-
-## Preconditions
-
-Let's assume the following project structure:
-
-```bash
-$ tree .
-.
-└── client.py
-├── driver.py
-├── requirements.txt
-```
-
-## Install dependencies
-
-```bash
-pip install -r requirements.txt
-```
-
-## Start the long-running Flower server
-
-```bash
-flower-server --insecure
-```
-
-## Start the long-running Flower client
-
-In a new terminal window, start the first long-running Flower client:
-
-```bash
-flower-client --callable client:flower
-```
-
-In yet another new terminal window, start the second long-running Flower client:
-
-```bash
-flower-client --callable client:flower
-```
-
-## Start the Driver script
-
-```bash
-python driver.py
-```
diff --git a/examples/mt-pytorch-callable/client.py b/examples/mt-pytorch-callable/client.py
deleted file mode 100644
index 6f9747784ae0..000000000000
--- a/examples/mt-pytorch-callable/client.py
+++ /dev/null
@@ -1,123 +0,0 @@
-import warnings
-from collections import OrderedDict
-
-import flwr as fl
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.data import DataLoader
-from torchvision.datasets import CIFAR10
-from torchvision.transforms import Compose, Normalize, ToTensor
-from tqdm import tqdm
-
-
-# #############################################################################
-# 1. Regular PyTorch pipeline: nn.Module, train, test, and DataLoader
-# #############################################################################
-
-warnings.filterwarnings("ignore", category=UserWarning)
-DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-
-class Net(nn.Module):
-    """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz')"""
-
-    def __init__(self) -> None:
-        super(Net, self).__init__()
-        self.conv1 = nn.Conv2d(3, 6, 5)
-        self.pool = nn.MaxPool2d(2, 2)
-        self.conv2 = nn.Conv2d(6, 16, 5)
-        self.fc1 = nn.Linear(16 * 5 * 5, 120)
-        self.fc2 = nn.Linear(120, 84)
-        self.fc3 = nn.Linear(84, 10)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = self.pool(F.relu(self.conv1(x)))
-        x = self.pool(F.relu(self.conv2(x)))
-        x = x.view(-1, 16 * 5 * 5)
-        x = F.relu(self.fc1(x))
-        x = F.relu(self.fc2(x))
-        return self.fc3(x)
-
-
-def train(net, trainloader, epochs):
-    """Train the model on the training set."""
-    criterion = torch.nn.CrossEntropyLoss()
-    optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
-    for _ in range(epochs):
-        for images, labels in tqdm(trainloader):
-            optimizer.zero_grad()
-            criterion(net(images.to(DEVICE)), labels.to(DEVICE)).backward()
-            optimizer.step()
-
-
-def test(net, testloader):
-    """Validate the model on the test set."""
-    criterion = torch.nn.CrossEntropyLoss()
-    correct, loss = 0, 0.0
-    with torch.no_grad():
-        for images, labels in tqdm(testloader):
-            outputs = net(images.to(DEVICE))
-            labels = labels.to(DEVICE)
-            loss += criterion(outputs, labels).item()
-            correct += (torch.max(outputs.data, 1)[1] == labels).sum().item()
-    accuracy = correct / len(testloader.dataset)
-    return loss, accuracy
-
-
-def load_data():
-    """Load CIFAR-10 (training and test set)."""
-    trf = Compose([ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
-    trainset = CIFAR10("./data", train=True, download=True, transform=trf)
-    testset = CIFAR10("./data", train=False, download=True, transform=trf)
-    return DataLoader(trainset, batch_size=32, shuffle=True), DataLoader(testset)
-
-
-# #############################################################################
-# 2. Federation of the pipeline with Flower
-# #############################################################################
-
-# Load model and data (simple CNN, CIFAR-10)
-net = Net().to(DEVICE)
-trainloader, testloader = load_data()
-
-
-# Define Flower client
-class FlowerClient(fl.client.NumPyClient):
-    def get_parameters(self, config):
-        return [val.cpu().numpy() for _, val in net.state_dict().items()]
-
-    def set_parameters(self, 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 fit(self, parameters, config):
-        self.set_parameters(parameters)
-        train(net, trainloader, epochs=1)
-        return self.get_parameters(config={}), len(trainloader.dataset), {}
-
-    def evaluate(self, parameters, config):
-        self.set_parameters(parameters)
-        loss, accuracy = test(net, testloader)
-        return loss, len(testloader.dataset), {"accuracy": accuracy}
-
-
-def client_fn(cid: str):
-    """."""
-    return FlowerClient().to_client()
-
-
-# To run this: `flower-client --callable client:flower`
-flower = fl.flower.Flower(
-    client_fn=client_fn,
-)
-
-
-if __name__ == "__main__":
-    # Start Flower client
-    fl.client.start_client(
-        server_address="0.0.0.0:9092",
-        client=FlowerClient().to_client(),
-        transport="grpc-rere",
-    )
diff --git a/examples/mt-pytorch-callable/driver.py b/examples/mt-pytorch-callable/driver.py
deleted file mode 100644
index 1248672b6813..000000000000
--- a/examples/mt-pytorch-callable/driver.py
+++ /dev/null
@@ -1,25 +0,0 @@
-from typing import List, Tuple
-
-import flwr as fl
-from flwr.common import Metrics
-
-
-# Define metric aggregation function
-def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
-    # Multiply accuracy of each client by number of examples used
-    accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics]
-    examples = [num_examples for num_examples, _ in metrics]
-
-    # Aggregate and return custom metric (weighted average)
-    return {"accuracy": sum(accuracies) / sum(examples)}
-
-
-# Define strategy
-strategy = fl.server.strategy.FedAvg(evaluate_metrics_aggregation_fn=weighted_average)
-
-# Start Flower driver
-fl.driver.start_driver(
-    server_address="0.0.0.0:9091",
-    config=fl.server.ServerConfig(num_rounds=3),
-    strategy=strategy,
-)
diff --git a/examples/mt-pytorch-callable/requirements.txt b/examples/mt-pytorch-callable/requirements.txt
deleted file mode 100644
index 797ca6db6244..000000000000
--- a/examples/mt-pytorch-callable/requirements.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-flwr>=1.0, <2.0
-torch==1.13.1
-torchvision==0.14.1
-tqdm==4.65.0
diff --git a/examples/mt-pytorch/README.md b/examples/mt-pytorch/README.md
deleted file mode 100644
index ef9516314e26..000000000000
--- a/examples/mt-pytorch/README.md
+++ /dev/null
@@ -1,73 +0,0 @@
-# Multi-Tenant Federated Learning with Flower and PyTorch
-
-This example contains experimental code. 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.
-
-## Setup
-
-```bash
-./dev/venv-reset.sh
-```
-
-## Run with Driver API
-
-Terminal 1: start Flower server
-
-```bash
-flower-server
-```
-
-Terminal 2+3: start two Flower client nodes
-
-```bash
-python client.py
-```
-
-Terminal 4: start Driver script
-
-Using:
-
-```bash
-python start_driver.py
-```
-
-Or, alternatively:
-
-```bash
-python driver.py
-```
-
-## Run in legacy mode
-
-Terminal 1: start Flower server
-
-```bash
-python server.py
-```
-
-Terminal 2+3: start two clients
-
-```bash
-python client.py
-```
-
-## Run with Driver API (REST transport layer)
-
-Terminal 1: start Flower server and enable REST transport layer
-
-```bash
-flower-server --rest
-```
-
-Terminal 2: start Driver script
-
-```bash
-python driver.py
-```
-
-Open file `client.py` adjust `server_address` and `transport`.
-
-Terminal 3+4: start two Flower client nodes
-
-```bash
-python client.py
-```
diff --git a/examples/mt-pytorch/client.py b/examples/mt-pytorch/client.py
deleted file mode 100644
index 23cc736fd62b..000000000000
--- a/examples/mt-pytorch/client.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from typing import Dict
-import flwr as fl
-from flwr.common import NDArrays, Scalar
-
-from task import (
-    Net,
-    DEVICE,
-    load_data,
-    get_parameters,
-    set_parameters,
-    train,
-    test,
-)
-
-
-# Load model and data (simple CNN, CIFAR-10)
-net = Net().to(DEVICE)
-trainloader, testloader = load_data()
-
-
-# Define Flower client
-class FlowerClient(fl.client.NumPyClient):
-    def get_parameters(self, config: Dict[str, Scalar]) -> NDArrays:
-        return get_parameters(net)
-
-    def fit(self, parameters, config):
-        set_parameters(net, parameters)
-        results = train(net, trainloader, testloader, epochs=1, device=DEVICE)
-        return get_parameters(net), len(trainloader.dataset), results
-
-    def evaluate(self, parameters, config):
-        set_parameters(net, parameters)
-        loss, accuracy = test(net, testloader)
-        return loss, len(testloader.dataset), {"accuracy": accuracy}
-
-
-# Start Flower client
-fl.client.start_numpy_client(
-    server_address="0.0.0.0:9092",  # "0.0.0.0:9093" for REST
-    client=FlowerClient(),
-    transport="grpc-rere",  # "rest" for REST
-)
diff --git a/examples/mt-pytorch/driver.py b/examples/mt-pytorch/driver.py
deleted file mode 100644
index fed760f021af..000000000000
--- a/examples/mt-pytorch/driver.py
+++ /dev/null
@@ -1,226 +0,0 @@
-from typing import List, Tuple
-import random
-import time
-
-from flwr.driver import GrpcDriver
-from flwr.common import (
-    ServerMessage,
-    FitIns,
-    ndarrays_to_parameters,
-    serde,
-    parameters_to_ndarrays,
-    ClientMessage,
-    NDArrays,
-    Code,
-)
-from flwr.proto import driver_pb2, task_pb2, node_pb2, transport_pb2
-from flwr.server.strategy.aggregate import aggregate
-from flwr.common import Metrics
-from flwr.server import History
-from flwr.common import serde
-from task import Net, get_parameters, set_parameters
-
-
-# Define metric aggregation function
-def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
-    examples = [num_examples for num_examples, _ in metrics]
-
-    # Multiply accuracy of each client by number of examples used
-    train_losses = [num_examples * m["train_loss"] for num_examples, m in metrics]
-    train_accuracies = [
-        num_examples * m["train_accuracy"] for num_examples, m in metrics
-    ]
-    val_losses = [num_examples * m["val_loss"] for num_examples, m in metrics]
-    val_accuracies = [num_examples * m["val_accuracy"] for num_examples, m in metrics]
-
-    # Aggregate and return custom metric (weighted average)
-    return {
-        "train_loss": sum(train_losses) / sum(examples),
-        "train_accuracy": sum(train_accuracies) / sum(examples),
-        "val_loss": sum(val_losses) / sum(examples),
-        "val_accuracy": sum(val_accuracies) / sum(examples),
-    }
-
-
-# -------------------------------------------------------------------------- Driver SDK
-driver = GrpcDriver(driver_service_address="0.0.0.0:9091", certificates=None)
-# -------------------------------------------------------------------------- Driver SDK
-
-anonymous_client_nodes = False
-num_client_nodes_per_round = 2
-sleep_time = 1
-num_rounds = 3
-parameters = ndarrays_to_parameters(get_parameters(net=Net()))
-
-# -------------------------------------------------------------------------- Driver SDK
-driver.connect()
-create_workload_res: driver_pb2.CreateWorkloadResponse = driver.create_workload(
-    req=driver_pb2.CreateWorkloadRequest()
-)
-# -------------------------------------------------------------------------- Driver SDK
-
-workload_id = create_workload_res.workload_id
-print(f"Created workload id {workload_id}")
-
-history = History()
-for server_round in range(num_rounds):
-    print(f"Commencing server round {server_round + 1}")
-
-    # List of sampled node IDs in this round
-    sampled_nodes: List[node_pb2.Node] = []
-
-    # Sample node ids
-    if anonymous_client_nodes:
-        # If we're working with anonymous clients, we don't know their identities, and
-        # we don't know how many of them we have. We, therefore, have to assume that
-        # enough anonymous client nodes are available or become available over time.
-        #
-        # To schedule a TaskIns for an anonymous client node, we set the node_id to 0
-        # (and `anonymous` to True)
-        # Here, we create an array with only zeros in it:
-        sampled_node_ids: List[int] = [0] * num_client_nodes_per_round
-        sampled_nodes = [
-            node_pb2.Node(node_id=node_id, anonymous=False)
-            for node_id in sampled_node_ids
-        ]
-    else:
-        # If our client nodes have identiy (i.e., they are not anonymous), we can get
-        # those IDs from the Driver API using `get_nodes`. If enough clients are
-        # available via the Driver API, we can select a subset by taking a random
-        # sample.
-        #
-        # The Driver API might not immediately return enough client node IDs, so we
-        # loop and wait until enough client nodes are available.
-        while True:
-            # Get a list of node ID's from the server
-            get_nodes_req = driver_pb2.GetNodesRequest(workload_id=workload_id)
-
-            # ---------------------------------------------------------------------- Driver SDK
-            get_nodes_res: driver_pb2.GetNodesResponse = driver.get_nodes(
-                req=get_nodes_req
-            )
-            # ---------------------------------------------------------------------- Driver SDK
-
-            all_nodes: List[node_pb2.Node] = get_nodes_res.nodes
-            print(f"Got {len(all_nodes)} client nodes")
-
-            if len(all_nodes) >= num_client_nodes_per_round:
-                # Sample client nodes
-                sampled_nodes = random.sample(all_nodes, num_client_nodes_per_round)
-                break
-
-            time.sleep(3)
-
-    # Log sampled node IDs
-    print(f"Sampled {len(sampled_nodes)} node IDs: {sampled_nodes}")
-    time.sleep(sleep_time)
-
-    # Schedule a task for all sampled nodes
-    fit_ins: FitIns = FitIns(parameters=parameters, config={})
-    server_message_proto: transport_pb2.ServerMessage = serde.server_message_to_proto(
-        server_message=ServerMessage(fit_ins=fit_ins)
-    )
-    task_ins_list: List[task_pb2.TaskIns] = []
-    for sampled_node in sampled_nodes:
-        new_task_ins = task_pb2.TaskIns(
-            task_id="",  # Do not set, will be created and set by the DriverAPI
-            group_id="",
-            workload_id=workload_id,
-            task=task_pb2.Task(
-                producer=node_pb2.Node(
-                    node_id=0,
-                    anonymous=True,
-                ),
-                consumer=sampled_node,
-                legacy_server_message=server_message_proto,
-            ),
-        )
-        task_ins_list.append(new_task_ins)
-
-    push_task_ins_req = driver_pb2.PushTaskInsRequest(task_ins_list=task_ins_list)
-
-    # ---------------------------------------------------------------------- Driver SDK
-    push_task_ins_res: driver_pb2.PushTaskInsResponse = driver.push_task_ins(
-        req=push_task_ins_req
-    )
-    # ---------------------------------------------------------------------- Driver SDK
-
-    print(
-        f"Scheduled {len(push_task_ins_res.task_ids)} tasks: {push_task_ins_res.task_ids}"
-    )
-
-    time.sleep(sleep_time)
-
-    # Wait for results, ignore empty task_ids
-    task_ids: List[str] = [
-        task_id for task_id in push_task_ins_res.task_ids if task_id != ""
-    ]
-    all_task_res: List[task_pb2.TaskRes] = []
-    while True:
-        pull_task_res_req = driver_pb2.PullTaskResRequest(
-            node=node_pb2.Node(node_id=0, anonymous=True),
-            task_ids=task_ids,
-        )
-
-        # ------------------------------------------------------------------ Driver SDK
-        pull_task_res_res: driver_pb2.PullTaskResResponse = driver.pull_task_res(
-            req=pull_task_res_req
-        )
-        # ------------------------------------------------------------------ Driver SDK
-
-        task_res_list: List[task_pb2.TaskRes] = pull_task_res_res.task_res_list
-        print(f"Got {len(task_res_list)} results")
-
-        time.sleep(sleep_time)
-
-        all_task_res += task_res_list
-        if len(all_task_res) == len(task_ids):
-            break
-
-    # Collect correct results
-    node_messages: List[ClientMessage] = []
-    for task_res in all_task_res:
-        if task_res.task.HasField("legacy_client_message"):
-            node_messages.append(task_res.task.legacy_client_message)
-    print(f"Received {len(node_messages)} results")
-
-    weights_results: List[Tuple[NDArrays, int]] = []
-    metrics_results: List = []
-    for node_message in node_messages:
-        if not node_message.fit_res:
-            continue
-        fit_res = node_message.fit_res
-        # Aggregate only if the status is OK
-        if fit_res.status.code != Code.OK.value:
-            continue
-        weights_results.append(
-            (parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples)
-        )
-        metrics_results.append(
-            (fit_res.num_examples, serde.metrics_from_proto(fit_res.metrics))
-        )
-
-    # Aggregate parameters (FedAvg)
-    parameters_aggregated = ndarrays_to_parameters(aggregate(weights_results))
-    parameters = parameters_aggregated
-
-    # Aggregate metrics
-    metrics_aggregated = weighted_average(metrics_results)
-    history.add_metrics_distributed_fit(
-        server_round=server_round, metrics=metrics_aggregated
-    )
-    print("Round ", server_round, " metrics: ", metrics_aggregated)
-
-    # Slow down the start of the next round
-    time.sleep(sleep_time)
-
-print("app_fit: losses_distributed %s", str(history.losses_distributed))
-print("app_fit: metrics_distributed_fit %s", str(history.metrics_distributed_fit))
-print("app_fit: metrics_distributed %s", str(history.metrics_distributed))
-print("app_fit: losses_centralized %s", str(history.losses_centralized))
-print("app_fit: metrics_centralized %s", str(history.metrics_centralized))
-
-# -------------------------------------------------------------------------- Driver SDK
-driver.disconnect()
-# -------------------------------------------------------------------------- Driver SDK
-print("Driver disconnected")
diff --git a/examples/mxnet-from-centralized-to-federated/README.md b/examples/mxnet-from-centralized-to-federated/README.md
index 839d3b16a1cf..2c3f240d8978 100644
--- a/examples/mxnet-from-centralized-to-federated/README.md
+++ b/examples/mxnet-from-centralized-to-federated/README.md
@@ -1,5 +1,7 @@
 # MXNet: From Centralized To Federated
 
+> Note the MXNet project has ended, and is now in [Attic](https://attic.apache.org/projects/mxnet.html). The MXNet GitHub has also [been archived](https://github.com/apache/mxnet). As a result, this example won't be receiving more updates. Using MXNet is no longer recommnended.
+
 This example demonstrates how an already existing centralized MXNet-based machine learning project can be federated with Flower.
 
 This introductory example for Flower uses MXNet, but you're not required to be a MXNet expert to run the example. The example will help you to understand how Flower can be used to build federated learning use cases based on an existing MXNet project.
diff --git a/examples/mxnet-from-centralized-to-federated/client.py b/examples/mxnet-from-centralized-to-federated/client.py
index 3a3a9de146ef..bb666a26508e 100644
--- a/examples/mxnet-from-centralized-to-federated/client.py
+++ b/examples/mxnet-from-centralized-to-federated/client.py
@@ -1,6 +1,5 @@
 """Flower client example using MXNet for MNIST classification."""
 
-
 from typing import Dict, List, Tuple
 
 import flwr as fl
diff --git a/examples/mxnet-from-centralized-to-federated/mxnet_mnist.py b/examples/mxnet-from-centralized-to-federated/mxnet_mnist.py
index a53ed018eb48..5cf39da7c9ca 100644
--- a/examples/mxnet-from-centralized-to-federated/mxnet_mnist.py
+++ b/examples/mxnet-from-centralized-to-federated/mxnet_mnist.py
@@ -5,7 +5,6 @@
 https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html
 """
 
-
 from typing import List, Tuple
 import mxnet as mx
 from mxnet import gluon
diff --git a/examples/mxnet-from-centralized-to-federated/pyproject.toml b/examples/mxnet-from-centralized-to-federated/pyproject.toml
index a0d31f76ebdd..b00b3ddfe412 100644
--- a/examples/mxnet-from-centralized-to-federated/pyproject.toml
+++ b/examples/mxnet-from-centralized-to-federated/pyproject.toml
@@ -6,11 +6,10 @@ build-backend = "poetry.core.masonry.api"
 name = "mxnet_example"
 version = "0.1.0"
 description = "MXNet example with MNIST and CNN"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
-flwr = ">=1.0,<2.0"
-# flwr = { path = "../../", develop = true }  # Development
-mxnet = "1.6.0"
+flwr = "1.6.0"
+mxnet = "1.9.1"
 numpy = "1.23.1"
diff --git a/examples/mxnet-from-centralized-to-federated/requirements.txt b/examples/mxnet-from-centralized-to-federated/requirements.txt
index 73060e27c70c..8dd6f7150dfd 100644
--- a/examples/mxnet-from-centralized-to-federated/requirements.txt
+++ b/examples/mxnet-from-centralized-to-federated/requirements.txt
@@ -1,3 +1,3 @@
-flwr>=1.0,<2.0
-mxnet==1.6.0
+flwr==1.6.0
+mxnet==1.9.1
 numpy==1.23.1
diff --git a/examples/mxnet-from-centralized-to-federated/server.py b/examples/mxnet-from-centralized-to-federated/server.py
index 29cbce1884d1..871aa4e8ec99 100644
--- a/examples/mxnet-from-centralized-to-federated/server.py
+++ b/examples/mxnet-from-centralized-to-federated/server.py
@@ -1,6 +1,5 @@
 """Flower server example."""
 
-
 import flwr as fl
 
 if __name__ == "__main__":
diff --git a/examples/opacus/dp_cifar_client.py b/examples/opacus/dp_cifar_client.py
index bab1451ba707..cc30e7728222 100644
--- a/examples/opacus/dp_cifar_client.py
+++ b/examples/opacus/dp_cifar_client.py
@@ -28,7 +28,7 @@ def load_data():
 
 model = Net()
 trainloader, testloader, sample_rate = load_data()
-fl.client.start_numpy_client(
+fl.client.start_client(
     server_address="127.0.0.1:8080",
-    client=DPCifarClient(model, trainloader, testloader),
+    client=DPCifarClient(model, trainloader, testloader).to_client(),
 )
diff --git a/examples/opacus/dp_cifar_simulation.py b/examples/opacus/dp_cifar_simulation.py
index 14a9d037685b..d957caf8785c 100644
--- a/examples/opacus/dp_cifar_simulation.py
+++ b/examples/opacus/dp_cifar_simulation.py
@@ -1,14 +1,14 @@
 import math
 from collections import OrderedDict
-from typing import Callable, Optional, Tuple
+from typing import Callable, Dict, Optional, Tuple
 
 import flwr as fl
 import numpy as np
 import torch
 import torchvision.transforms as transforms
-from opacus.dp_model_inspector import DPModelInspector
 from torch.utils.data import DataLoader
 from torchvision.datasets import CIFAR10
+from flwr.common.typing import Scalar
 
 from dp_cifar_main import DEVICE, PARAMS, DPCifarClient, Net, test
 
@@ -23,8 +23,6 @@ def client_fn(cid: str) -> fl.client.Client:
     # Load model.
     model = Net()
     # Check model is compatible with Opacus.
-    # inspector = DPModelInspector()
-    # print(f"Is the model valid? {inspector.validate(model)}")
 
     # Load data partition (divide CIFAR10 into NUM_CLIENTS distinct partitions, using 30% for validation).
     transform = transforms.Compose(
@@ -45,12 +43,14 @@ def client_fn(cid: str) -> fl.client.Client:
     client_trainloader = DataLoader(client_trainset, PARAMS["batch_size"])
     client_testloader = DataLoader(client_testset, PARAMS["batch_size"])
 
-    return DPCifarClient(model, client_trainloader, client_testloader)
+    return DPCifarClient(model, client_trainloader, client_testloader).to_client()
 
 
 # Define an evaluation function for centralized evaluation (using whole CIFAR10 testset).
 def get_evaluate_fn() -> Callable[[fl.common.NDArrays], Optional[Tuple[float, float]]]:
-    def evaluate(weights: fl.common.NDArrays) -> Optional[Tuple[float, float]]:
+    def evaluate(
+        server_round: int, parameters: fl.common.NDArrays, config: Dict[str, Scalar]
+    ):
         transform = transforms.Compose(
             [
                 transforms.ToTensor(),
@@ -63,7 +63,7 @@ def evaluate(weights: fl.common.NDArrays) -> Optional[Tuple[float, float]]:
         state_dict = OrderedDict(
             {
                 k: torch.tensor(np.atleast_1d(v))
-                for k, v in zip(model.state_dict().keys(), weights)
+                for k, v in zip(model.state_dict().keys(), parameters)
             }
         )
         model.load_state_dict(state_dict, strict=True)
@@ -82,7 +82,7 @@ def main() -> None:
         client_fn=client_fn,
         num_clients=NUM_CLIENTS,
         client_resources={"num_cpus": 1},
-        num_rounds=3,
+        config=fl.server.ServerConfig(num_rounds=3),
         strategy=fl.server.strategy.FedAvg(
             fraction_fit=0.1, fraction_evaluate=0.1, evaluate_fn=get_evaluate_fn()
         ),
diff --git a/examples/opacus/pyproject.toml b/examples/opacus/pyproject.toml
index af0eaf596fbf..26914fa27aa4 100644
--- a/examples/opacus/pyproject.toml
+++ b/examples/opacus/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "flwr_opacus"
 version = "0.1.0"
 description = "Differentially Private Federated Learning with Opacus and Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/pytorch-federated-variational-autoencoder/client.py b/examples/pytorch-federated-variational-autoencoder/client.py
index ceb55c79f564..fc71f7e70c0b 100644
--- a/examples/pytorch-federated-variational-autoencoder/client.py
+++ b/examples/pytorch-federated-variational-autoencoder/client.py
@@ -93,7 +93,9 @@ def evaluate(self, parameters, config):
             loss = test(net, testloader)
             return float(loss), len(testloader), {}
 
-    fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=CifarClient())
+    fl.client.start_client(
+        server_address="127.0.0.1:8080", client=CifarClient().to_client()
+    )
 
 
 if __name__ == "__main__":
diff --git a/examples/pytorch-federated-variational-autoencoder/pyproject.toml b/examples/pytorch-federated-variational-autoencoder/pyproject.toml
index 116140306f62..bc1f85803682 100644
--- a/examples/pytorch-federated-variational-autoencoder/pyproject.toml
+++ b/examples/pytorch-federated-variational-autoencoder/pyproject.toml
@@ -2,7 +2,7 @@
 name = "pytorch_federated_variational_autoencoder"
 version = "0.1.0"
 description = "Federated Variational Autoencoder Example"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/pytorch-from-centralized-to-federated/README.md b/examples/pytorch-from-centralized-to-federated/README.md
index 40f7f40e5adc..06ee89dddcac 100644
--- a/examples/pytorch-from-centralized-to-federated/README.md
+++ b/examples/pytorch-from-centralized-to-federated/README.md
@@ -2,7 +2,7 @@
 
 This example demonstrates how an already existing centralized PyTorch-based machine learning project can be federated with Flower.
 
-This introductory example for Flower uses PyTorch, but you're not required to be a PyTorch expert to run the example. The example will help you to understand how Flower can be used to build federated learning use cases based on existing machine learning projects.
+This introductory example for Flower uses PyTorch, but you're not required to be a PyTorch expert to run the example. The example will help you to understand how Flower can be used to build federated learning use cases based on existing machine learning projects. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the CIFAR-10 dataset.
 
 ## Project Setup
 
diff --git a/examples/pytorch-from-centralized-to-federated/__init__.py b/examples/pytorch-from-centralized-to-federated/__init__.py
deleted file mode 100644
index e69de29bb2d1..000000000000
diff --git a/examples/pytorch-from-centralized-to-federated/cifar.py b/examples/pytorch-from-centralized-to-federated/cifar.py
index 3c1d67d2f445..277a21da2e70 100644
--- a/examples/pytorch-from-centralized-to-federated/cifar.py
+++ b/examples/pytorch-from-centralized-to-federated/cifar.py
@@ -6,22 +6,20 @@
 https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html
 """
 
-
 # mypy: ignore-errors
 # pylint: disable=W0223
 
 
-from typing import Tuple, Dict
+from typing import Tuple
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-import torchvision
-import torchvision.transforms as transforms
 from torch import Tensor
-from torchvision.datasets import CIFAR10
+from torch.utils.data import DataLoader
+from torchvision.transforms import Compose, ToTensor, Normalize
 
-DATA_ROOT = "./dataset"
+from flwr_datasets import FederatedDataset
 
 
 # pylint: disable=unsubscriptable-object
@@ -53,19 +51,25 @@ def forward(self, x: Tensor) -> Tensor:
         return x
 
 
-def load_data() -> (
-    Tuple[torch.utils.data.DataLoader, torch.utils.data.DataLoader, Dict]
-):
-    """Load CIFAR-10 (training and test set)."""
-    transform = transforms.Compose(
-        [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
+def load_data(partition_id: int):
+    """Load partition CIFAR10 data."""
+    fds = FederatedDataset(dataset="cifar10", partitioners={"train": 10})
+    partition = fds.load_partition(partition_id)
+    # Divide data on each node: 80% train, 20% test
+    partition_train_test = partition.train_test_split(test_size=0.2)
+    pytorch_transforms = Compose(
+        [ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
     )
-    trainset = CIFAR10(DATA_ROOT, train=True, download=True, transform=transform)
-    trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True)
-    testset = CIFAR10(DATA_ROOT, train=False, download=True, transform=transform)
-    testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False)
-    num_examples = {"trainset": len(trainset), "testset": len(testset)}
-    return trainloader, testloader, num_examples
+
+    def apply_transforms(batch):
+        """Apply transforms to the partition from FederatedDataset."""
+        batch["img"] = [pytorch_transforms(img) for img in batch["img"]]
+        return batch
+
+    partition_train_test = partition_train_test.with_transform(apply_transforms)
+    trainloader = DataLoader(partition_train_test["train"], batch_size=32, shuffle=True)
+    testloader = DataLoader(partition_train_test["test"], batch_size=32)
+    return trainloader, testloader
 
 
 def train(
@@ -87,7 +91,7 @@ def train(
     for epoch in range(epochs):  # loop over the dataset multiple times
         running_loss = 0.0
         for i, data in enumerate(trainloader, 0):
-            images, labels = data[0].to(device), data[1].to(device)
+            images, labels = data["img"].to(device), data["label"].to(device)
 
             # zero the parameter gradients
             optimizer.zero_grad()
@@ -120,7 +124,7 @@ def test(
     net.eval()
     with torch.no_grad():
         for data in testloader:
-            images, labels = data[0].to(device), data[1].to(device)
+            images, labels = data["img"].to(device), data["label"].to(device)
             outputs = net(images)
             loss += criterion(outputs, labels).item()
             _, predicted = torch.max(outputs.data, 1)  # pylint: disable=no-member
@@ -133,7 +137,7 @@ def main():
     DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
     print("Centralized PyTorch training")
     print("Load data")
-    trainloader, testloader, _ = load_data()
+    trainloader, testloader = load_data(0)
     net = Net().to(DEVICE)
     net.eval()
     print("Start training")
diff --git a/examples/pytorch-from-centralized-to-federated/client.py b/examples/pytorch-from-centralized-to-federated/client.py
index 88678e0569b7..9df4739e0aab 100644
--- a/examples/pytorch-from-centralized-to-federated/client.py
+++ b/examples/pytorch-from-centralized-to-federated/client.py
@@ -1,24 +1,23 @@
 """Flower client example using PyTorch for CIFAR-10 image classification."""
 
-
-import os
-import sys
-import timeit
+import argparse
 from collections import OrderedDict
 from typing import Dict, List, Tuple
 
-import flwr as fl
 import numpy as np
 import torch
-import torchvision
+from datasets.utils.logging import disable_progress_bar
+from torch.utils.data import DataLoader
 
 import cifar
+import flwr as fl
+
+disable_progress_bar()
+
 
 USE_FEDBN: bool = True
 
-# pylint: disable=no-member
-DEVICE: str = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-# pylint: enable=no-member
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
 
 # Flower Client
@@ -28,19 +27,18 @@ class CifarClient(fl.client.NumPyClient):
     def __init__(
         self,
         model: cifar.Net,
-        trainloader: torch.utils.data.DataLoader,
-        testloader: torch.utils.data.DataLoader,
-        num_examples: Dict,
+        trainloader: DataLoader,
+        testloader: DataLoader,
     ) -> None:
         self.model = model
         self.trainloader = trainloader
         self.testloader = testloader
-        self.num_examples = num_examples
 
     def get_parameters(self, config: Dict[str, str]) -> List[np.ndarray]:
         self.model.train()
         if USE_FEDBN:
-            # Return model parameters as a list of NumPy ndarrays, excluding parameters of BN layers when using FedBN
+            # Return model parameters as a list of NumPy ndarrays, excluding
+            # parameters of BN layers when using FedBN
             return [
                 val.cpu().numpy()
                 for name, val in self.model.state_dict().items()
@@ -69,7 +67,7 @@ def fit(
         # Set model parameters, train model, return updated model parameters
         self.set_parameters(parameters)
         cifar.train(self.model, self.trainloader, epochs=1, device=DEVICE)
-        return self.get_parameters(config={}), self.num_examples["trainset"], {}
+        return self.get_parameters(config={}), len(self.trainloader.dataset), {}
 
     def evaluate(
         self, parameters: List[np.ndarray], config: Dict[str, str]
@@ -77,24 +75,27 @@ def evaluate(
         # Set model parameters, evaluate model on local test dataset, return result
         self.set_parameters(parameters)
         loss, accuracy = cifar.test(self.model, self.testloader, device=DEVICE)
-        return float(loss), self.num_examples["testset"], {"accuracy": float(accuracy)}
+        return float(loss), len(self.testloader.dataset), {"accuracy": float(accuracy)}
 
 
 def main() -> None:
     """Load data, start CifarClient."""
+    parser = argparse.ArgumentParser(description="Flower")
+    parser.add_argument("--partition-id", type=int, required=True, choices=range(0, 10))
+    args = parser.parse_args()
 
     # Load data
-    trainloader, testloader, num_examples = cifar.load_data()
+    trainloader, testloader = cifar.load_data(args.partition_id)
 
     # Load model
     model = cifar.Net().to(DEVICE).train()
 
     # Perform a single forward pass to properly initialize BatchNorm
-    _ = model(next(iter(trainloader))[0].to(DEVICE))
+    _ = model(next(iter(trainloader))["img"].to(DEVICE))
 
     # Start client
-    client = CifarClient(model, trainloader, testloader, num_examples)
-    fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=client)
+    client = CifarClient(model, trainloader, testloader).to_client()
+    fl.client.start_client(server_address="127.0.0.1:8080", client=client)
 
 
 if __name__ == "__main__":
diff --git a/examples/pytorch-from-centralized-to-federated/pyproject.toml b/examples/pytorch-from-centralized-to-federated/pyproject.toml
index 73999a9e6cd4..3d1559e3a515 100644
--- a/examples/pytorch-from-centralized-to-federated/pyproject.toml
+++ b/examples/pytorch-from-centralized-to-federated/pyproject.toml
@@ -6,10 +6,11 @@ build-backend = "poetry.masonry.api"
 name = "quickstart-pytorch"
 version = "0.1.0"
 description = "PyTorch: From Centralized To Federated with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = ">=1.0,<2.0"
+flwr-datasets = { extras = ["vision"], version = ">=0.0.2,<1.0.0" }
 torch = "1.13.1"
 torchvision = "0.14.1"
diff --git a/examples/pytorch-from-centralized-to-federated/requirements.txt b/examples/pytorch-from-centralized-to-federated/requirements.txt
index f3caddbc875e..ba4afad9c288 100644
--- a/examples/pytorch-from-centralized-to-federated/requirements.txt
+++ b/examples/pytorch-from-centralized-to-federated/requirements.txt
@@ -1,3 +1,4 @@
 flwr>=1.0, <2.0
+flwr-datasets[vision]>=0.0.2, <1.0.0
 torch==1.13.1
 torchvision==0.14.1
diff --git a/examples/pytorch-from-centralized-to-federated/run.sh b/examples/pytorch-from-centralized-to-federated/run.sh
index c64f362086aa..6ddf6ad476b4 100755
--- a/examples/pytorch-from-centralized-to-federated/run.sh
+++ b/examples/pytorch-from-centralized-to-federated/run.sh
@@ -4,9 +4,9 @@ echo "Starting server"
 python server.py &
 sleep 3  # Sleep for 3s to give the server enough time to start
 
-for i in `seq 0 1`; do
+for i in $(seq 0 1); do
     echo "Starting client $i"
-    python client.py &
+    python client.py --partition-id $i &
 done
 
 # This will allow you to use CTRL+C to stop all background processes
diff --git a/examples/pytorch-from-centralized-to-federated/server.py b/examples/pytorch-from-centralized-to-federated/server.py
index 29cbce1884d1..5190d690dc20 100644
--- a/examples/pytorch-from-centralized-to-federated/server.py
+++ b/examples/pytorch-from-centralized-to-federated/server.py
@@ -1,10 +1,27 @@
 """Flower server example."""
 
+from typing import List, Tuple
 
 import flwr as fl
+from flwr.common import Metrics
 
-if __name__ == "__main__":
-    fl.server.start_server(
-        server_address="0.0.0.0:8080",
-        config=fl.server.ServerConfig(num_rounds=3),
-    )
+
+# Define metric aggregation function
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+    # Multiply accuracy of each client by number of examples used
+    accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics]
+    examples = [num_examples for num_examples, _ in metrics]
+
+    # Aggregate and return custom metric (weighted average)
+    return {"accuracy": sum(accuracies) / sum(examples)}
+
+
+# Define strategy
+strategy = fl.server.strategy.FedAvg(evaluate_metrics_aggregation_fn=weighted_average)
+
+# Start Flower server
+fl.server.start_server(
+    server_address="0.0.0.0:8080",
+    config=fl.server.ServerConfig(num_rounds=10),
+    strategy=strategy,
+)
diff --git a/examples/quickstart-cpp/driver.py b/examples/quickstart-cpp/driver.py
index 037623ee77cf..f19cf0e9bd98 100644
--- a/examples/quickstart-cpp/driver.py
+++ b/examples/quickstart-cpp/driver.py
@@ -3,7 +3,7 @@
 
 # Start Flower server for three rounds of federated learning
 if __name__ == "__main__":
-    fl.driver.start_driver(
+    fl.server.start_driver(
         server_address="0.0.0.0:9091",
         config=fl.server.ServerConfig(num_rounds=3),
         strategy=FedAvgCpp(),
diff --git a/examples/quickstart-fastai/client.py b/examples/quickstart-fastai/client.py
index a88abbe525dc..6bb2a751d544 100644
--- a/examples/quickstart-fastai/client.py
+++ b/examples/quickstart-fastai/client.py
@@ -43,7 +43,7 @@ def evaluate(self, parameters, config):
 
 
 # Start Flower client
-fl.client.start_numpy_client(
+fl.client.start_client(
     server_address="127.0.0.1:8080",
-    client=FlowerClient(),
+    client=FlowerClient().to_client(),
 )
diff --git a/examples/quickstart-fastai/pyproject.toml b/examples/quickstart-fastai/pyproject.toml
index ffaa97267493..19a25291a6af 100644
--- a/examples/quickstart-fastai/pyproject.toml
+++ b/examples/quickstart-fastai/pyproject.toml
@@ -6,9 +6,11 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-fastai"
 version = "0.1.0"
 description = "Fastai Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
-python = ">=3.8,<3.10"
-flwr = "^1.0.0"
-fastai = "^2.7.10"
+python = ">=3.8,<3.11"
+flwr = ">=1.0,<2.0"
+fastai = "2.7.14"
+torch = "2.2.0"
+torchvision = "0.17.0"
diff --git a/examples/quickstart-fastai/requirements.txt b/examples/quickstart-fastai/requirements.txt
index 0a7f315018a2..9c6e8d77293a 100644
--- a/examples/quickstart-fastai/requirements.txt
+++ b/examples/quickstart-fastai/requirements.txt
@@ -1,3 +1,4 @@
-fastai~=2.7.12
-flwr~=1.4.0
-torch~=2.0.1
+flwr>=1.0, <2.0
+fastai==2.7.14
+torch==2.2.0
+torchvision==0.17.0
diff --git a/examples/quickstart-huggingface/README.md b/examples/quickstart-huggingface/README.md
index c1e3cc4edc06..ce7790cd4af5 100644
--- a/examples/quickstart-huggingface/README.md
+++ b/examples/quickstart-huggingface/README.md
@@ -1,6 +1,6 @@
 # Federated HuggingFace Transformers using Flower and PyTorch
 
-This introductory example to using [HuggingFace](https://huggingface.co) Transformers with Flower with PyTorch. This example has been extended from the [quickstart-pytorch](https://flower.dev/docs/examples/quickstart-pytorch.html) example. The training script closely follows the [HuggingFace course](https://huggingface.co/course/chapter3?fw=pt), so you are encouraged to check that out for detailed explaination for the transformer pipeline.
+This introductory example to using [HuggingFace](https://huggingface.co) Transformers with Flower with PyTorch. This example has been extended from the [quickstart-pytorch](https://flower.ai/docs/examples/quickstart-pytorch.html) example. The training script closely follows the [HuggingFace course](https://huggingface.co/course/chapter3?fw=pt), so you are encouraged to check that out for a detailed explanation of the transformer pipeline.
 
 Like `quickstart-pytorch`, running this example in itself is also meant to be quite easy.
 
@@ -62,13 +62,13 @@ Now you are ready to start the Flower clients which will participate in the lear
 Start client 1 in the first terminal:
 
 ```shell
-python3 client.py
+python3 client.py --partition-id 0
 ```
 
 Start client 2 in the second terminal:
 
 ```shell
-python3 client.py
+python3 client.py --partition-id 1
 ```
 
 You will see that PyTorch is starting a federated training.
diff --git a/examples/quickstart-huggingface/client.py b/examples/quickstart-huggingface/client.py
index 8717d710ad9c..9be08d0cbcf4 100644
--- a/examples/quickstart-huggingface/client.py
+++ b/examples/quickstart-huggingface/client.py
@@ -1,58 +1,48 @@
-from collections import OrderedDict
+import argparse
 import warnings
+from collections import OrderedDict
 
 import flwr as fl
 import torch
-import numpy as np
-
-import random
-from torch.utils.data import DataLoader
-
-from datasets import load_dataset
 from evaluate import load as load_metric
-
-from transformers import AutoTokenizer, DataCollatorWithPadding
+from torch.optim import AdamW
+from torch.utils.data import DataLoader
 from transformers import AutoModelForSequenceClassification
-from transformers import AdamW
+from transformers import AutoTokenizer, DataCollatorWithPadding
+
+from flwr_datasets import FederatedDataset
 
 warnings.filterwarnings("ignore", category=UserWarning)
 DEVICE = torch.device("cpu")
 CHECKPOINT = "distilbert-base-uncased"  # transformer model checkpoint
 
 
-def load_data():
+def load_data(partition_id):
     """Load IMDB data (training and eval)"""
-    raw_datasets = load_dataset("imdb")
-    raw_datasets = raw_datasets.shuffle(seed=42)
-
-    # remove unnecessary data split
-    del raw_datasets["unsupervised"]
+    fds = FederatedDataset(dataset="imdb", partitioners={"train": 1_000})
+    partition = fds.load_partition(partition_id)
+    # Divide data: 80% train, 20% test
+    partition_train_test = partition.train_test_split(test_size=0.2)
 
     tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT)
 
     def tokenize_function(examples):
         return tokenizer(examples["text"], truncation=True)
 
-    # random 100 samples
-    population = random.sample(range(len(raw_datasets["train"])), 100)
-
-    tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
-    tokenized_datasets["train"] = tokenized_datasets["train"].select(population)
-    tokenized_datasets["test"] = tokenized_datasets["test"].select(population)
-
-    tokenized_datasets = tokenized_datasets.remove_columns("text")
-    tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
+    partition_train_test = partition_train_test.map(tokenize_function, batched=True)
+    partition_train_test = partition_train_test.remove_columns("text")
+    partition_train_test = partition_train_test.rename_column("label", "labels")
 
     data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
     trainloader = DataLoader(
-        tokenized_datasets["train"],
+        partition_train_test["train"],
         shuffle=True,
         batch_size=32,
         collate_fn=data_collator,
     )
 
     testloader = DataLoader(
-        tokenized_datasets["test"], batch_size=32, collate_fn=data_collator
+        partition_train_test["test"], batch_size=32, collate_fn=data_collator
     )
 
     return trainloader, testloader
@@ -88,12 +78,12 @@ def test(net, testloader):
     return loss, accuracy
 
 
-def main():
+def main(partition_id):
     net = AutoModelForSequenceClassification.from_pretrained(
         CHECKPOINT, num_labels=2
     ).to(DEVICE)
 
-    trainloader, testloader = load_data()
+    trainloader, testloader = load_data(partition_id)
 
     # Flower client
     class IMDBClient(fl.client.NumPyClient):
@@ -118,8 +108,20 @@ def evaluate(self, parameters, config):
             return float(loss), len(testloader), {"accuracy": float(accuracy)}
 
     # Start client
-    fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=IMDBClient())
+    fl.client.start_client(
+        server_address="127.0.0.1:8080", client=IMDBClient().to_client()
+    )
 
 
 if __name__ == "__main__":
-    main()
+    parser = argparse.ArgumentParser(description="Flower")
+    parser.add_argument(
+        "--partition-id",
+        choices=list(range(1_000)),
+        required=True,
+        type=int,
+        help="Partition of the dataset divided into 1,000 iid partitions created "
+        "artificially.",
+    )
+    partition_id = parser.parse_args().partition_id
+    main(partition_id)
diff --git a/examples/quickstart-huggingface/pyproject.toml b/examples/quickstart-huggingface/pyproject.toml
index eb9687c5152c..2b46804d7b45 100644
--- a/examples/quickstart-huggingface/pyproject.toml
+++ b/examples/quickstart-huggingface/pyproject.toml
@@ -7,13 +7,14 @@ name = "quickstart-huggingface"
 version = "0.1.0"
 description = "Hugging Face Transformers Federated Learning Quickstart with Flower"
 authors = [
-    "The Flower Authors <hello@flower.dev>",
-    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>"
+    "The Flower Authors <hello@flower.ai>",
+    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>",
 ]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = ">=1.0,<2.0"
+flwr-datasets = ">=0.0.2,<1.0.0"
 torch = ">=1.13.1,<2.0"
 transformers = ">=4.30.0,<5.0"
 evaluate = ">=0.4.0,<1.0"
diff --git a/examples/quickstart-huggingface/requirements.txt b/examples/quickstart-huggingface/requirements.txt
index aeb2d13fc4a4..3cd5735625ba 100644
--- a/examples/quickstart-huggingface/requirements.txt
+++ b/examples/quickstart-huggingface/requirements.txt
@@ -1,4 +1,5 @@
 flwr>=1.0, <2.0
+flwr-datasets>=0.0.2, <1.0.0
 torch>=1.13.1, <2.0
 transformers>=4.30.0, <5.0
 evaluate>=0.4.0, <1.0
diff --git a/examples/quickstart-huggingface/run.sh b/examples/quickstart-huggingface/run.sh
index c64f362086aa..fa989eab1471 100755
--- a/examples/quickstart-huggingface/run.sh
+++ b/examples/quickstart-huggingface/run.sh
@@ -6,7 +6,7 @@ sleep 3  # Sleep for 3s to give the server enough time to start
 
 for i in `seq 0 1`; do
     echo "Starting client $i"
-    python client.py &
+    python client.py --partition-id ${i}&
 done
 
 # This will allow you to use CTRL+C to stop all background processes
diff --git a/examples/quickstart-jax/README.md b/examples/quickstart-jax/README.md
index 9208337b86de..836adf558d88 100644
--- a/examples/quickstart-jax/README.md
+++ b/examples/quickstart-jax/README.md
@@ -52,7 +52,7 @@ pip install -r requirements.txt
 
 ## Run JAX Federated
 
-This JAX example is based on the [Linear Regression with JAX](https://coax.readthedocs.io/en/latest/examples/linear_regression/jax.html) tutorial and uses a sklearn dataset (generating a random dataset for a regression pronlem). Feel free to consult the tutorial if you want to get a better understanding of JAX. If you play around with the dataset, please keep in mind that the data samples are generated randomly depending on the settings being done while calling the dataset function. Please checkout out the [scikit-learn tutorial for further information](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html). The file `jax_training.py` contains all the steps that are described in the tutorial. It loads the train and test dataset and a linear regression model, trains the model with the training set, and evaluates the trained model on the test set.
+This JAX example is based on the [Linear Regression with JAX](https://coax.readthedocs.io/en/latest/examples/linear_regression/jax.html) tutorial and uses a sklearn dataset (generating a random dataset for a regression problem). Feel free to consult the tutorial if you want to get a better understanding of JAX. If you play around with the dataset, please keep in mind that the data samples are generated randomly depending on the settings being done while calling the dataset function. Please checkout out the [scikit-learn tutorial for further information](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_regression.html). The file `jax_training.py` contains all the steps that are described in the tutorial. It loads the train and test dataset and a linear regression model, trains the model with the training set, and evaluates the trained model on the test set.
 
 The only things we need are a simple Flower server (in `server.py`) and a Flower client (in `client.py`). The Flower client basically takes model and training code tells Flower how to call it.
 
diff --git a/examples/quickstart-jax/client.py b/examples/quickstart-jax/client.py
index f9b056276deb..2257a3d6daa3 100644
--- a/examples/quickstart-jax/client.py
+++ b/examples/quickstart-jax/client.py
@@ -1,6 +1,5 @@
 """Flower client example using JAX for linear regression."""
 
-
 from typing import Dict, List, Tuple, Callable
 
 import flwr as fl
@@ -52,4 +51,6 @@ def evaluate(
 
 
 # Start Flower client
-fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=FlowerClient())
+fl.client.start_client(
+    server_address="127.0.0.1:8080", client=FlowerClient().to_client()
+)
diff --git a/examples/quickstart-jax/jax_training.py b/examples/quickstart-jax/jax_training.py
index 2b523a08516e..a2e23a0927bc 100644
--- a/examples/quickstart-jax/jax_training.py
+++ b/examples/quickstart-jax/jax_training.py
@@ -7,7 +7,6 @@
 please read the JAX documentation or the mentioned tutorial.
 """
 
-
 from typing import Dict, List, Tuple, Callable
 import jax
 import jax.numpy as jnp
diff --git a/examples/quickstart-jax/pyproject.toml b/examples/quickstart-jax/pyproject.toml
index 41b4462d0a14..c956191369b5 100644
--- a/examples/quickstart-jax/pyproject.toml
+++ b/examples/quickstart-jax/pyproject.toml
@@ -2,7 +2,7 @@
 name = "jax_example"
 version = "0.1.0"
 description = "JAX example training a linear regression model with federated learning"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/quickstart-mlcube/client.py b/examples/quickstart-mlcube/client.py
index 0a1962d8da8a..46ddd45f52ce 100644
--- a/examples/quickstart-mlcube/client.py
+++ b/examples/quickstart-mlcube/client.py
@@ -43,8 +43,9 @@ def main():
         os.path.dirname(os.path.abspath(__file__)), "workspaces", workspace_name
     )
 
-    fl.client.start_numpy_client(
-        server_address="0.0.0.0:8080", client=MLCubeClient(workspace=workspace)
+    fl.client.start_client(
+        server_address="0.0.0.0:8080",
+        client=MLCubeClient(workspace=workspace).to_client(),
     )
 
 
diff --git a/examples/quickstart-mlcube/pyproject.toml b/examples/quickstart-mlcube/pyproject.toml
index 0d42fc3b2898..a2862bd5ebb7 100644
--- a/examples/quickstart-mlcube/pyproject.toml
+++ b/examples/quickstart-mlcube/pyproject.toml
@@ -6,13 +6,13 @@ build-backend = "poetry.masonry.api"
 name = "quickstart-ml-cube"
 version = "0.1.0"
 description = "Keras Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = ">=1.0,<2.0"  # For development: { path = "../../", develop = true }
-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-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\"" }
 mlcube = "0.0.9"
 mlcube-docker = "0.0.9"
 tensorflow-estimator = ">=2.9.1,<2.11.1 || >2.11.1"
diff --git a/examples/quickstart-mlx/README.md b/examples/quickstart-mlx/README.md
index d94a87a014f7..cca55bcb946a 100644
--- a/examples/quickstart-mlx/README.md
+++ b/examples/quickstart-mlx/README.md
@@ -66,19 +66,19 @@ following commands.
 Start a first client in the first terminal:
 
 ```shell
-python3 client.py --node-id 0
+python3 client.py --partition-id 0
 ```
 
 And another one in the second terminal:
 
 ```shell
-python3 client.py --node-id 1
+python3 client.py --partition-id 1
 ```
 
 If you want to utilize your GPU, you can use the `--gpu` argument:
 
 ```shell
-python3 client.py --gpu --node-id 2
+python3 client.py --gpu --partition-id 2
 ```
 
 Note that you can start many more clients if you want, but each will have to be in its own terminal.
@@ -96,7 +96,7 @@ We will use `flwr_datasets` to easily download and partition the `MNIST` dataset
 
 ```python
 fds = FederatedDataset(dataset="mnist", partitioners={"train": 3})
-partition = fds.load_partition(node_id = args.node_id)
+partition = fds.load_partition(partition_id = args.partition_id)
 partition_splits = partition.train_test_split(test_size=0.2)
 
 partition_splits['train'].set_format("numpy")
diff --git a/examples/quickstart-mlx/client.py b/examples/quickstart-mlx/client.py
index 3b506399a5f1..faba2b94d6bd 100644
--- a/examples/quickstart-mlx/client.py
+++ b/examples/quickstart-mlx/client.py
@@ -89,7 +89,7 @@ def evaluate(self, parameters, config):
     parser = argparse.ArgumentParser("Train a simple MLP on MNIST with MLX.")
     parser.add_argument("--gpu", action="store_true", help="Use the Metal back-end.")
     parser.add_argument(
-        "--node-id",
+        "--partition-id",
         choices=[0, 1, 2],
         type=int,
         help="Partition of the dataset divided into 3 iid partitions created artificially.",
@@ -106,7 +106,7 @@ def evaluate(self, parameters, config):
     learning_rate = 1e-1
 
     fds = FederatedDataset(dataset="mnist", partitioners={"train": 3})
-    partition = fds.load_partition(node_id=args.node_id)
+    partition = fds.load_partition(partition_id=args.partition_id)
     partition_splits = partition.train_test_split(test_size=0.2)
 
     partition_splits["train"].set_format("numpy")
diff --git a/examples/quickstart-mlx/pyproject.toml b/examples/quickstart-mlx/pyproject.toml
index deb541c5ba9c..752040b6aaa9 100644
--- a/examples/quickstart-mlx/pyproject.toml
+++ b/examples/quickstart-mlx/pyproject.toml
@@ -6,11 +6,11 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-mlx"
 version = "0.1.0"
 description = "MLX Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = ">=1.0,<2.0"
 mlx = "==0.0.3"
 numpy = "==1.24.4"
-flwr-datasets = {extras = ["vision"], version = "^0.0.2"}
+flwr-datasets = { extras = ["vision"], version = "^0.0.2" }
diff --git a/examples/quickstart-mlx/run.sh b/examples/quickstart-mlx/run.sh
index 70281049517d..40d211848c07 100755
--- a/examples/quickstart-mlx/run.sh
+++ b/examples/quickstart-mlx/run.sh
@@ -8,7 +8,7 @@ sleep 3  # Sleep for 3s to give the server enough time to start
 
 for i in $(seq 0 1); do
     echo "Starting client $i"
-    python client.py --node-id $i &
+    python client.py --partition-id $i &
 done
 
 # Enable CTRL+C to stop all background processes
diff --git a/examples/quickstart-monai/.gitignore b/examples/quickstart-monai/.gitignore
new file mode 100644
index 000000000000..a218cab9669e
--- /dev/null
+++ b/examples/quickstart-monai/.gitignore
@@ -0,0 +1 @@
+MedNIST*
diff --git a/examples/quickstart-monai/README.md b/examples/quickstart-monai/README.md
new file mode 100644
index 000000000000..4a9afef4f86a
--- /dev/null
+++ b/examples/quickstart-monai/README.md
@@ -0,0 +1,85 @@
+# Flower Example using MONAI
+
+This introductory example to Flower uses MONAI, but deep knowledge of MONAI is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case.
+Running this example in itself is quite easy.
+
+[MONAI](https://docs.monai.io/en/latest/index.html)(Medical Open Network for AI) is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of the PyTorch Ecosystem.
+
+Its ambitions are:
+
+- developing a community of academic, industrial and clinical researchers collaborating on a common foundation;
+
+- creating state-of-the-art, end-to-end training workflows for healthcare imaging;
+
+- providing researchers with an optimized and standardized way to create and evaluate deep learning models.
+
+## 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/quickstart-monai . && rm -rf _tmp && cd quickstart-monai
+```
+
+This will create a new directory called `quickstart-monai` containing the following files:
+
+```shell
+-- pyproject.toml
+-- requirements.txt
+-- client.py
+-- data.py
+-- model.py
+-- server.py
+-- README.md
+```
+
+### Installing Dependencies
+
+Project dependencies (such as `monai` 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
+
+```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 MONAI 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
+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 terminal windows and run the following commands.  Clients will train a [DenseNet121](https://docs.monai.io/en/stable/networks.html#densenet121) from MONAI. If a GPU is present in your system, clients will use it.
+
+Start client 1 in the first terminal:
+
+```shell
+python3 client.py --partition-id 0
+```
+
+Start client 2 in the second terminal:
+
+```shell
+python3 client.py --partition-id 1
+```
+
+You will see that the federated training is starting. Look at the [code](https://github.com/adap/flower/tree/main/examples/quickstart-monai) for a detailed explanation.
diff --git a/examples/quickstart-monai/client.py b/examples/quickstart-monai/client.py
new file mode 100644
index 000000000000..0ed943da83cc
--- /dev/null
+++ b/examples/quickstart-monai/client.py
@@ -0,0 +1,61 @@
+import argparse
+import warnings
+from collections import OrderedDict
+
+import torch
+from data import load_data
+from model import test, train
+from monai.networks.nets.densenet import DenseNet121
+
+import flwr as fl
+
+warnings.filterwarnings("ignore", category=UserWarning)
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+# Define Flower client
+class FlowerClient(fl.client.NumPyClient):
+    def __init__(self, net, trainloader, testloader, device):
+        self.net = net
+        self.trainloader = trainloader
+        self.testloader = testloader
+        self.device = device
+
+    def get_parameters(self, config):
+        return [val.cpu().numpy() for _, val in self.net.state_dict().items()]
+
+    def set_parameters(self, parameters):
+        params_dict = zip(self.net.state_dict().keys(), parameters)
+        state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
+        self.net.load_state_dict(state_dict, strict=True)
+
+    def fit(self, parameters, config):
+        self.set_parameters(parameters)
+        train(self.net, self.trainloader, epoch_num=1, device=self.device)
+        return self.get_parameters(config={}), len(self.trainloader), {}
+
+    def evaluate(self, parameters, config):
+        self.set_parameters(parameters)
+        loss, accuracy = test(self.net, self.testloader, self.device)
+        return loss, len(self.testloader), {"accuracy": accuracy}
+
+
+if __name__ == "__main__":
+    total_partitions = 10
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--partition-id", type=int, choices=range(total_partitions), required=True
+    )
+    args = parser.parse_args()
+
+    # Load model and data (simple CNN, CIFAR-10)
+    trainloader, _, testloader, num_class = load_data(
+        total_partitions, args.partition_id
+    )
+    net = DenseNet121(spatial_dims=2, in_channels=1, out_channels=num_class).to(DEVICE)
+
+    # Start Flower client
+    fl.client.start_numpy_client(
+        server_address="127.0.0.1:8080",
+        client=FlowerClient(net, trainloader, testloader, DEVICE),
+    )
diff --git a/examples/quickstart-monai/data.py b/examples/quickstart-monai/data.py
new file mode 100644
index 000000000000..d184476522e8
--- /dev/null
+++ b/examples/quickstart-monai/data.py
@@ -0,0 +1,158 @@
+import os
+import tarfile
+from urllib import request
+
+import numpy as np
+from monai.data import DataLoader, Dataset
+from monai.transforms import (
+    Compose,
+    EnsureChannelFirst,
+    LoadImage,
+    RandFlip,
+    RandRotate,
+    RandZoom,
+    ScaleIntensity,
+    ToTensor,
+)
+
+
+def _partition(files_list, labels_list, num_shards, index):
+    total_size = len(files_list)
+    assert total_size == len(
+        labels_list
+    ), f"List of datapoints and labels must be of the same length"
+    shard_size = total_size // num_shards
+
+    # Calculate start and end indices for the shard
+    start_idx = index * shard_size
+    if index == num_shards - 1:
+        # Last shard takes the remainder
+        end_idx = total_size
+    else:
+        end_idx = start_idx + shard_size
+
+    # Create a subset for the shard
+    files = files_list[start_idx:end_idx]
+    labels = labels_list[start_idx:end_idx]
+    return files, labels
+
+
+def load_data(num_shards, index):
+    image_file_list, image_label_list, _, num_class = _download_data()
+
+    # Get partition given index
+    files_list, labels_list = _partition(
+        image_file_list, image_label_list, num_shards, index
+    )
+
+    trainX, trainY, valX, valY, testX, testY = _split_data(
+        files_list, labels_list, len(files_list)
+    )
+    train_transforms, val_transforms = _get_transforms()
+
+    train_ds = MedNISTDataset(trainX, trainY, train_transforms)
+    train_loader = DataLoader(train_ds, batch_size=300, shuffle=True)
+
+    val_ds = MedNISTDataset(valX, valY, val_transforms)
+    val_loader = DataLoader(val_ds, batch_size=300)
+
+    test_ds = MedNISTDataset(testX, testY, val_transforms)
+    test_loader = DataLoader(test_ds, batch_size=300)
+
+    return train_loader, val_loader, test_loader, num_class
+
+
+class MedNISTDataset(Dataset):
+    def __init__(self, image_files, labels, transforms):
+        self.image_files = image_files
+        self.labels = labels
+        self.transforms = transforms
+
+    def __len__(self):
+        return len(self.image_files)
+
+    def __getitem__(self, index):
+        return self.transforms(self.image_files[index]), self.labels[index]
+
+
+def _download_data():
+    data_dir = "./MedNIST/"
+    _download_and_extract(
+        "https://dl.dropboxusercontent.com/s/5wwskxctvcxiuea/MedNIST.tar.gz",
+        os.path.join(data_dir),
+    )
+
+    class_names = sorted(
+        [x for x in os.listdir(data_dir) if os.path.isdir(os.path.join(data_dir, x))]
+    )
+    num_class = len(class_names)
+    image_files = [
+        [
+            os.path.join(data_dir, class_name, x)
+            for x in os.listdir(os.path.join(data_dir, class_name))
+        ]
+        for class_name in class_names
+    ]
+    image_file_list = []
+    image_label_list = []
+    for i, class_name in enumerate(class_names):
+        image_file_list.extend(image_files[i])
+        image_label_list.extend([i] * len(image_files[i]))
+    num_total = len(image_label_list)
+    return image_file_list, image_label_list, num_total, num_class
+
+
+def _split_data(image_file_list, image_label_list, num_total):
+    valid_frac, test_frac = 0.1, 0.1
+    trainX, trainY = [], []
+    valX, valY = [], []
+    testX, testY = [], []
+
+    for i in range(num_total):
+        rann = np.random.random()
+        if rann < valid_frac:
+            valX.append(image_file_list[i])
+            valY.append(image_label_list[i])
+        elif rann < test_frac + valid_frac:
+            testX.append(image_file_list[i])
+            testY.append(image_label_list[i])
+        else:
+            trainX.append(image_file_list[i])
+            trainY.append(image_label_list[i])
+
+    return trainX, trainY, valX, valY, testX, testY
+
+
+def _get_transforms():
+    train_transforms = Compose(
+        [
+            LoadImage(image_only=True),
+            EnsureChannelFirst(),
+            ScaleIntensity(),
+            RandRotate(range_x=15, prob=0.5, keep_size=True),
+            RandFlip(spatial_axis=0, prob=0.5),
+            RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5, keep_size=True),
+            ToTensor(),
+        ]
+    )
+
+    val_transforms = Compose(
+        [LoadImage(image_only=True), EnsureChannelFirst(), ScaleIntensity(), ToTensor()]
+    )
+
+    return train_transforms, val_transforms
+
+
+def _download_and_extract(url, dest_folder):
+    if not os.path.isdir(dest_folder):
+        # Download the tar.gz file
+        tar_gz_filename = url.split("/")[-1]
+        if not os.path.isfile(tar_gz_filename):
+            with request.urlopen(url) as response, open(
+                tar_gz_filename, "wb"
+            ) as out_file:
+                out_file.write(response.read())
+
+        # Extract the tar.gz file
+        with tarfile.open(tar_gz_filename, "r:gz") as tar_ref:
+            tar_ref.extractall()
diff --git a/examples/quickstart-monai/model.py b/examples/quickstart-monai/model.py
new file mode 100644
index 000000000000..4c74d50553e4
--- /dev/null
+++ b/examples/quickstart-monai/model.py
@@ -0,0 +1,33 @@
+import torch
+
+
+def train(model, train_loader, epoch_num, device):
+    loss_function = torch.nn.CrossEntropyLoss()
+    optimizer = torch.optim.Adam(model.parameters(), 1e-5)
+    for _ in range(epoch_num):
+        model.train()
+        for inputs, labels in train_loader:
+            optimizer.zero_grad()
+            loss_function(model(inputs.to(device)), labels.to(device)).backward()
+            optimizer.step()
+
+
+def test(model, test_loader, device):
+    model.eval()
+    loss = 0.0
+    y_true = list()
+    y_pred = list()
+    loss_function = torch.nn.CrossEntropyLoss()
+    with torch.no_grad():
+        for test_images, test_labels in test_loader:
+            out = model(test_images.to(device))
+            test_labels = test_labels.to(device)
+            loss += loss_function(out, test_labels).item()
+            pred = out.argmax(dim=1)
+            for i in range(len(pred)):
+                y_true.append(test_labels[i].item())
+                y_pred.append(pred[i].item())
+    accuracy = sum([1 if t == p else 0 for t, p in zip(y_true, y_pred)]) / len(
+        test_loader.dataset
+    )
+    return loss, accuracy
diff --git a/examples/mt-pytorch-callable/pyproject.toml b/examples/quickstart-monai/pyproject.toml
similarity index 51%
rename from examples/mt-pytorch-callable/pyproject.toml
rename to examples/quickstart-monai/pyproject.toml
index 0d1a91836006..66a56ee2270b 100644
--- a/examples/mt-pytorch-callable/pyproject.toml
+++ b/examples/quickstart-monai/pyproject.toml
@@ -3,14 +3,17 @@ requires = ["poetry-core>=1.4.0"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
-name = "quickstart-pytorch"
+name = "quickstart-monai"
 version = "0.1.0"
-description = "PyTorch Federated Learning Quickstart with Flower"
+description = "MONAI Federated Learning Quickstart with Flower"
 authors = ["The Flower Authors <hello@flower.dev>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
-flwr = { path = "../../", develop = true, extras = ["simulation", "rest"] }
+flwr = ">=1.0,<2.0"
 torch = "1.13.1"
-torchvision = "0.14.1"
 tqdm = "4.65.0"
+scikit-learn = "1.3.1"
+monai = { version = "1.3.0", extras=["gdown", "nibabel", "tqdm", "itk"] }
+numpy = "1.24.4"
+pillow = "10.2.0"
diff --git a/examples/quickstart-monai/requirements.txt b/examples/quickstart-monai/requirements.txt
new file mode 100644
index 000000000000..e3f1e463c629
--- /dev/null
+++ b/examples/quickstart-monai/requirements.txt
@@ -0,0 +1,7 @@
+flwr>=1.0, <2.0
+torch==1.13.1
+tqdm==4.65.0
+scikit-learn==1.3.1
+monai[gdown,nibabel,tqdm,itk]==1.3.0
+numpy==1.24.4
+pillow==10.2.0
diff --git a/examples/mt-pytorch-callable/run.sh b/examples/quickstart-monai/run.sh
similarity index 74%
rename from examples/mt-pytorch-callable/run.sh
rename to examples/quickstart-monai/run.sh
index d2bf34f834b1..1da60bccb86d 100755
--- a/examples/mt-pytorch-callable/run.sh
+++ b/examples/quickstart-monai/run.sh
@@ -2,8 +2,7 @@
 set -e
 cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/
 
-# Download the CIFAR-10 dataset
-python -c "from torchvision.datasets import CIFAR10; CIFAR10('./data', download=True)"
+python -c "from data import _download_data; _download_data()"
 
 echo "Starting server"
 python server.py &
@@ -11,7 +10,7 @@ sleep 3  # Sleep for 3s to give the server enough time to start
 
 for i in `seq 0 1`; do
     echo "Starting client $i"
-    python client.py &
+    python client.py --partition-id $i &
 done
 
 # Enable CTRL+C to stop all background processes
diff --git a/examples/mt-pytorch-callable/server.py b/examples/quickstart-monai/server.py
similarity index 100%
rename from examples/mt-pytorch-callable/server.py
rename to examples/quickstart-monai/server.py
diff --git a/examples/quickstart-mxnet/README.md b/examples/quickstart-mxnet/README.md
index 930cec5acdfd..37e01ef2707c 100644
--- a/examples/quickstart-mxnet/README.md
+++ b/examples/quickstart-mxnet/README.md
@@ -1,5 +1,7 @@
 # Flower Example using MXNet
 
+> Note the MXNet project has ended, and is now in [Attic](https://attic.apache.org/projects/mxnet.html). The MXNet GitHub has also [been archived](https://github.com/apache/mxnet). As a result, this example won't be receiving more updates. Using MXNet is no longer recommnended.
+
 This example demonstrates how to run a MXNet machine learning project federated with Flower.
 
 This introductory example for Flower uses MXNet, but you're not required to be a MXNet expert to run the example. The example will help you to understand how Flower can be used to build federated learning use cases based on an existing MXNet projects.
diff --git a/examples/quickstart-mxnet/client.py b/examples/quickstart-mxnet/client.py
index b0c937f7350f..6c2b2e99775d 100644
--- a/examples/quickstart-mxnet/client.py
+++ b/examples/quickstart-mxnet/client.py
@@ -5,7 +5,6 @@
 https://mxnet.apache.org/api/python/docs/tutorials/packages/gluon/image/mnist.html
 """
 
-
 import flwr as fl
 import numpy as np
 import mxnet as mx
diff --git a/examples/quickstart-mxnet/pyproject.toml b/examples/quickstart-mxnet/pyproject.toml
index a0d31f76ebdd..b00b3ddfe412 100644
--- a/examples/quickstart-mxnet/pyproject.toml
+++ b/examples/quickstart-mxnet/pyproject.toml
@@ -6,11 +6,10 @@ build-backend = "poetry.core.masonry.api"
 name = "mxnet_example"
 version = "0.1.0"
 description = "MXNet example with MNIST and CNN"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
-flwr = ">=1.0,<2.0"
-# flwr = { path = "../../", develop = true }  # Development
-mxnet = "1.6.0"
+flwr = "1.6.0"
+mxnet = "1.9.1"
 numpy = "1.23.1"
diff --git a/examples/quickstart-mxnet/requirements.txt b/examples/quickstart-mxnet/requirements.txt
index 73060e27c70c..8dd6f7150dfd 100644
--- a/examples/quickstart-mxnet/requirements.txt
+++ b/examples/quickstart-mxnet/requirements.txt
@@ -1,3 +1,3 @@
-flwr>=1.0,<2.0
-mxnet==1.6.0
+flwr==1.6.0
+mxnet==1.9.1
 numpy==1.23.1
diff --git a/examples/quickstart-mxnet/server.py b/examples/quickstart-mxnet/server.py
index 29cbce1884d1..871aa4e8ec99 100644
--- a/examples/quickstart-mxnet/server.py
+++ b/examples/quickstart-mxnet/server.py
@@ -1,6 +1,5 @@
 """Flower server example."""
 
-
 import flwr as fl
 
 if __name__ == "__main__":
diff --git a/examples/quickstart-pandas/README.md b/examples/quickstart-pandas/README.md
index 2defc468c2ef..dd69f3ead3cb 100644
--- a/examples/quickstart-pandas/README.md
+++ b/examples/quickstart-pandas/README.md
@@ -1,6 +1,7 @@
 # Flower Example using Pandas
 
-This introductory example to Flower uses Pandas, but deep knowledge of Pandas is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case.
+This introductory example to Flower uses Pandas, but deep knowledge of Pandas is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to
+download, partition and preprocess the dataset.
 Running this example in itself is quite easy.
 
 ## Project Setup
@@ -69,13 +70,13 @@ Now you are ready to start the Flower clients which will participate in the lear
 Start client 1 in the first terminal:
 
 ```shell
-$ python3 client.py
+$ python3 client.py --partition-id 0
 ```
 
 Start client 2 in the second terminal:
 
 ```shell
-$ python3 client.py
+$ python3 client.py --partition-id 1
 ```
 
-You will see that the server is printing aggregated statistics about the dataset distributed amongst clients. Have a look to the [Flower Quickstarter documentation](https://flower.dev/docs/quickstart-pandas.html) for a detailed explanation.
+You will see that the server is printing aggregated statistics about the dataset distributed amongst clients. Have a look to the [Flower Quickstarter documentation](https://flower.ai/docs/quickstart-pandas.html) for a detailed explanation.
diff --git a/examples/quickstart-pandas/client.py b/examples/quickstart-pandas/client.py
index 3feab3f6a0f4..c52b7c65b04c 100644
--- a/examples/quickstart-pandas/client.py
+++ b/examples/quickstart-pandas/client.py
@@ -1,4 +1,4 @@
-import warnings
+import argparse
 from typing import Dict, List, Tuple
 
 import numpy as np
@@ -6,10 +6,10 @@
 
 import flwr as fl
 
+from flwr_datasets import FederatedDataset
 
-df = pd.read_csv("./data/client.csv")
 
-column_names = ["sepal length (cm)", "sepal width (cm)"]
+column_names = ["sepal_length", "sepal_width"]
 
 
 def compute_hist(df: pd.DataFrame, col_name: str) -> np.ndarray:
@@ -19,23 +19,47 @@ def compute_hist(df: pd.DataFrame, col_name: str) -> np.ndarray:
 
 # Define Flower client
 class FlowerClient(fl.client.NumPyClient):
+    def __init__(self, X: pd.DataFrame):
+        self.X = X
+
     def fit(
         self, parameters: List[np.ndarray], config: Dict[str, str]
     ) -> Tuple[List[np.ndarray], int, Dict]:
         hist_list = []
         # Execute query locally
-        for c in column_names:
-            hist = compute_hist(df, c)
+        for c in self.X.columns:
+            hist = compute_hist(self.X, c)
             hist_list.append(hist)
         return (
             hist_list,
-            len(df),
+            len(self.X),
             {},
         )
 
 
-# Start Flower client
-fl.client.start_numpy_client(
-    server_address="127.0.0.1:8080",
-    client=FlowerClient(),
-)
+if __name__ == "__main__":
+    N_CLIENTS = 2
+
+    parser = argparse.ArgumentParser(description="Flower")
+    parser.add_argument(
+        "--partition-id",
+        type=int,
+        choices=range(0, N_CLIENTS),
+        required=True,
+        help="Specifies the partition id of artificially partitioned datasets.",
+    )
+    args = parser.parse_args()
+    partition_id = args.partition_id
+
+    # Load the partition data
+    fds = FederatedDataset(dataset="hitorilabs/iris", partitioners={"train": N_CLIENTS})
+
+    dataset = fds.load_partition(partition_id, "train").with_format("pandas")[:]
+    # Use just the specified columns
+    X = dataset[column_names]
+
+    # Start Flower client
+    fl.client.start_client(
+        server_address="127.0.0.1:8080",
+        client=FlowerClient(X).to_client(),
+    )
diff --git a/examples/quickstart-pandas/pyproject.toml b/examples/quickstart-pandas/pyproject.toml
index de20eaf61d63..2e6b1424bb54 100644
--- a/examples/quickstart-pandas/pyproject.toml
+++ b/examples/quickstart-pandas/pyproject.toml
@@ -7,11 +7,11 @@ name = "quickstart-pandas"
 version = "0.1.0"
 description = "Pandas Federated Analytics Quickstart with Flower"
 authors = ["Ragy Haddad <ragy202@gmail.com>"]
-maintainers = ["The Flower Authors <hello@flower.dev>"]
+maintainers = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = ">=1.0,<2.0"
+flwr-datasets = { extras = ["vision"], version = ">=0.0.2,<1.0.0" }
 numpy = "1.23.2"
 pandas = "2.0.0"
-scikit-learn = "1.3.1"
diff --git a/examples/quickstart-pandas/requirements.txt b/examples/quickstart-pandas/requirements.txt
index 14308a55faaf..d44a3c6adab9 100644
--- a/examples/quickstart-pandas/requirements.txt
+++ b/examples/quickstart-pandas/requirements.txt
@@ -1,4 +1,4 @@
 flwr>=1.0, <2.0
+flwr-datasets[vision]>=0.0.2, <1.0.0
 numpy==1.23.2
 pandas==2.0.0
-scikit-learn==1.3.1
diff --git a/examples/quickstart-pandas/run.sh b/examples/quickstart-pandas/run.sh
index 6b85ce30bf45..2ae1e582b8cf 100755
--- a/examples/quickstart-pandas/run.sh
+++ b/examples/quickstart-pandas/run.sh
@@ -2,13 +2,9 @@ echo "Starting server"
 python server.py &
 sleep 3  # Sleep for 3s to give the server enough time to start
 
-# Download data
-mkdir -p ./data
-python -c "from sklearn.datasets import load_iris; load_iris(as_frame=True)['data'].to_csv('./data/client.csv')"
-
 for i in `seq 0 1`; do
     echo "Starting client $i"
-    python client.py &
+    python client.py --partition-id ${i} &
 done
 
 # This will allow you to use CTRL+C to stop all background processes
diff --git a/examples/quickstart-pandas/server.py b/examples/quickstart-pandas/server.py
index c82304374836..af4c2a796788 100644
--- a/examples/quickstart-pandas/server.py
+++ b/examples/quickstart-pandas/server.py
@@ -1,5 +1,4 @@
-import pickle
-from typing import Callable, Dict, List, Optional, Tuple, Union
+from typing import Dict, List, Optional, Tuple, Union
 
 import numpy as np
 
@@ -9,9 +8,6 @@
     EvaluateRes,
     FitIns,
     FitRes,
-    Metrics,
-    MetricsAggregationFn,
-    NDArrays,
     Parameters,
     Scalar,
     ndarrays_to_parameters,
@@ -23,11 +19,6 @@
 
 
 class FedAnalytics(Strategy):
-    def __init__(
-        self, compute_fns: List[Callable] = None, col_names: List[str] = None
-    ) -> None:
-        super().__init__()
-
     def initialize_parameters(
         self, client_manager: Optional[ClientManager] = None
     ) -> Optional[Parameters]:
diff --git a/examples/quickstart-pytorch-lightning/README.md b/examples/quickstart-pytorch-lightning/README.md
index 360efb8f6261..fb29c7e9e9ea 100644
--- a/examples/quickstart-pytorch-lightning/README.md
+++ b/examples/quickstart-pytorch-lightning/README.md
@@ -1 +1,76 @@
-# Flower Examples using PyTorch Lightning
+# Flower Example using PyTorch Lightning
+
+This introductory example to Flower uses PyTorch, but deep knowledge of PyTorch Lightning is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case. Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the MNIST dataset.
+
+## 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/quickstart-pytorch-lightning . && rm -rf flower && cd quickstart-pytorch-lightning
+```
+
+This will create a new directory called `quickstart-pytorch-lightning` containing the following files:
+
+```shell
+-- pyproject.toml
+-- requirements.txt
+-- client.py  # client-side code
+-- server.py # server-side code (including the strategy)
+-- README.md
+-- run.sh # runs server, then two clients
+-- mnist.py # run a centralised version of this example
+```
+
+### Installing Dependencies
+
+Project dependencies (such as `torch` 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
+
+```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 python -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 PyTorch 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
+python server.py
+```
+
+Now you are ready to start the Flower clients which will participate in the learning. We need to specify the partition id to
+use different partitions of the data on different nodes.  To do so simply open two more terminal windows and run the
+following commands.
+
+Start client 1 in the first terminal:
+
+```shell
+python client.py --partition-id 0
+```
+
+Start client 2 in the second terminal:
+
+```shell
+python client.py --partition-id 1
+```
+
+You will see that PyTorch is starting a federated training. Look at the [code](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch) for a detailed explanation.
diff --git a/examples/quickstart-pytorch-lightning/client.py b/examples/quickstart-pytorch-lightning/client.py
index e810d639974d..6e21259cc492 100644
--- a/examples/quickstart-pytorch-lightning/client.py
+++ b/examples/quickstart-pytorch-lightning/client.py
@@ -1,8 +1,14 @@
-import flwr as fl
-import mnist
-import pytorch_lightning as pl
+import argparse
 from collections import OrderedDict
+
+import pytorch_lightning as pl
 import torch
+from datasets.utils.logging import disable_progress_bar
+
+import flwr as fl
+import mnist
+
+disable_progress_bar()
 
 
 class FlowerClient(fl.client.NumPyClient):
@@ -50,13 +56,24 @@ def _set_parameters(model, parameters):
 
 
 def main() -> None:
+    parser = argparse.ArgumentParser(description="Flower")
+    parser.add_argument(
+        "--partition-id",
+        type=int,
+        choices=range(0, 10),
+        required=True,
+        help="Specifies the artificial data partition",
+    )
+    args = parser.parse_args()
+    partition_id = args.partition_id
+
     # Model and data
     model = mnist.LitAutoEncoder()
-    train_loader, val_loader, test_loader = mnist.load_data()
+    train_loader, val_loader, test_loader = mnist.load_data(partition_id)
 
     # Flower client
-    client = FlowerClient(model, train_loader, val_loader, test_loader)
-    fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=client)
+    client = FlowerClient(model, train_loader, val_loader, test_loader).to_client()
+    fl.client.start_client(server_address="127.0.0.1:8080", client=client)
 
 
 if __name__ == "__main__":
diff --git a/examples/quickstart-pytorch-lightning/mnist.py b/examples/quickstart-pytorch-lightning/mnist.py
index c8f8374ecc04..95342f4fb9b3 100644
--- a/examples/quickstart-pytorch-lightning/mnist.py
+++ b/examples/quickstart-pytorch-lightning/mnist.py
@@ -3,14 +3,13 @@
 Source: pytorchlightning.ai (2021/02/04)
 """
 
-
+from flwr_datasets import FederatedDataset
+import pytorch_lightning as pl
 import torch
 from torch import nn
 from torch.nn import functional as F
-from torch.utils.data import DataLoader, random_split
+from torch.utils.data import DataLoader
 from torchvision import transforms
-from torchvision.datasets import MNIST
-import pytorch_lightning as pl
 
 
 class LitAutoEncoder(pl.LightningModule):
@@ -60,25 +59,56 @@ def _evaluate(self, batch, stage=None):
             self.log(f"{stage}_loss", loss, prog_bar=True)
 
 
-def load_data():
-    # Training / validation set
-    trainset = MNIST("", train=True, download=True, transform=transforms.ToTensor())
-    mnist_train, mnist_val = random_split(trainset, [55000, 5000])
-    train_loader = DataLoader(mnist_train, batch_size=32, shuffle=True, num_workers=16)
-    val_loader = DataLoader(mnist_val, batch_size=32, shuffle=False, num_workers=16)
+def collate_fn(batch):
+    """Change the dictionary to tuple to keep the exact dataloader behavior."""
+    images = [item["image"] for item in batch]
+    labels = [item["label"] for item in batch]
+
+    images_tensor = torch.stack(images)
+    labels_tensor = torch.tensor(labels)
+
+    return images_tensor, labels_tensor
+
+
+def apply_transforms(batch):
+    """Apply transforms to the partition from FederatedDataset."""
+    batch["image"] = [transforms.functional.to_tensor(img) for img in batch["image"]]
+    return batch
+
 
-    # Test set
-    testset = MNIST("", train=False, download=True, transform=transforms.ToTensor())
-    test_loader = DataLoader(testset, batch_size=32, shuffle=False, num_workers=16)
+def load_data(partition):
+    fds = FederatedDataset(dataset="mnist", partitioners={"train": 10})
+    partition = fds.load_partition(partition, "train")
 
-    return train_loader, val_loader, test_loader
+    partition = partition.with_transform(apply_transforms)
+    # 20 % for on federated evaluation
+    partition_full = partition.train_test_split(test_size=0.2)
+    # 60 % for the federated train and 20 % for the federated validation (both in fit)
+    partition_train_valid = partition_full["train"].train_test_split(train_size=0.75)
+    trainloader = DataLoader(
+        partition_train_valid["train"],
+        batch_size=32,
+        shuffle=True,
+        collate_fn=collate_fn,
+        num_workers=1,
+    )
+    valloader = DataLoader(
+        partition_train_valid["test"],
+        batch_size=32,
+        collate_fn=collate_fn,
+        num_workers=1,
+    )
+    testloader = DataLoader(
+        partition_full["test"], batch_size=32, collate_fn=collate_fn, num_workers=1
+    )
+    return trainloader, valloader, testloader
 
 
 def main() -> None:
     """Centralized training."""
 
     # Load data
-    train_loader, val_loader, test_loader = load_data()
+    train_loader, val_loader, test_loader = load_data(0)
 
     # Load model
     model = LitAutoEncoder()
diff --git a/examples/quickstart-pytorch-lightning/pyproject.toml b/examples/quickstart-pytorch-lightning/pyproject.toml
index 0a1e1376b8cb..a09aaa3d65b5 100644
--- a/examples/quickstart-pytorch-lightning/pyproject.toml
+++ b/examples/quickstart-pytorch-lightning/pyproject.toml
@@ -6,11 +6,12 @@ build-backend = "poetry.masonry.api"
 name = "quickstart-pytorch-lightning"
 version = "0.1.0"
 description = "Federated Learning Quickstart with Flower and PyTorch Lightning"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = "^3.8"
 flwr = ">=1.0,<2.0"
 # flwr = { path = "../../", develop = true }  # Development
+flwr-datasets = { extras = ["vision"], version = ">=0.0.2,<1.0.0" }
 pytorch-lightning = "1.6.0"
 torchvision = "0.14.1"
diff --git a/examples/quickstart-pytorch-lightning/requirements.txt b/examples/quickstart-pytorch-lightning/requirements.txt
index 1cd0b31fa0b5..6530dcc8c52c 100644
--- a/examples/quickstart-pytorch-lightning/requirements.txt
+++ b/examples/quickstart-pytorch-lightning/requirements.txt
@@ -1,3 +1,4 @@
 flwr>=1.0, <2.0
+flwr-datasets[vision]>=0.0.2, <1.0.0
 pytorch_lightning>=1.4.7
 torchvision==0.14.1
diff --git a/examples/quickstart-pytorch-lightning/run.sh b/examples/quickstart-pytorch-lightning/run.sh
index 2b6507bc154c..62a1dac199bd 100755
--- a/examples/quickstart-pytorch-lightning/run.sh
+++ b/examples/quickstart-pytorch-lightning/run.sh
@@ -4,9 +4,9 @@ echo "Starting server"
 python server.py &
 sleep 3 # Sleep for 3s to give the server enough time to start
 
-for i in `seq 0 1`; do
+for i in $(seq 0 1); do
     echo "Starting client $i"
-    python client.py &
+    python client.py --partition-id "${i}" &
 done
 
 # This will allow you to use CTRL+C to stop all background processes
diff --git a/examples/quickstart-pytorch-lightning/server.py b/examples/quickstart-pytorch-lightning/server.py
index 370186ae1d98..a104a1fffd26 100644
--- a/examples/quickstart-pytorch-lightning/server.py
+++ b/examples/quickstart-pytorch-lightning/server.py
@@ -11,7 +11,7 @@ def main() -> None:
     # Start Flower server for three rounds of federated learning
     fl.server.start_server(
         server_address="0.0.0.0:8080",
-        config=fl.server.ServerConfig(num_rounds=10),
+        config=fl.server.ServerConfig(num_rounds=3),
         strategy=strategy,
     )
 
diff --git a/examples/quickstart-pytorch/README.md b/examples/quickstart-pytorch/README.md
index 6de0dcf7ab32..02c9b4b38498 100644
--- a/examples/quickstart-pytorch/README.md
+++ b/examples/quickstart-pytorch/README.md
@@ -1,6 +1,6 @@
 # Flower Example using PyTorch
 
-This introductory example to Flower uses PyTorch, but deep knowledge of PyTorch is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case. Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.dev/docs/datasets/) to download, partition and preprocess the CIFAR-10 dataset.
+This introductory example to Flower uses PyTorch, but deep knowledge of PyTorch is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case. Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the CIFAR-10 dataset.
 
 ## Project Setup
 
@@ -55,20 +55,20 @@ Afterwards you are ready to start the Flower server as well as the clients. You
 python3 server.py
 ```
 
-Now you are ready to start the Flower clients which will participate in the learning. We need to specify the node id to
+Now you are ready to start the Flower clients which will participate in the learning. We need to specify the partition id to
 use different partitions of the data on different nodes.  To do so simply open two more terminal windows and run the
 following commands.
 
 Start client 1 in the first terminal:
 
 ```shell
-python3 client.py --node-id 0
+python3 client.py --partition-id 0
 ```
 
 Start client 2 in the second terminal:
 
 ```shell
-python3 client.py --node-id 1
+python3 client.py --partition-id 1
 ```
 
 You will see that PyTorch is starting a federated training. Look at the [code](https://github.com/adap/flower/tree/main/examples/quickstart-pytorch) for a detailed explanation.
diff --git a/examples/quickstart-pytorch/client.py b/examples/quickstart-pytorch/client.py
index ad57645002f8..e640ce111dff 100644
--- a/examples/quickstart-pytorch/client.py
+++ b/examples/quickstart-pytorch/client.py
@@ -69,10 +69,10 @@ def test(net, testloader):
     return loss, accuracy
 
 
-def load_data(node_id):
+def load_data(partition_id):
     """Load partition CIFAR10 data."""
     fds = FederatedDataset(dataset="cifar10", partitioners={"train": 3})
-    partition = fds.load_partition(node_id)
+    partition = fds.load_partition(partition_id)
     # Divide data on each node: 80% train, 20% test
     partition_train_test = partition.train_test_split(test_size=0.2)
     pytorch_transforms = Compose(
@@ -94,19 +94,20 @@ def apply_transforms(batch):
 # 2. Federation of the pipeline with Flower
 # #############################################################################
 
-# Get node id
+# Get partition id
 parser = argparse.ArgumentParser(description="Flower")
 parser.add_argument(
-    "--node-id",
+    "--partition-id",
     choices=[0, 1, 2],
+    required=True,
     type=int,
     help="Partition of the dataset divided into 3 iid partitions created artificially.",
 )
-node_id = parser.parse_args().node_id
+partition_id = parser.parse_args().partition_id
 
 # Load model and data (simple CNN, CIFAR-10)
 net = Net().to(DEVICE)
-trainloader, testloader = load_data(node_id=node_id)
+trainloader, testloader = load_data(partition_id=partition_id)
 
 
 # Define Flower client
@@ -131,7 +132,7 @@ def evaluate(self, parameters, config):
 
 
 # Start Flower client
-fl.client.start_numpy_client(
+fl.client.start_client(
     server_address="127.0.0.1:8080",
-    client=FlowerClient(),
+    client=FlowerClient().to_client(),
 )
diff --git a/examples/quickstart-pytorch/pyproject.toml b/examples/quickstart-pytorch/pyproject.toml
index ec6a3af8c5b4..d8e1503dd8a7 100644
--- a/examples/quickstart-pytorch/pyproject.toml
+++ b/examples/quickstart-pytorch/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-pytorch"
 version = "0.1.0"
 description = "PyTorch Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/quickstart-pytorch/run.sh b/examples/quickstart-pytorch/run.sh
index cdace99bb8df..6ca9c8cafec9 100755
--- a/examples/quickstart-pytorch/run.sh
+++ b/examples/quickstart-pytorch/run.sh
@@ -8,7 +8,7 @@ sleep 3  # Sleep for 3s to give the server enough time to start
 
 for i in $(seq 0 1); do
     echo "Starting client $i"
-    python client.py --node-id "$i" &
+    python client.py --partition-id "$i" &
 done
 
 # Enable CTRL+C to stop all background processes
diff --git a/examples/quickstart-sklearn-tabular/README.md b/examples/quickstart-sklearn-tabular/README.md
new file mode 100644
index 000000000000..a975a9392800
--- /dev/null
+++ b/examples/quickstart-sklearn-tabular/README.md
@@ -0,0 +1,77 @@
+# Flower Example using scikit-learn
+
+This example of Flower uses `scikit-learn`'s `LogisticRegression` model to train a federated learning system on
+"iris" (tabular) dataset.
+It will help you understand how to adapt Flower for use with `scikit-learn`.
+Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to
+download, partition and preprocess the dataset.
+
+## 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/quickstart-sklearn-tabular . && rm -rf flower && cd quickstart-sklearn-tabular
+```
+
+This will create a new directory called `quickstart-sklearn-tabular` containing the following files:
+
+```shell
+-- pyproject.toml
+-- requirements.txt
+-- client.py
+-- server.py
+-- utils.py
+-- README.md
+```
+
+### Installing Dependencies
+
+Project dependencies (such as `scikit-learn` 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
+
+```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 scikit-learn 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
+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
+poetry run python3 client.py --partition-id 0 # partition-id should be any of {0,1,2}
+```
+
+Alternatively you can run all of it in one shell as follows:
+
+```shell
+poetry run python3 server.py &
+poetry run python3 client.py --partition-id 0 &
+poetry run python3 client.py --partition-id 1
+```
+
+You will see that Flower is starting a federated training.
diff --git a/examples/quickstart-sklearn-tabular/client.py b/examples/quickstart-sklearn-tabular/client.py
new file mode 100644
index 000000000000..b7e3046c822d
--- /dev/null
+++ b/examples/quickstart-sklearn-tabular/client.py
@@ -0,0 +1,73 @@
+import argparse
+import warnings
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import log_loss
+
+import flwr as fl
+import utils
+from flwr_datasets import FederatedDataset
+
+if __name__ == "__main__":
+    N_CLIENTS = 3
+
+    parser = argparse.ArgumentParser(description="Flower")
+    parser.add_argument(
+        "--partition-id",
+        type=int,
+        choices=range(0, N_CLIENTS),
+        required=True,
+        help="Specifies the artificial data partition",
+    )
+    args = parser.parse_args()
+    partition_id = args.partition_id
+
+    # Load the partition data
+    fds = FederatedDataset(dataset="hitorilabs/iris", partitioners={"train": N_CLIENTS})
+
+    dataset = fds.load_partition(partition_id, "train").with_format("pandas")[:]
+    X = dataset[["petal_length", "petal_width", "sepal_length", "sepal_width"]]
+    y = dataset["species"]
+    unique_labels = fds.load_split("train").unique("species")
+    # Split the on edge data: 80% train, 20% test
+    X_train, X_test = X[: int(0.8 * len(X))], X[int(0.8 * len(X)) :]
+    y_train, y_test = y[: int(0.8 * len(y))], y[int(0.8 * len(y)) :]
+
+    # Create LogisticRegression Model
+    model = LogisticRegression(
+        penalty="l2",
+        max_iter=1,  # local epoch
+        warm_start=True,  # prevent refreshing weights when fitting
+    )
+
+    # Setting initial parameters, akin to model.compile for keras models
+    utils.set_initial_params(model, n_features=X_train.shape[1], n_classes=3)
+
+    # Define Flower client
+    class IrisClient(fl.client.NumPyClient):
+        def get_parameters(self, config):  # type: ignore
+            return utils.get_model_parameters(model)
+
+        def fit(self, parameters, config):  # type: ignore
+            utils.set_model_params(model, parameters)
+            # Ignore convergence failure due to low local epochs
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                model.fit(X_train, y_train)
+            accuracy = model.score(X_train, y_train)
+            return (
+                utils.get_model_parameters(model),
+                len(X_train),
+                {"train_accuracy": accuracy},
+            )
+
+        def evaluate(self, parameters, config):  # type: ignore
+            utils.set_model_params(model, parameters)
+            loss = log_loss(y_test, model.predict_proba(X_test), labels=unique_labels)
+            accuracy = model.score(X_test, y_test)
+            return loss, len(X_test), {"test_accuracy": accuracy}
+
+    # Start Flower client
+    fl.client.start_client(
+        server_address="0.0.0.0:8080", client=IrisClient().to_client()
+    )
diff --git a/examples/quickstart-sklearn-tabular/pyproject.toml b/examples/quickstart-sklearn-tabular/pyproject.toml
new file mode 100644
index 000000000000..86eab5c38df0
--- /dev/null
+++ b/examples/quickstart-sklearn-tabular/pyproject.toml
@@ -0,0 +1,18 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "sklearn-mnist"
+version = "0.1.0"
+description = "Federated learning with scikit-learn and Flower"
+authors = [
+    "The Flower Authors <hello@flower.ai>",
+    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>",
+]
+
+[tool.poetry.dependencies]
+python = "^3.8"
+flwr = ">=1.0,<2.0"
+flwr-datasets = { extras = ["vision"], version = ">=0.0.2,<1.0.0" }
+scikit-learn = "^1.3.0"
diff --git a/examples/quickstart-sklearn-tabular/requirements.txt b/examples/quickstart-sklearn-tabular/requirements.txt
new file mode 100644
index 000000000000..e0f15b31f3f7
--- /dev/null
+++ b/examples/quickstart-sklearn-tabular/requirements.txt
@@ -0,0 +1,3 @@
+flwr>=1.0, <2.0
+flwr-datasets[vision]>=0.0.2, <1.0.0
+scikit-learn>=1.3.0
diff --git a/examples/quickstart-sklearn-tabular/run.sh b/examples/quickstart-sklearn-tabular/run.sh
new file mode 100755
index 000000000000..f770ca05f8f4
--- /dev/null
+++ b/examples/quickstart-sklearn-tabular/run.sh
@@ -0,0 +1,17 @@
+#!/bin/bash
+set -e
+cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/
+
+echo "Starting server"
+python server.py &
+sleep 3  # Sleep for 3s to give the server enough time to start
+
+for i in $(seq 0 1); do
+    echo "Starting client $i"
+    python client.py --partition-id "${i}" &
+done
+
+# This will allow you to use CTRL+C to stop all background processes
+trap 'trap - SIGTERM && kill -- -$$' SIGINT SIGTERM
+# Wait for all background processes to complete
+wait
diff --git a/examples/quickstart-sklearn-tabular/server.py b/examples/quickstart-sklearn-tabular/server.py
new file mode 100644
index 000000000000..0c779c52a8d6
--- /dev/null
+++ b/examples/quickstart-sklearn-tabular/server.py
@@ -0,0 +1,19 @@
+import flwr as fl
+import utils
+from sklearn.linear_model import LogisticRegression
+
+
+# Start Flower server for five rounds of federated learning
+if __name__ == "__main__":
+    model = LogisticRegression()
+    utils.set_initial_params(model, n_classes=3, n_features=4)
+    strategy = fl.server.strategy.FedAvg(
+        min_available_clients=2,
+        fit_metrics_aggregation_fn=utils.weighted_average,
+        evaluate_metrics_aggregation_fn=utils.weighted_average,
+    )
+    fl.server.start_server(
+        server_address="0.0.0.0:8080",
+        strategy=strategy,
+        config=fl.server.ServerConfig(num_rounds=25),
+    )
diff --git a/examples/quickstart-sklearn-tabular/utils.py b/examples/quickstart-sklearn-tabular/utils.py
new file mode 100644
index 000000000000..e154f44ef8bf
--- /dev/null
+++ b/examples/quickstart-sklearn-tabular/utils.py
@@ -0,0 +1,75 @@
+from typing import List, Tuple, Dict
+
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+
+from flwr.common import NDArrays, Metrics, Scalar
+
+
+def get_model_parameters(model: LogisticRegression) -> NDArrays:
+    """Return the parameters of a sklearn LogisticRegression model."""
+    if model.fit_intercept:
+        params = [
+            model.coef_,
+            model.intercept_,
+        ]
+    else:
+        params = [
+            model.coef_,
+        ]
+    return params
+
+
+def set_model_params(model: LogisticRegression, params: NDArrays) -> LogisticRegression:
+    """Set the parameters of a sklean LogisticRegression model."""
+    model.coef_ = params[0]
+    if model.fit_intercept:
+        model.intercept_ = params[1]
+    return model
+
+
+def set_initial_params(model: LogisticRegression, n_classes: int, n_features: int):
+    """Set 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.
+    """
+    model.classes_ = np.array([i for i in range(n_classes)])
+
+    model.coef_ = np.zeros((n_classes, n_features))
+    if model.fit_intercept:
+        model.intercept_ = np.zeros((n_classes,))
+
+
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Dict[str, Scalar]:
+    """Compute weighted average.
+
+    It is generic implementation that averages only over floats and ints and drops the
+    other data types of the Metrics.
+    """
+    print(metrics)
+    # num_samples_list can represent number of sample or batches depending on the client
+    num_samples_list = [n_batches for n_batches, _ in metrics]
+    num_samples_sum = sum(num_samples_list)
+    metrics_lists: Dict[str, List[float]] = {}
+    for num_samples, all_metrics_dict in metrics:
+        #  Calculate each metric one by one
+        for single_metric, value in all_metrics_dict.items():
+            if isinstance(value, (float, int)):
+                metrics_lists[single_metric] = []
+        # Just one iteration needed to initialize the keywords
+        break
+
+    for num_samples, all_metrics_dict in metrics:
+        # Calculate each metric one by one
+        for single_metric, value in all_metrics_dict.items():
+            # Add weighted metric
+            if isinstance(value, (float, int)):
+                metrics_lists[single_metric].append(float(num_samples * value))
+
+    weighted_metrics: Dict[str, Scalar] = {}
+    for metric_name, metric_values in metrics_lists.items():
+        weighted_metrics[metric_name] = sum(metric_values) / num_samples_sum
+
+    return weighted_metrics
diff --git a/examples/quickstart-tabnet/client.py b/examples/quickstart-tabnet/client.py
index da391a95710a..2289b1b55b3d 100644
--- a/examples/quickstart-tabnet/client.py
+++ b/examples/quickstart-tabnet/client.py
@@ -79,4 +79,6 @@ def evaluate(self, parameters, config):
 
 
 # Start Flower client
-fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=TabNetClient())
+fl.client.start_client(
+    server_address="127.0.0.1:8080", client=TabNetClient().to_client()
+)
diff --git a/examples/quickstart-tabnet/pyproject.toml b/examples/quickstart-tabnet/pyproject.toml
index 948eaf085b86..18f1979791bd 100644
--- a/examples/quickstart-tabnet/pyproject.toml
+++ b/examples/quickstart-tabnet/pyproject.toml
@@ -6,12 +6,12 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-tabnet"
 version = "0.1.0"
 description = "Tabnet Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 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-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 = "4.8.3"
 tabnet = "0.1.6"
diff --git a/examples/quickstart-tensorflow/README.md b/examples/quickstart-tensorflow/README.md
index 7ada48797d03..8d5e9434b086 100644
--- a/examples/quickstart-tensorflow/README.md
+++ b/examples/quickstart-tensorflow/README.md
@@ -1,7 +1,7 @@
 # Flower Example using TensorFlow/Keras
 
-This introductory example to Flower uses Keras but deep knowledge of Keras is not necessarily required to run the example. However, it will help you understanding how to adapt Flower to your use-cases.
-Running this example in itself is quite easy.
+This introductory example to Flower uses Keras but deep knowledge of Keras is not necessarily required to run the example. However, it will help you understand how to adapt Flower to your use case.
+Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the CIFAR-10 dataset.
 
 ## Project Setup
 
@@ -50,7 +50,7 @@ pip install -r requirements.txt
 
 ## Run Federated Learning with TensorFlow/Keras 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:
+Afterward, 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
 poetry run python3 server.py
@@ -62,7 +62,7 @@ Now you are ready to start the Flower clients which will participate in the lear
 poetry run python3 client.py
 ```
 
-Alternatively you can run all of it in one shell as follows:
+Alternatively, you can run all of it in one shell as follows:
 
 ```shell
 poetry run python3 server.py &
diff --git a/examples/quickstart-tensorflow/client.py b/examples/quickstart-tensorflow/client.py
index fc367e2c3053..3e2035c09311 100644
--- a/examples/quickstart-tensorflow/client.py
+++ b/examples/quickstart-tensorflow/client.py
@@ -1,16 +1,38 @@
+import argparse
 import os
 
 import flwr as fl
 import tensorflow as tf
-
+from flwr_datasets import FederatedDataset
 
 # Make TensorFlow log less verbose
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 
+# Parse arguments
+parser = argparse.ArgumentParser(description="Flower")
+parser.add_argument(
+    "--partition-id",
+    type=int,
+    choices=[0, 1, 2],
+    required=True,
+    help="Partition of the dataset (0,1 or 2). "
+    "The dataset is divided into 3 partitions created artificially.",
+)
+args = parser.parse_args()
+
 # Load model and data (MobileNetV2, CIFAR-10)
 model = tf.keras.applications.MobileNetV2((32, 32, 3), classes=10, weights=None)
 model.compile("adam", "sparse_categorical_crossentropy", metrics=["accuracy"])
-(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
+
+# Download and partition dataset
+fds = FederatedDataset(dataset="cifar10", partitioners={"train": 3})
+partition = fds.load_partition(args.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)
+x_train, y_train = partition["train"]["img"] / 255.0, partition["train"]["label"]
+x_test, y_test = partition["test"]["img"] / 255.0, partition["test"]["label"]
 
 
 # Define Flower client
@@ -30,4 +52,6 @@ def evaluate(self, parameters, config):
 
 
 # Start Flower client
-fl.client.start_numpy_client(server_address="127.0.0.1:8080", client=CifarClient())
+fl.client.start_client(
+    server_address="127.0.0.1:8080", client=CifarClient().to_client()
+)
diff --git a/examples/quickstart-tensorflow/pyproject.toml b/examples/quickstart-tensorflow/pyproject.toml
index 68d4f9aada52..98aeb932cab9 100644
--- a/examples/quickstart-tensorflow/pyproject.toml
+++ b/examples/quickstart-tensorflow/pyproject.toml
@@ -6,10 +6,11 @@ build-backend = "poetry.core.masonry.api"
 name = "quickstart-tensorflow"
 version = "0.1.0"
 description = "Keras Federated Learning Quickstart with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 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\""}
+flwr-datasets = { extras = ["vision"], version = ">=0.0.2,<1.0.0" }
+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\"" }
diff --git a/examples/quickstart-tensorflow/requirements.txt b/examples/quickstart-tensorflow/requirements.txt
index 6420aab25ec8..7f025975cae9 100644
--- a/examples/quickstart-tensorflow/requirements.txt
+++ b/examples/quickstart-tensorflow/requirements.txt
@@ -1,3 +1,4 @@
 flwr>=1.0, <2.0
+flwr-datasets[vision]>=0.0.2, <1.0.0
 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"
diff --git a/examples/quickstart-tensorflow/run.sh b/examples/quickstart-tensorflow/run.sh
index c64f362086aa..76188f197e3e 100755
--- a/examples/quickstart-tensorflow/run.sh
+++ b/examples/quickstart-tensorflow/run.sh
@@ -6,7 +6,7 @@ sleep 3  # Sleep for 3s to give the server enough time to start
 
 for i in `seq 0 1`; do
     echo "Starting client $i"
-    python client.py &
+    python client.py --partition-id $i &
 done
 
 # This will allow you to use CTRL+C to stop all background processes
diff --git a/examples/quickstart-tensorflow/server.py b/examples/quickstart-tensorflow/server.py
index 39c350388c1b..fe691a88aba0 100644
--- a/examples/quickstart-tensorflow/server.py
+++ b/examples/quickstart-tensorflow/server.py
@@ -1,8 +1,25 @@
+from typing import List, Tuple
+
 import flwr as fl
+from flwr.common import Metrics
+
+
+# Define metric aggregation function
+def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
+    # Multiply accuracy of each client by number of examples used
+    accuracies = [num_examples * m["accuracy"] for num_examples, m in metrics]
+    examples = [num_examples for num_examples, _ in metrics]
+
+    # Aggregate and return custom metric (weighted average)
+    return {"accuracy": sum(accuracies) / sum(examples)}
+
 
+# Define strategy
+strategy = fl.server.strategy.FedAvg(evaluate_metrics_aggregation_fn=weighted_average)
 
 # Start Flower server
 fl.server.start_server(
     server_address="0.0.0.0:8080",
     config=fl.server.ServerConfig(num_rounds=3),
+    strategy=strategy,
 )
diff --git a/examples/quickstart-xgboost-horizontal/.gitignore b/examples/quickstart-xgboost-horizontal/.gitignore
deleted file mode 100644
index 4a6ddf5b9142..000000000000
--- a/examples/quickstart-xgboost-horizontal/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-dataset
-
diff --git a/examples/quickstart-xgboost-horizontal/README.md b/examples/quickstart-xgboost-horizontal/README.md
deleted file mode 100644
index 346a33da7412..000000000000
--- a/examples/quickstart-xgboost-horizontal/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
-# Federated XGBoost in Horizontal Setting (PyTorch)
-
-[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/adap/flower/blob/main/examples/quickstart-xgboost-horizontal/code_horizontal.ipynb) (or open the [Jupyter Notebook](https://github.com/adap/flower/blob/main/examples/quickstart-xgboost-horizontal/code_horizontal.ipynb))
-
-This example demonstrates a federated XGBoost using Flower with PyTorch. This is a novel method to conduct federated XGBoost in the horizontal setting. It differs from the previous methods in the following ways:
-
-- We aggregate and conduct federated learning on client tree’s prediction outcomes by sending clients' built XGBoost trees to the server and then sharing to the clients.
-- The exchange of privacy-sensitive information (gradients) is not needed.
-- The model is a CNN with 1D convolution kernel size = the number of XGBoost trees in the client tree ensembles.
-- Using 1D convolution, we make the tree learning rate (a hyperparameter of XGBoost) learnable.
-
-## Project Setup
-
-This implementation can be easily run in Google Colab with the button at the top of the README or as a standalone Jupyter notebook,
-it will automatically download and extract the example data inside a `dataset` folder and `binary_classification` and `regression` sub-folders.
-
-## Datasets
-
-This implementation supports both binary classification and regression datasets in SVM light format, loaded from ([LIBSVM Data](https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/)). Simply download the dataset files from the website and put them in the folder location indicated above.
diff --git a/examples/quickstart-xgboost-horizontal/code_horizontal.ipynb b/examples/quickstart-xgboost-horizontal/code_horizontal.ipynb
deleted file mode 100644
index 4d76e0c26023..000000000000
--- a/examples/quickstart-xgboost-horizontal/code_horizontal.ipynb
+++ /dev/null
@@ -1,1560 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Initialization"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 15871,
-     "status": "ok",
-     "timestamp": 1670356049976,
-     "user": {
-      "displayName": "Chenyang Ma",
-      "userId": "17975430055716133031"
-     },
-     "user_tz": 0
-    },
-    "outputId": "2c588ea0-a383-4461-e633-794e73d0f57a"
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import urllib.request\n",
-    "import bz2\n",
-    "import shutil\n",
-    "\n",
-    "CLASSIFICATION_PATH = os.path.join(\"dataset\", \"binary_classification\")\n",
-    "REGRESSION_PATH = os.path.join(\"dataset\", \"regression\")\n",
-    "\n",
-    "if not os.path.exists(CLASSIFICATION_PATH):\n",
-    "    os.makedirs(CLASSIFICATION_PATH)\n",
-    "    urllib.request.urlretrieve(\n",
-    "        \"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/cod-rna\",\n",
-    "        f\"{os.path.join(CLASSIFICATION_PATH, 'cod-rna')}\",\n",
-    "    )\n",
-    "    urllib.request.urlretrieve(\n",
-    "        \"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/cod-rna.t\",\n",
-    "        f\"{os.path.join(CLASSIFICATION_PATH, 'cod-rna.t')}\",\n",
-    "    )\n",
-    "    urllib.request.urlretrieve(\n",
-    "        \"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/cod-rna.r\",\n",
-    "        f\"{os.path.join(CLASSIFICATION_PATH, 'cod-rna.r')}\",\n",
-    "    )\n",
-    "    urllib.request.urlretrieve(\n",
-    "        \"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/ijcnn1.t.bz2\",\n",
-    "        f\"{os.path.join(CLASSIFICATION_PATH, 'ijcnn1.t.bz2')}\",\n",
-    "    )\n",
-    "    urllib.request.urlretrieve(\n",
-    "        \"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/ijcnn1.tr.bz2\",\n",
-    "        f\"{os.path.join(CLASSIFICATION_PATH, 'ijcnn1.tr.bz2')}\",\n",
-    "    )\n",
-    "    for filepath in os.listdir(CLASSIFICATION_PATH):\n",
-    "        if filepath[-3:] == \"bz2\":\n",
-    "            abs_filepath = os.path.join(CLASSIFICATION_PATH, filepath)\n",
-    "            with bz2.BZ2File(abs_filepath) as fr, open(abs_filepath[:-4], \"wb\") as fw:\n",
-    "                shutil.copyfileobj(fr, fw)\n",
-    "\n",
-    "if not os.path.exists(REGRESSION_PATH):\n",
-    "    os.makedirs(REGRESSION_PATH)\n",
-    "    urllib.request.urlretrieve(\n",
-    "        \"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/eunite2001\",\n",
-    "        f\"{os.path.join(REGRESSION_PATH, 'eunite2001')}\",\n",
-    "    )\n",
-    "    urllib.request.urlretrieve(\n",
-    "        \"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/eunite2001.t\",\n",
-    "        f\"{os.path.join(REGRESSION_PATH, 'eunite2001.t')}\",\n",
-    "    )\n",
-    "    urllib.request.urlretrieve(\n",
-    "        \"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/YearPredictionMSD.bz2\",\n",
-    "        f\"{os.path.join(REGRESSION_PATH, 'YearPredictionMSD.bz2')}\",\n",
-    "    )\n",
-    "    urllib.request.urlretrieve(\n",
-    "        \"https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/YearPredictionMSD.t.bz2\",\n",
-    "        f\"{os.path.join(REGRESSION_PATH, 'YearPredictionMSD.t.bz2')}\",\n",
-    "    )\n",
-    "    for filepath in os.listdir(REGRESSION_PATH):\n",
-    "        if filepath[-3:] == \"bz2\":\n",
-    "            abs_filepath = os.path.join(REGRESSION_PATH, filepath)\n",
-    "            with bz2.BZ2File(abs_filepath) as fr, open(abs_filepath[:-4], \"wb\") as fw:\n",
-    "                shutil.copyfileobj(fr, fw)\n",
-    "\n",
-    "\n",
-    "!nvidia-smi\n",
-    "!pip install matplotlib scikit-learn tqdm torch torchmetrics torchsummary xgboost\n",
-    "!pip install -U \"flwr-nightly[simulation]\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Import relevant modules"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 7,
-     "status": "ok",
-     "timestamp": 1670356049977,
-     "user": {
-      "displayName": "Chenyang Ma",
-      "userId": "17975430055716133031"
-     },
-     "user_tz": 0
-    },
-    "outputId": "5289e33e-e18e-491b-d536-6b1052598994"
-   },
-   "outputs": [],
-   "source": [
-    "import xgboost as xgb\n",
-    "from xgboost import XGBClassifier, XGBRegressor\n",
-    "from sklearn.metrics import mean_squared_error, accuracy_score\n",
-    "from sklearn.datasets import load_svmlight_file\n",
-    "\n",
-    "import numpy as np\n",
-    "import torch, torch.nn as nn\n",
-    "import torch.nn.functional as F\n",
-    "import torchvision\n",
-    "from torchmetrics import Accuracy, MeanSquaredError\n",
-    "from tqdm import trange, tqdm\n",
-    "from torchsummary import summary\n",
-    "from torch.utils.data import DataLoader, Dataset, random_split\n",
-    "\n",
-    "print(\"Imported modules.\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Import Flower relevant modules for Federated XGBoost"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import flwr as fl\n",
-    "from flwr.common.typing import Parameters\n",
-    "from collections import OrderedDict\n",
-    "from typing import Any, Dict, List, Optional, Tuple, Union\n",
-    "from flwr.common import NDArray, NDArrays\n",
-    "\n",
-    "print(\"Imported modules.\")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Define utility function for xgboost trees"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from matplotlib import pyplot as plt  # pylint: disable=E0401\n",
-    "\n",
-    "\n",
-    "def plot_xgbtree(tree: Union[XGBClassifier, XGBRegressor], n_tree: int) -> None:\n",
-    "    \"\"\"Visualize the built xgboost tree.\"\"\"\n",
-    "    xgb.plot_tree(tree, num_trees=n_tree)\n",
-    "    plt.rcParams[\"figure.figsize\"] = [50, 10]\n",
-    "    plt.show()\n",
-    "\n",
-    "\n",
-    "def construct_tree(\n",
-    "    dataset: Dataset, label: NDArray, n_estimators: int, tree_type: str\n",
-    ") -> Union[XGBClassifier, XGBRegressor]:\n",
-    "    \"\"\"Construct a xgboost tree form tabular dataset.\"\"\"\n",
-    "    if tree_type == \"BINARY\":\n",
-    "        tree = xgb.XGBClassifier(\n",
-    "            objective=\"binary:logistic\",\n",
-    "            learning_rate=0.1,\n",
-    "            max_depth=8,\n",
-    "            n_estimators=n_estimators,\n",
-    "            subsample=0.8,\n",
-    "            colsample_bylevel=1,\n",
-    "            colsample_bynode=1,\n",
-    "            colsample_bytree=1,\n",
-    "            alpha=5,\n",
-    "            gamma=5,\n",
-    "            num_parallel_tree=1,\n",
-    "            min_child_weight=1,\n",
-    "        )\n",
-    "\n",
-    "    elif tree_type == \"REG\":\n",
-    "        tree = xgb.XGBRegressor(\n",
-    "            objective=\"reg:squarederror\",\n",
-    "            learning_rate=0.1,\n",
-    "            max_depth=8,\n",
-    "            n_estimators=n_estimators,\n",
-    "            subsample=0.8,\n",
-    "            colsample_bylevel=1,\n",
-    "            colsample_bynode=1,\n",
-    "            colsample_bytree=1,\n",
-    "            alpha=5,\n",
-    "            gamma=5,\n",
-    "            num_parallel_tree=1,\n",
-    "            min_child_weight=1,\n",
-    "        )\n",
-    "\n",
-    "    tree.fit(dataset, label)\n",
-    "    return tree\n",
-    "\n",
-    "\n",
-    "def construct_tree_from_loader(\n",
-    "    dataset_loader: DataLoader, n_estimators: int, tree_type: str\n",
-    ") -> Union[XGBClassifier, XGBRegressor]:\n",
-    "    \"\"\"Construct a xgboost tree form tabular dataset loader.\"\"\"\n",
-    "    for dataset in dataset_loader:\n",
-    "        data, label = dataset[0], dataset[1]\n",
-    "    return construct_tree(data, label, n_estimators, tree_type)\n",
-    "\n",
-    "\n",
-    "def single_tree_prediction(\n",
-    "    tree: Union[XGBClassifier, XGBRegressor], n_tree: int, dataset: NDArray\n",
-    ") -> Optional[NDArray]:\n",
-    "    \"\"\"Extract the prediction result of a single tree in the xgboost tree\n",
-    "    ensemble.\"\"\"\n",
-    "    # How to access a single tree\n",
-    "    # https://github.com/bmreiniger/datascience.stackexchange/blob/master/57905.ipynb\n",
-    "    num_t = len(tree.get_booster().get_dump())\n",
-    "    if n_tree > num_t:\n",
-    "        print(\n",
-    "            \"The tree index to be extracted is larger than the total number of trees.\"\n",
-    "        )\n",
-    "        return None\n",
-    "\n",
-    "    return tree.predict(  # type: ignore\n",
-    "        dataset, iteration_range=(n_tree, n_tree + 1), output_margin=True\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "def tree_encoding(  # pylint: disable=R0914\n",
-    "    trainloader: DataLoader,\n",
-    "    client_trees: Union[\n",
-    "        Tuple[XGBClassifier, int],\n",
-    "        Tuple[XGBRegressor, int],\n",
-    "        List[Union[Tuple[XGBClassifier, int], Tuple[XGBRegressor, int]]],\n",
-    "    ],\n",
-    "    client_tree_num: int,\n",
-    "    client_num: int,\n",
-    ") -> Optional[Tuple[NDArray, NDArray]]:\n",
-    "    \"\"\"Transform the tabular dataset into prediction results using the\n",
-    "    aggregated xgboost tree ensembles from all clients.\"\"\"\n",
-    "    if trainloader is None:\n",
-    "        return None\n",
-    "\n",
-    "    for local_dataset in trainloader:\n",
-    "        x_train, y_train = local_dataset[0], local_dataset[1]\n",
-    "\n",
-    "    x_train_enc = np.zeros((x_train.shape[0], client_num * client_tree_num))\n",
-    "    x_train_enc = np.array(x_train_enc, copy=True)\n",
-    "\n",
-    "    temp_trees: Any = None\n",
-    "    if isinstance(client_trees, list) is False:\n",
-    "        temp_trees = [client_trees[0]] * client_num\n",
-    "    elif isinstance(client_trees, list) and len(client_trees) != client_num:\n",
-    "        temp_trees = [client_trees[0][0]] * client_num\n",
-    "    else:\n",
-    "        cids = []\n",
-    "        temp_trees = []\n",
-    "        for i, _ in enumerate(client_trees):\n",
-    "            temp_trees.append(client_trees[i][0])  # type: ignore\n",
-    "            cids.append(client_trees[i][1])  # type: ignore\n",
-    "        sorted_index = np.argsort(np.asarray(cids))\n",
-    "        temp_trees = np.asarray(temp_trees)[sorted_index]\n",
-    "\n",
-    "    for i, _ in enumerate(temp_trees):\n",
-    "        for j in range(client_tree_num):\n",
-    "            x_train_enc[:, i * client_tree_num + j] = single_tree_prediction(\n",
-    "                temp_trees[i], j, x_train\n",
-    "            )\n",
-    "\n",
-    "    x_train_enc32: Any = np.float32(x_train_enc)\n",
-    "    y_train32: Any = np.float32(y_train)\n",
-    "\n",
-    "    x_train_enc32, y_train32 = torch.from_numpy(\n",
-    "        np.expand_dims(x_train_enc32, axis=1)  # type: ignore\n",
-    "    ), torch.from_numpy(\n",
-    "        np.expand_dims(y_train32, axis=-1)  # type: ignore\n",
-    "    )\n",
-    "    return x_train_enc32, y_train32"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Manually download and load the tabular dataset from LIBSVM data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 26613,
-     "status": "ok",
-     "timestamp": 1670356076585,
-     "user": {
-      "displayName": "Chenyang Ma",
-      "userId": "17975430055716133031"
-     },
-     "user_tz": 0
-    },
-    "outputId": "22843504-faf0-44cf-aedd-1df8d0ec87a6"
-   },
-   "outputs": [],
-   "source": [
-    "# Datasets can be downloaded from LIBSVM Data: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/\n",
-    "binary_train = [\"cod-rna.t\", \"cod-rna\", \"ijcnn1.t\"]\n",
-    "binary_test = [\"cod-rna.r\", \"cod-rna.t\", \"ijcnn1.tr\"]\n",
-    "reg_train = [\"eunite2001\", \"YearPredictionMSD\"]\n",
-    "reg_test = [\"eunite2001.t\", \"YearPredictionMSD.t\"]\n",
-    "\n",
-    "# Define the type of training task. Binary classification: BINARY; Regression: REG\n",
-    "task_types = [\"BINARY\", \"REG\"]\n",
-    "task_type = task_types[0]\n",
-    "\n",
-    "# Select the downloaded training and test dataset\n",
-    "if task_type == \"BINARY\":\n",
-    "    dataset_path = \"dataset/binary_classification/\"\n",
-    "    train = binary_train[0]\n",
-    "    test = binary_test[0]\n",
-    "elif task_type == \"REG\":\n",
-    "    dataset_path = \"dataset/regression/\"\n",
-    "    train = reg_train[0]\n",
-    "    test = reg_test[0]\n",
-    "\n",
-    "data_train = load_svmlight_file(dataset_path + train, zero_based=False)\n",
-    "data_test = load_svmlight_file(dataset_path + test, zero_based=False)\n",
-    "\n",
-    "print(\"Task type selected is: \" + task_type)\n",
-    "print(\"Training dataset is: \" + train)\n",
-    "print(\"Test dataset is: \" + test)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Preprocess the tabular dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class TreeDataset(Dataset):\n",
-    "    def __init__(self, data: NDArray, labels: NDArray) -> None:\n",
-    "        self.labels = labels\n",
-    "        self.data = data\n",
-    "\n",
-    "    def __len__(self) -> int:\n",
-    "        return len(self.labels)\n",
-    "\n",
-    "    def __getitem__(self, idx: int) -> Dict[int, NDArray]:\n",
-    "        label = self.labels[idx]\n",
-    "        data = self.data[idx, :]\n",
-    "        sample = {0: data, 1: label}\n",
-    "        return sample\n",
-    "\n",
-    "\n",
-    "X_train = data_train[0].toarray()\n",
-    "y_train = data_train[1]\n",
-    "X_test = data_test[0].toarray()\n",
-    "y_test = data_test[1]\n",
-    "X_train.flags.writeable = True\n",
-    "y_train.flags.writeable = True\n",
-    "X_test.flags.writeable = True\n",
-    "y_test.flags.writeable = True\n",
-    "\n",
-    "# If the feature dimensions of the trainset and testset do not agree,\n",
-    "# specify n_features in the load_svmlight_file function in the above cell.\n",
-    "# https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_svmlight_file.html\n",
-    "print(\"Feature dimension of the dataset:\", X_train.shape[1])\n",
-    "print(\"Size of the trainset:\", X_train.shape[0])\n",
-    "print(\"Size of the testset:\", X_test.shape[0])\n",
-    "assert X_train.shape[1] == X_test.shape[1]\n",
-    "\n",
-    "if task_type == \"BINARY\":\n",
-    "    y_train[y_train == -1] = 0\n",
-    "    y_test[y_test == -1] = 0\n",
-    "\n",
-    "trainset = TreeDataset(np.array(X_train, copy=True), np.array(y_train, copy=True))\n",
-    "testset = TreeDataset(np.array(X_test, copy=True), np.array(y_test, copy=True))"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Conduct tabular dataset partition for Federated Learning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_dataloader(\n",
-    "    dataset: Dataset, partition: str, batch_size: Union[int, str]\n",
-    ") -> DataLoader:\n",
-    "    if batch_size == \"whole\":\n",
-    "        batch_size = len(dataset)\n",
-    "    return DataLoader(\n",
-    "        dataset, batch_size=batch_size, pin_memory=True, shuffle=(partition == \"train\")\n",
-    "    )\n",
-    "\n",
-    "\n",
-    "# https://github.com/adap/flower\n",
-    "def do_fl_partitioning(\n",
-    "    trainset: Dataset,\n",
-    "    testset: Dataset,\n",
-    "    pool_size: int,\n",
-    "    batch_size: Union[int, str],\n",
-    "    val_ratio: float = 0.0,\n",
-    ") -> Tuple[DataLoader, DataLoader, DataLoader]:\n",
-    "    # Split training set into `num_clients` partitions to simulate different local datasets\n",
-    "    partition_size = len(trainset) // pool_size\n",
-    "    lengths = [partition_size] * pool_size\n",
-    "    if sum(lengths) != len(trainset):\n",
-    "        lengths[-1] = len(trainset) - sum(lengths[0:-1])\n",
-    "    datasets = random_split(trainset, lengths, torch.Generator().manual_seed(0))\n",
-    "\n",
-    "    # Split each partition into train/val and create DataLoader\n",
-    "    trainloaders = []\n",
-    "    valloaders = []\n",
-    "    for ds in datasets:\n",
-    "        len_val = int(len(ds) * val_ratio)\n",
-    "        len_train = len(ds) - len_val\n",
-    "        lengths = [len_train, len_val]\n",
-    "        ds_train, ds_val = random_split(ds, lengths, torch.Generator().manual_seed(0))\n",
-    "        trainloaders.append(get_dataloader(ds_train, \"train\", batch_size))\n",
-    "        if len_val != 0:\n",
-    "            valloaders.append(get_dataloader(ds_val, \"val\", batch_size))\n",
-    "        else:\n",
-    "            valloaders = None\n",
-    "    testloader = get_dataloader(testset, \"test\", batch_size)\n",
-    "    return trainloaders, valloaders, testloader"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Define global variables for Federated XGBoost Learning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# The number of clients participated in the federated learning\n",
-    "client_num = 5\n",
-    "\n",
-    "# The number of XGBoost trees in the tree ensemble that will be built for each client\n",
-    "client_tree_num = 500 // client_num"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Build global XGBoost tree for comparison"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 1080216,
-     "status": "ok",
-     "timestamp": 1670357156788,
-     "user": {
-      "displayName": "Chenyang Ma",
-      "userId": "17975430055716133031"
-     },
-     "user_tz": 0
-    },
-    "outputId": "d56f2821-5cd5-49ff-c5dc-f8d088eed799"
-   },
-   "outputs": [],
-   "source": [
-    "global_tree = construct_tree(X_train, y_train, client_tree_num, task_type)\n",
-    "preds_train = global_tree.predict(X_train)\n",
-    "preds_test = global_tree.predict(X_test)\n",
-    "\n",
-    "if task_type == \"BINARY\":\n",
-    "    result_train = accuracy_score(y_train, preds_train)\n",
-    "    result_test = accuracy_score(y_test, preds_test)\n",
-    "    print(\"Global XGBoost Training Accuracy: %f\" % (result_train))\n",
-    "    print(\"Global XGBoost Testing Accuracy: %f\" % (result_test))\n",
-    "elif task_type == \"REG\":\n",
-    "    result_train = mean_squared_error(y_train, preds_train)\n",
-    "    result_test = mean_squared_error(y_test, preds_test)\n",
-    "    print(\"Global XGBoost Training MSE: %f\" % (result_train))\n",
-    "    print(\"Global XGBoost Testing MSE: %f\" % (result_test))\n",
-    "\n",
-    "print(global_tree)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Simulate local XGBoost trees on clients for comparison"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "executionInfo": {
-     "elapsed": 242310,
-     "status": "ok",
-     "timestamp": 1670357399084,
-     "user": {
-      "displayName": "Chenyang Ma",
-      "userId": "17975430055716133031"
-     },
-     "user_tz": 0
-    },
-    "outputId": "0739df9f-84de-4749-8de1-7bd7c6a32ccc"
-   },
-   "outputs": [],
-   "source": [
-    "client_trees_comparison = []\n",
-    "trainloaders, _, testloader = do_fl_partitioning(\n",
-    "    trainset, testset, pool_size=client_num, batch_size=\"whole\", val_ratio=0.0\n",
-    ")\n",
-    "\n",
-    "for i, trainloader in enumerate(trainloaders):\n",
-    "    for local_dataset in trainloader:\n",
-    "        local_X_train, local_y_train = local_dataset[0], local_dataset[1]\n",
-    "        tree = construct_tree(local_X_train, local_y_train, client_tree_num, task_type)\n",
-    "        client_trees_comparison.append(tree)\n",
-    "\n",
-    "        preds_train = client_trees_comparison[-1].predict(local_X_train)\n",
-    "        preds_test = client_trees_comparison[-1].predict(X_test)\n",
-    "\n",
-    "        if task_type == \"BINARY\":\n",
-    "            result_train = accuracy_score(local_y_train, preds_train)\n",
-    "            result_test = accuracy_score(y_test, preds_test)\n",
-    "            print(\"Local Client %d XGBoost Training Accuracy: %f\" % (i, result_train))\n",
-    "            print(\"Local Client %d XGBoost Testing Accuracy: %f\" % (i, result_test))\n",
-    "        elif task_type == \"REG\":\n",
-    "            result_train = mean_squared_error(local_y_train, preds_train)\n",
-    "            result_test = mean_squared_error(y_test, preds_test)\n",
-    "            print(\"Local Client %d XGBoost Training MSE: %f\" % (i, result_train))\n",
-    "            print(\"Local Client %d XGBoost Testing MSE: %f\" % (i, result_test))"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Centralized Federated XGBoost\n",
-    "#### Create 1D convolutional neural network on trees prediction results. \n",
-    "#### 1D kernel size == client_tree_num\n",
-    "#### Make the learning rate of the tree ensembles learnable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 38,
-     "status": "ok",
-     "timestamp": 1670363021675,
-     "user": {
-      "displayName": "Chenyang Ma",
-      "userId": "17975430055716133031"
-     },
-     "user_tz": 0
-    }
-   },
-   "outputs": [],
-   "source": [
-    "class CNN(nn.Module):\n",
-    "    def __init__(self, n_channel: int = 64) -> None:\n",
-    "        super(CNN, self).__init__()\n",
-    "        n_out = 1\n",
-    "        self.task_type = task_type\n",
-    "        self.conv1d = nn.Conv1d(\n",
-    "            1, n_channel, kernel_size=client_tree_num, stride=client_tree_num, padding=0\n",
-    "        )\n",
-    "        self.layer_direct = nn.Linear(n_channel * client_num, n_out)\n",
-    "        self.ReLU = nn.ReLU()\n",
-    "        self.Sigmoid = nn.Sigmoid()\n",
-    "        self.Identity = nn.Identity()\n",
-    "\n",
-    "        # Add weight initialization\n",
-    "        for layer in self.modules():\n",
-    "            if isinstance(layer, nn.Linear):\n",
-    "                nn.init.kaiming_uniform_(\n",
-    "                    layer.weight, mode=\"fan_in\", nonlinearity=\"relu\"\n",
-    "                )\n",
-    "\n",
-    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
-    "        x = self.ReLU(self.conv1d(x))\n",
-    "        x = x.flatten(start_dim=1)\n",
-    "        x = self.ReLU(x)\n",
-    "        if self.task_type == \"BINARY\":\n",
-    "            x = self.Sigmoid(self.layer_direct(x))\n",
-    "        elif self.task_type == \"REG\":\n",
-    "            x = self.Identity(self.layer_direct(x))\n",
-    "        return x\n",
-    "\n",
-    "    def get_weights(self) -> fl.common.NDArrays:\n",
-    "        \"\"\"Get model weights as a list of NumPy ndarrays.\"\"\"\n",
-    "        return [\n",
-    "            np.array(val.cpu().numpy(), copy=True)\n",
-    "            for _, val in self.state_dict().items()\n",
-    "        ]\n",
-    "\n",
-    "    def set_weights(self, weights: fl.common.NDArrays) -> None:\n",
-    "        \"\"\"Set model weights from a list of NumPy ndarrays.\"\"\"\n",
-    "        layer_dict = {}\n",
-    "        for k, v in zip(self.state_dict().keys(), weights):\n",
-    "            if v.ndim != 0:\n",
-    "                layer_dict[k] = torch.Tensor(np.array(v, copy=True))\n",
-    "        state_dict = OrderedDict(layer_dict)\n",
-    "        self.load_state_dict(state_dict, strict=True)\n",
-    "\n",
-    "\n",
-    "def train(\n",
-    "    task_type: str,\n",
-    "    net: CNN,\n",
-    "    trainloader: DataLoader,\n",
-    "    device: torch.device,\n",
-    "    num_iterations: int,\n",
-    "    log_progress: bool = True,\n",
-    ") -> Tuple[float, float, int]:\n",
-    "    # Define loss and optimizer\n",
-    "    if task_type == \"BINARY\":\n",
-    "        criterion = nn.BCELoss()\n",
-    "    elif task_type == \"REG\":\n",
-    "        criterion = nn.MSELoss()\n",
-    "    # optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-6)\n",
-    "    optimizer = torch.optim.Adam(net.parameters(), lr=0.0001, betas=(0.9, 0.999))\n",
-    "\n",
-    "    def cycle(iterable):\n",
-    "        \"\"\"Repeats the contents of the train loader, in case it gets exhausted in 'num_iterations'.\"\"\"\n",
-    "        while True:\n",
-    "            for x in iterable:\n",
-    "                yield x\n",
-    "\n",
-    "    # Train the network\n",
-    "    net.train()\n",
-    "    total_loss, total_result, n_samples = 0.0, 0.0, 0\n",
-    "    pbar = (\n",
-    "        tqdm(iter(cycle(trainloader)), total=num_iterations, desc=f\"TRAIN\")\n",
-    "        if log_progress\n",
-    "        else iter(cycle(trainloader))\n",
-    "    )\n",
-    "\n",
-    "    # Unusually, this training is formulated in terms of number of updates/iterations/batches processed\n",
-    "    # by the network. This will be helpful later on, when partitioning the data across clients: resulting\n",
-    "    # in differences between dataset sizes and hence inconsistent numbers of updates per 'epoch'.\n",
-    "    for i, data in zip(range(num_iterations), pbar):\n",
-    "        tree_outputs, labels = data[0].to(device), data[1].to(device)\n",
-    "        optimizer.zero_grad()\n",
-    "\n",
-    "        outputs = net(tree_outputs)\n",
-    "        loss = criterion(outputs, labels)\n",
-    "        loss.backward()\n",
-    "        optimizer.step()\n",
-    "\n",
-    "        # Collected training loss and accuracy statistics\n",
-    "        total_loss += loss.item()\n",
-    "        n_samples += labels.size(0)\n",
-    "\n",
-    "        if task_type == \"BINARY\":\n",
-    "            acc = Accuracy(task=\"binary\")(outputs, labels.type(torch.int))\n",
-    "            total_result += acc * labels.size(0)\n",
-    "        elif task_type == \"REG\":\n",
-    "            mse = MeanSquaredError()(outputs, labels.type(torch.int))\n",
-    "            total_result += mse * labels.size(0)\n",
-    "\n",
-    "        if log_progress:\n",
-    "            if task_type == \"BINARY\":\n",
-    "                pbar.set_postfix(\n",
-    "                    {\n",
-    "                        \"train_loss\": total_loss / n_samples,\n",
-    "                        \"train_acc\": total_result / n_samples,\n",
-    "                    }\n",
-    "                )\n",
-    "            elif task_type == \"REG\":\n",
-    "                pbar.set_postfix(\n",
-    "                    {\n",
-    "                        \"train_loss\": total_loss / n_samples,\n",
-    "                        \"train_mse\": total_result / n_samples,\n",
-    "                    }\n",
-    "                )\n",
-    "    if log_progress:\n",
-    "        print(\"\\n\")\n",
-    "\n",
-    "    return total_loss / n_samples, total_result / n_samples, n_samples\n",
-    "\n",
-    "\n",
-    "def test(\n",
-    "    task_type: str,\n",
-    "    net: CNN,\n",
-    "    testloader: DataLoader,\n",
-    "    device: torch.device,\n",
-    "    log_progress: bool = True,\n",
-    ") -> Tuple[float, float, int]:\n",
-    "    \"\"\"Evaluates the network on test data.\"\"\"\n",
-    "    if task_type == \"BINARY\":\n",
-    "        criterion = nn.BCELoss()\n",
-    "    elif task_type == \"REG\":\n",
-    "        criterion = nn.MSELoss()\n",
-    "\n",
-    "    total_loss, total_result, n_samples = 0.0, 0.0, 0\n",
-    "    net.eval()\n",
-    "    with torch.no_grad():\n",
-    "        pbar = tqdm(testloader, desc=\"TEST\") if log_progress else testloader\n",
-    "        for data in pbar:\n",
-    "            tree_outputs, labels = data[0].to(device), data[1].to(device)\n",
-    "            outputs = net(tree_outputs)\n",
-    "\n",
-    "            # Collected testing loss and accuracy statistics\n",
-    "            total_loss += criterion(outputs, labels).item()\n",
-    "            n_samples += labels.size(0)\n",
-    "\n",
-    "            if task_type == \"BINARY\":\n",
-    "                acc = Accuracy(task=\"binary\")(\n",
-    "                    outputs.cpu(), labels.type(torch.int).cpu()\n",
-    "                )\n",
-    "                total_result += acc * labels.size(0)\n",
-    "            elif task_type == \"REG\":\n",
-    "                mse = MeanSquaredError()(outputs.cpu(), labels.type(torch.int).cpu())\n",
-    "                total_result += mse * labels.size(0)\n",
-    "\n",
-    "    if log_progress:\n",
-    "        print(\"\\n\")\n",
-    "\n",
-    "    return total_loss / n_samples, total_result / n_samples, n_samples"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Flower custom client\n",
-    "## Import Flower custom client relevant modules"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Flower client\n",
-    "from flwr.common import (\n",
-    "    EvaluateIns,\n",
-    "    EvaluateRes,\n",
-    "    FitIns,\n",
-    "    FitRes,\n",
-    "    GetPropertiesIns,\n",
-    "    GetPropertiesRes,\n",
-    "    GetParametersIns,\n",
-    "    GetParametersRes,\n",
-    "    Status,\n",
-    "    Code,\n",
-    "    parameters_to_ndarrays,\n",
-    "    ndarrays_to_parameters,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 36,
-     "status": "ok",
-     "timestamp": 1670363021676,
-     "user": {
-      "displayName": "Chenyang Ma",
-      "userId": "17975430055716133031"
-     },
-     "user_tz": 0
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def tree_encoding_loader(\n",
-    "    dataloader: DataLoader,\n",
-    "    batch_size: int,\n",
-    "    client_trees: Union[\n",
-    "        Tuple[XGBClassifier, int],\n",
-    "        Tuple[XGBRegressor, int],\n",
-    "        List[Union[Tuple[XGBClassifier, int], Tuple[XGBRegressor, int]]],\n",
-    "    ],\n",
-    "    client_tree_num: int,\n",
-    "    client_num: int,\n",
-    ") -> DataLoader:\n",
-    "    encoding = tree_encoding(dataloader, client_trees, client_tree_num, client_num)\n",
-    "    if encoding is None:\n",
-    "        return None\n",
-    "    data, labels = encoding\n",
-    "    tree_dataset = TreeDataset(data, labels)\n",
-    "    return get_dataloader(tree_dataset, \"tree\", batch_size)\n",
-    "\n",
-    "\n",
-    "class FL_Client(fl.client.Client):\n",
-    "    def __init__(\n",
-    "        self,\n",
-    "        task_type: str,\n",
-    "        trainloader: DataLoader,\n",
-    "        valloader: DataLoader,\n",
-    "        client_tree_num: int,\n",
-    "        client_num: int,\n",
-    "        cid: str,\n",
-    "        log_progress: bool = False,\n",
-    "    ):\n",
-    "        \"\"\"\n",
-    "        Creates a client for training `network.Net` on tabular dataset.\n",
-    "        \"\"\"\n",
-    "        self.task_type = task_type\n",
-    "        self.cid = cid\n",
-    "        self.tree = construct_tree_from_loader(trainloader, client_tree_num, task_type)\n",
-    "        self.trainloader_original = trainloader\n",
-    "        self.valloader_original = valloader\n",
-    "        self.trainloader = None\n",
-    "        self.valloader = None\n",
-    "        self.client_tree_num = client_tree_num\n",
-    "        self.client_num = client_num\n",
-    "        self.properties = {\"tensor_type\": \"numpy.ndarray\"}\n",
-    "        self.log_progress = log_progress\n",
-    "\n",
-    "        # instantiate model\n",
-    "        self.net = CNN()\n",
-    "\n",
-    "        # determine device\n",
-    "        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
-    "    def get_properties(self, ins: GetPropertiesIns) -> GetPropertiesRes:\n",
-    "        return GetPropertiesRes(properties=self.properties)\n",
-    "\n",
-    "    def get_parameters(\n",
-    "        self, ins: GetParametersIns\n",
-    "    ) -> Tuple[\n",
-    "        GetParametersRes, Union[Tuple[XGBClassifier, int], Tuple[XGBRegressor, int]]\n",
-    "    ]:\n",
-    "        return [\n",
-    "            GetParametersRes(\n",
-    "                status=Status(Code.OK, \"\"),\n",
-    "                parameters=ndarrays_to_parameters(self.net.get_weights()),\n",
-    "            ),\n",
-    "            (self.tree, int(self.cid)),\n",
-    "        ]\n",
-    "\n",
-    "    def set_parameters(\n",
-    "        self,\n",
-    "        parameters: Tuple[\n",
-    "            Parameters,\n",
-    "            Union[\n",
-    "                Tuple[XGBClassifier, int],\n",
-    "                Tuple[XGBRegressor, int],\n",
-    "                List[Union[Tuple[XGBClassifier, int], Tuple[XGBRegressor, int]]],\n",
-    "            ],\n",
-    "        ],\n",
-    "    ) -> Union[\n",
-    "        Tuple[XGBClassifier, int],\n",
-    "        Tuple[XGBRegressor, int],\n",
-    "        List[Union[Tuple[XGBClassifier, int], Tuple[XGBRegressor, int]]],\n",
-    "    ]:\n",
-    "        self.net.set_weights(parameters_to_ndarrays(parameters[0]))\n",
-    "        return parameters[1]\n",
-    "\n",
-    "    def fit(self, fit_params: FitIns) -> FitRes:\n",
-    "        # Process incoming request to train\n",
-    "        num_iterations = fit_params.config[\"num_iterations\"]\n",
-    "        batch_size = fit_params.config[\"batch_size\"]\n",
-    "        aggregated_trees = self.set_parameters(fit_params.parameters)\n",
-    "\n",
-    "        if type(aggregated_trees) is list:\n",
-    "            print(\"Client \" + self.cid + \": recieved\", len(aggregated_trees), \"trees\")\n",
-    "        else:\n",
-    "            print(\"Client \" + self.cid + \": only had its own tree\")\n",
-    "        self.trainloader = tree_encoding_loader(\n",
-    "            self.trainloader_original,\n",
-    "            batch_size,\n",
-    "            aggregated_trees,\n",
-    "            self.client_tree_num,\n",
-    "            self.client_num,\n",
-    "        )\n",
-    "        self.valloader = tree_encoding_loader(\n",
-    "            self.valloader_original,\n",
-    "            batch_size,\n",
-    "            aggregated_trees,\n",
-    "            self.client_tree_num,\n",
-    "            self.client_num,\n",
-    "        )\n",
-    "\n",
-    "        # num_iterations = None special behaviour: train(...) runs for a single epoch, however many updates it may be\n",
-    "        num_iterations = num_iterations or len(self.trainloader)\n",
-    "\n",
-    "        # Train the model\n",
-    "        print(f\"Client {self.cid}: training for {num_iterations} iterations/updates\")\n",
-    "        self.net.to(self.device)\n",
-    "        train_loss, train_result, num_examples = train(\n",
-    "            self.task_type,\n",
-    "            self.net,\n",
-    "            self.trainloader,\n",
-    "            device=self.device,\n",
-    "            num_iterations=num_iterations,\n",
-    "            log_progress=self.log_progress,\n",
-    "        )\n",
-    "        print(\n",
-    "            f\"Client {self.cid}: training round complete, {num_examples} examples processed\"\n",
-    "        )\n",
-    "\n",
-    "        # Return training information: model, number of examples processed and metrics\n",
-    "        if self.task_type == \"BINARY\":\n",
-    "            return FitRes(\n",
-    "                status=Status(Code.OK, \"\"),\n",
-    "                parameters=self.get_parameters(fit_params.config),\n",
-    "                num_examples=num_examples,\n",
-    "                metrics={\"loss\": train_loss, \"accuracy\": train_result},\n",
-    "            )\n",
-    "        elif self.task_type == \"REG\":\n",
-    "            return FitRes(\n",
-    "                status=Status(Code.OK, \"\"),\n",
-    "                parameters=self.get_parameters(fit_params.config),\n",
-    "                num_examples=num_examples,\n",
-    "                metrics={\"loss\": train_loss, \"mse\": train_result},\n",
-    "            )\n",
-    "\n",
-    "    def evaluate(self, eval_params: EvaluateIns) -> EvaluateRes:\n",
-    "        # Process incoming request to evaluate\n",
-    "        self.set_parameters(eval_params.parameters)\n",
-    "\n",
-    "        # Evaluate the model\n",
-    "        self.net.to(self.device)\n",
-    "        loss, result, num_examples = test(\n",
-    "            self.task_type,\n",
-    "            self.net,\n",
-    "            self.valloader,\n",
-    "            device=self.device,\n",
-    "            log_progress=self.log_progress,\n",
-    "        )\n",
-    "\n",
-    "        # Return evaluation information\n",
-    "        if self.task_type == \"BINARY\":\n",
-    "            print(\n",
-    "                f\"Client {self.cid}: evaluation on {num_examples} examples: loss={loss:.4f}, accuracy={result:.4f}\"\n",
-    "            )\n",
-    "            return EvaluateRes(\n",
-    "                status=Status(Code.OK, \"\"),\n",
-    "                loss=loss,\n",
-    "                num_examples=num_examples,\n",
-    "                metrics={\"accuracy\": result},\n",
-    "            )\n",
-    "        elif self.task_type == \"REG\":\n",
-    "            print(\n",
-    "                f\"Client {self.cid}: evaluation on {num_examples} examples: loss={loss:.4f}, mse={result:.4f}\"\n",
-    "            )\n",
-    "            return EvaluateRes(\n",
-    "                status=Status(Code.OK, \"\"),\n",
-    "                loss=loss,\n",
-    "                num_examples=num_examples,\n",
-    "                metrics={\"mse\": result},\n",
-    "            )"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create Flower custom server\n",
-    "## Import Flower custom server relevant modules"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Flower server\n",
-    "import functools\n",
-    "from flwr.server.strategy import FedXgbNnAvg\n",
-    "from flwr.server.app import ServerConfig\n",
-    "\n",
-    "import timeit\n",
-    "from logging import DEBUG, INFO\n",
-    "from typing import Dict, List, Optional, Tuple, Union\n",
-    "\n",
-    "from flwr.common import DisconnectRes, Parameters, ReconnectIns, Scalar\n",
-    "from flwr.common.logger import log\n",
-    "from flwr.common.typing import GetParametersIns\n",
-    "from flwr.server.client_manager import ClientManager, SimpleClientManager\n",
-    "from flwr.server.client_proxy import ClientProxy\n",
-    "from flwr.server.history import History\n",
-    "from flwr.server.strategy import Strategy\n",
-    "from flwr.server.server import (\n",
-    "    reconnect_clients,\n",
-    "    reconnect_client,\n",
-    "    fit_clients,\n",
-    "    fit_client,\n",
-    "    _handle_finished_future_after_fit,\n",
-    "    evaluate_clients,\n",
-    "    evaluate_client,\n",
-    "    _handle_finished_future_after_evaluate,\n",
-    ")\n",
-    "\n",
-    "FitResultsAndFailures = Tuple[\n",
-    "    List[Tuple[ClientProxy, FitRes]],\n",
-    "    List[Union[Tuple[ClientProxy, FitRes], BaseException]],\n",
-    "]\n",
-    "EvaluateResultsAndFailures = Tuple[\n",
-    "    List[Tuple[ClientProxy, EvaluateRes]],\n",
-    "    List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class FL_Server(fl.server.Server):\n",
-    "    \"\"\"Flower server.\"\"\"\n",
-    "\n",
-    "    def __init__(\n",
-    "        self, *, client_manager: ClientManager, strategy: Optional[Strategy] = None\n",
-    "    ) -> None:\n",
-    "        self._client_manager: ClientManager = client_manager\n",
-    "        self.parameters: Parameters = Parameters(\n",
-    "            tensors=[], tensor_type=\"numpy.ndarray\"\n",
-    "        )\n",
-    "        self.strategy: Strategy = strategy\n",
-    "        self.max_workers: Optional[int] = None\n",
-    "\n",
-    "    # pylint: disable=too-many-locals\n",
-    "    def fit(self, num_rounds: int, timeout: Optional[float]) -> History:\n",
-    "        \"\"\"Run federated averaging for a number of rounds.\"\"\"\n",
-    "        history = History()\n",
-    "\n",
-    "        # Initialize parameters\n",
-    "        log(INFO, \"Initializing global parameters\")\n",
-    "        self.parameters = self._get_initial_parameters(timeout=timeout)\n",
-    "\n",
-    "        log(INFO, \"Evaluating initial parameters\")\n",
-    "        res = self.strategy.evaluate(0, parameters=self.parameters)\n",
-    "        if res is not None:\n",
-    "            log(\n",
-    "                INFO,\n",
-    "                \"initial parameters (loss, other metrics): %s, %s\",\n",
-    "                res[0],\n",
-    "                res[1],\n",
-    "            )\n",
-    "            history.add_loss_centralized(server_round=0, loss=res[0])\n",
-    "            history.add_metrics_centralized(server_round=0, metrics=res[1])\n",
-    "\n",
-    "        # Run federated learning for num_rounds\n",
-    "        log(INFO, \"FL starting\")\n",
-    "        start_time = timeit.default_timer()\n",
-    "\n",
-    "        for current_round in range(1, num_rounds + 1):\n",
-    "            # Train model and replace previous global model\n",
-    "            res_fit = self.fit_round(server_round=current_round, timeout=timeout)\n",
-    "            if res_fit:\n",
-    "                parameters_prime, _, _ = res_fit  # fit_metrics_aggregated\n",
-    "                if parameters_prime:\n",
-    "                    self.parameters = parameters_prime\n",
-    "\n",
-    "            # Evaluate model using strategy implementation\n",
-    "            res_cen = self.strategy.evaluate(current_round, parameters=self.parameters)\n",
-    "            if res_cen is not None:\n",
-    "                loss_cen, metrics_cen = res_cen\n",
-    "                log(\n",
-    "                    INFO,\n",
-    "                    \"fit progress: (%s, %s, %s, %s)\",\n",
-    "                    current_round,\n",
-    "                    loss_cen,\n",
-    "                    metrics_cen,\n",
-    "                    timeit.default_timer() - start_time,\n",
-    "                )\n",
-    "                history.add_loss_centralized(server_round=current_round, loss=loss_cen)\n",
-    "                history.add_metrics_centralized(\n",
-    "                    server_round=current_round, metrics=metrics_cen\n",
-    "                )\n",
-    "\n",
-    "            # Evaluate model on a sample of available clients\n",
-    "            res_fed = self.evaluate_round(server_round=current_round, timeout=timeout)\n",
-    "            if res_fed:\n",
-    "                loss_fed, evaluate_metrics_fed, _ = res_fed\n",
-    "                if loss_fed:\n",
-    "                    history.add_loss_distributed(\n",
-    "                        server_round=current_round, loss=loss_fed\n",
-    "                    )\n",
-    "                    history.add_metrics_distributed(\n",
-    "                        server_round=current_round, metrics=evaluate_metrics_fed\n",
-    "                    )\n",
-    "\n",
-    "        # Bookkeeping\n",
-    "        end_time = timeit.default_timer()\n",
-    "        elapsed = end_time - start_time\n",
-    "        log(INFO, \"FL finished in %s\", elapsed)\n",
-    "        return history\n",
-    "\n",
-    "    def evaluate_round(\n",
-    "        self,\n",
-    "        server_round: int,\n",
-    "        timeout: Optional[float],\n",
-    "    ) -> Optional[\n",
-    "        Tuple[Optional[float], Dict[str, Scalar], EvaluateResultsAndFailures]\n",
-    "    ]:\n",
-    "        \"\"\"Validate current global model on a number of clients.\"\"\"\n",
-    "\n",
-    "        # Get clients and their respective instructions from strategy\n",
-    "        client_instructions = self.strategy.configure_evaluate(\n",
-    "            server_round=server_round,\n",
-    "            parameters=self.parameters,\n",
-    "            client_manager=self._client_manager,\n",
-    "        )\n",
-    "        if not client_instructions:\n",
-    "            log(INFO, \"evaluate_round %s: no clients selected, cancel\", server_round)\n",
-    "            return None\n",
-    "        log(\n",
-    "            DEBUG,\n",
-    "            \"evaluate_round %s: strategy sampled %s clients (out of %s)\",\n",
-    "            server_round,\n",
-    "            len(client_instructions),\n",
-    "            self._client_manager.num_available(),\n",
-    "        )\n",
-    "\n",
-    "        # Collect `evaluate` results from all clients participating in this round\n",
-    "        results, failures = evaluate_clients(\n",
-    "            client_instructions,\n",
-    "            max_workers=self.max_workers,\n",
-    "            timeout=timeout,\n",
-    "        )\n",
-    "        log(\n",
-    "            DEBUG,\n",
-    "            \"evaluate_round %s received %s results and %s failures\",\n",
-    "            server_round,\n",
-    "            len(results),\n",
-    "            len(failures),\n",
-    "        )\n",
-    "\n",
-    "        # Aggregate the evaluation results\n",
-    "        aggregated_result: Tuple[\n",
-    "            Optional[float],\n",
-    "            Dict[str, Scalar],\n",
-    "        ] = self.strategy.aggregate_evaluate(server_round, results, failures)\n",
-    "\n",
-    "        loss_aggregated, metrics_aggregated = aggregated_result\n",
-    "        return loss_aggregated, metrics_aggregated, (results, failures)\n",
-    "\n",
-    "    def fit_round(\n",
-    "        self,\n",
-    "        server_round: int,\n",
-    "        timeout: Optional[float],\n",
-    "    ) -> Optional[\n",
-    "        Tuple[\n",
-    "            Optional[\n",
-    "                Tuple[\n",
-    "                    Parameters,\n",
-    "                    Union[\n",
-    "                        Tuple[XGBClassifier, int],\n",
-    "                        Tuple[XGBRegressor, int],\n",
-    "                        List[\n",
-    "                            Union[Tuple[XGBClassifier, int], Tuple[XGBRegressor, int]]\n",
-    "                        ],\n",
-    "                    ],\n",
-    "                ]\n",
-    "            ],\n",
-    "            Dict[str, Scalar],\n",
-    "            FitResultsAndFailures,\n",
-    "        ]\n",
-    "    ]:\n",
-    "        \"\"\"Perform a single round of federated averaging.\"\"\"\n",
-    "\n",
-    "        # Get clients and their respective instructions from strategy\n",
-    "        client_instructions = self.strategy.configure_fit(\n",
-    "            server_round=server_round,\n",
-    "            parameters=self.parameters,\n",
-    "            client_manager=self._client_manager,\n",
-    "        )\n",
-    "\n",
-    "        if not client_instructions:\n",
-    "            log(INFO, \"fit_round %s: no clients selected, cancel\", server_round)\n",
-    "            return None\n",
-    "        log(\n",
-    "            DEBUG,\n",
-    "            \"fit_round %s: strategy sampled %s clients (out of %s)\",\n",
-    "            server_round,\n",
-    "            len(client_instructions),\n",
-    "            self._client_manager.num_available(),\n",
-    "        )\n",
-    "\n",
-    "        # Collect `fit` results from all clients participating in this round\n",
-    "        results, failures = fit_clients(\n",
-    "            client_instructions=client_instructions,\n",
-    "            max_workers=self.max_workers,\n",
-    "            timeout=timeout,\n",
-    "        )\n",
-    "\n",
-    "        log(\n",
-    "            DEBUG,\n",
-    "            \"fit_round %s received %s results and %s failures\",\n",
-    "            server_round,\n",
-    "            len(results),\n",
-    "            len(failures),\n",
-    "        )\n",
-    "\n",
-    "        # Aggregate training results\n",
-    "        NN_aggregated: Parameters\n",
-    "        trees_aggregated: Union[\n",
-    "            Tuple[XGBClassifier, int],\n",
-    "            Tuple[XGBRegressor, int],\n",
-    "            List[Union[Tuple[XGBClassifier, int], Tuple[XGBRegressor, int]]],\n",
-    "        ]\n",
-    "        metrics_aggregated: Dict[str, Scalar]\n",
-    "        aggregated, metrics_aggregated = self.strategy.aggregate_fit(\n",
-    "            server_round, results, failures\n",
-    "        )\n",
-    "        NN_aggregated, trees_aggregated = aggregated[0], aggregated[1]\n",
-    "\n",
-    "        if type(trees_aggregated) is list:\n",
-    "            print(\"Server side aggregated\", len(trees_aggregated), \"trees.\")\n",
-    "        else:\n",
-    "            print(\"Server side did not aggregate trees.\")\n",
-    "\n",
-    "        return (\n",
-    "            [NN_aggregated, trees_aggregated],\n",
-    "            metrics_aggregated,\n",
-    "            (results, failures),\n",
-    "        )\n",
-    "\n",
-    "    def _get_initial_parameters(\n",
-    "        self, timeout: Optional[float]\n",
-    "    ) -> Tuple[Parameters, Union[Tuple[XGBClassifier, int], Tuple[XGBRegressor, int]]]:\n",
-    "        \"\"\"Get initial parameters from one of the available clients.\"\"\"\n",
-    "\n",
-    "        # Server-side parameter initialization\n",
-    "        parameters: Optional[Parameters] = self.strategy.initialize_parameters(\n",
-    "            client_manager=self._client_manager\n",
-    "        )\n",
-    "        if parameters is not None:\n",
-    "            log(INFO, \"Using initial parameters provided by strategy\")\n",
-    "            return parameters\n",
-    "\n",
-    "        # Get initial parameters from one of the clients\n",
-    "        log(INFO, \"Requesting initial parameters from one random client\")\n",
-    "        random_client = self._client_manager.sample(1)[0]\n",
-    "        ins = GetParametersIns(config={})\n",
-    "        get_parameters_res_tree = random_client.get_parameters(ins=ins, timeout=timeout)\n",
-    "        parameters = [get_parameters_res_tree[0].parameters, get_parameters_res_tree[1]]\n",
-    "        log(INFO, \"Received initial parameters from one random client\")\n",
-    "\n",
-    "        return parameters"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Create server-side evaluation and experiment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "executionInfo": {
-     "elapsed": 35,
-     "status": "ok",
-     "timestamp": 1670363021676,
-     "user": {
-      "displayName": "Chenyang Ma",
-      "userId": "17975430055716133031"
-     },
-     "user_tz": 0
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def print_model_layers(model: nn.Module) -> None:\n",
-    "    print(model)\n",
-    "    for param_tensor in model.state_dict():\n",
-    "        print(param_tensor, \"\\t\", model.state_dict()[param_tensor].size())\n",
-    "\n",
-    "\n",
-    "def serverside_eval(\n",
-    "    server_round: int,\n",
-    "    parameters: Tuple[\n",
-    "        Parameters,\n",
-    "        Union[\n",
-    "            Tuple[XGBClassifier, int],\n",
-    "            Tuple[XGBRegressor, int],\n",
-    "            List[Union[Tuple[XGBClassifier, int], Tuple[XGBRegressor, int]]],\n",
-    "        ],\n",
-    "    ],\n",
-    "    config: Dict[str, Scalar],\n",
-    "    task_type: str,\n",
-    "    testloader: DataLoader,\n",
-    "    batch_size: int,\n",
-    "    client_tree_num: int,\n",
-    "    client_num: int,\n",
-    ") -> Tuple[float, Dict[str, float]]:\n",
-    "    \"\"\"An evaluation function for centralized/serverside evaluation over the entire test set.\"\"\"\n",
-    "    # device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
-    "    device = \"cpu\"\n",
-    "    model = CNN()\n",
-    "    # print_model_layers(model)\n",
-    "\n",
-    "    model.set_weights(parameters_to_ndarrays(parameters[0]))\n",
-    "    model.to(device)\n",
-    "\n",
-    "    trees_aggregated = parameters[1]\n",
-    "    testloader = tree_encoding_loader(\n",
-    "        testloader, batch_size, trees_aggregated, client_tree_num, client_num\n",
-    "    )\n",
-    "    loss, result, _ = test(\n",
-    "        task_type, model, testloader, device=device, log_progress=False\n",
-    "    )\n",
-    "\n",
-    "    if task_type == \"BINARY\":\n",
-    "        print(\n",
-    "            f\"Evaluation on the server: test_loss={loss:.4f}, test_accuracy={result:.4f}\"\n",
-    "        )\n",
-    "        return loss, {\"accuracy\": result}\n",
-    "    elif task_type == \"REG\":\n",
-    "        print(f\"Evaluation on the server: test_loss={loss:.4f}, test_mse={result:.4f}\")\n",
-    "        return loss, {\"mse\": result}\n",
-    "\n",
-    "\n",
-    "def start_experiment(\n",
-    "    task_type: str,\n",
-    "    trainset: Dataset,\n",
-    "    testset: Dataset,\n",
-    "    num_rounds: int = 5,\n",
-    "    client_tree_num: int = 50,\n",
-    "    client_pool_size: int = 5,\n",
-    "    num_iterations: int = 100,\n",
-    "    fraction_fit: float = 1.0,\n",
-    "    min_fit_clients: int = 2,\n",
-    "    batch_size: int = 32,\n",
-    "    val_ratio: float = 0.1,\n",
-    ") -> History:\n",
-    "    client_resources = {\"num_cpus\": 0.5}  # 2 clients per CPU\n",
-    "\n",
-    "    # Partition the dataset into subsets reserved for each client.\n",
-    "    # - 'val_ratio' controls the proportion of the (local) client reserved as a local test set\n",
-    "    # (good for testing how the final model performs on the client's local unseen data)\n",
-    "    trainloaders, valloaders, testloader = do_fl_partitioning(\n",
-    "        trainset,\n",
-    "        testset,\n",
-    "        batch_size=\"whole\",\n",
-    "        pool_size=client_pool_size,\n",
-    "        val_ratio=val_ratio,\n",
-    "    )\n",
-    "    print(\n",
-    "        f\"Data partitioned across {client_pool_size} clients\"\n",
-    "        f\" and {val_ratio} of local dataset reserved for validation.\"\n",
-    "    )\n",
-    "\n",
-    "    # Configure the strategy\n",
-    "    def fit_config(server_round: int) -> Dict[str, Scalar]:\n",
-    "        print(f\"Configuring round {server_round}\")\n",
-    "        return {\n",
-    "            \"num_iterations\": num_iterations,\n",
-    "            \"batch_size\": batch_size,\n",
-    "        }\n",
-    "\n",
-    "    # FedXgbNnAvg\n",
-    "    strategy = FedXgbNnAvg(\n",
-    "        fraction_fit=fraction_fit,\n",
-    "        fraction_evaluate=fraction_fit if val_ratio > 0.0 else 0.0,\n",
-    "        min_fit_clients=min_fit_clients,\n",
-    "        min_evaluate_clients=min_fit_clients,\n",
-    "        min_available_clients=client_pool_size,  # all clients should be available\n",
-    "        on_fit_config_fn=fit_config,\n",
-    "        on_evaluate_config_fn=(lambda r: {\"batch_size\": batch_size}),\n",
-    "        evaluate_fn=functools.partial(\n",
-    "            serverside_eval,\n",
-    "            task_type=task_type,\n",
-    "            testloader=testloader,\n",
-    "            batch_size=batch_size,\n",
-    "            client_tree_num=client_tree_num,\n",
-    "            client_num=client_num,\n",
-    "        ),\n",
-    "        accept_failures=False,\n",
-    "    )\n",
-    "\n",
-    "    print(\n",
-    "        f\"FL experiment configured for {num_rounds} rounds with {client_pool_size} client in the pool.\"\n",
-    "    )\n",
-    "    print(\n",
-    "        f\"FL round will proceed with {fraction_fit * 100}% of clients sampled, at least {min_fit_clients}.\"\n",
-    "    )\n",
-    "\n",
-    "    def client_fn(cid: str) -> fl.client.Client:\n",
-    "        \"\"\"Creates a federated learning client\"\"\"\n",
-    "        if val_ratio > 0.0 and val_ratio <= 1.0:\n",
-    "            return FL_Client(\n",
-    "                task_type,\n",
-    "                trainloaders[int(cid)],\n",
-    "                valloaders[int(cid)],\n",
-    "                client_tree_num,\n",
-    "                client_pool_size,\n",
-    "                cid,\n",
-    "                log_progress=False,\n",
-    "            )\n",
-    "        else:\n",
-    "            return FL_Client(\n",
-    "                task_type,\n",
-    "                trainloaders[int(cid)],\n",
-    "                None,\n",
-    "                client_tree_num,\n",
-    "                client_pool_size,\n",
-    "                cid,\n",
-    "                log_progress=False,\n",
-    "            )\n",
-    "\n",
-    "    # Start the simulation\n",
-    "    history = fl.simulation.start_simulation(\n",
-    "        client_fn=client_fn,\n",
-    "        server=FL_Server(client_manager=SimpleClientManager(), strategy=strategy),\n",
-    "        num_clients=client_pool_size,\n",
-    "        client_resources=client_resources,\n",
-    "        config=ServerConfig(num_rounds=num_rounds),\n",
-    "        strategy=strategy,\n",
-    "    )\n",
-    "\n",
-    "    print(history)\n",
-    "\n",
-    "    return history"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Start federated training and inference\n",
-    "#### High-level workflow: \n",
-    "#### At round 1, each client first builds their own local XGBoost tree, and sends to the server. The server aggregates all trees and sends to all clients. \n",
-    "#### After round 1, each client calculates every other client tree’s prediction results, and trains a convolutional neural network with 1D convolution kernel size == the number of XGBoost trees in the tree ensemble. \n",
-    "#### The sharing of privacy-sensitive information is not needed, and the learning rate (a hyperparameter for XGBoost) is learnable using 1D convolution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 624
-    },
-    "executionInfo": {
-     "elapsed": 7610,
-     "status": "error",
-     "timestamp": 1670363029252,
-     "user": {
-      "displayName": "Chenyang Ma",
-      "userId": "17975430055716133031"
-     },
-     "user_tz": 0
-    },
-    "outputId": "ee2b7146-07ec-4f97-ba44-5b12b35bbeaf"
-   },
-   "outputs": [],
-   "source": [
-    "start_experiment(\n",
-    "    task_type=task_type,\n",
-    "    trainset=trainset,\n",
-    "    testset=testset,\n",
-    "    num_rounds=20,\n",
-    "    client_tree_num=client_tree_num,\n",
-    "    client_pool_size=client_num,\n",
-    "    num_iterations=100,\n",
-    "    batch_size=64,\n",
-    "    fraction_fit=1.0,\n",
-    "    min_fit_clients=1,\n",
-    "    val_ratio=0.0,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "gpuClass": "premium",
-  "kernelspec": {
-   "display_name": "FedXGBoost",
-   "language": "python",
-   "name": "python3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
diff --git a/examples/secaggplus-mt/README.md b/examples/secaggplus-mt/README.md
deleted file mode 100644
index 0b3b4db3942e..000000000000
--- a/examples/secaggplus-mt/README.md
+++ /dev/null
@@ -1,36 +0,0 @@
-# Secure Aggregation with Driver API
-
-This example contains highly experimental code. 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.
-
-## Installing Dependencies
-
-Project dependencies (such as and `flwr`) are defined in `pyproject.toml`. 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)), 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"
-```
-
-### 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
-```
-
-If you don't see any errors you're good to go!
-
-## Run with Driver API
-
-```bash
-./run.sh
-```
diff --git a/examples/secaggplus-mt/client.py b/examples/secaggplus-mt/client.py
deleted file mode 100644
index f0f1348ee378..000000000000
--- a/examples/secaggplus-mt/client.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import time
-
-import numpy as np
-
-import flwr as fl
-from flwr.common import Status, FitIns, FitRes, Code
-from flwr.common.parameter import ndarrays_to_parameters
-from flwr.client.secure_aggregation import SecAggPlusHandler
-
-
-# Define Flower client with the SecAgg+ protocol
-class FlowerClient(fl.client.Client, SecAggPlusHandler):
-    def fit(self, fit_ins: FitIns) -> FitRes:
-        ret_vec = [np.ones(3)]
-        ret = FitRes(
-            status=Status(code=Code.OK, message="Success"),
-            parameters=ndarrays_to_parameters(ret_vec),
-            num_examples=1,
-            metrics={},
-        )
-        # Force a significant delay for testing purposes
-        if self._shared_state.sid == 0:
-            print(f"Client {self._shared_state.sid} dropped for testing purposes.")
-            time.sleep(4)
-            return ret
-        print(f"Client {self._shared_state.sid} uploading {ret_vec[0]}...")
-        return ret
-
-
-# Start Flower client
-fl.client.start_client(
-    server_address="0.0.0.0:9092",
-    client=FlowerClient(),
-    transport="grpc-rere",
-)
diff --git a/examples/secaggplus-mt/driver.py b/examples/secaggplus-mt/driver.py
deleted file mode 100644
index d9f795766f6d..000000000000
--- a/examples/secaggplus-mt/driver.py
+++ /dev/null
@@ -1,206 +0,0 @@
-import random
-import time
-from typing import Dict, List, Tuple
-
-import numpy as np
-from workflows import get_workflow_factory
-
-from flwr.common import Metrics, ndarrays_to_parameters
-from flwr.driver import GrpcDriver
-from flwr.proto import driver_pb2, node_pb2, task_pb2
-from flwr.server import History
-
-
-# Convert instruction/result dict to/from list of TaskIns/TaskRes
-def task_dict_to_task_ins_list(
-    task_dict: Dict[int, task_pb2.Task]
-) -> List[task_pb2.TaskIns]:
-    def merge(_task: task_pb2.Task, _merge_task: task_pb2.Task) -> task_pb2.Task:
-        _task.MergeFrom(_merge_task)
-        return _task
-
-    return [
-        task_pb2.TaskIns(
-            task_id="",  # Do not set, will be created and set by the DriverAPI
-            group_id="",
-            workload_id=workload_id,
-            task=merge(
-                task,
-                task_pb2.Task(
-                    producer=node_pb2.Node(
-                        node_id=0,
-                        anonymous=True,
-                    ),
-                    consumer=node_pb2.Node(
-                        node_id=sampled_node_id,
-                        # Must be False for this Secure Aggregation example
-                        anonymous=False,
-                    ),
-                ),
-            ),
-        )
-        for sampled_node_id, task in task_dict.items()
-    ]
-
-
-def task_res_list_to_task_dict(
-    task_res_list: List[task_pb2.TaskRes],
-) -> Dict[int, task_pb2.Task]:
-    return {task_res.task.producer.node_id: task_res.task for task_res in task_res_list}
-
-
-# Define metric aggregation function
-def weighted_average(metrics: List[Tuple[int, Metrics]]) -> Metrics:
-    examples = [num_examples for num_examples, _ in metrics]
-
-    # Multiply accuracy of each client by number of examples used
-    train_losses = [num_examples * m["train_loss"] for num_examples, m in metrics]
-    train_accuracies = [
-        num_examples * m["train_accuracy"] for num_examples, m in metrics
-    ]
-    val_losses = [num_examples * m["val_loss"] for num_examples, m in metrics]
-    val_accuracies = [num_examples * m["val_accuracy"] for num_examples, m in metrics]
-
-    # Aggregate and return custom metric (weighted average)
-    return {
-        "train_loss": sum(train_losses) / sum(examples),
-        "train_accuracy": sum(train_accuracies) / sum(examples),
-        "val_loss": sum(val_losses) / sum(examples),
-        "val_accuracy": sum(val_accuracies) / sum(examples),
-    }
-
-
-# -------------------------------------------------------------------------- Driver SDK
-driver = GrpcDriver(driver_service_address="0.0.0.0:9091", certificates=None)
-# -------------------------------------------------------------------------- Driver SDK
-
-anonymous_client_nodes = False
-num_client_nodes_per_round = 5
-sleep_time = 0.5
-time_out = 3.9
-num_rounds = 3
-parameters = ndarrays_to_parameters([np.ones(3)])
-wf_factory = get_workflow_factory()
-
-# -------------------------------------------------------------------------- Driver SDK
-driver.connect()
-create_workload_res: driver_pb2.CreateWorkloadResponse = driver.create_workload(
-    req=driver_pb2.CreateWorkloadRequest()
-)
-# -------------------------------------------------------------------------- Driver SDK
-
-workload_id = create_workload_res.workload_id
-print(f"Created workload id {workload_id}")
-
-history = History()
-for server_round in range(num_rounds):
-    print(f"Commencing server round {server_round + 1}")
-
-    # List of sampled node IDs in this round
-    sampled_node_ids: List[int] = []
-
-    # Sample node ids
-    if anonymous_client_nodes:
-        # If we're working with anonymous clients, we don't know their identities, and
-        # we don't know how many of them we have. We, therefore, have to assume that
-        # enough anonymous client nodes are available or become available over time.
-        #
-        # To schedule a TaskIns for an anonymous client node, we set the node_id to 0
-        # (and `anonymous` to True)
-        # Here, we create an array with only zeros in it:
-        sampled_node_ids = [0] * num_client_nodes_per_round
-    else:
-        # If our client nodes have identiy (i.e., they are not anonymous), we can get
-        # those IDs from the Driver API using `get_nodes`. If enough clients are
-        # available via the Driver API, we can select a subset by taking a random
-        # sample.
-        #
-        # The Driver API might not immediately return enough client node IDs, so we
-        # loop and wait until enough client nodes are available.
-        while True:
-            # Get a list of node ID's from the server
-            get_nodes_req = driver_pb2.GetNodesRequest(workload_id=workload_id)
-
-            # ---------------------------------------------------------------------- Driver SDK
-            get_nodes_res: driver_pb2.GetNodesResponse = driver.get_nodes(
-                req=get_nodes_req
-            )
-            # ---------------------------------------------------------------------- Driver SDK
-
-            all_node_ids: List[int] = [node.node_id for node in get_nodes_res.nodes]
-
-            if len(all_node_ids) >= num_client_nodes_per_round:
-                # Sample client nodes
-                sampled_node_ids = random.sample(
-                    all_node_ids, num_client_nodes_per_round
-                )
-                break
-
-            time.sleep(3)
-
-    # Log sampled node IDs
-    time.sleep(sleep_time)
-
-    workflow = wf_factory(parameters, sampled_node_ids)
-    node_messages = None
-
-    while True:
-        try:
-            instructions: Dict[int, task_pb2.Task] = workflow.send(node_messages)
-            next(workflow)
-        except StopIteration:
-            break
-        # Schedule a task for all sampled nodes
-        task_ins_list: List[task_pb2.TaskIns] = task_dict_to_task_ins_list(instructions)
-
-        push_task_ins_req = driver_pb2.PushTaskInsRequest(task_ins_list=task_ins_list)
-
-        # ---------------------------------------------------------------------- Driver SDK
-        push_task_ins_res: driver_pb2.PushTaskInsResponse = driver.push_task_ins(
-            req=push_task_ins_req
-        )
-        # ---------------------------------------------------------------------- Driver SDK
-
-        time.sleep(sleep_time)
-
-        # Wait for results, ignore empty task_ids
-        start_time = time.time()
-        task_ids: List[str] = [
-            task_id for task_id in push_task_ins_res.task_ids if task_id != ""
-        ]
-        all_task_res: List[task_pb2.TaskRes] = []
-        while True:
-            if time.time() - start_time >= time_out:
-                break
-            pull_task_res_req = driver_pb2.PullTaskResRequest(
-                node=node_pb2.Node(node_id=0, anonymous=True),
-                task_ids=task_ids,
-            )
-
-            # ------------------------------------------------------------------ Driver SDK
-            pull_task_res_res: driver_pb2.PullTaskResResponse = driver.pull_task_res(
-                req=pull_task_res_req
-            )
-            # ------------------------------------------------------------------ Driver SDK
-
-            task_res_list: List[task_pb2.TaskRes] = pull_task_res_res.task_res_list
-
-            time.sleep(sleep_time)
-
-            all_task_res += task_res_list
-            if len(all_task_res) == len(task_ids):
-                break
-
-        # Collect correct results
-        node_messages = task_res_list_to_task_dict(
-            [res for res in all_task_res if res.task.HasField("sa")]
-        )
-    workflow.close()
-
-    # Slow down the start of the next round
-    time.sleep(sleep_time)
-
-# -------------------------------------------------------------------------- Driver SDK
-driver.disconnect()
-# -------------------------------------------------------------------------- Driver SDK
-print("Driver disconnected")
diff --git a/examples/secaggplus-mt/pyproject.toml b/examples/secaggplus-mt/pyproject.toml
deleted file mode 100644
index 94d8defa3316..000000000000
--- a/examples/secaggplus-mt/pyproject.toml
+++ /dev/null
@@ -1,13 +0,0 @@
-[build-system]
-requires = ["poetry-core>=1.4.0"]
-build-backend = "poetry.core.masonry.api"
-
-[tool.poetry]
-name = "secaggplus-mt"
-version = "0.1.0"
-description = "Secure Aggregation with Driver API"
-authors = ["The Flower Authors <hello@flower.dev>"]
-
-[tool.poetry.dependencies]
-python = ">=3.8,<3.11"
-flwr-nightly = { version = "^1.5.0.dev20230629", extras = ["simulation", "rest"] }
diff --git a/examples/secaggplus-mt/requirements.txt b/examples/secaggplus-mt/requirements.txt
deleted file mode 100644
index eeed6941afc7..000000000000
--- a/examples/secaggplus-mt/requirements.txt
+++ /dev/null
@@ -1 +0,0 @@
-flwr-nightly[simulation,rest]
diff --git a/examples/secaggplus-mt/run.sh b/examples/secaggplus-mt/run.sh
deleted file mode 100755
index 5cc769f6cbd8..000000000000
--- a/examples/secaggplus-mt/run.sh
+++ /dev/null
@@ -1,35 +0,0 @@
-#!/bin/bash
-# Kill any currently running client.py processes
-pkill -f 'python client.py'
-
-# Kill any currently running flower-server processes with --grpc-rere option
-pkill -f 'flower-server --grpc-rere'
-
-# Start the flower server
-echo "Starting flower server in background..."
-flower-server --grpc-rere > /dev/null 2>&1 &
-sleep 2
-
-# Number of client processes to start
-N=5 # Replace with your desired value
-
-echo "Starting $N clients in background..."
-
-# Start N client processes
-for i in $(seq 1 $N)
-do
-  python client.py > /dev/null 2>&1 &
-  # python client.py &
-  sleep 0.1
-done
-
-echo "Starting driver..."
-python driver.py
-
-echo "Clearing background processes..."
-
-# Kill any currently running client.py processes
-pkill -f 'python client.py'
-
-# Kill any currently running flower-server processes with --grpc-rere option
-pkill -f 'flower-server --grpc-rere'
diff --git a/examples/secaggplus-mt/workflows.py b/examples/secaggplus-mt/workflows.py
deleted file mode 100644
index 3117e308a498..000000000000
--- a/examples/secaggplus-mt/workflows.py
+++ /dev/null
@@ -1,372 +0,0 @@
-import random
-from logging import WARNING
-from typing import Callable, Dict, Generator, List
-
-import numpy as np
-
-from flwr.common import (
-    Parameters,
-    Scalar,
-    bytes_to_ndarray,
-    log,
-    ndarray_to_bytes,
-    ndarrays_to_parameters,
-    parameters_to_ndarrays,
-)
-from flwr.common.secure_aggregation.crypto.shamir import combine_shares
-from flwr.common.secure_aggregation.crypto.symmetric_encryption import (
-    bytes_to_private_key,
-    bytes_to_public_key,
-    generate_shared_key,
-)
-from flwr.common.secure_aggregation.ndarrays_arithmetic import (
-    factor_extract,
-    get_parameters_shape,
-    get_zero_parameters,
-    parameters_addition,
-    parameters_mod,
-    parameters_subtraction,
-)
-from flwr.common.secure_aggregation.quantization import dequantize, quantize
-from flwr.common.secure_aggregation.secaggplus_constants import (
-    KEY_ACTIVE_SECURE_ID_LIST,
-    KEY_CIPHERTEXT_LIST,
-    KEY_CLIPPING_RANGE,
-    KEY_DEAD_SECURE_ID_LIST,
-    KEY_DESTINATION_LIST,
-    KEY_MASKED_PARAMETERS,
-    KEY_MOD_RANGE,
-    KEY_PARAMETERS,
-    KEY_PUBLIC_KEY_1,
-    KEY_PUBLIC_KEY_2,
-    KEY_SAMPLE_NUMBER,
-    KEY_SECURE_ID,
-    KEY_SECURE_ID_LIST,
-    KEY_SHARE_LIST,
-    KEY_SHARE_NUMBER,
-    KEY_SOURCE_LIST,
-    KEY_STAGE,
-    KEY_TARGET_RANGE,
-    KEY_THRESHOLD,
-    STAGE_COLLECT_MASKED_INPUT,
-    STAGE_SETUP,
-    STAGE_SHARE_KEYS,
-    STAGE_UNMASK,
-)
-from flwr.common.secure_aggregation.secaggplus_utils import pseudo_rand_gen
-from flwr.common.serde import named_values_from_proto, named_values_to_proto
-from flwr.common.typing import Value
-from flwr.proto.task_pb2 import SecureAggregation, Task
-
-
-LOG_EXPLAIN = True
-
-
-def get_workflow_factory() -> (
-    Callable[[Parameters, List[int]], Generator[Dict[int, Task], Dict[int, Task], None]]
-):
-    return _wrap_workflow_with_sec_agg
-
-
-def _wrap_in_task(named_values: Dict[str, Value]) -> Task:
-    return Task(sa=SecureAggregation(named_values=named_values_to_proto(named_values)))
-
-
-def _get_from_task(task: Task) -> Dict[str, Value]:
-    return named_values_from_proto(task.sa.named_values)
-
-
-_secure_aggregation_configuration = {
-    KEY_SHARE_NUMBER: 3,
-    KEY_THRESHOLD: 2,
-    KEY_CLIPPING_RANGE: 3.0,
-    KEY_TARGET_RANGE: 1 << 20,
-    KEY_MOD_RANGE: 1 << 30,
-}
-
-
-def workflow_with_sec_agg(
-    parameters: Parameters,
-    sampled_node_ids: List[int],
-    sec_agg_config: Dict[str, Scalar],
-) -> Generator[Dict[int, Task], Dict[int, Task], None]:
-    """
-    =============== Setup stage ===============
-    """
-    # Protocol config
-    num_samples = len(sampled_node_ids)
-    num_shares = sec_agg_config[KEY_SHARE_NUMBER]
-    threshold = sec_agg_config[KEY_THRESHOLD]
-    mod_range = sec_agg_config[KEY_MOD_RANGE]
-    # Quantization config
-    clipping_range = sec_agg_config[KEY_CLIPPING_RANGE]
-    target_range = sec_agg_config[KEY_TARGET_RANGE]
-
-    if LOG_EXPLAIN:
-        _quantized = quantize(
-            [np.ones(3) for _ in range(num_samples)], clipping_range, target_range
-        )
-        print(
-            "\n\n################################ Introduction ################################\n"
-            "In the example, each client will upload a vector [1.0, 1.0, 1.0] instead of\n"
-            "model updates for demonstration purposes.\n"
-            "Client 0 is configured to drop out before uploading the masked vector.\n"
-            f"After quantization, the raw vectors will be:"
-        )
-        for i in range(1, num_samples):
-            print(f"\t{_quantized[i]} from Client {i}")
-        print(
-            f"Numbers are rounded to integers stochastically during the quantization\n"
-            ", and thus not all entries are identical."
-        )
-        print(
-            "The above raw vectors are hidden from the driver through adding masks.\n"
-        )
-        print(
-            "########################## Secure Aggregation Start ##########################"
-        )
-    cfg = {
-        KEY_STAGE: STAGE_SETUP,
-        KEY_SAMPLE_NUMBER: num_samples,
-        KEY_SHARE_NUMBER: num_shares,
-        KEY_THRESHOLD: threshold,
-        KEY_CLIPPING_RANGE: clipping_range,
-        KEY_TARGET_RANGE: target_range,
-        KEY_MOD_RANGE: mod_range,
-    }
-    # The number of shares should better be odd in the SecAgg+ protocol.
-    if num_samples != num_shares and num_shares & 0x1 == 0:
-        log(WARNING, "Number of shares in the SecAgg+ protocol should be odd.")
-        num_shares += 1
-
-    # Randomly assign secure IDs to clients
-    sids = [i for i in range(len(sampled_node_ids))]
-    random.shuffle(sids)
-    nid2sid = dict(zip(sampled_node_ids, sids))
-    sid2nid = {sid: nid for nid, sid in nid2sid.items()}
-    # Build neighbour relations (node ID -> secure IDs of neighbours)
-    half_share = num_shares >> 1
-    nid2neighbours = {
-        node_id: {
-            (nid2sid[node_id] + offset) % num_samples
-            for offset in range(-half_share, half_share + 1)
-        }
-        for node_id in sampled_node_ids
-    }
-
-    surviving_node_ids = sampled_node_ids
-    if LOG_EXPLAIN:
-        print(
-            f"Sending configurations to {num_samples} clients and allocating secure IDs..."
-        )
-    # Send setup configuration to clients
-    yield {
-        node_id: _wrap_in_task(
-            named_values={
-                **cfg,
-                KEY_SECURE_ID: nid2sid[node_id],
-            }
-        )
-        for node_id in surviving_node_ids
-    }
-    # Receive public keys from clients and build the dict
-    node_messages = yield
-    surviving_node_ids = [node_id for node_id in node_messages]
-
-    if LOG_EXPLAIN:
-        print(f"Received public keys from {len(surviving_node_ids)} clients.")
-
-    sid2public_keys = {}
-    for node_id, task in node_messages.items():
-        key_dict = _get_from_task(task)
-        pk1, pk2 = key_dict[KEY_PUBLIC_KEY_1], key_dict[KEY_PUBLIC_KEY_2]
-        sid2public_keys[nid2sid[node_id]] = [pk1, pk2]
-
-    """
-    =============== Share keys stage ===============   
-    """
-    if LOG_EXPLAIN:
-        print(f"\nForwarding public keys...")
-    # Broadcast public keys to clients
-    yield {
-        node_id: _wrap_in_task(
-            named_values={
-                KEY_STAGE: STAGE_SHARE_KEYS,
-                **{
-                    str(sid): value
-                    for sid, value in sid2public_keys.items()
-                    if sid in nid2neighbours[node_id]
-                },
-            }
-        )
-        for node_id in surviving_node_ids
-    }
-
-    # Receive secret key shares from clients
-    node_messages = yield
-    surviving_node_ids = [node_id for node_id in node_messages]
-    if LOG_EXPLAIN:
-        print(f"Received encrypted key shares from {len(surviving_node_ids)} clients.")
-    # Build forward packet list dictionary
-    srcs, dsts, ciphertexts = [], [], []
-    fwd_ciphertexts: Dict[int, List[bytes]] = {
-        nid2sid[nid]: [] for nid in surviving_node_ids
-    }  # dest secure ID -> list of ciphertexts
-    fwd_srcs: Dict[int, List[bytes]] = {
-        sid: [] for sid in fwd_ciphertexts
-    }  # dest secure ID -> list of src secure IDs
-    for node_id, task in node_messages.items():
-        res_dict = _get_from_task(task)
-        srcs += [nid2sid[node_id]] * len(res_dict[KEY_DESTINATION_LIST])
-        dsts += res_dict[KEY_DESTINATION_LIST]
-        ciphertexts += res_dict[KEY_CIPHERTEXT_LIST]
-
-    for src, dst, ciphertext in zip(srcs, dsts, ciphertexts):
-        if dst in fwd_ciphertexts:
-            fwd_ciphertexts[dst].append(ciphertext)
-            fwd_srcs[dst].append(src)
-
-    """
-    =============== Collect masked input stage ===============   
-    """
-
-    if LOG_EXPLAIN:
-        print(f"\nForwarding encrypted key shares and requesting masked input...")
-    # Send encrypted secret key shares to clients (plus model parameters)
-    weights = parameters_to_ndarrays(parameters)
-    yield {
-        node_id: _wrap_in_task(
-            named_values={
-                KEY_STAGE: STAGE_COLLECT_MASKED_INPUT,
-                KEY_CIPHERTEXT_LIST: fwd_ciphertexts[nid2sid[node_id]],
-                KEY_SOURCE_LIST: fwd_srcs[nid2sid[node_id]],
-                KEY_PARAMETERS: [ndarray_to_bytes(arr) for arr in weights],
-            }
-        )
-        for node_id in surviving_node_ids
-    }
-    # Collect masked input from clients
-    node_messages = yield
-    surviving_node_ids = [node_id for node_id in node_messages]
-    # Get shape of vector sent by first client
-    masked_vector = [np.array([0], dtype=int)] + get_zero_parameters(
-        [w.shape for w in weights]
-    )
-    # Add all collected masked vectors and compuute available and dropout clients set
-    dead_sids = {
-        nid2sid[node_id]
-        for node_id in sampled_node_ids
-        if node_id not in surviving_node_ids
-    }
-    active_sids = {nid2sid[node_id] for node_id in surviving_node_ids}
-    if LOG_EXPLAIN:
-        for sid in dead_sids:
-            print(f"Client {sid} dropped out.")
-    for node_id, task in node_messages.items():
-        named_values = _get_from_task(task)
-        client_masked_vec = named_values[KEY_MASKED_PARAMETERS]
-        client_masked_vec = [bytes_to_ndarray(b) for b in client_masked_vec]
-        if LOG_EXPLAIN:
-            print(f"Received {client_masked_vec[1]} from Client {nid2sid[node_id]}.")
-        masked_vector = parameters_addition(masked_vector, client_masked_vec)
-    masked_vector = parameters_mod(masked_vector, mod_range)
-    """
-    =============== Unmask stage ===============   
-    """
-
-    if LOG_EXPLAIN:
-        print("\nRequesting key shares to unmask the aggregate vector...")
-    # Send secure IDs of active and dead clients.
-    yield {
-        node_id: _wrap_in_task(
-            named_values={
-                KEY_STAGE: STAGE_UNMASK,
-                KEY_DEAD_SECURE_ID_LIST: list(dead_sids & nid2neighbours[node_id]),
-                KEY_ACTIVE_SECURE_ID_LIST: list(active_sids & nid2neighbours[node_id]),
-            }
-        )
-        for node_id in surviving_node_ids
-    }
-    # Collect key shares from clients
-    node_messages = yield
-    surviving_node_ids = [node_id for node_id in node_messages]
-    if LOG_EXPLAIN:
-        print(f"Received key shares from {len(surviving_node_ids)} clients.")
-    # Build collected shares dict
-    collected_shares_dict: Dict[int, List[bytes]] = {}
-    for nid in sampled_node_ids:
-        collected_shares_dict[nid2sid[nid]] = []
-
-    if len(surviving_node_ids) < threshold:
-        raise Exception("Not enough available clients after unmask vectors stage")
-    for _, task in node_messages.items():
-        named_values = _get_from_task(task)
-        for owner_sid, share in zip(
-            named_values[KEY_SECURE_ID_LIST], named_values[KEY_SHARE_LIST]
-        ):
-            collected_shares_dict[owner_sid].append(share)
-    # Remove mask for every client who is available before ask vectors stage,
-    # divide vector by first element
-    active_sids, dead_sids = set(active_sids), set(dead_sids)
-    for sid, share_list in collected_shares_dict.items():
-        if len(share_list) < threshold:
-            raise Exception(
-                "Not enough shares to recover secret in unmask vectors stage"
-            )
-        secret = combine_shares(share_list)
-        if sid in active_sids:
-            # The seed for PRG is the private mask seed of an active client.
-            private_mask = pseudo_rand_gen(
-                secret, mod_range, get_parameters_shape(masked_vector)
-            )
-            masked_vector = parameters_subtraction(masked_vector, private_mask)
-        else:
-            # The seed for PRG is the secret key 1 of a dropped client.
-            neighbor_list = list(nid2neighbours[sid2nid[sid]])
-            neighbor_list.remove(sid)
-
-            for neighbor_sid in neighbor_list:
-                shared_key = generate_shared_key(
-                    bytes_to_private_key(secret),
-                    bytes_to_public_key(sid2public_keys[neighbor_sid][0]),
-                )
-                pairwise_mask = pseudo_rand_gen(
-                    shared_key, mod_range, get_parameters_shape(masked_vector)
-                )
-                if sid > neighbor_sid:
-                    masked_vector = parameters_addition(masked_vector, pairwise_mask)
-                else:
-                    masked_vector = parameters_subtraction(masked_vector, pairwise_mask)
-    recon_parameters = parameters_mod(masked_vector, mod_range)
-    # Divide vector by number of clients who have given us their masked vector
-    # i.e. those participating in final unmask vectors stage
-    total_weights_factor, recon_parameters = factor_extract(recon_parameters)
-    if LOG_EXPLAIN:
-        print(f"Unmasked sum of vectors (quantized): {recon_parameters[0]}")
-    # recon_parameters = parameters_divide(recon_parameters, total_weights_factor)
-    aggregated_vector = dequantize(
-        quantized_parameters=recon_parameters,
-        clipping_range=clipping_range,
-        target_range=target_range,
-    )
-    aggregated_vector[0] -= (len(active_sids) - 1) * clipping_range
-    if LOG_EXPLAIN:
-        print(f"Unmasked sum of vectors (dequantized): {aggregated_vector[0]}")
-        print(
-            f"Aggregate vector using FedAvg: {aggregated_vector[0] / len(active_sids)}"
-        )
-        print(
-            "########################### Secure Aggregation End ###########################\n\n"
-        )
-    aggregated_parameters = ndarrays_to_parameters(aggregated_vector)
-    # Update model parameters
-    parameters.tensors = aggregated_parameters.tensors
-    parameters.tensor_type = aggregated_parameters.tensor_type
-
-
-def _wrap_workflow_with_sec_agg(
-    parameters: Parameters, sampled_node_ids: List[int]
-) -> Generator[Dict[int, Task], Dict[int, Task], None]:
-    return workflow_with_sec_agg(
-        parameters, sampled_node_ids, sec_agg_config=_secure_aggregation_configuration
-    )
diff --git a/examples/simulation-pytorch/README.md b/examples/simulation-pytorch/README.md
index 11b7a3364376..93f9e1acbac7 100644
--- a/examples/simulation-pytorch/README.md
+++ b/examples/simulation-pytorch/README.md
@@ -1,6 +1,6 @@
 # Flower Simulation example using PyTorch
 
-This introductory example uses the simulation capabilities of Flower to simulate a large number of clients on a single machine. Take a look at the [Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html) for a deep dive into how Flower simulation works. This example uses [Flower Datasets](https://flower.dev/docs/datasets/) to download, partition and preprocess the MNIST dataset. This examples uses 100 clients by default.
+This introductory example uses the simulation capabilities of Flower to simulate a large number of clients on a single machine. Take a look at the [Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) for a deep dive into how Flower simulation works. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the MNIST dataset. This examples uses 100 clients by default.
 
 ## Running the example (via Jupyter Notebook)
 
@@ -54,17 +54,13 @@ Write the command below in your terminal to install the dependencies according t
 pip install -r requirements.txt
 ```
 
-### Run Federated Learning Example
+### Run with `start_simulation()`
 
-```bash
-# You can run the example without activating your environemnt
-poetry run python sim.py
+Ensure you have activated your environment then:
 
-# Or by first activating it
-poetry shell
+```bash
 # and then run the example
 python sim.py
-# you can exit your environment by typing "exit"
 ```
 
 You can adjust the CPU/GPU resources you assign to each of your virtual clients. By default, your clients will only use 1xCPU core. For example:
@@ -73,10 +69,29 @@ You can adjust the CPU/GPU resources you assign to each of your virtual clients.
 # Will assign 2xCPUs to each client
 python sim.py --num_cpus=2
 
-# Will assign 2xCPUs and 20% of the GPU's VRAM to each client
-# This means that you can have 5 concurrent clients on each GPU
+# Will assign 2xCPUs and 25% of the GPU's VRAM to each client
+# This means that you can have 4 concurrent clients on each GPU
 # (assuming you have enough CPUs)
-python sim.py --num_cpus=2 --num_gpus=0.2
+python sim.py --num_cpus=2 --num_gpus=0.25
+```
+
+### Run with Flower Next (preview)
+
+Ensure you have activated your environment, then execute the command below. All `ClientApp` instances will run on CPU but the `ServerApp` will run on the GPU if one is available. Note that this is the case because the `Simulation Engine` only exposes certain resources to the `ClientApp` (based on the `client_resources` in `--backend-config`).
+
+```bash
+# Run with the default backend-config.
+# `--server-app` points to the `server` object in the sim.py file in this example.
+# `--client-app` points to the `client` object in the sim.py file in this example.
+flower-simulation --client-app=sim:client --server-app=sim:server --num-supernodes=100
+```
+
+You can change the default resources assigned to each `ClientApp` by means of the `--backend-config` argument:
+
+```bash
+# Tells the VCE to reserve 2x CPUs and 25% of available VRAM for each ClientApp
+flower-simulation --client-app=sim:client --server-app=sim:server --num-supernodes=100 \
+    --backend-config='{"client_resources": {"num_cpus":2, "num_gpus":0.25}}'
 ```
 
-Take a look at the [Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html) for more details on how you can customise your simulation.
+Take a look at the [Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) for more details on how you can customise your simulation.
diff --git a/examples/simulation-pytorch/pyproject.toml b/examples/simulation-pytorch/pyproject.toml
index 07918c0cd17c..5978c17f2c60 100644
--- a/examples/simulation-pytorch/pyproject.toml
+++ b/examples/simulation-pytorch/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "simulation-pytorch"
 version = "0.1.0"
 description = "Federated Learning Simulation with Flower and PyTorch"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
@@ -17,4 +17,3 @@ torchvision = "0.16.1"
 
 [tool.poetry.group.dev.dependencies]
 ipykernel = "^6.27.0"
-
diff --git a/examples/simulation-pytorch/sim.ipynb b/examples/simulation-pytorch/sim.ipynb
index 508630cf9422..6dda1ef9319d 100644
--- a/examples/simulation-pytorch/sim.ipynb
+++ b/examples/simulation-pytorch/sim.ipynb
@@ -30,7 +30,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will be using the _simulation_ mode in Flower, which allows you to run a large number of clients without the overheads of manually managing devices. This is achieved via the [Virtual Client Engine](https://flower.dev/docs/framework/how-to-run-simulations.html) in Flower. With simulation, you can dynamically scale your experiments whether you run the code on your laptop, a machine with a single GPU, a server with multiple GPUs os even on a cluster with multiple servers. The `Virtual Client Engine` handles everything transparently and it allows you to specify how many resources (e.g. CPU cores, GPU VRAM) should be assigned to each virtual client."
+    "We will be using the _simulation_ mode in Flower, which allows you to run a large number of clients without the overheads of manually managing devices. This is achieved via the [Virtual Client Engine](https://flower.ai/docs/framework/how-to-run-simulations.html) in Flower. With simulation, you can dynamically scale your experiments whether you run the code on your laptop, a machine with a single GPU, a server with multiple GPUs os even on a cluster with multiple servers. The `Virtual Client Engine` handles everything transparently and it allows you to specify how many resources (e.g. CPU cores, GPU VRAM) should be assigned to each virtual client."
    ]
   },
   {
@@ -178,7 +178,7 @@
     "\n",
     "To start designing a Federated Learning pipeline we need to meet one of the key properties in FL: each client has its own data partition. To accomplish this with the MNIST dataset, we are going to generate N random partitions, where N is the total number of clients in our FL system.\n",
     "\n",
-    "We can use [Flower Datasets](https://flower.dev/docs/datasets/) to effortlessly obtain an off-the-shelf partitioned dataset or partition one that isn't pre-partitioned. Let's choose MNIST."
+    "We can use [Flower Datasets](https://flower.ai/docs/datasets/) to effortlessly obtain an off-the-shelf partitioned dataset or partition one that isn't pre-partitioned. Let's choose MNIST."
    ]
   },
   {
@@ -197,7 +197,7 @@
     "# Download MNIST dataset and partition the \"train\" partition (so one can be assigned to each client)\n",
     "mnist_fds = FederatedDataset(dataset=\"mnist\", partitioners={\"train\": NUM_CLIENTS})\n",
     "# Let's keep the test set as is, and use it to evaluate the global model on the server\n",
-    "centralized_testset = mnist_fds.load_full(\"test\")"
+    "centralized_testset = mnist_fds.load_split(\"test\")"
    ]
   },
   {
@@ -290,7 +290,7 @@
     "        self.model.to(self.device)  # send model to device\n",
     "\n",
     "    def set_parameters(self, parameters):\n",
-    "        \"\"\"With the model paramters received from the server,\n",
+    "        \"\"\"With the model parameters received from the server,\n",
     "        overwrite the uninitialise model in this class with them.\"\"\"\n",
     "\n",
     "        params_dict = zip(self.model.state_dict().keys(), parameters)\n",
@@ -509,7 +509,7 @@
     "        valloader = DataLoader(valset.with_transform(apply_transforms), batch_size=32)\n",
     "\n",
     "        # Create and return client\n",
-    "        return FlowerClient(trainloader, valloader)\n",
+    "        return FlowerClient(trainloader, valloader).to_client()\n",
     "\n",
     "    return client_fn\n",
     "\n",
@@ -605,11 +605,11 @@
     "\n",
     "Get all resources you need!\n",
     "\n",
-    "* **[DOCS]** Our complete documenation: https://flower.dev/docs/\n",
-    "* **[Examples]** All Flower examples: https://flower.dev/docs/examples/\n",
+    "* **[DOCS]** Our complete documenation: https://flower.ai/docs/\n",
+    "* **[Examples]** All Flower examples: https://flower.ai/docs/examples/\n",
     "* **[VIDEO]** Our Youtube channel: https://www.youtube.com/@flowerlabs\n",
     "\n",
-    "Don't forget to join our Slack channel: https://flower.dev/join-slack/\n"
+    "Don't forget to join our Slack channel: https://flower.ai/join-slack/\n"
    ]
   }
  ],
diff --git a/examples/simulation-pytorch/sim.py b/examples/simulation-pytorch/sim.py
index 68d9426e83ab..6fb750f2e59c 100644
--- a/examples/simulation-pytorch/sim.py
+++ b/examples/simulation-pytorch/sim.py
@@ -29,9 +29,9 @@
     default=0.0,
     help="Ratio of GPU memory to assign to a virtual client",
 )
-parser.add_argument("--num_rounds", type=int, default=10, help="Number of FL rounds.")
 
 NUM_CLIENTS = 100
+NUM_ROUNDS = 10
 
 
 # Flower client, adapted from Pytorch quickstart example
@@ -104,7 +104,7 @@ def client_fn(cid: str) -> fl.client.Client:
         valset = valset.with_transform(apply_transforms)
 
         # Create and return client
-        return FlowerClient(trainset, valset)
+        return FlowerClient(trainset, valset).to_client()
 
     return client_fn
 
@@ -167,28 +167,36 @@ def evaluate(
     return evaluate
 
 
+# Download MNIST dataset and partition it
+mnist_fds = FederatedDataset(dataset="mnist", partitioners={"train": NUM_CLIENTS})
+centralized_testset = mnist_fds.load_split("test")
+
+# Configure the strategy
+strategy = fl.server.strategy.FedAvg(
+    fraction_fit=0.1,  # Sample 10% of available clients for training
+    fraction_evaluate=0.05,  # Sample 5% of available clients for evaluation
+    min_available_clients=10,
+    on_fit_config_fn=fit_config,
+    evaluate_metrics_aggregation_fn=weighted_average,  # Aggregate federated metrics
+    evaluate_fn=get_evaluate_fn(centralized_testset),  # Global evaluation function
+)
+
+# ClientApp for Flower-Next
+client = fl.client.ClientApp(
+    client_fn=get_client_fn(mnist_fds),
+)
+
+# ServerApp for Flower-Next
+server = fl.server.ServerApp(
+    config=fl.server.ServerConfig(num_rounds=NUM_ROUNDS),
+    strategy=strategy,
+)
+
+
 def main():
     # Parse input arguments
     args = parser.parse_args()
 
-    # Download MNIST dataset and partition it
-    mnist_fds = FederatedDataset(dataset="mnist", partitioners={"train": NUM_CLIENTS})
-    centralized_testset = mnist_fds.load_full("test")
-
-    # Configure the strategy
-    strategy = fl.server.strategy.FedAvg(
-        fraction_fit=0.1,  # Sample 10% of available clients for training
-        fraction_evaluate=0.05,  # Sample 5% of available clients for evaluation
-        min_fit_clients=10,  # Never sample less than 10 clients for training
-        min_evaluate_clients=5,  # Never sample less than 5 clients for evaluation
-        min_available_clients=int(
-            NUM_CLIENTS * 0.75
-        ),  # Wait until at least 75 clients are available
-        on_fit_config_fn=fit_config,
-        evaluate_metrics_aggregation_fn=weighted_average,  # Aggregate federated metrics
-        evaluate_fn=get_evaluate_fn(centralized_testset),  # Global evaluation function
-    )
-
     # Resources to be assigned to each virtual client
     client_resources = {
         "num_cpus": args.num_cpus,
@@ -200,7 +208,7 @@ def main():
         client_fn=get_client_fn(mnist_fds),
         num_clients=NUM_CLIENTS,
         client_resources=client_resources,
-        config=fl.server.ServerConfig(num_rounds=args.num_rounds),
+        config=fl.server.ServerConfig(num_rounds=NUM_ROUNDS),
         strategy=strategy,
         actor_kwargs={
             "on_actor_init_fn": disable_progress_bar  # disable tqdm on each actor/process spawning virtual clients
diff --git a/examples/simulation-tensorflow/README.md b/examples/simulation-tensorflow/README.md
index f0d94f343d37..917d7b34c7af 100644
--- a/examples/simulation-tensorflow/README.md
+++ b/examples/simulation-tensorflow/README.md
@@ -1,6 +1,6 @@
 # Flower Simulation example using TensorFlow/Keras
 
-This introductory example uses the simulation capabilities of Flower to simulate a large number of clients on a single machine. Take a look at the [Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html) for a deep dive into how Flower simulation works. This example uses [Flower Datasets](https://flower.dev/docs/datasets/) to download, partition and preprocess the MNIST dataset. This examples uses 100 clients by default.
+This introductory example uses the simulation capabilities of Flower to simulate a large number of clients on a single machine. Take a look at the [Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) for a deep dive into how Flower simulation works. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the MNIST dataset. This examples uses 100 clients by default.
 
 ## Running the example (via Jupyter Notebook)
 
@@ -53,29 +53,46 @@ Write the command below in your terminal to install the dependencies according t
 pip install -r requirements.txt
 ```
 
-### Run Federated Learning Example
+### Run with `start_simulation()`
 
-```bash
-# You can run the example without activating your environemnt
-poetry run python sim.py
+Ensure you have activated your environment then:
 
-# Or by first activating it
-poetry shell
+```bash
 # and then run the example
 python sim.py
-# you can exit your environment by typing "exit"
 ```
 
-You can adjust the CPU/GPU resources you assign to each of your virtual clients. By default, your clients will only use 1xCPU core. For example:
+You can adjust the CPU/GPU resources you assign to each of your virtual clients. By default, your clients will only use 2xCPU core. For example:
 
 ```bash
 # Will assign 2xCPUs to each client
 python sim.py --num_cpus=2
 
-# Will assign 2xCPUs and 20% of the GPU's VRAM to each client
-# This means that you can have 5 concurrent clients on each GPU
+# Will assign 2xCPUs and 25% of the GPU's VRAM to each client
+# This means that you can have 4 concurrent clients on each GPU
 # (assuming you have enough CPUs)
-python sim.py --num_cpus=2 --num_gpus=0.2
+python sim.py --num_cpus=2 --num_gpus=0.25
+```
+
+Because TensorFlow by default maps all the available VRAM, we need to [enable GPU memory growth](https://www.tensorflow.org/guide/gpu#limiting_gpu_memory_growth), see how it is done in the example (`sim.py`) for both the "main" process (where the server/strategy runs) and for the clients (using the `actor_kwargs`)
+
+### Run with Flower Next (preview)
+
+Ensure you have activated your environment, then execute the command below. All `ClientApp` instances will run on CPU but the `ServerApp` will run on the GPU if one is available. Note that this is the case because the `Simulation Engine` only exposes certain resources to the `ClientApp` (based on the `client_resources` in `--backend-config`). For TensorFlow simulations, it is desirable to make use of TF's [memory growth](https://www.tensorflow.org/api_docs/python/tf/config/experimental/set_memory_growth) feature. You can enable that easily with the `--enable-tf-gpu-growth` flag.
+
+```bash
+# Run with the default backend-config.
+# `--server-app` points to the `server` object in the sim.py file in this example.
+# `--client-app` points to the `client` object in the sim.py file in this example.
+flower-simulation --client-app=sim:client --server-app=sim:server --num-supernodes=100 --enable-tf-gpu-growth
+```
+
+You can change the default resources assigned to each `ClientApp` using the `--backend-config` argument.
+
+```bash
+# Tells the VCE to reserve 2x CPUs and 25% of available VRAM for each ClientApp
+flower-simulation --client-app=sim:client --server-app=sim:server --num-supernodes=100 \
+    --backend-config='{"client_resources": {"num_cpus":2, "num_gpus":0.25}}' --enable-tf-gpu-growth
 ```
 
-Take a look at the [Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html) for more details on how you can customise your simulation.
+Take a look at the [Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) for more details on how you can customise your simulation.
diff --git a/examples/simulation-tensorflow/pyproject.toml b/examples/simulation-tensorflow/pyproject.toml
index f2e7bd3006c0..ad8cc2032b2d 100644
--- a/examples/simulation-tensorflow/pyproject.toml
+++ b/examples/simulation-tensorflow/pyproject.toml
@@ -6,11 +6,11 @@ build-backend = "poetry.core.masonry.api"
 name = "simulation-tensorflow"
 version = "0.1.0"
 description = "Federated Learning Simulation with Flower and Tensorflow"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
 flwr = { extras = ["simulation"], version = ">=1.0,<2.0" }
 flwr-datasets = { extras = ["vision"], version = ">=0.0.2,<1.0.0" }
-tensorflow = {version = "^2.9.1, !=2.11.1", markers="platform_machine == 'x86_64'"}
-tensorflow-macos = {version = "^2.9.1, !=2.11.1", markers="sys_platform == 'darwin' and platform_machine == 'arm64'"}
+tensorflow = { version = "^2.9.1, !=2.11.1", markers = "platform_machine == 'x86_64'" }
+tensorflow-macos = { version = "^2.9.1, !=2.11.1", markers = "sys_platform == 'darwin' and platform_machine == 'arm64'" }
diff --git a/examples/simulation-tensorflow/sim.ipynb b/examples/simulation-tensorflow/sim.ipynb
index 575b437018f3..797e2dcc603e 100644
--- a/examples/simulation-tensorflow/sim.ipynb
+++ b/examples/simulation-tensorflow/sim.ipynb
@@ -189,7 +189,7 @@
     "        )\n",
     "\n",
     "        # Create and return client\n",
-    "        return FlowerClient(trainset, valset)\n",
+    "        return FlowerClient(trainset, valset).to_client()\n",
     "\n",
     "    return client_fn\n",
     "\n",
@@ -232,7 +232,7 @@
     "\n",
     "Flower comes with 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 - actually starts the simulation.\n",
     "\n",
-    "We can use [Flower Datasets](https://flower.dev/docs/datasets/) to effortlessly obtain an off-the-shelf partitioned dataset or partition one that isn't pre-partitioned. Let's choose MNIST."
+    "We can use [Flower Datasets](https://flower.ai/docs/datasets/) to effortlessly obtain an off-the-shelf partitioned dataset or partition one that isn't pre-partitioned. Let's choose MNIST."
    ]
   },
   {
@@ -247,7 +247,7 @@
     "# Download MNIST dataset and partition it\n",
     "mnist_fds = FederatedDataset(dataset=\"mnist\", partitioners={\"train\": NUM_CLIENTS})\n",
     "# Get the whole test set for centralised evaluation\n",
-    "centralized_testset = mnist_fds.load_full(\"test\").to_tf_dataset(\n",
+    "centralized_testset = mnist_fds.load_split(\"test\").to_tf_dataset(\n",
     "    columns=\"image\", label_cols=\"label\", batch_size=64\n",
     ")\n",
     "\n",
@@ -323,11 +323,11 @@
     "\n",
     "Get all resources you need!\n",
     "\n",
-    "* **[DOCS]** Our complete documenation: https://flower.dev/docs/\n",
-    "* **[Examples]** All Flower examples: https://flower.dev/docs/examples/\n",
+    "* **[DOCS]** Our complete documenation: https://flower.ai/docs/\n",
+    "* **[Examples]** All Flower examples: https://flower.ai/docs/examples/\n",
     "* **[VIDEO]** Our Youtube channel: https://www.youtube.com/@flowerlabs\n",
     "\n",
-    "Don't forget to join our Slack channel: https://flower.dev/join-slack/"
+    "Don't forget to join our Slack channel: https://flower.ai/join-slack/"
    ]
   }
  ],
diff --git a/examples/simulation-tensorflow/sim.py b/examples/simulation-tensorflow/sim.py
index 490e25fe8c8d..e94e5ec96850 100644
--- a/examples/simulation-tensorflow/sim.py
+++ b/examples/simulation-tensorflow/sim.py
@@ -1,5 +1,4 @@
 import os
-import math
 import argparse
 from typing import Dict, List, Tuple
 
@@ -29,9 +28,9 @@
     default=0.0,
     help="Ratio of GPU memory to assign to a virtual client",
 )
-parser.add_argument("--num_rounds", type=int, default=10, help="Number of FL rounds.")
 
 NUM_CLIENTS = 100
+NUM_ROUNDS = 10
 VERBOSE = 0
 
 
@@ -94,7 +93,7 @@ def client_fn(cid: str) -> fl.client.Client:
         )
 
         # Create and return client
-        return FlowerClient(trainset, valset)
+        return FlowerClient(trainset, valset).to_client()
 
     return client_fn
 
@@ -129,30 +128,39 @@ def evaluate(
     return evaluate
 
 
+# Download MNIST dataset and partition it
+mnist_fds = FederatedDataset(dataset="mnist", partitioners={"train": NUM_CLIENTS})
+# Get the whole test set for centralised evaluation
+centralized_testset = mnist_fds.load_split("test").to_tf_dataset(
+    columns="image", label_cols="label", batch_size=64
+)
+
+# Create FedAvg strategy
+strategy = fl.server.strategy.FedAvg(
+    fraction_fit=0.1,  # Sample 10% of available clients for training
+    fraction_evaluate=0.05,  # Sample 5% of available clients for evaluation
+    min_fit_clients=10,  # Never sample less than 10 clients for training
+    evaluate_metrics_aggregation_fn=weighted_average,  # aggregates federated metrics
+    evaluate_fn=get_evaluate_fn(centralized_testset),  # global evaluation function
+)
+
+
+# ClientApp for Flower-Next
+client = fl.client.ClientApp(
+    client_fn=get_client_fn(mnist_fds),
+)
+
+# ServerApp for Flower-Next
+server = fl.server.ServerApp(
+    config=fl.server.ServerConfig(num_rounds=NUM_ROUNDS),
+    strategy=strategy,
+)
+
+
 def main() -> None:
     # Parse input arguments
     args = parser.parse_args()
 
-    # Download MNIST dataset and partition it
-    mnist_fds = FederatedDataset(dataset="mnist", partitioners={"train": NUM_CLIENTS})
-    # Get the whole test set for centralised evaluation
-    centralized_testset = mnist_fds.load_full("test").to_tf_dataset(
-        columns="image", label_cols="label", batch_size=64
-    )
-
-    # Create FedAvg strategy
-    strategy = fl.server.strategy.FedAvg(
-        fraction_fit=0.1,  # Sample 10% of available clients for training
-        fraction_evaluate=0.05,  # Sample 5% of available clients for evaluation
-        min_fit_clients=10,  # Never sample less than 10 clients for training
-        min_evaluate_clients=5,  # Never sample less than 5 clients for evaluation
-        min_available_clients=int(
-            NUM_CLIENTS * 0.75
-        ),  # Wait until at least 75 clients are available
-        evaluate_metrics_aggregation_fn=weighted_average,  # aggregates federated metrics
-        evaluate_fn=get_evaluate_fn(centralized_testset),  # global evaluation function
-    )
-
     # With a dictionary, you tell Flower's VirtualClientEngine that each
     # client needs exclusive access to these many resources in order to run
     client_resources = {
@@ -164,7 +172,7 @@ def main() -> None:
     fl.simulation.start_simulation(
         client_fn=get_client_fn(mnist_fds),
         num_clients=NUM_CLIENTS,
-        config=fl.server.ServerConfig(num_rounds=args.num_rounds),
+        config=fl.server.ServerConfig(NUM_ROUNDS),
         strategy=strategy,
         client_resources=client_resources,
         actor_kwargs={
diff --git a/examples/sklearn-logreg-mnist/README.md b/examples/sklearn-logreg-mnist/README.md
index 79ed63a64233..12b1a5e3bc1a 100644
--- a/examples/sklearn-logreg-mnist/README.md
+++ b/examples/sklearn-logreg-mnist/README.md
@@ -1,7 +1,7 @@
 # Flower Example using scikit-learn
 
 This example of Flower uses `scikit-learn`'s `LogisticRegression` model to train a federated learning system. It will help you understand how to adapt Flower for use with `scikit-learn`.
-Running this example in itself is quite easy.
+Running this example in itself is quite easy. This example uses [Flower Datasets](https://flower.ai/docs/datasets/) to download, partition and preprocess the MNIST dataset.
 
 ## Project Setup
 
@@ -57,18 +57,24 @@ Afterwards you are ready to start the Flower server as well as the clients. You
 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:
+Now you are ready to start the Flower clients which will participate in the learning. To do so simply open two or more terminals and run the following command in each:
+
+Start client 1 in the first terminal:
 
 ```shell
-poetry run python3 client.py
+python3 client.py --partition-id 0 # or any integer in {0-9}
 ```
 
-Alternatively you can run all of it in one shell as follows:
+Start client 2 in the second terminal:
 
 ```shell
-poetry run python3 server.py &
-poetry run python3 client.py &
-poetry run python3 client.py
+python3 client.py --partition-id 1 # or any integer in {0-9}
+```
+
+Alternatively, you can run all of it in one shell as follows:
+
+```bash
+bash run.sh
 ```
 
 You will see that Flower is starting a federated training.
diff --git a/examples/sklearn-logreg-mnist/client.py b/examples/sklearn-logreg-mnist/client.py
index dbf0f2f462a7..1e9349df1acc 100644
--- a/examples/sklearn-logreg-mnist/client.py
+++ b/examples/sklearn-logreg-mnist/client.py
@@ -1,19 +1,35 @@
+import argparse
 import warnings
-import flwr as fl
-import numpy as np
 
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import log_loss
 
+import flwr as fl
 import utils
+from flwr_datasets import FederatedDataset
 
 if __name__ == "__main__":
-    # Load MNIST dataset from https://www.openml.org/d/554
-    (X_train, y_train), (X_test, y_test) = utils.load_mnist()
+    N_CLIENTS = 10
+
+    parser = argparse.ArgumentParser(description="Flower")
+    parser.add_argument(
+        "--partition-id",
+        type=int,
+        choices=range(0, N_CLIENTS),
+        required=True,
+        help="Specifies the artificial data partition",
+    )
+    args = parser.parse_args()
+    partition_id = args.partition_id
+
+    # Load the partition data
+    fds = FederatedDataset(dataset="mnist", partitioners={"train": N_CLIENTS})
 
-    # Split train set into 10 partitions and randomly use one for training.
-    partition_id = np.random.choice(10)
-    (X_train, y_train) = utils.partition(X_train, y_train, 10)[partition_id]
+    dataset = fds.load_partition(partition_id, "train").with_format("numpy")
+    X, y = dataset["image"].reshape((len(dataset), -1)), dataset["label"]
+    # Split the on edge data: 80% train, 20% test
+    X_train, X_test = X[: int(0.8 * len(X))], X[int(0.8 * len(X)) :]
+    y_train, y_test = y[: int(0.8 * len(y))], y[int(0.8 * len(y)) :]
 
     # Create LogisticRegression Model
     model = LogisticRegression(
@@ -46,4 +62,6 @@ def evaluate(self, parameters, config):  # type: ignore
             return loss, len(X_test), {"accuracy": accuracy}
 
     # Start Flower client
-    fl.client.start_numpy_client(server_address="0.0.0.0:8080", client=MnistClient())
+    fl.client.start_client(
+        server_address="0.0.0.0:8080", client=MnistClient().to_client()
+    )
diff --git a/examples/sklearn-logreg-mnist/pyproject.toml b/examples/sklearn-logreg-mnist/pyproject.toml
index 7c13b3f3d492..58cc5ca4a02e 100644
--- a/examples/sklearn-logreg-mnist/pyproject.toml
+++ b/examples/sklearn-logreg-mnist/pyproject.toml
@@ -7,13 +7,13 @@ name = "sklearn-mnist"
 version = "0.1.0"
 description = "Federated learning with scikit-learn and Flower"
 authors = [
-    "The Flower Authors <hello@flower.dev>",
-    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>"
+    "The Flower Authors <hello@flower.ai>",
+    "Kaushik Amar Das <kaushik.das@iiitg.ac.in>",
 ]
 
 [tool.poetry.dependencies]
 python = "^3.8"
-flwr = "^1.0.0"
+flwr = ">=1.0,<2.0"
 # flwr = { path = "../../", develop = true }  # Development
+flwr-datasets = { extras = ["vision"], version = ">=0.0.2,<1.0.0" }
 scikit-learn = "^1.1.1"
-openml = "^0.12.2"
diff --git a/examples/sklearn-logreg-mnist/requirements.txt b/examples/sklearn-logreg-mnist/requirements.txt
index eec2e1a3c4bd..50da9ace3630 100644
--- a/examples/sklearn-logreg-mnist/requirements.txt
+++ b/examples/sklearn-logreg-mnist/requirements.txt
@@ -1,4 +1,4 @@
-flwr~=1.4.0
+flwr>=1.0, <2.0
+flwr-datasets[vision]>=0.0.2, <1.0.0
 numpy~=1.21.1
-openml~=0.13.1
 scikit_learn~=1.2.2
diff --git a/examples/sklearn-logreg-mnist/run.sh b/examples/sklearn-logreg-mnist/run.sh
index c64f362086aa..f770ca05f8f4 100755
--- a/examples/sklearn-logreg-mnist/run.sh
+++ b/examples/sklearn-logreg-mnist/run.sh
@@ -1,15 +1,17 @@
 #!/bin/bash
+set -e
+cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/
 
 echo "Starting server"
 python server.py &
 sleep 3  # Sleep for 3s to give the server enough time to start
 
-for i in `seq 0 1`; do
+for i in $(seq 0 1); do
     echo "Starting client $i"
-    python client.py &
+    python client.py --partition-id "${i}" &
 done
 
 # This will allow you to use CTRL+C to stop all background processes
-trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM
+trap 'trap - SIGTERM && kill -- -$$' SIGINT SIGTERM
 # Wait for all background processes to complete
 wait
diff --git a/examples/sklearn-logreg-mnist/server.py b/examples/sklearn-logreg-mnist/server.py
index 77e7a89dd668..e0af91fabcee 100644
--- a/examples/sklearn-logreg-mnist/server.py
+++ b/examples/sklearn-logreg-mnist/server.py
@@ -4,6 +4,8 @@
 from sklearn.linear_model import LogisticRegression
 from typing import Dict
 
+from flwr_datasets import FederatedDataset
+
 
 def fit_round(server_round: int) -> Dict:
     """Send round number to client."""
@@ -14,7 +16,9 @@ def get_evaluate_fn(model: LogisticRegression):
     """Return an evaluation function for server-side evaluation."""
 
     # Load test data here to avoid the overhead of doing it in `evaluate` itself
-    _, (X_test, y_test) = utils.load_mnist()
+    fds = FederatedDataset(dataset="mnist", partitioners={"train": 10})
+    dataset = fds.load_split("test").with_format("numpy")
+    X_test, y_test = dataset["image"].reshape((len(dataset), -1)), dataset["label"]
 
     # The `evaluate` function will be called after every round
     def evaluate(server_round, parameters: fl.common.NDArrays, config):
diff --git a/examples/sklearn-logreg-mnist/utils.py b/examples/sklearn-logreg-mnist/utils.py
index 6a6d6c12ac73..b279a0d1a4b3 100644
--- a/examples/sklearn-logreg-mnist/utils.py
+++ b/examples/sklearn-logreg-mnist/utils.py
@@ -1,16 +1,11 @@
-from typing import Tuple, Union, List
 import numpy as np
 from sklearn.linear_model import LogisticRegression
-import openml
 
-XY = Tuple[np.ndarray, np.ndarray]
-Dataset = Tuple[XY, XY]
-LogRegParams = Union[XY, Tuple[np.ndarray]]
-XYList = List[XY]
+from flwr.common import NDArrays
 
 
-def get_model_parameters(model: LogisticRegression) -> LogRegParams:
-    """Returns the paramters of a sklearn LogisticRegression model."""
+def get_model_parameters(model: LogisticRegression) -> NDArrays:
+    """Returns the parameters of a sklearn LogisticRegression model."""
     if model.fit_intercept:
         params = [
             model.coef_,
@@ -23,9 +18,7 @@ def get_model_parameters(model: LogisticRegression) -> LogRegParams:
     return params
 
 
-def set_model_params(
-    model: LogisticRegression, params: LogRegParams
-) -> LogisticRegression:
+def set_model_params(model: LogisticRegression, params: NDArrays) -> LogisticRegression:
     """Sets the parameters of a sklean LogisticRegression model."""
     model.coef_ = params[0]
     if model.fit_intercept:
@@ -47,32 +40,3 @@ def set_initial_params(model: LogisticRegression):
     model.coef_ = np.zeros((n_classes, n_features))
     if model.fit_intercept:
         model.intercept_ = np.zeros((n_classes,))
-
-
-def load_mnist() -> Dataset:
-    """Loads the MNIST dataset using OpenML.
-
-    OpenML dataset link: https://www.openml.org/d/554
-    """
-    mnist_openml = openml.datasets.get_dataset(554)
-    Xy, _, _, _ = mnist_openml.get_data(dataset_format="array")
-    X = Xy[:, :-1]  # the last column contains labels
-    y = Xy[:, -1]
-    # First 60000 samples consist of the train set
-    x_train, y_train = X[:60000], y[:60000]
-    x_test, y_test = X[60000:], y[60000:]
-    return (x_train, y_train), (x_test, y_test)
-
-
-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))
-    )
diff --git a/examples/vertical-fl/README.md b/examples/vertical-fl/README.md
index d5ab0ab9c30d..78588180d3d6 100644
--- a/examples/vertical-fl/README.md
+++ b/examples/vertical-fl/README.md
@@ -295,7 +295,7 @@ class ServerModel(nn.Module):
 
 It comprises a single linear layer that accepts the concatenated outputs from
 all client models as its input. The number of inputs to this layer equals the
-total number of outputs from the client models ( $3 \times 4 = 12$ ). After processing
+total number of outputs from the client models (3 x 4 = 12). After processing
 these inputs, the linear layer's output is passed through a sigmoid activation
 function (`nn.Sigmoid()`), which maps the result to a `(0, 1)` range, providing
 a probability score indicative of the likelihood of survival.
diff --git a/examples/vertical-fl/pyproject.toml b/examples/vertical-fl/pyproject.toml
index 14771c70062f..19dcd0e7a842 100644
--- a/examples/vertical-fl/pyproject.toml
+++ b/examples/vertical-fl/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "vertical-fl"
 version = "0.1.0"
 description = "PyTorch Vertical FL with Flower"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
diff --git a/examples/vit-finetune/README.md b/examples/vit-finetune/README.md
new file mode 100644
index 000000000000..ac1652acf02d
--- /dev/null
+++ b/examples/vit-finetune/README.md
@@ -0,0 +1,94 @@
+# Federated finetuning of a ViT
+
+This example shows how to use Flower's Simulation Engine to federate the finetuning of a Vision Transformer ([ViT-Base-16](https://pytorch.org/vision/main/models/generated/torchvision.models.vit_b_16.html#torchvision.models.vit_b_16)) that has been pretrained on ImageNet. To keep things simple we'll be finetuning it to [Oxford Flower-102](https://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html) datasset, creating 20 partitions using [Flower Datasets](https://flower.ai/docs/datasets/). We'll be finetuning just the exit `head` of the ViT, this means that the training is not that costly and each client requires just ~1GB of VRAM (for a batch size of 32 images).
+
+## Running the example
+
+If you haven't cloned the Flower repository already you might want to clone code example and discard the rest. 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/vit-finetune . && rm -rf flower && cd vit-finetune
+```
+
+This will create a new directory called `vit-finetune` containing the following files:
+
+```
+-- README.md         <- Your're reading this right now
+-- main.py           <- Main file that launches the simulation
+-- client.py         <- Contains Flower client code and ClientApp
+-- server.py         <- Contains Flower server code and ServerApp
+-- model.py          <- Defines model and train/eval functions
+-- dataset.py        <- Downloads, partitions and processes dataset
+-- pyproject.toml    <- Example dependencies, installable using Poetry
+-- requirements.txt  <- Example dependencies, installable using pip
+```
+
+### Installing Dependencies
+
+Project dependencies (such as `torch` 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
+
+```shell
+poetry install
+poetry shell
+```
+
+#### pip
+
+With an activated environemnt, install the dependencies for this example:
+
+```shell
+pip install -r requirements.txt
+```
+
+### Run with `start_simulation()`
+
+Running the example is quite straightforward. You can control the number of rounds `--num-rounds` (which defaults to 20).
+
+```bash
+python main.py
+```
+
+![](_static/central_evaluation.png)
+
+Running the example as-is on an RTX 3090Ti should take ~15s/round running 5 clients in parallel (plus the _global model_ during centralized evaluation stages) in a single GPU. Note that more clients could fit in VRAM, but since the GPU utilization is high (99%-100%) we are probably better off not doing that (at least in this case).
+
+You can adjust the `client_resources` passed to `start_simulation()` so more/less clients run at the same time in the GPU. Take a look at the [Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) for more details on how you can customise your simulation.
+
+```bash
++---------------------------------------------------------------------------------------+
+| NVIDIA-SMI 535.161.07             Driver Version: 535.161.07   CUDA Version: 12.2     |
+|-----------------------------------------+----------------------+----------------------+
+| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
+|                                         |                      |               MIG M. |
+|=========================================+======================+======================|
+|   0  NVIDIA GeForce RTX 3090 Ti     Off | 00000000:0B:00.0 Off |                  Off |
+| 44%   74C    P2             441W / 450W |   7266MiB / 24564MiB |    100%      Default |
+|                                         |                      |                  N/A |
++-----------------------------------------+----------------------+----------------------+
+
++---------------------------------------------------------------------------------------+
+| Processes:                                                                            |
+|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |
+|        ID   ID                                                             Usage      |
+|=======================================================================================|
+|    0   N/A  N/A    173812      C   python                                     1966MiB |
+|    0   N/A  N/A    174510      C   ray::ClientAppActor.run                    1056MiB |
+|    0   N/A  N/A    174512      C   ray::ClientAppActor.run                    1056MiB |
+|    0   N/A  N/A    174513      C   ray::ClientAppActor.run                    1056MiB |
+|    0   N/A  N/A    174514      C   ray::ClientAppActor.run                    1056MiB |
+|    0   N/A  N/A    174516      C   ray::ClientAppActor.run                    1056MiB |
++---------------------------------------------------------------------------------------+
+```
+
+### Run with Flower Next (preview)
+
+```bash
+flower-simulation \
+    --client-app=client:app \
+    --server-app=server:app \
+    --num-supernodes=20 \
+    --backend-config='{"client_resources": {"num_cpus":4, "num_gpus":0.25}}'
+```
diff --git a/examples/vit-finetune/_static/central_evaluation.png b/examples/vit-finetune/_static/central_evaluation.png
new file mode 100644
index 000000000000..d0d53ba353a1
Binary files /dev/null and b/examples/vit-finetune/_static/central_evaluation.png differ
diff --git a/examples/vit-finetune/client.py b/examples/vit-finetune/client.py
new file mode 100644
index 000000000000..68d98926feeb
--- /dev/null
+++ b/examples/vit-finetune/client.py
@@ -0,0 +1,82 @@
+import torch
+from torch.utils.data import DataLoader
+
+import flwr
+from flwr.client import NumPyClient
+from dataset import apply_transforms, get_dataset_with_partitions
+from model import get_model, set_parameters, train
+
+
+class FedViTClient(NumPyClient):
+
+    def __init__(self, trainset):
+
+        self.trainset = trainset
+        self.model = get_model()
+
+        # Determine device
+        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.model.to(self.device)  # send model to device
+
+    def set_for_finetuning(self):
+        """Freeze all parameter except those in the final head.
+
+        Only output MLP will be updated by the client and therefore, the only part of
+        the model that will be federated (hence, communicated back to the server for
+        aggregation.)
+        """
+
+        # Disable gradients for everything
+        self.model.requires_grad_(False)
+        # Now enable just for output head
+        self.model.heads.requires_grad_(True)
+
+    def get_parameters(self, config):
+        """Get locally updated parameters."""
+        finetune_layers = self.model.heads
+        return [val.cpu().numpy() for _, val in finetune_layers.state_dict().items()]
+
+    def fit(self, parameters, config):
+        set_parameters(self.model, parameters)
+
+        # Get some info from the config
+        # Get batchsize and LR set from server
+        batch_size = config["batch_size"]
+        lr = config["lr"]
+
+        trainloader = DataLoader(
+            self.trainset, batch_size=batch_size, num_workers=2, shuffle=True
+        )
+
+        # Set optimizer
+        optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)
+        # Train locally
+        avg_train_loss = train(
+            self.model, trainloader, optimizer, epochs=1, device=self.device
+        )
+        # Return locally-finetuned part of the model
+        return (
+            self.get_parameters(config={}),
+            len(trainloader.dataset),
+            {"train_loss": avg_train_loss},
+        )
+
+
+# Downloads and partition dataset
+federated_ox_flowers, _ = get_dataset_with_partitions(num_partitions=20)
+
+
+def client_fn(cid: str):
+    """Return a FedViTClient that trains with the cid-th data partition."""
+
+    trainset_for_this_client = federated_ox_flowers.load_partition(int(cid), "train")
+
+    trainset = trainset_for_this_client.with_transform(apply_transforms)
+
+    return FedViTClient(trainset).to_client()
+
+
+# To be used with Flower Next
+app = flwr.client.ClientApp(
+    client_fn=client_fn,
+)
diff --git a/examples/vit-finetune/dataset.py b/examples/vit-finetune/dataset.py
new file mode 100644
index 000000000000..42e0af560a17
--- /dev/null
+++ b/examples/vit-finetune/dataset.py
@@ -0,0 +1,52 @@
+from torchvision.transforms import (
+    Compose,
+    Normalize,
+    ToTensor,
+    RandomResizedCrop,
+    Resize,
+    CenterCrop,
+)
+
+from flwr_datasets import FederatedDataset
+
+
+def get_dataset_with_partitions(num_partitions: int):
+    """Get Oxford Flowers datasets and partition it.
+
+    Return partitioned dataset as well as the whole test set.
+    """
+
+    # Get Oxford Flowers-102 and divide it into 20 IID partitions
+    ox_flowers_fds = FederatedDataset(
+        dataset="nelorth/oxford-flowers", partitioners={"train": num_partitions}
+    )
+
+    centralized_testset = ox_flowers_fds.load_split("test")
+    return ox_flowers_fds, centralized_testset
+
+
+def apply_eval_transforms(batch):
+    """Apply a very standard set of image transforms."""
+    transforms = Compose(
+        [
+            Resize((256, 256)),
+            CenterCrop((224, 224)),
+            ToTensor(),
+            Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ]
+    )
+    batch["image"] = [transforms(img) for img in batch["image"]]
+    return batch
+
+
+def apply_transforms(batch):
+    """Apply a very standard set of image transforms."""
+    transforms = Compose(
+        [
+            RandomResizedCrop((224, 224)),
+            ToTensor(),
+            Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ]
+    )
+    batch["image"] = [transforms(img) for img in batch["image"]]
+    return batch
diff --git a/examples/vit-finetune/main.py b/examples/vit-finetune/main.py
new file mode 100644
index 000000000000..1257246304a1
--- /dev/null
+++ b/examples/vit-finetune/main.py
@@ -0,0 +1,58 @@
+import argparse
+
+import flwr as fl
+import matplotlib.pyplot as plt
+
+from server import strategy
+from client import client_fn
+
+parser = argparse.ArgumentParser(
+    description="Finetuning of a ViT with Flower Simulation."
+)
+
+parser.add_argument(
+    "--num-rounds",
+    type=int,
+    default=20,
+    help="Number of rounds.",
+)
+
+
+def main():
+
+    args = parser.parse_args()
+
+    # To control the degree of parallelism
+    # With default settings in this example,
+    # each client should take just ~1GB of VRAM.
+    client_resources = {
+        "num_cpus": 4,
+        "num_gpus": 0.2,
+    }
+
+    # Launch simulation
+    history = fl.simulation.start_simulation(
+        client_fn=client_fn,
+        num_clients=20,
+        client_resources=client_resources,
+        config=fl.server.ServerConfig(num_rounds=args.num_rounds),
+        strategy=strategy,
+    )
+
+    print(history)
+
+    # Basic plotting
+    global_accuracy_centralised = history.metrics_centralized["accuracy"]
+    round = [int(data[0]) for data in global_accuracy_centralised]
+    acc = [100.0 * data[1] for data in global_accuracy_centralised]
+    plt.plot(round, acc)
+    plt.xticks(round)
+    plt.grid()
+    plt.ylabel("Accuracy (%)")
+    plt.xlabel("Round")
+    plt.title("Federated finetuning of ViT for Flowers-102")
+    plt.savefig("central_evaluation.png")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/vit-finetune/model.py b/examples/vit-finetune/model.py
new file mode 100644
index 000000000000..ca7dc1cd9864
--- /dev/null
+++ b/examples/vit-finetune/model.py
@@ -0,0 +1,71 @@
+from collections import OrderedDict
+
+import torch
+from torchvision.models import vit_b_16, ViT_B_16_Weights
+
+
+def get_model():
+    """Return a pretrained ViT with all layers frozen except output head."""
+
+    # Instantiate a pre-trained ViT-B on ImageNet
+    model = vit_b_16(weights=ViT_B_16_Weights.IMAGENET1K_V1)
+
+    # We're going to federated the finetuning of this model
+    # using the Oxford Flowers-102 dataset. One easy way to achieve
+    # this is by re-initializing the output block of the ViT so it
+    # outputs 102 clases instead of the default 1k
+    in_features = model.heads[-1].in_features
+    model.heads[-1] = torch.nn.Linear(in_features, 102)
+
+    # Disable gradients for everything
+    model.requires_grad_(False)
+    # Now enable just for output head
+    model.heads.requires_grad_(True)
+
+    return model
+
+
+def set_parameters(model, parameters):
+    """Apply the parameters to the model.
+
+    Recall this example only federates the head of the ViT so that's the only part of
+    the model we need to load.
+    """
+    finetune_layers = model.heads
+    params_dict = zip(finetune_layers.state_dict().keys(), parameters)
+    state_dict = OrderedDict({k: torch.tensor(v) for k, v in params_dict})
+    finetune_layers.load_state_dict(state_dict, strict=True)
+
+
+def train(net, trainloader, optimizer, epochs, device):
+    """Train the model on the training set."""
+    criterion = torch.nn.CrossEntropyLoss()
+    net.train()
+    avg_loss = 0
+    # A very standard training loop for image classification
+    for _ in range(epochs):
+        for batch in trainloader:
+            images, labels = batch["image"].to(device), batch["label"].to(device)
+            optimizer.zero_grad()
+            loss = criterion(net(images), labels)
+            avg_loss += loss.item() / labels.shape[0]
+            loss.backward()
+            optimizer.step()
+
+    return avg_loss / len(trainloader)
+
+
+def test(net, testloader, device: str):
+    """Validate the network on the entire test set."""
+    criterion = torch.nn.CrossEntropyLoss()
+    correct, loss = 0, 0.0
+    net.eval()
+    with torch.no_grad():
+        for data in testloader:
+            images, labels = data["image"].to(device), data["label"].to(device)
+            outputs = net(images)
+            loss += criterion(outputs, labels).item()
+            _, predicted = torch.max(outputs.data, 1)
+            correct += (predicted == labels).sum().item()
+    accuracy = correct / len(testloader.dataset)
+    return loss, accuracy
diff --git a/examples/vit-finetune/pyproject.toml b/examples/vit-finetune/pyproject.toml
new file mode 100644
index 000000000000..d014d6b6fb2a
--- /dev/null
+++ b/examples/vit-finetune/pyproject.toml
@@ -0,0 +1,17 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "vit-finetune"
+version = "0.1.0"
+description = "FL finetuning of a Vision Transformer with Flower."
+authors = ["The Flower Authors <hello@flower.ai>"]
+
+[tool.poetry.dependencies]
+python = ">=3.8,<3.11"
+flwr = { extras = ["simulation"], version = ">=1.0,<2.0" }
+flwr-datasets = { extras = ["vision"], version = ">=0.0.2,<1.0.0" }
+torch = "2.2.1"
+torchvision = "0.17.1"
+matplotlib = "3.8.3"
diff --git a/examples/vit-finetune/requirements.txt b/examples/vit-finetune/requirements.txt
new file mode 100644
index 000000000000..3692be0d6c2c
--- /dev/null
+++ b/examples/vit-finetune/requirements.txt
@@ -0,0 +1,5 @@
+flwr[simulation]>=1.0, <2.0
+flwr-datasets[vision]>=0.0.2, <1.0.0
+matplotlib==3.8.3
+torch==2.2.1
+torchvision==0.17.1
\ No newline at end of file
diff --git a/examples/vit-finetune/server.py b/examples/vit-finetune/server.py
new file mode 100644
index 000000000000..698bcd45cece
--- /dev/null
+++ b/examples/vit-finetune/server.py
@@ -0,0 +1,61 @@
+import torch
+from datasets import Dataset
+from torch.utils.data import DataLoader
+import flwr as fl
+
+from dataset import apply_eval_transforms, get_dataset_with_partitions
+from model import get_model, set_parameters, test
+
+
+def fit_config(server_round: int):
+    """Return a configuration with static batch size and (local) epochs."""
+    config = {
+        "lr": 0.01,  # Learning rate used by clients
+        "batch_size": 32,  # Batch size to use by clients during fit()
+    }
+    return config
+
+
+def get_evaluate_fn(
+    centralized_testset: Dataset,
+):
+    """Return an evaluation function for centralized evaluation."""
+
+    def evaluate(server_round, parameters, config):
+        """Use the entire Oxford Flowers-102 test set for evaluation."""
+
+        # Determine device
+        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+        model = get_model()
+        set_parameters(model, parameters)
+        model.to(device)
+
+        # Apply transform to dataset
+        testset = centralized_testset.with_transform(apply_eval_transforms)
+
+        testloader = DataLoader(testset, batch_size=128)
+        # Run evaluation
+        loss, accuracy = test(model, testloader, device=device)
+
+        return loss, {"accuracy": accuracy}
+
+    return evaluate
+
+
+# Downloads and partition dataset
+_, centralized_testset = get_dataset_with_partitions(num_partitions=20)
+
+# Configure the strategy
+strategy = fl.server.strategy.FedAvg(
+    fraction_fit=0.5,  # Sample 50% of available clients for training each round
+    fraction_evaluate=0.0,  # No federated evaluation
+    on_fit_config_fn=fit_config,
+    evaluate_fn=get_evaluate_fn(centralized_testset),  # Global evaluation function
+)
+
+# To be used with Flower Next
+app = fl.server.ServerApp(
+    config=fl.server.ServerConfig(num_rounds=3),
+    strategy=strategy,
+)
diff --git a/examples/whisper-federated-finetuning/README.md b/examples/whisper-federated-finetuning/README.md
index e89a09519fed..ddebe51247b2 100644
--- a/examples/whisper-federated-finetuning/README.md
+++ b/examples/whisper-federated-finetuning/README.md
@@ -110,7 +110,7 @@ An overview of the FL pipeline built with Flower for this example is illustrated
 3. Once on-site training is completed, each client sends back the (now updated) classification head to the Flower server.
 4. The Flower server aggregates (via FedAvg) the classification heads in order to obtain a new _global_ classification head. This head will be shared with clients in the next round.
 
-Flower supports two ways of doing Federated Learning: simulated and non-simulated FL. The former, managed by the [`VirtualClientEngine`](https://flower.dev/docs/framework/how-to-run-simulations.html), allows you to run large-scale workloads in a system-aware manner, that scales with the resources available on your system (whether it is a laptop, a desktop with a single GPU, or a cluster of GPU servers). The latter is better suited for settings where clients are unique devices (e.g. a server, a smart device, etc). This example shows you how to use both.
+Flower supports two ways of doing Federated Learning: simulated and non-simulated FL. The former, managed by the [`VirtualClientEngine`](https://flower.ai/docs/framework/how-to-run-simulations.html), allows you to run large-scale workloads in a system-aware manner, that scales with the resources available on your system (whether it is a laptop, a desktop with a single GPU, or a cluster of GPU servers). The latter is better suited for settings where clients are unique devices (e.g. a server, a smart device, etc). This example shows you how to use both.
 
 ### Preparing the dataset
 
@@ -147,7 +147,7 @@ INFO flwr 2023-11-08 14:03:57,557 | app.py:229 | app_fit: metrics_centralized {'
 
 With just 5 FL rounds, the global model should be reaching ~95% validation accuracy. A test accuracy of 97% can be reached with 10 rounds of FL training using the default hyperparameters. On an RTX 3090Ti, each round takes ~20-30s depending on the amount of data the clients selected in a round have.
 
-Take a look at the [Documentation](https://flower.dev/docs/framework/how-to-run-simulations.html) for more details on how you can customize your simulation.
+Take a look at the [Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html) for more details on how you can customize your simulation.
 
 ### Federated Finetuning (non-simulated)
 
diff --git a/examples/whisper-federated-finetuning/client.py b/examples/whisper-federated-finetuning/client.py
index 2bfeadfbdae6..d3bb217933f8 100644
--- a/examples/whisper-federated-finetuning/client.py
+++ b/examples/whisper-federated-finetuning/client.py
@@ -146,13 +146,13 @@ def client_fn(cid: str):
 
         return WhisperFlowerClient(
             full_train_dataset, num_classes, disable_tqdm, compile
-        )
+        ).to_client()
 
     return client_fn
 
 
-def run_client():
-    """Run clinet."""
+def main():
+    """Run client."""
 
     # Parse input arguments
     args = parser.parse_args()
@@ -174,10 +174,11 @@ def run_client():
         client_data_path=CLIENT_DATA,
     )
 
-    fl.client.start_numpy_client(
-        server_address=f"{args.server_address}:8080", client=client_fn(args.cid)
+    fl.client.start_client(
+        server_address=f"{args.server_address}:8080",
+        client=client_fn(args.cid),
     )
 
 
 if __name__ == "__main__":
-    run_client()
+    main()
diff --git a/examples/whisper-federated-finetuning/pyproject.toml b/examples/whisper-federated-finetuning/pyproject.toml
index dd5578b8b3d0..27a89578c5a0 100644
--- a/examples/whisper-federated-finetuning/pyproject.toml
+++ b/examples/whisper-federated-finetuning/pyproject.toml
@@ -6,7 +6,7 @@ build-backend = "poetry.core.masonry.api"
 name = "whisper-flower"
 version = "0.1.0"
 description = "On-device Federated Downstreaming for Speech Classification"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
@@ -16,4 +16,4 @@ tokenizers = "0.13.3"
 datasets = "2.14.6"
 soundfile = "0.12.1"
 librosa = "0.10.1"
-# this example was tested with pytorch 2.1.0
\ No newline at end of file
+# this example was tested with pytorch 2.1.0
diff --git a/examples/whisper-federated-finetuning/requirements.txt b/examples/whisper-federated-finetuning/requirements.txt
index eb4a5d7eb47b..f16b3d6993ce 100644
--- a/examples/whisper-federated-finetuning/requirements.txt
+++ b/examples/whisper-federated-finetuning/requirements.txt
@@ -3,5 +3,4 @@ tokenizers==0.13.3
 datasets==2.14.6
 soundfile==0.12.1
 librosa==0.10.1
-flwr==1.5.0
-ray==2.6.3
\ No newline at end of file
+flwr[simulation]>=1.0, <2.0
\ No newline at end of file
diff --git a/examples/xgboost-comprehensive/README.md b/examples/xgboost-comprehensive/README.md
index 11c4c3f9a08b..dc6d7e3872d6 100644
--- a/examples/xgboost-comprehensive/README.md
+++ b/examples/xgboost-comprehensive/README.md
@@ -1,7 +1,7 @@
 # Flower Example using XGBoost (Comprehensive)
 
 This example demonstrates a comprehensive federated learning setup using Flower with XGBoost.
-We use [HIGGS](https://archive.ics.uci.edu/dataset/280/higgs) dataset to perform a binary classification task.
+We use [HIGGS](https://archive.ics.uci.edu/dataset/280/higgs) dataset to perform a binary classification task. This examples uses [Flower Datasets](https://flower.ai/docs/datasets/) to retrieve, partition and preprocess the data for each Flower client.
 It differs from the [xgboost-quickstart](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart) example in the following ways:
 
 - Arguments parsers of server and clients for hyperparameters selection.
@@ -10,6 +10,32 @@ It differs from the [xgboost-quickstart](https://github.com/adap/flower/tree/mai
 - Customised partitioner type (uniform, linear, square, exponential).
 - Centralised/distributed evaluation.
 - Bagging/cyclic training methods.
+- You can run it with Flower Simulation
+
+## Training Strategies
+
+This example provides two training strategies, **bagging aggregation** and **cyclic training**.
+
+### Bagging Aggregation
+
+Bagging (bootstrap) aggregation is an ensemble meta-algorithm in machine learning,
+used for enhancing the stability and accuracy of machine learning algorithms.
+Here, we leverage this algorithm for XGBoost trees.
+
+Specifically, each client is treated as a bootstrap by random subsampling (data partitioning in FL).
+At each FL round, all clients boost a number of trees (in this example, 1 tree) based on the local bootstrap samples.
+Then, the clients' trees are aggregated on the server, and concatenates them to the global model from previous round.
+The aggregated tree ensemble is regarded as a new global model.
+
+This way, let's consider a scenario with M clients.
+Given FL round R, the bagging models consist of (M * R) trees.
+
+### Cyclic Training
+
+Cyclic XGBoost training 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.
 
 ## Project Setup
 
@@ -26,7 +52,10 @@ This will create a new directory called `xgboost-comprehensive` containing the f
 -- server.py         <- Defines the server-side logic
 -- client.py         <- Defines the client-side logic
 -- dataset.py        <- Defines the functions of data loading and partitioning
--- utils.py          <- Defines the arguments parser for clients and server
+-- utils.py          <- Defines the arguments parser and hyper-parameters
+-- client_utils.py   <- Defines the client utility functions
+-- server_utils.py   <- Defines the server utility functions
+-- sim.py            <- Example of using Flower simulation
 -- run_bagging.sh    <- Commands to run bagging experiments
 -- run_cyclic.sh     <- Commands to run cyclic experiments
 -- pyproject.toml    <- Example dependencies (if you use Poetry)
@@ -47,7 +76,7 @@ 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"
+poetry run python -c "import flwr"
 ```
 
 If you don't see any errors you're good to go!
@@ -62,44 +91,76 @@ pip install -r requirements.txt
 
 ## Run Federated Learning with XGBoost and Flower
 
+You can run this example in two ways: either by manually launching the server, and then several clients that connect to it; or by launching a Flower simulation. Both run the same workload, yielding identical results. The former is ideal for deployments on different machines, while the latter makes it easy to simulate large client cohorts in a resource-aware manner. You can read more about how Flower Simulation works in the [Documentation](https://flower.ai/docs/framework/how-to-run-simulations.html). The commands shown below assume you have activated your environment (if you decide to use Poetry, you can activate it via `poetry shell`).
+
+### Independent Client/Server Setup
+
 We have two scripts to run bagging and cyclic (client-by-client) experiments.
 The included `run_bagging.sh` or `run_cyclic.sh` will start the Flower server (using `server.py`),
 sleep for 15 seconds to ensure that the server is up,
 and then start 5 Flower clients (using `client.py`) with a small subset of the data from exponential partition distribution.
+
 You can simply start everything in a terminal as follows:
 
 ```shell
-poetry run ./run_bagging.sh
+./run_bagging.sh
 ```
 
 Or
 
 ```shell
-poetry run ./run_cyclic.sh
+./run_cyclic.sh
+```
+
+The script starts processes in the background so that you don't have to open six terminal windows.
+
+You can also run the example without the scripts. First, launch the server:
+
+```bash
+python server.py --train-method=bagging/cyclic --pool-size=N --num-clients-per-round=N
+```
+
+Then run at least two clients (each on a new terminal or computer in your network) passing different `PARTITION_ID` and all using the same `N` (denoting the total number of clients or data partitions):
+
+```bash
+python client.py --train-method=bagging/cyclic --partition-id=PARTITION_ID --num-partitions=N
+```
+
+### Flower Simulation Setup
+
+We also provide an example 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. With default arguments, each client will use 2 CPUs.
+
+To run bagging aggregation with 5 clients for 30 rounds evaluated on centralised test set:
+
+```shell
+python sim.py --train-method=bagging --pool-size=5 --num-clients-per-round=5 --num-rounds=30 --centralised-eval
 ```
 
-The script starts processes in the background so that you don't have to open eleven terminal windows.
-If you experiment with the code example and something goes wrong, simply using `CTRL + C` on Linux (or `CMD + C` on macOS) wouldn't normally kill all these processes,
-which is why the script ends with `trap "trap - SIGTERM && kill -- -$$" SIGINT SIGTERM EXIT` and `wait`.
-This simply allows you to stop the experiment using `CTRL + C` (or `CMD + C`).
-If you change the script and anything goes wrong you can still use `killall python` (or `killall python3`)
-to kill all background processes (or a more specific command if you have other Python processes running that you don't want to kill).
+To run cyclic training with 5 clients for 30 rounds evaluated on centralised test set:
 
-You can also manually run `poetry run python3 server.py --train-method=bagging/cyclic --pool-size=N --num-clients-per-round=N`
-and `poetry run python3 client.py --train-method=bagging/cyclic --node-id=NODE_ID --num-partitions=N` for as many clients as you want,
-but you have to make sure that each command is run in a different terminal window (or a different computer on the network).
+```shell
+python sim.py --train-method=cyclic --pool-size=5 --num-rounds=30 --centralised-eval-client
+```
 
 In addition, we provide more options to customise the experimental settings, including data partitioning and centralised/distributed evaluation (see `utils.py`).
-Look at the [code](https://github.com/adap/flower/tree/main/examples/xgboost-comprehensive)
-and [tutorial](https://flower.dev/docs/framework/tutorial-quickstart-xgboost.html) for a detailed explanation.
+Check the [tutorial](https://flower.ai/docs/framework/tutorial-quickstart-xgboost.html) for a detailed explanation.
 
 ### Expected Experimental Results
 
 #### Bagging aggregation experiment
 
-![](_static/xgboost_flower_auc.png)
+![](_static/xgboost_flower_auc_bagging.png)
 
-The figure above shows the centralised tested AUC performance over FL rounds on 4 experimental settings.
+The figure above shows the centralised tested AUC performance over FL rounds with bagging aggregation strategy on 4 experimental settings.
 One can see that all settings obtain stable performance boost over FL rounds (especially noticeable at the start of training).
-As expected, uniform client distribution shows higher AUC values (beyond 83% at the end) than square/exponential setup.
-Feel free to explore more interesting experiments by yourself!
+As expected, uniform client distribution shows higher AUC values than square/exponential setup.
+
+#### Cyclic training experiment
+
+![](_static/xgboost_flower_auc_cyclic.png)
+
+This figure shows the cyclic training results on centralised test set.
+The models with cyclic training requires more rounds to converge
+because only a single client participate in the training per round.
+
+Feel free to explore more interesting experiments by yourself !
diff --git a/examples/xgboost-comprehensive/_static/xgboost_flower_auc.png b/examples/xgboost-comprehensive/_static/xgboost_flower_auc.png
deleted file mode 100644
index e6a4bfb83250..000000000000
Binary files a/examples/xgboost-comprehensive/_static/xgboost_flower_auc.png and /dev/null differ
diff --git a/examples/xgboost-comprehensive/_static/xgboost_flower_auc_bagging.png b/examples/xgboost-comprehensive/_static/xgboost_flower_auc_bagging.png
new file mode 100644
index 000000000000..e192df214471
Binary files /dev/null and b/examples/xgboost-comprehensive/_static/xgboost_flower_auc_bagging.png differ
diff --git a/examples/xgboost-comprehensive/_static/xgboost_flower_auc_cyclic.png b/examples/xgboost-comprehensive/_static/xgboost_flower_auc_cyclic.png
new file mode 100644
index 000000000000..731d0fc3fbbc
Binary files /dev/null and b/examples/xgboost-comprehensive/_static/xgboost_flower_auc_cyclic.png differ
diff --git a/examples/xgboost-comprehensive/client.py b/examples/xgboost-comprehensive/client.py
index ff7a4adf7977..2d54c3fd63c7 100644
--- a/examples/xgboost-comprehensive/client.py
+++ b/examples/xgboost-comprehensive/client.py
@@ -1,21 +1,9 @@
 import warnings
 from logging import INFO
-import xgboost as xgb
 
 import flwr as fl
 from flwr_datasets import FederatedDataset
 from flwr.common.logger import log
-from flwr.common import (
-    Code,
-    EvaluateIns,
-    EvaluateRes,
-    FitIns,
-    FitRes,
-    GetParametersIns,
-    GetParametersRes,
-    Parameters,
-    Status,
-)
 
 from dataset import (
     instantiate_partitioner,
@@ -23,7 +11,8 @@
     transform_dataset_to_dmatrix,
     resplit,
 )
-from utils import client_args_parser, BST_PARAMS
+from utils import client_args_parser, BST_PARAMS, NUM_LOCAL_ROUND
+from client_utils import XgbClient
 
 
 warnings.filterwarnings("ignore", category=UserWarning)
@@ -32,15 +21,13 @@
 # Parse arguments for experimental settings
 args = client_args_parser()
 
-# Load (HIGGS) dataset and conduct partitioning
-num_partitions = args.num_partitions
-
-# Partitioner type is chosen from ["uniform", "linear", "square", "exponential"]
-partitioner_type = args.partitioner_type
+# Train method (bagging or cyclic)
+train_method = args.train_method
 
-# Instantiate partitioner
+# Load (HIGGS) dataset and conduct partitioning
+# Instantiate partitioner from ["uniform", "linear", "square", "exponential"]
 partitioner = instantiate_partitioner(
-    partitioner_type=partitioner_type, num_partitions=num_partitions
+    partitioner_type=args.partitioner_type, num_partitions=args.num_partitions
 )
 fds = FederatedDataset(
     dataset="jxie/higgs",
@@ -48,25 +35,22 @@
     resplitter=resplit,
 )
 
-# Load the partition for this `node_id`
+# Load the partition for this `partition_id`
 log(INFO, "Loading partition...")
-node_id = args.node_id
-partition = fds.load_partition(node_id=node_id, split="train")
+partition = fds.load_partition(partition_id=args.partition_id, split="train")
 partition.set_format("numpy")
 
 if args.centralised_eval:
     # Use centralised test set for evaluation
     train_data = partition
-    valid_data = fds.load_full("test")
+    valid_data = fds.load_split("test")
     valid_data.set_format("numpy")
     num_train = train_data.shape[0]
     num_val = valid_data.shape[0]
 else:
     # Train/test splitting
-    SEED = args.seed
-    test_fraction = args.test_fraction
     train_data, valid_data, num_train, num_val = train_test_split(
-        partition, test_fraction=test_fraction, seed=SEED
+        partition, test_fraction=args.test_fraction, seed=args.seed
     )
 
 # Reformat data to DMatrix for xgboost
@@ -74,101 +58,25 @@
 train_dmatrix = transform_dataset_to_dmatrix(train_data)
 valid_dmatrix = transform_dataset_to_dmatrix(valid_data)
 
-
 # Hyper-parameters for xgboost training
-num_local_round = 1
+num_local_round = NUM_LOCAL_ROUND
 params = BST_PARAMS
 
-
-# Define Flower client
-class XgbClient(fl.client.Client):
-    def __init__(self):
-        self.bst = None
-        self.config = None
-
-    def get_parameters(self, ins: GetParametersIns) -> GetParametersRes:
-        _ = (self, ins)
-        return GetParametersRes(
-            status=Status(
-                code=Code.OK,
-                message="OK",
-            ),
-            parameters=Parameters(tensor_type="", tensors=[]),
-        )
-
-    def _local_boost(self):
-        # Update trees based on local training data.
-        for i in range(num_local_round):
-            self.bst.update(train_dmatrix, self.bst.num_boosted_rounds())
-
-        # Bagging: extract the last N=num_local_round trees for sever aggregation
-        # Cyclic: return the entire model
-        bst = (
-            self.bst[
-                self.bst.num_boosted_rounds()
-                - num_local_round : self.bst.num_boosted_rounds()
-            ]
-            if args.train_method == "bagging"
-            else self.bst
-        )
-
-        return bst
-
-    def fit(self, ins: FitIns) -> FitRes:
-        if not self.bst:
-            # 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.config = bst.save_config()
-            self.bst = bst
-        else:
-            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 = self._local_boost()
-
-        local_model = bst.save_raw("json")
-        local_model_bytes = bytes(local_model)
-
-        return FitRes(
-            status=Status(
-                code=Code.OK,
-                message="OK",
-            ),
-            parameters=Parameters(tensor_type="", tensors=[local_model_bytes]),
-            num_examples=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,
-        )
-        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,
-            metrics={"AUC": auc},
-        )
-
+# Setup learning rate
+if args.train_method == "bagging" and args.scaled_lr:
+    new_lr = params["eta"] / args.num_partitions
+    params.update({"eta": new_lr})
 
 # Start Flower client
-fl.client.start_client(server_address="127.0.0.1:8080", client=XgbClient())
+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,
+        train_method,
+    ),
+)
diff --git a/examples/xgboost-comprehensive/client_utils.py b/examples/xgboost-comprehensive/client_utils.py
new file mode 100644
index 000000000000..d2e07677ef97
--- /dev/null
+++ b/examples/xgboost-comprehensive/client_utils.py
@@ -0,0 +1,126 @@
+from logging import INFO
+import xgboost as xgb
+
+import flwr as fl
+from flwr.common.logger import log
+from flwr.common import (
+    Code,
+    EvaluateIns,
+    EvaluateRes,
+    FitIns,
+    FitRes,
+    GetParametersIns,
+    GetParametersRes,
+    Parameters,
+    Status,
+)
+
+
+class XgbClient(fl.client.Client):
+    def __init__(
+        self,
+        train_dmatrix,
+        valid_dmatrix,
+        num_train,
+        num_val,
+        num_local_round,
+        params,
+        train_method,
+    ):
+        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
+        self.train_method = train_method
+
+    def get_parameters(self, ins: GetParametersIns) -> GetParametersRes:
+        _ = (self, ins)
+        return GetParametersRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            parameters=Parameters(tensor_type="", tensors=[]),
+        )
+
+    def _local_boost(self, bst_input):
+        # Update trees based on local training data.
+        for i in range(self.num_local_round):
+            bst_input.update(self.train_dmatrix, bst_input.num_boosted_rounds())
+
+        # Bagging: extract the last N=num_local_round trees for sever aggregation
+        # Cyclic: return the entire model
+        bst = (
+            bst_input[
+                bst_input.num_boosted_rounds()
+                - self.num_local_round : bst_input.num_boosted_rounds()
+            ]
+            if self.train_method == "bagging"
+            else bst_input
+        )
+
+        return bst
+
+    def fit(self, ins: FitIns) -> FitRes:
+        global_round = int(ins.config["global_round"])
+        if global_round == 1:
+            # First round local training
+            bst = xgb.train(
+                self.params,
+                self.train_dmatrix,
+                num_boost_round=self.num_local_round,
+                evals=[(self.valid_dmatrix, "validate"), (self.train_dmatrix, "train")],
+            )
+        else:
+            bst = xgb.Booster(params=self.params)
+            for item in ins.parameters.tensors:
+                global_model = bytearray(item)
+
+            # Load global model into booster
+            bst.load_model(global_model)
+
+            # Local training
+            bst = self._local_boost(bst)
+
+        # Save model
+        local_model = bst.save_raw("json")
+        local_model_bytes = bytes(local_model)
+
+        return FitRes(
+            status=Status(
+                code=Code.OK,
+                message="OK",
+            ),
+            parameters=Parameters(tensor_type="", tensors=[local_model_bytes]),
+            num_examples=self.num_train,
+            metrics={},
+        )
+
+    def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
+        # 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=self.num_val,
+            metrics={"AUC": auc},
+        )
diff --git a/examples/xgboost-comprehensive/dataset.py b/examples/xgboost-comprehensive/dataset.py
index bcf2e00b30af..94959925f833 100644
--- a/examples/xgboost-comprehensive/dataset.py
+++ b/examples/xgboost-comprehensive/dataset.py
@@ -39,12 +39,18 @@ def train_test_split(partition: Dataset, test_fraction: float, seed: int):
 
 def transform_dataset_to_dmatrix(data: Union[Dataset, DatasetDict]) -> xgb.core.DMatrix:
     """Transform dataset to DMatrix format for xgboost."""
-    x = data["inputs"]
-    y = data["label"]
+    x, y = separate_xy(data)
     new_data = xgb.DMatrix(x, label=y)
     return new_data
 
 
+def separate_xy(data: Union[Dataset, DatasetDict]):
+    """Return outputs of x (data) and y (labels) ."""
+    x = data["inputs"]
+    y = data["label"]
+    return x, y
+
+
 def resplit(dataset: DatasetDict) -> DatasetDict:
     """Increase the quantity of centralised test samples from 500K to 1M."""
     return DatasetDict(
diff --git a/examples/xgboost-comprehensive/pyproject.toml b/examples/xgboost-comprehensive/pyproject.toml
index bbfbb4134b8d..b257801cb420 100644
--- a/examples/xgboost-comprehensive/pyproject.toml
+++ b/examples/xgboost-comprehensive/pyproject.toml
@@ -6,10 +6,10 @@ build-backend = "poetry.core.masonry.api"
 name = "xgboost-comprehensive"
 version = "0.1.0"
 description = "Federated XGBoost with Flower (comprehensive)"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
-flwr-nightly = ">=1.0,<2.0"
+flwr-nightly = { extras = ["simulation"], version = ">=1.7.0,<2.0" }
 flwr-datasets = ">=0.0.2,<1.0.0"
 xgboost = ">=2.0.0,<3.0.0"
diff --git a/examples/xgboost-comprehensive/requirements.txt b/examples/xgboost-comprehensive/requirements.txt
index c37ac2b6ad6d..b5b1d83bcdd1 100644
--- a/examples/xgboost-comprehensive/requirements.txt
+++ b/examples/xgboost-comprehensive/requirements.txt
@@ -1,3 +1,3 @@
-flwr-nightly>=1.0, <2.0
+flwr[simulation]>=1.7.0, <2.0
 flwr-datasets>=0.0.2, <1.0.0
 xgboost>=2.0.0, <3.0.0
diff --git a/examples/xgboost-comprehensive/run_bagging.sh b/examples/xgboost-comprehensive/run_bagging.sh
index 7920f6bf5e55..a6300b781a06 100755
--- a/examples/xgboost-comprehensive/run_bagging.sh
+++ b/examples/xgboost-comprehensive/run_bagging.sh
@@ -3,12 +3,12 @@ set -e
 cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/
 
 echo "Starting server"
-python3 server.py --pool-size=5 --num-rounds=50 --num-clients-per-round=5 --centralised-eval &
-sleep 15  # Sleep for 15s to give the server enough time to start
+python3 server.py --pool-size=5 --num-rounds=30 --num-clients-per-round=5 --centralised-eval &
+sleep 30  # Sleep for 30s to give the server enough time to start
 
 for i in `seq 0 4`; do
     echo "Starting client $i"
-    python3 client.py --node-id=$i --num-partitions=5 --partitioner-type=exponential &
+    python3 client.py --partition-id=$i --num-partitions=5 --partitioner-type=exponential &
 done
 
 # Enable CTRL+C to stop all background processes
diff --git a/examples/xgboost-comprehensive/run_cyclic.sh b/examples/xgboost-comprehensive/run_cyclic.sh
index 47e09fd8faef..258bdf2fe0d8 100755
--- a/examples/xgboost-comprehensive/run_cyclic.sh
+++ b/examples/xgboost-comprehensive/run_cyclic.sh
@@ -8,7 +8,7 @@ sleep 15  # Sleep for 15s to give the server enough time to start
 
 for i in `seq 0 4`; do
     echo "Starting client $i"
-    python3 client.py --node-id=$i --train-method=cyclic --num-partitions=5 --partitioner-type=exponential --centralised-eval &
+    python3 client.py --partition-id=$i --train-method=cyclic --num-partitions=5 --partitioner-type=exponential --centralised-eval &
 done
 
 # Enable CTRL+C to stop all background processes
diff --git a/examples/xgboost-comprehensive/server.py b/examples/xgboost-comprehensive/server.py
index 1cf4ba79fa50..939819641438 100644
--- a/examples/xgboost-comprehensive/server.py
+++ b/examples/xgboost-comprehensive/server.py
@@ -1,19 +1,19 @@
 import warnings
-from typing import Dict, List, Optional
 from logging import INFO
-import xgboost as xgb
 
 import flwr as fl
 from flwr.common.logger import log
-from flwr.common import Parameters, Scalar
 from flwr_datasets import FederatedDataset
-from flwr.server.strategy import FedXgbBagging
-from flwr.server.strategy import FedXgbCyclic
-from flwr.server.client_proxy import ClientProxy
-from flwr.server.criterion import Criterion
-from flwr.server.client_manager import SimpleClientManager
-
-from utils import server_args_parser, BST_PARAMS
+from flwr.server.strategy import FedXgbBagging, FedXgbCyclic
+
+from utils import server_args_parser
+from server_utils import (
+    eval_config,
+    fit_config,
+    evaluate_metrics_aggregation,
+    get_evaluate_fn,
+    CyclicClientManager,
+)
 from dataset import resplit, transform_dataset_to_dmatrix
 
 
@@ -34,97 +34,11 @@
     fds = FederatedDataset(
         dataset="jxie/higgs", partitioners={"train": 20}, resplitter=resplit
     )
-    test_set = fds.load_full("test")
+    log(INFO, "Loading centralised test set...")
+    test_set = fds.load_split("test")
     test_set.set_format("numpy")
     test_dmatrix = transform_dataset_to_dmatrix(test_set)
 
-# Hyper-parameters used for initialisation
-params = BST_PARAMS
-
-
-def eval_config(rnd: int) -> Dict[str, str]:
-    """Return a configuration with global epochs."""
-    config = {
-        "global_round": str(rnd),
-    }
-    return config
-
-
-def evaluate_metrics_aggregation(eval_metrics):
-    """Return an aggregated metric (AUC) for evaluation."""
-    total_num = sum([num for num, _ in eval_metrics])
-    auc_aggregated = (
-        sum([metrics["AUC"] * num for num, metrics in eval_metrics]) / total_num
-    )
-    metrics_aggregated = {"AUC": auc_aggregated}
-    return metrics_aggregated
-
-
-def get_evaluate_fn(test_data):
-    """Return a function for centralised evaluation."""
-
-    def evaluate_fn(
-        server_round: int, parameters: Parameters, config: Dict[str, Scalar]
-    ):
-        # If at the first round, skip the evaluation
-        if server_round == 0:
-            return 0, {}
-        else:
-            bst = xgb.Booster(params=params)
-            for para in parameters.tensors:
-                para_b = bytearray(para)
-
-            # Load global model
-            bst.load_model(para_b)
-            # Run evaluation
-            eval_results = bst.eval_set(
-                evals=[(test_data, "valid")],
-                iteration=bst.num_boosted_rounds() - 1,
-            )
-            auc = round(float(eval_results.split("\t")[1].split(":")[1]), 4)
-            log(INFO, f"AUC = {auc} at round {server_round}")
-
-            return 0, {"AUC": auc}
-
-    return evaluate_fn
-
-
-class CyclicClientManager(SimpleClientManager):
-    """Provides a cyclic client selection rule."""
-
-    def sample(
-        self,
-        num_clients: int,
-        min_num_clients: Optional[int] = None,
-        criterion: Optional[Criterion] = None,
-    ) -> List[ClientProxy]:
-        """Sample a number of Flower ClientProxy instances."""
-
-        # Block until at least num_clients are connected.
-        if min_num_clients is None:
-            min_num_clients = num_clients
-        self.wait_for(min_num_clients)
-
-        # Sample clients which meet the criterion
-        available_cids = list(self.clients)
-        if criterion is not None:
-            available_cids = [
-                cid for cid in available_cids if criterion.select(self.clients[cid])
-            ]
-
-        if num_clients > len(available_cids):
-            log(
-                INFO,
-                "Sampling failed: number of available clients"
-                " (%s) is less than number of requested clients (%s).",
-                len(available_cids),
-                num_clients,
-            )
-            return []
-
-        # Return all available clients
-        return [self.clients[cid] for cid in available_cids]
-
 
 # Define strategy
 if train_method == "bagging":
@@ -137,9 +51,10 @@ def sample(
         min_evaluate_clients=num_evaluate_clients if not centralised_eval else 0,
         fraction_evaluate=1.0 if not centralised_eval else 0.0,
         on_evaluate_config_fn=eval_config,
-        evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation
-        if not centralised_eval
-        else None,
+        on_fit_config_fn=fit_config,
+        evaluate_metrics_aggregation_fn=(
+            evaluate_metrics_aggregation if not centralised_eval else None
+        ),
     )
 else:
     # Cyclic training
@@ -149,6 +64,7 @@ def sample(
         fraction_evaluate=1.0,
         evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation,
         on_evaluate_config_fn=eval_config,
+        on_fit_config_fn=fit_config,
     )
 
 # Start Flower server
diff --git a/examples/xgboost-comprehensive/server_utils.py b/examples/xgboost-comprehensive/server_utils.py
new file mode 100644
index 000000000000..35a31bd9adac
--- /dev/null
+++ b/examples/xgboost-comprehensive/server_utils.py
@@ -0,0 +1,101 @@
+from typing import Dict, List, Optional
+from logging import INFO
+import xgboost as xgb
+from flwr.common.logger import log
+from flwr.common import Parameters, Scalar
+from flwr.server.client_manager import SimpleClientManager
+from flwr.server.client_proxy import ClientProxy
+from flwr.server.criterion import Criterion
+from utils import BST_PARAMS
+
+
+def eval_config(rnd: int) -> Dict[str, str]:
+    """Return a configuration with global epochs."""
+    config = {
+        "global_round": str(rnd),
+    }
+    return config
+
+
+def fit_config(rnd: int) -> Dict[str, str]:
+    """Return a configuration with global epochs."""
+    config = {
+        "global_round": str(rnd),
+    }
+    return config
+
+
+def evaluate_metrics_aggregation(eval_metrics):
+    """Return an aggregated metric (AUC) for evaluation."""
+    total_num = sum([num for num, _ in eval_metrics])
+    auc_aggregated = (
+        sum([metrics["AUC"] * num for num, metrics in eval_metrics]) / total_num
+    )
+    metrics_aggregated = {"AUC": auc_aggregated}
+    return metrics_aggregated
+
+
+def get_evaluate_fn(test_data):
+    """Return a function for centralised evaluation."""
+
+    def evaluate_fn(
+        server_round: int, parameters: Parameters, config: Dict[str, Scalar]
+    ):
+        # If at the first round, skip the evaluation
+        if server_round == 0:
+            return 0, {}
+        else:
+            bst = xgb.Booster(params=BST_PARAMS)
+            for para in parameters.tensors:
+                para_b = bytearray(para)
+
+            # Load global model
+            bst.load_model(para_b)
+            # Run evaluation
+            eval_results = bst.eval_set(
+                evals=[(test_data, "valid")],
+                iteration=bst.num_boosted_rounds() - 1,
+            )
+            auc = round(float(eval_results.split("\t")[1].split(":")[1]), 4)
+            log(INFO, f"AUC = {auc} at round {server_round}")
+
+            return 0, {"AUC": auc}
+
+    return evaluate_fn
+
+
+class CyclicClientManager(SimpleClientManager):
+    """Provides a cyclic client selection rule."""
+
+    def sample(
+        self,
+        num_clients: int,
+        min_num_clients: Optional[int] = None,
+        criterion: Optional[Criterion] = None,
+    ) -> List[ClientProxy]:
+        """Sample a number of Flower ClientProxy instances."""
+
+        # Block until at least num_clients are connected.
+        if min_num_clients is None:
+            min_num_clients = num_clients
+        self.wait_for(min_num_clients)
+
+        # Sample clients which meet the criterion
+        available_cids = list(self.clients)
+        if criterion is not None:
+            available_cids = [
+                cid for cid in available_cids if criterion.select(self.clients[cid])
+            ]
+
+        if num_clients > len(available_cids):
+            log(
+                INFO,
+                "Sampling failed: number of available clients"
+                " (%s) is less than number of requested clients (%s).",
+                len(available_cids),
+                num_clients,
+            )
+            return []
+
+        # Return all available clients
+        return [self.clients[cid] for cid in available_cids]
diff --git a/examples/xgboost-comprehensive/sim.py b/examples/xgboost-comprehensive/sim.py
new file mode 100644
index 000000000000..c9481f1cdd5d
--- /dev/null
+++ b/examples/xgboost-comprehensive/sim.py
@@ -0,0 +1,188 @@
+import warnings
+from logging import INFO
+import xgboost as xgb
+from tqdm import tqdm
+
+import flwr as fl
+from flwr_datasets import FederatedDataset
+from flwr.common.logger import log
+from flwr.server.strategy import FedXgbBagging, FedXgbCyclic
+
+from dataset import (
+    instantiate_partitioner,
+    train_test_split,
+    transform_dataset_to_dmatrix,
+    separate_xy,
+    resplit,
+)
+from utils import (
+    sim_args_parser,
+    NUM_LOCAL_ROUND,
+    BST_PARAMS,
+)
+from server_utils import (
+    eval_config,
+    fit_config,
+    evaluate_metrics_aggregation,
+    get_evaluate_fn,
+    CyclicClientManager,
+)
+from client_utils import XgbClient
+
+
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+def get_client_fn(
+    train_data_list, valid_data_list, train_method, params, num_local_round
+):
+    """Return a function to construct a client.
+
+    The VirtualClientEngine will execute this function whenever a client is sampled by
+    the strategy to participate.
+    """
+
+    def client_fn(cid: str) -> fl.client.Client:
+        """Construct a FlowerClient with its own dataset partition."""
+        x_train, y_train = train_data_list[int(cid)][0]
+        x_valid, y_valid = valid_data_list[int(cid)][0]
+
+        # Reformat data to DMatrix
+        train_dmatrix = xgb.DMatrix(x_train, label=y_train)
+        valid_dmatrix = xgb.DMatrix(x_valid, label=y_valid)
+
+        # Fetch the number of examples
+        num_train = train_data_list[int(cid)][1]
+        num_val = valid_data_list[int(cid)][1]
+
+        # Create and return client
+        return XgbClient(
+            train_dmatrix,
+            valid_dmatrix,
+            num_train,
+            num_val,
+            num_local_round,
+            params,
+            train_method,
+        )
+
+    return client_fn
+
+
+def main():
+    # Parse arguments for experimental settings
+    args = sim_args_parser()
+
+    # Load (HIGGS) dataset and conduct partitioning
+    partitioner = instantiate_partitioner(
+        partitioner_type=args.partitioner_type, num_partitions=args.pool_size
+    )
+    fds = FederatedDataset(
+        dataset="jxie/higgs",
+        partitioners={"train": partitioner},
+        resplitter=resplit,
+    )
+
+    # Load centralised test set
+    if args.centralised_eval or args.centralised_eval_client:
+        log(INFO, "Loading centralised test set...")
+        test_data = fds.load_split("test")
+        test_data.set_format("numpy")
+        num_test = test_data.shape[0]
+        test_dmatrix = transform_dataset_to_dmatrix(test_data)
+
+    # Load partitions and reformat data to DMatrix for xgboost
+    log(INFO, "Loading client local partitions...")
+    train_data_list = []
+    valid_data_list = []
+
+    # Load and process all client partitions. This upfront cost is amortized soon
+    # after the simulation begins since clients wont need to preprocess their partition.
+    for partition_id in tqdm(range(args.pool_size), desc="Extracting client partition"):
+        # Extract partition for client with partition_id
+        partition = fds.load_partition(partition_id=partition_id, split="train")
+        partition.set_format("numpy")
+
+        if args.centralised_eval_client:
+            # Use centralised test set for evaluation
+            train_data = partition
+            num_train = train_data.shape[0]
+            x_test, y_test = separate_xy(test_data)
+            valid_data_list.append(((x_test, y_test), num_test))
+        else:
+            # Train/test splitting
+            train_data, valid_data, num_train, num_val = train_test_split(
+                partition, test_fraction=args.test_fraction, seed=args.seed
+            )
+            x_valid, y_valid = separate_xy(valid_data)
+            valid_data_list.append(((x_valid, y_valid), num_val))
+
+        x_train, y_train = separate_xy(train_data)
+        train_data_list.append(((x_train, y_train), num_train))
+
+    # Define strategy
+    if args.train_method == "bagging":
+        # Bagging training
+        strategy = FedXgbBagging(
+            evaluate_function=(
+                get_evaluate_fn(test_dmatrix) if args.centralised_eval else None
+            ),
+            fraction_fit=(float(args.num_clients_per_round) / args.pool_size),
+            min_fit_clients=args.num_clients_per_round,
+            min_available_clients=args.pool_size,
+            min_evaluate_clients=(
+                args.num_evaluate_clients if not args.centralised_eval else 0
+            ),
+            fraction_evaluate=1.0 if not args.centralised_eval else 0.0,
+            on_evaluate_config_fn=eval_config,
+            on_fit_config_fn=fit_config,
+            evaluate_metrics_aggregation_fn=(
+                evaluate_metrics_aggregation if not args.centralised_eval else None
+            ),
+        )
+    else:
+        # Cyclic training
+        strategy = FedXgbCyclic(
+            fraction_fit=1.0,
+            min_available_clients=args.pool_size,
+            fraction_evaluate=1.0,
+            evaluate_metrics_aggregation_fn=evaluate_metrics_aggregation,
+            on_evaluate_config_fn=eval_config,
+            on_fit_config_fn=fit_config,
+        )
+
+    # Resources to be assigned to each virtual client
+    # In this example we use CPU by default
+    client_resources = {
+        "num_cpus": args.num_cpus_per_client,
+        "num_gpus": 0.0,
+    }
+
+    # Hyper-parameters for xgboost training
+    num_local_round = NUM_LOCAL_ROUND
+    params = BST_PARAMS
+
+    # Setup learning rate
+    if args.train_method == "bagging" and args.scaled_lr:
+        new_lr = params["eta"] / args.pool_size
+        params.update({"eta": new_lr})
+
+    # Start simulation
+    fl.simulation.start_simulation(
+        client_fn=get_client_fn(
+            train_data_list,
+            valid_data_list,
+            args.train_method,
+            params,
+            num_local_round,
+        ),
+        num_clients=args.pool_size,
+        client_resources=client_resources,
+        config=fl.server.ServerConfig(num_rounds=args.num_rounds),
+        strategy=strategy,
+        client_manager=CyclicClientManager() if args.train_method == "cyclic" else None,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/xgboost-comprehensive/utils.py b/examples/xgboost-comprehensive/utils.py
index 8acdbbb88a7e..abc100da1ade 100644
--- a/examples/xgboost-comprehensive/utils.py
+++ b/examples/xgboost-comprehensive/utils.py
@@ -1,6 +1,8 @@
 import argparse
 
 
+# Hyper-parameters for xgboost training
+NUM_LOCAL_ROUND = 1
 BST_PARAMS = {
     "objective": "binary:logistic",
     "eta": 0.1,  # Learning rate
@@ -35,10 +37,10 @@ def client_args_parser():
         help="Partitioner types.",
     )
     parser.add_argument(
-        "--node-id",
+        "--partition-id",
         default=0,
         type=int,
-        help="Node ID used for the current client.",
+        help="Partition ID used for the current client.",
     )
     parser.add_argument(
         "--seed", default=42, type=int, help="Seed used for train/test splitting."
@@ -52,7 +54,12 @@ def client_args_parser():
     parser.add_argument(
         "--centralised-eval",
         action="store_true",
-        help="Conduct centralised evaluation (True), or client evaluation on hold-out data (False).",
+        help="Conduct evaluation on centralised test set (True), or on hold-out data (False).",
+    )
+    parser.add_argument(
+        "--scaled-lr",
+        action="store_true",
+        help="Perform scaled learning rate based on the number of clients (True).",
     )
 
     args = parser.parse_args()
@@ -96,3 +103,78 @@ def server_args_parser():
 
     args = parser.parse_args()
     return args
+
+
+def sim_args_parser():
+    """Parse arguments to define experimental settings on server side."""
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--train-method",
+        default="bagging",
+        type=str,
+        choices=["bagging", "cyclic"],
+        help="Training methods selected from bagging aggregation or cyclic training.",
+    )
+
+    # Server side
+    parser.add_argument(
+        "--pool-size", default=5, type=int, help="Number of total clients."
+    )
+    parser.add_argument(
+        "--num-rounds", default=30, type=int, help="Number of FL rounds."
+    )
+    parser.add_argument(
+        "--num-clients-per-round",
+        default=5,
+        type=int,
+        help="Number of clients participate in training each round.",
+    )
+    parser.add_argument(
+        "--num-evaluate-clients",
+        default=5,
+        type=int,
+        help="Number of clients selected for evaluation.",
+    )
+    parser.add_argument(
+        "--centralised-eval",
+        action="store_true",
+        help="Conduct centralised evaluation (True), or client evaluation on hold-out data (False).",
+    )
+    parser.add_argument(
+        "--num-cpus-per-client",
+        default=2,
+        type=int,
+        help="Number of CPUs used for per client.",
+    )
+
+    # Client side
+    parser.add_argument(
+        "--partitioner-type",
+        default="uniform",
+        type=str,
+        choices=["uniform", "linear", "square", "exponential"],
+        help="Partitioner types.",
+    )
+    parser.add_argument(
+        "--seed", default=42, type=int, help="Seed used for train/test splitting."
+    )
+    parser.add_argument(
+        "--test-fraction",
+        default=0.2,
+        type=float,
+        help="Test fraction for train/test splitting.",
+    )
+    parser.add_argument(
+        "--centralised-eval-client",
+        action="store_true",
+        help="Conduct evaluation on centralised test set (True), or on hold-out data (False).",
+    )
+    parser.add_argument(
+        "--scaled-lr",
+        action="store_true",
+        help="Perform scaled learning rate based on the number of clients (True).",
+    )
+
+    args = parser.parse_args()
+    return args
diff --git a/examples/xgboost-quickstart/README.md b/examples/xgboost-quickstart/README.md
index 5174c236c668..72dde5706e8d 100644
--- a/examples/xgboost-quickstart/README.md
+++ b/examples/xgboost-quickstart/README.md
@@ -67,13 +67,13 @@ To do so simply open two more terminal windows and run the following commands.
 Start client 1 in the first terminal:
 
 ```shell
-python3 client.py --node-id=0
+python3 client.py --partition-id=0
 ```
 
 Start client 2 in the second terminal:
 
 ```shell
-python3 client.py --node-id=1
+python3 client.py --partition-id=1
 ```
 
 You will see that XGBoost is starting a federated training.
@@ -85,4 +85,4 @@ poetry run ./run.sh
 ```
 
 Look at the [code](https://github.com/adap/flower/tree/main/examples/xgboost-quickstart)
-and [tutorial](https://flower.dev/docs/framework/tutorial-quickstart-xgboost.html) for a detailed explanation.
+and [tutorial](https://flower.ai/docs/framework/tutorial-quickstart-xgboost.html) for a detailed explanation.
diff --git a/examples/xgboost-quickstart/client.py b/examples/xgboost-quickstart/client.py
index b5eab59ba14d..6ac23ae15148 100644
--- a/examples/xgboost-quickstart/client.py
+++ b/examples/xgboost-quickstart/client.py
@@ -24,13 +24,13 @@
 
 warnings.filterwarnings("ignore", category=UserWarning)
 
-# Define arguments parser for the client/node ID.
+# Define arguments parser for the client/partition ID.
 parser = argparse.ArgumentParser()
 parser.add_argument(
-    "--node-id",
+    "--partition-id",
     default=0,
     type=int,
-    help="Node ID used for the current client.",
+    help="Partition ID used for the current client.",
 )
 args = parser.parse_args()
 
@@ -61,9 +61,9 @@ def transform_dataset_to_dmatrix(data: Union[Dataset, DatasetDict]) -> xgb.core.
 partitioner = IidPartitioner(num_partitions=30)
 fds = FederatedDataset(dataset="jxie/higgs", partitioners={"train": partitioner})
 
-# Load the partition for this `node_id`
+# Load the partition for this `partition_id`
 log(INFO, "Loading partition...")
-partition = fds.load_partition(node_id=args.node_id, split="train")
+partition = fds.load_partition(partition_id=args.partition_id, split="train")
 partition.set_format("numpy")
 
 # Train/test splitting
@@ -173,4 +173,4 @@ def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
 
 
 # Start Flower client
-fl.client.start_client(server_address="127.0.0.1:8080", client=XgbClient())
+fl.client.start_client(server_address="127.0.0.1:8080", client=XgbClient().to_client())
diff --git a/examples/xgboost-quickstart/pyproject.toml b/examples/xgboost-quickstart/pyproject.toml
index 7b3cbd9659a2..c16542ea7ffe 100644
--- a/examples/xgboost-quickstart/pyproject.toml
+++ b/examples/xgboost-quickstart/pyproject.toml
@@ -6,10 +6,10 @@ build-backend = "poetry.core.masonry.api"
 name = "xgboost-quickstart"
 version = "0.1.0"
 description = "Federated XGBoost with Flower (quickstart)"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 
 [tool.poetry.dependencies]
 python = ">=3.8,<3.11"
-flwr = ">=1.6.0,<2.0"
+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/requirements.txt b/examples/xgboost-quickstart/requirements.txt
index 4ccd5587bfc3..c6949e0651c5 100644
--- a/examples/xgboost-quickstart/requirements.txt
+++ b/examples/xgboost-quickstart/requirements.txt
@@ -1,3 +1,3 @@
-flwr>=1.6.0, <2.0
+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/run.sh b/examples/xgboost-quickstart/run.sh
index 6287145bfb5f..b35af58222ab 100755
--- a/examples/xgboost-quickstart/run.sh
+++ b/examples/xgboost-quickstart/run.sh
@@ -8,7 +8,7 @@ sleep 5  # Sleep for 5s to give the server enough time to start
 
 for i in `seq 0 1`; do
     echo "Starting client $i"
-    python3 client.py --node-id=$i &
+    python3 client.py --partition-id=$i &
 done
 
 # Enable CTRL+C to stop all background processes
diff --git a/pyproject.toml b/pyproject.toml
index 2349d554a409..e0514254ecac 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,14 +4,14 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "flwr"
-version = "1.7.0"
+version = "1.8.0"
 description = "Flower: A Friendly Federated Learning Framework"
 license = "Apache-2.0"
-authors = ["The Flower Authors <hello@flower.dev>"]
+authors = ["The Flower Authors <hello@flower.ai>"]
 readme = "README.md"
-homepage = "https://flower.dev"
+homepage = "https://flower.ai"
 repository = "https://github.com/adap/flower"
-documentation = "https://flower.dev"
+documentation = "https://flower.ai"
 keywords = [
     "flower",
     "fl",
@@ -52,21 +52,26 @@ 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-server = "flwr.server:run_server"
-flower-client = "flwr.client:run_client"
+flower-superlink = "flwr.server:run_superlink"
+flower-client-app = "flwr.client:run_client_app"
+flower-server-app = "flwr.server:run_server_app"
+flower-simulation = "flwr.simulation:run_simulation_from_cli"
 
 [tool.poetry.dependencies]
 python = "^3.8"
 # Mandatory dependencies
 numpy = "^1.21.0"
-grpcio = "^1.48.2,!=1.52.0"
-protobuf = "^3.19.0"
-cryptography = "^41.0.2"
+grpcio = "^1.60.0"
+protobuf = "^4.25.2"
+cryptography = "^42.0.4"
 pycryptodome = "^3.18.0"
 iterators = "^0.0.2"
-# Optional dependencies (VCE)
+typer = { version = "^0.9.0", extras=["all"] }
+tomli = "^2.0.1"
+# Optional dependencies (Simulation Engine)
 ray = { version = "==2.6.3", optional = true }
 pydantic = { version = "<2.0.0", optional = true }
 # Optional dependencies (REST transport layer)
@@ -81,21 +86,21 @@ rest = ["requests", "starlette", "uvicorn"]
 [tool.poetry.group.dev.dependencies]
 types-dataclasses = "==0.6.6"
 types-protobuf = "==3.19.18"
-types-requests = "==2.31.0.2"
-types-setuptools = "==68.2.0.0"
-clang-format = "==17.0.4"
-isort = "==5.12.0"
-black = { version = "==23.10.1", extras = ["jupyter"] }
+types-requests = "==2.31.0.20240125"
+types-setuptools = "==69.0.0.20240125"
+clang-format = "==17.0.6"
+isort = "==5.13.2"
+black = { version = "==24.2.0", extras = ["jupyter"] }
 docformatter = "==1.7.5"
-mypy = "==1.6.1"
-pylint = "==2.13.9"
+mypy = "==1.8.0"
+pylint = "==3.0.3"
 flake8 = "==5.0.4"
-pytest = "==7.4.3"
+pytest = "==7.4.4"
 pytest-cov = "==4.1.0"
-pytest-watch = "==4.2.0"
-grpcio-tools = "==1.48.2"
+pytest-watcher = "==0.4.1"
+grpcio-tools = "==1.60.0"
 mypy-protobuf = "==3.2.0"
-jupyterlab = "==4.0.8"
+jupyterlab = "==4.0.12"
 rope = "==1.11.0"
 semver = "==3.0.2"
 sphinx = "==6.2.1"
@@ -109,7 +114,7 @@ furo = "==2023.9.10"
 sphinx-reredirects = "==0.1.3"
 nbsphinx = "==0.9.3"
 nbstripout = "==0.6.1"
-ruff = "==0.1.4"
+ruff = "==0.1.9"
 sphinx-argparse = "==0.4.0"
 pipreqs = "==0.4.13"
 mdformat-gfm = "==0.3.5"
@@ -120,7 +125,8 @@ twine = "==4.0.2"
 pyroma = "==4.2"
 check-wheel-contents = "==0.4.0"
 GitPython = "==3.1.32"
-licensecheck = "==2023.5.1"
+PyGithub = "==2.1.1"
+licensecheck = "==2024"
 
 [tool.isort]
 line_length = 88
@@ -136,7 +142,7 @@ line-length = 88
 target-version = ["py38", "py39", "py310", "py311"]
 
 [tool.pylint."MESSAGES CONTROL"]
-disable = "bad-continuation,duplicate-code,too-few-public-methods,useless-import-alias"
+disable = "duplicate-code,too-few-public-methods,useless-import-alias"
 
 [tool.pytest.ini_options]
 minversion = "6.2"
@@ -145,6 +151,16 @@ testpaths = [
     "src/py/flwr",
     "src/py/flwr_tool",
 ]
+filterwarnings = "ignore::DeprecationWarning"
+
+[tool.pytest-watcher]
+now = false
+clear = true
+delay = 0.2
+runner = "pytest"
+runner_args = ["-s", "-vvvvv"]
+patterns = ["*.py"]
+ignore_patterns = []
 
 [tool.mypy]
 plugins = [
@@ -183,7 +199,7 @@ target-version = "py38"
 line-length = 88
 select = ["D", "E", "F", "W", "B", "ISC", "C4", "UP"]
 fixable = ["D", "E", "F", "W", "B", "ISC", "C4", "UP"]
-ignore = ["B024", "B027"]
+ignore = ["B024", "B027", "D205", "D209"]
 exclude = [
     ".bzr",
     ".direnv",
diff --git a/src/docker/client/Dockerfile b/src/docker/client/Dockerfile
new file mode 100644
index 000000000000..0755a7989281
--- /dev/null
+++ b/src/docker/client/Dockerfile
@@ -0,0 +1,8 @@
+# Copyright 2023 Flower Labs GmbH. All Rights Reserved.
+
+ARG BASE_REPOSITORY=flwr/base
+ARG BASE_IMAGE_TAG
+FROM $BASE_REPOSITORY:$BASE_IMAGE_TAG
+
+ARG FLWR_VERSION
+RUN python -m pip install -U --no-cache-dir flwr[rest]==${FLWR_VERSION}
diff --git a/src/docker/server/Dockerfile b/src/docker/server/Dockerfile
index 9bf3214bb42c..faa9cf2e56fe 100644
--- a/src/docker/server/Dockerfile
+++ b/src/docker/server/Dockerfile
@@ -1,13 +1,14 @@
 # Copyright 2023 Flower Labs GmbH. All Rights Reserved.
 
-ARG BASE_IMAGE_VERSION=py3.11-ubuntu22.04
-FROM flwr/base:$BASE_IMAGE_VERSION as server
+ARG BASE_REPOSITORY=flwr/base
+ARG BASE_IMAGE_TAG=py3.11-ubuntu22.04
+FROM $BASE_REPOSITORY:$BASE_IMAGE_TAG as server
 
 WORKDIR /app
 ARG FLWR_VERSION
 RUN python -m pip install -U --no-cache-dir flwr[rest]==${FLWR_VERSION}
-ENTRYPOINT ["python", "-c", "from flwr.server import run_server; run_server()"]
+ENTRYPOINT ["python", "-c", "from flwr.server import run_superlink; run_superlink()"]
 
 # Test if Flower can be successfully installed and imported
 FROM server as test
-RUN python -c "from flwr.server import run_server"
+RUN python -c "from flwr.server import run_superlink"
diff --git a/src/kotlin/flwr/src/main/AndroidManifest.xml b/src/kotlin/flwr/src/main/AndroidManifest.xml
index 8bdb7e14b389..3cb3262db448 100644
--- a/src/kotlin/flwr/src/main/AndroidManifest.xml
+++ b/src/kotlin/flwr/src/main/AndroidManifest.xml
@@ -1,4 +1,5 @@
 <?xml version="1.0" encoding="utf-8"?>
 <manifest xmlns:android="http://schemas.android.com/apk/res/android">
-
+    <uses-permission android:name="android.permission.INTERNET" />
+    <uses-permission android:name="android.permission.ACCESS_NETWORK_STATE" />
 </manifest>
diff --git a/src/proto/flwr/proto/driver.proto b/src/proto/flwr/proto/driver.proto
index eb948217a4de..bc0062c4a51f 100644
--- a/src/proto/flwr/proto/driver.proto
+++ b/src/proto/flwr/proto/driver.proto
@@ -21,8 +21,8 @@ import "flwr/proto/node.proto";
 import "flwr/proto/task.proto";
 
 service Driver {
-  // Request workload_id
-  rpc CreateWorkload(CreateWorkloadRequest) returns (CreateWorkloadResponse) {}
+  // Request run_id
+  rpc CreateRun(CreateRunRequest) returns (CreateRunResponse) {}
 
   // Return a set of nodes
   rpc GetNodes(GetNodesRequest) returns (GetNodesResponse) {}
@@ -34,12 +34,12 @@ service Driver {
   rpc PullTaskRes(PullTaskResRequest) returns (PullTaskResResponse) {}
 }
 
-// CreateWorkload
-message CreateWorkloadRequest {}
-message CreateWorkloadResponse { sint64 workload_id = 1; }
+// CreateRun
+message CreateRunRequest {}
+message CreateRunResponse { sint64 run_id = 1; }
 
 // GetNodes messages
-message GetNodesRequest { sint64 workload_id = 1; }
+message GetNodesRequest { sint64 run_id = 1; }
 message GetNodesResponse { repeated Node nodes = 1; }
 
 // PushTaskIns messages
diff --git a/src/proto/flwr/proto/error.proto b/src/proto/flwr/proto/error.proto
new file mode 100644
index 000000000000..a35af7f8af67
--- /dev/null
+++ b/src/proto/flwr/proto/error.proto
@@ -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.
+// ==============================================================================
+
+syntax = "proto3";
+
+package flwr.proto;
+
+message Error {
+  sint64 code = 1;
+  string reason = 2;
+}
diff --git a/src/proto/flwr/proto/recordset.proto b/src/proto/flwr/proto/recordset.proto
new file mode 100644
index 000000000000..d51d0f9ce416
--- /dev/null
+++ b/src/proto/flwr/proto/recordset.proto
@@ -0,0 +1,76 @@
+// 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 DoubleList { repeated double vals = 1; }
+message Sint64List { repeated sint64 vals = 1; }
+message BoolList { repeated bool vals = 1; }
+message StringList { repeated string vals = 1; }
+message BytesList { repeated bytes vals = 1; }
+
+message Array {
+  string dtype = 1;
+  repeated int32 shape = 2;
+  string stype = 3;
+  bytes data = 4;
+}
+
+message MetricsRecordValue {
+  oneof value {
+    // Single element
+    double double = 1;
+    sint64 sint64 = 2;
+
+    // List types
+    DoubleList double_list = 21;
+    Sint64List sint64_list = 22;
+  }
+}
+
+message ConfigsRecordValue {
+  oneof value {
+    // Single element
+    double double = 1;
+    sint64 sint64 = 2;
+    bool bool = 3;
+    string string = 4;
+    bytes bytes = 5;
+
+    // List types
+    DoubleList double_list = 21;
+    Sint64List sint64_list = 22;
+    BoolList bool_list = 23;
+    StringList string_list = 24;
+    BytesList bytes_list = 25;
+  }
+}
+
+message ParametersRecord {
+  repeated string data_keys = 1;
+  repeated Array data_values = 2;
+}
+
+message MetricsRecord { map<string, MetricsRecordValue> data = 1; }
+
+message ConfigsRecord { map<string, ConfigsRecordValue> data = 1; }
+
+message RecordSet {
+  map<string, ParametersRecord> parameters = 1;
+  map<string, MetricsRecord> metrics = 2;
+  map<string, ConfigsRecord> configs = 3;
+}
diff --git a/src/proto/flwr/proto/task.proto b/src/proto/flwr/proto/task.proto
index 2205ef2815c8..423df76f1335 100644
--- a/src/proto/flwr/proto/task.proto
+++ b/src/proto/flwr/proto/task.proto
@@ -18,7 +18,9 @@ syntax = "proto3";
 package flwr.proto;
 
 import "flwr/proto/node.proto";
+import "flwr/proto/recordset.proto";
 import "flwr/proto/transport.proto";
+import "flwr/proto/error.proto";
 
 message Task {
   Node producer = 1;
@@ -27,48 +29,21 @@ message Task {
   string delivered_at = 4;
   string ttl = 5;
   repeated string ancestry = 6;
-  SecureAggregation sa = 7;
-
-  ServerMessage legacy_server_message = 101 [ deprecated = true ];
-  ClientMessage legacy_client_message = 102 [ deprecated = true ];
+  string task_type = 7;
+  RecordSet recordset = 8;
+  Error error = 9;
 }
 
 message TaskIns {
   string task_id = 1;
   string group_id = 2;
-  sint64 workload_id = 3;
+  sint64 run_id = 3;
   Task task = 4;
 }
 
 message TaskRes {
   string task_id = 1;
   string group_id = 2;
-  sint64 workload_id = 3;
+  sint64 run_id = 3;
   Task task = 4;
 }
-
-message Value {
-  message DoubleList { repeated double vals = 1; }
-  message Sint64List { repeated sint64 vals = 1; }
-  message BoolList { repeated bool vals = 1; }
-  message StringList { repeated string vals = 1; }
-  message BytesList { repeated bytes vals = 1; }
-
-  oneof value {
-    // Single element
-    double double = 1;
-    sint64 sint64 = 2;
-    bool bool = 3;
-    string string = 4;
-    bytes bytes = 5;
-
-    // List types
-    DoubleList double_list = 21;
-    Sint64List sint64_list = 22;
-    BoolList bool_list = 23;
-    StringList string_list = 24;
-    BytesList bytes_list = 25;
-  }
-}
-
-message SecureAggregation { map<string, Value> named_values = 1; }
diff --git a/src/py/flwr/__init__.py b/src/py/flwr/__init__.py
index e05799280339..ccaf07c6012f 100644
--- a/src/py/flwr/__init__.py
+++ b/src/py/flwr/__init__.py
@@ -17,13 +17,11 @@
 
 from flwr.common.version import package_version as _package_version
 
-from . import client, common, driver, flower, server, simulation
+from . import client, common, server, simulation
 
 __all__ = [
     "client",
     "common",
-    "driver",
-    "flower",
     "server",
     "simulation",
 ]
diff --git a/src/py/flwr/cli/__init__.py b/src/py/flwr/cli/__init__.py
new file mode 100644
index 000000000000..d4d3b8ac4d48
--- /dev/null
+++ b/src/py/flwr/cli/__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 command line interface."""
diff --git a/src/py/flwr/cli/app.py b/src/py/flwr/cli/app.py
new file mode 100644
index 000000000000..1c6ee6c97841
--- /dev/null
+++ b/src/py/flwr/cli/app.py
@@ -0,0 +1,37 @@
+# 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."""
+
+import typer
+
+from .example import example
+from .new import new
+from .run import run
+
+app = typer.Typer(
+    help=typer.style(
+        "flwr is the Flower command line interface.",
+        fg=typer.colors.BRIGHT_YELLOW,
+        bold=True,
+    ),
+    no_args_is_help=True,
+)
+
+app.command()(new)
+app.command()(example)
+app.command()(run)
+
+if __name__ == "__main__":
+    app()
diff --git a/src/py/flwr/cli/example.py b/src/py/flwr/cli/example.py
new file mode 100644
index 000000000000..625ca8729640
--- /dev/null
+++ b/src/py/flwr/cli/example.py
@@ -0,0 +1,64 @@
+# 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 `example` command."""
+
+import json
+import os
+import subprocess
+import tempfile
+import urllib.request
+
+from .utils import prompt_options
+
+
+def example() -> None:
+    """Clone a Flower example.
+
+    All examples available in the Flower repository are available through this command.
+    """
+    # Load list of examples directly from GitHub
+    url = "https://api.github.com/repos/adap/flower/git/trees/main"
+    with urllib.request.urlopen(url) as res:
+        data = json.load(res)
+        examples_directory_url = [
+            item["url"] for item in data["tree"] if item["path"] == "examples"
+        ][0]
+
+    with urllib.request.urlopen(examples_directory_url) as res:
+        data = json.load(res)
+        example_names = [
+            item["path"] for item in data["tree"] if item["path"] not in [".gitignore"]
+        ]
+
+    example_name = prompt_options(
+        "Please select example by typing in the number",
+        example_names,
+    )
+
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        subprocess.check_output(
+            [
+                "git",
+                "clone",
+                "--depth=1",
+                "https://github.com/adap/flower.git",
+                tmpdirname,
+            ]
+        )
+        examples_dir = os.path.join(tmpdirname, "examples", example_name)
+        subprocess.check_output(["mv", examples_dir, "."])
+
+        print()
+        print(f"Example ready to use in {os.path.join(os.getcwd(), example_name)}")
diff --git a/src/py/flwr/cli/flower_toml.py b/src/py/flwr/cli/flower_toml.py
new file mode 100644
index 000000000000..103f83532054
--- /dev/null
+++ b/src/py/flwr/cli/flower_toml.py
@@ -0,0 +1,140 @@
+# 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.
+# ==============================================================================
+"""Utility to validate the `flower.toml` file."""
+
+import os
+from typing import Any, Dict, List, Optional, Tuple
+
+import tomli
+
+from flwr.common import object_ref
+
+
+def load_and_validate_with_defaults(
+    path: Optional[str] = None,
+) -> Tuple[Optional[Dict[str, Any]], List[str], List[str]]:
+    """Load and validate flower.toml as dict.
+
+    Returns
+    -------
+    Tuple[Optional[config], List[str], List[str]]
+        A tuple with the optional config in case it exists and is valid
+        and associated errors and warnings.
+    """
+    config = load(path)
+
+    if config is None:
+        errors = [
+            "Project configuration could not be loaded. flower.toml does not exist."
+        ]
+        return (None, errors, [])
+
+    is_valid, errors, warnings = validate(config)
+
+    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)
+
+
+def load(path: Optional[str] = None) -> Optional[Dict[str, Any]]:
+    """Load flower.toml and return as dict."""
+    if path is None:
+        cur_dir = os.getcwd()
+        toml_path = os.path.join(cur_dir, "flower.toml")
+    else:
+        toml_path = path
+
+    if not os.path.isfile(toml_path):
+        return None
+
+    with open(toml_path, encoding="utf-8") as toml_file:
+        data = tomli.loads(toml_file.read())
+        return data
+
+
+def validate_fields(config: Dict[str, Any]) -> Tuple[bool, List[str], List[str]]:
+    """Validate flower.toml fields."""
+    errors = []
+    warnings = []
+
+    if "project" not in config:
+        errors.append("Missing [project] section")
+    else:
+        if "name" not in config["project"]:
+            errors.append('Property "name" missing in [project]')
+        if "version" not in config["project"]:
+            errors.append('Property "version" missing in [project]')
+        if "description" not in config["project"]:
+            warnings.append('Recommended property "description" missing in [project]')
+        if "license" not in config["project"]:
+            warnings.append('Recommended property "license" missing in [project]')
+        if "authors" not in config["project"]:
+            warnings.append('Recommended property "authors" missing in [project]')
+
+    if "flower" not in config:
+        errors.append("Missing [flower] section")
+    elif "components" not in config["flower"]:
+        errors.append("Missing [flower.components] section")
+    else:
+        if "serverapp" not in config["flower"]["components"]:
+            errors.append('Property "serverapp" missing in [flower.components]')
+        if "clientapp" not in config["flower"]["components"]:
+            errors.append('Property "clientapp" missing in [flower.components]')
+
+    return len(errors) == 0, errors, warnings
+
+
+def validate(config: Dict[str, Any]) -> Tuple[bool, List[str], List[str]]:
+    """Validate flower.toml."""
+    is_valid, errors, warnings = validate_fields(config)
+
+    if not is_valid:
+        return False, errors, warnings
+
+    # Validate serverapp
+    is_valid, reason = object_ref.validate(config["flower"]["components"]["serverapp"])
+    if not is_valid and isinstance(reason, str):
+        return False, [reason], []
+
+    # Validate clientapp
+    is_valid, reason = object_ref.validate(config["flower"]["components"]["clientapp"])
+
+    if not is_valid and isinstance(reason, str):
+        return False, [reason], []
+
+    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
diff --git a/src/py/flwr/cli/flower_toml_test.py b/src/py/flwr/cli/flower_toml_test.py
new file mode 100644
index 000000000000..72a52e4e8b9b
--- /dev/null
+++ b/src/py/flwr/cli/flower_toml_test.py
@@ -0,0 +1,284 @@
+# 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 for Flower command line interface `run` command."""
+
+import os
+import textwrap
+from typing import Any, Dict
+
+from .flower_toml import load, validate, validate_fields
+
+
+def test_load_flower_toml_load_from_cwd(tmp_path: str) -> None:
+    """Test if load_template returns a string."""
+    # Prepare
+    flower_toml_content = """
+        [project]
+        name = "fedgpt"
+
+        [flower.components]
+        serverapp = "fedgpt.server:app"
+        clientapp = "fedgpt.client:app"
+
+        [flower.engine]
+        name = "simulation" # optional
+
+        [flower.engine.simulation.supernode]
+        count = 10 # optional
+    """
+    expected_config = {
+        "project": {
+            "name": "fedgpt",
+        },
+        "flower": {
+            "components": {
+                "serverapp": "fedgpt.server:app",
+                "clientapp": "fedgpt.client:app",
+            },
+            "engine": {
+                "name": "simulation",
+                "simulation": {"supernode": {"count": 10}},
+            },
+        },
+    }
+
+    # Current directory
+    origin = os.getcwd()
+
+    try:
+        # Change into the temporary directory
+        os.chdir(tmp_path)
+        with open("flower.toml", "w", encoding="utf-8") as f:
+            f.write(textwrap.dedent(flower_toml_content))
+
+        # Execute
+        config = load()
+
+        # Assert
+        assert config == expected_config
+    finally:
+        os.chdir(origin)
+
+
+def test_load_flower_toml_from_path(tmp_path: str) -> None:
+    """Test if load_template returns a string."""
+    # Prepare
+    flower_toml_content = """
+        [project]
+        name = "fedgpt"
+
+        [flower.components]
+        serverapp = "fedgpt.server:app"
+        clientapp = "fedgpt.client:app"
+
+        [flower.engine]
+        name = "simulation" # optional
+
+        [flower.engine.simulation.supernode]
+        count = 10 # optional
+    """
+    expected_config = {
+        "project": {
+            "name": "fedgpt",
+        },
+        "flower": {
+            "components": {
+                "serverapp": "fedgpt.server:app",
+                "clientapp": "fedgpt.client:app",
+            },
+            "engine": {
+                "name": "simulation",
+                "simulation": {"supernode": {"count": 10}},
+            },
+        },
+    }
+
+    # Current directory
+    origin = os.getcwd()
+
+    try:
+        # Change into the temporary directory
+        os.chdir(tmp_path)
+        with open("flower.toml", "w", encoding="utf-8") as f:
+            f.write(textwrap.dedent(flower_toml_content))
+
+        # Execute
+        config = load(path=os.path.join(tmp_path, "flower.toml"))
+
+        # Assert
+        assert config == expected_config
+    finally:
+        os.chdir(origin)
+
+
+def test_validate_flower_toml_fields_empty() -> None:
+    """Test that validate_flower_toml_fields fails correctly."""
+    # Prepare
+    config: Dict[str, Any] = {}
+
+    # Execute
+    is_valid, errors, warnings = validate_fields(config)
+
+    # Assert
+    assert not is_valid
+    assert len(errors) == 2
+    assert len(warnings) == 0
+
+
+def test_validate_flower_toml_fields_no_flower() -> None:
+    """Test that validate_flower_toml_fields fails correctly."""
+    # Prepare
+    config = {
+        "project": {
+            "name": "fedgpt",
+            "version": "1.0.0",
+            "description": "",
+            "license": "",
+            "authors": [],
+        }
+    }
+
+    # Execute
+    is_valid, errors, warnings = validate_fields(config)
+
+    # Assert
+    assert not is_valid
+    assert len(errors) == 1
+    assert len(warnings) == 0
+
+
+def test_validate_flower_toml_fields_no_flower_components() -> None:
+    """Test that validate_flower_toml_fields fails correctly."""
+    # Prepare
+    config = {
+        "project": {
+            "name": "fedgpt",
+            "version": "1.0.0",
+            "description": "",
+            "license": "",
+            "authors": [],
+        },
+        "flower": {},
+    }
+
+    # Execute
+    is_valid, errors, warnings = validate_fields(config)
+
+    # Assert
+    assert not is_valid
+    assert len(errors) == 1
+    assert len(warnings) == 0
+
+
+def test_validate_flower_toml_fields_no_server_and_client_app() -> None:
+    """Test that validate_flower_toml_fields fails correctly."""
+    # Prepare
+    config = {
+        "project": {
+            "name": "fedgpt",
+            "version": "1.0.0",
+            "description": "",
+            "license": "",
+            "authors": [],
+        },
+        "flower": {"components": {}},
+    }
+
+    # Execute
+    is_valid, errors, warnings = validate_fields(config)
+
+    # Assert
+    assert not is_valid
+    assert len(errors) == 2
+    assert len(warnings) == 0
+
+
+def test_validate_flower_toml_fields() -> None:
+    """Test that validate_flower_toml_fields succeeds correctly."""
+    # Prepare
+    config = {
+        "project": {
+            "name": "fedgpt",
+            "version": "1.0.0",
+            "description": "",
+            "license": "",
+            "authors": [],
+        },
+        "flower": {"components": {"serverapp": "", "clientapp": ""}},
+    }
+
+    # Execute
+    is_valid, errors, warnings = validate_fields(config)
+
+    # Assert
+    assert is_valid
+    assert len(errors) == 0
+    assert len(warnings) == 0
+
+
+def test_validate_flower_toml() -> None:
+    """Test that validate_flower_toml succeeds correctly."""
+    # Prepare
+    config = {
+        "project": {
+            "name": "fedgpt",
+            "version": "1.0.0",
+            "description": "",
+            "license": "",
+            "authors": [],
+        },
+        "flower": {
+            "components": {
+                "serverapp": "flwr.cli.run:run",
+                "clientapp": "flwr.cli.run:run",
+            }
+        },
+    }
+
+    # Execute
+    is_valid, errors, warnings = validate(config)
+
+    # Assert
+    assert is_valid
+    assert not errors
+    assert not warnings
+
+
+def test_validate_flower_toml_fail() -> None:
+    """Test that validate_flower_toml fails correctly."""
+    # Prepare
+    config = {
+        "project": {
+            "name": "fedgpt",
+            "version": "1.0.0",
+            "description": "",
+            "license": "",
+            "authors": [],
+        },
+        "flower": {
+            "components": {
+                "serverapp": "flwr.cli.run:run",
+                "clientapp": "flwr.cli.run:runa",
+            }
+        },
+    }
+
+    # Execute
+    is_valid, errors, warnings = validate(config)
+
+    # Assert
+    assert not is_valid
+    assert len(errors) == 1
+    assert len(warnings) == 0
diff --git a/src/py/flwr/flower/__init__.py b/src/py/flwr/cli/new/__init__.py
similarity index 72%
rename from src/py/flwr/flower/__init__.py
rename to src/py/flwr/cli/new/__init__.py
index 892a7ce5afdc..a973f47021c3 100644
--- a/src/py/flwr/flower/__init__.py
+++ b/src/py/flwr/cli/new/__init__.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Adap 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.
@@ -12,15 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Flower callable package."""
+"""Flower command line interface `new` command."""
 
-
-from flwr.client.flower import Flower as Flower
-from flwr.client.typing import Bwd as Bwd
-from flwr.client.typing import Fwd as Fwd
+from .new import new as new
 
 __all__ = [
-    "Flower",
-    "Fwd",
-    "Bwd",
+    "new",
 ]
diff --git a/src/py/flwr/cli/new/new.py b/src/py/flwr/cli/new/new.py
new file mode 100644
index 000000000000..7eb47e3e3548
--- /dev/null
+++ b/src/py/flwr/cli/new/new.py
@@ -0,0 +1,154 @@
+# 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 `new` command."""
+
+import os
+from enum import Enum
+from string import Template
+from typing import Dict, Optional
+
+import typer
+from typing_extensions import Annotated
+
+from ..utils import prompt_options, prompt_text
+
+
+class MlFramework(str, Enum):
+    """Available frameworks."""
+
+    NUMPY = "NumPy"
+    PYTORCH = "PyTorch"
+    TENSORFLOW = "TensorFlow"
+
+
+class TemplateNotFound(Exception):
+    """Raised when template does not exist."""
+
+
+def load_template(name: str) -> str:
+    """Load template from template directory and return as text."""
+    tpl_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "templates"))
+    tpl_file_path = os.path.join(tpl_dir, name)
+
+    if not os.path.isfile(tpl_file_path):
+        raise TemplateNotFound(f"Template '{name}' not found")
+
+    with open(tpl_file_path, encoding="utf-8") as tpl_file:
+        return tpl_file.read()
+
+
+def render_template(template: str, data: Dict[str, str]) -> str:
+    """Render template."""
+    tpl_file = load_template(template)
+    tpl = Template(tpl_file)
+    result = tpl.substitute(data)
+    return result
+
+
+def create_file(file_path: str, content: str) -> None:
+    """Create file including all nessecary directories and write content into file."""
+    os.makedirs(os.path.dirname(file_path), exist_ok=True)
+    with open(file_path, "w", encoding="utf-8") as f:
+        f.write(content)
+
+
+def render_and_create(file_path: str, template: str, context: Dict[str, str]) -> None:
+    """Render template and write to file."""
+    content = render_template(template, context)
+    create_file(file_path, content)
+
+
+def new(
+    project_name: Annotated[
+        Optional[str],
+        typer.Argument(metavar="project_name", help="The name of the project"),
+    ] = None,
+    framework: Annotated[
+        Optional[MlFramework],
+        typer.Option(case_sensitive=False, help="The ML framework to use"),
+    ] = None,
+) -> None:
+    """Create new Flower project."""
+    print(
+        typer.style(
+            f"🔨 Creating Flower project {project_name}...",
+            fg=typer.colors.GREEN,
+            bold=True,
+        )
+    )
+
+    if project_name is None:
+        project_name = prompt_text("Please provide project name")
+
+    if framework is not None:
+        framework_str = str(framework.value)
+    else:
+        framework_value = prompt_options(
+            "Please select ML framework by typing in the number",
+            [mlf.value for mlf in MlFramework],
+        )
+        selected_value = [
+            name
+            for name, value in vars(MlFramework).items()
+            if value == framework_value
+        ]
+        framework_str = selected_value[0]
+
+    framework_str = framework_str.lower()
+
+    # Set project directory path
+    cwd = os.getcwd()
+    pnl = project_name.lower()
+    project_dir = os.path.join(cwd, pnl)
+
+    # List of files to render
+    files = {
+        "README.md": {"template": "app/README.md.tpl"},
+        "requirements.txt": {"template": f"app/requirements.{framework_str}.txt.tpl"},
+        "flower.toml": {"template": "app/flower.toml.tpl"},
+        "pyproject.toml": {"template": f"app/pyproject.{framework_str}.toml.tpl"},
+        f"{pnl}/__init__.py": {"template": "app/code/__init__.py.tpl"},
+        f"{pnl}/server.py": {"template": f"app/code/server.{framework_str}.py.tpl"},
+        f"{pnl}/client.py": {"template": f"app/code/client.{framework_str}.py.tpl"},
+    }
+
+    # In case framework is MlFramework.PYTORCH generate additionally the task.py file
+    if framework_str == MlFramework.PYTORCH.value.lower():
+        files[f"{pnl}/task.py"] = {"template": f"app/code/task.{framework_str}.py.tpl"}
+
+    context = {"project_name": project_name}
+
+    for file_path, value in files.items():
+        render_and_create(
+            file_path=os.path.join(project_dir, file_path),
+            template=value["template"],
+            context=context,
+        )
+
+    print(
+        typer.style(
+            "🎊 Project creation successful.\n\n"
+            "Use the following command to run your project:\n",
+            fg=typer.colors.GREEN,
+            bold=True,
+        )
+    )
+    print(
+        typer.style(
+            f"	cd {project_name}\n" + "	pip install -e .\n	flwr run\n",
+            fg=typer.colors.BRIGHT_CYAN,
+            bold=True,
+        )
+    )
diff --git a/src/py/flwr/cli/new/new_test.py b/src/py/flwr/cli/new/new_test.py
new file mode 100644
index 000000000000..cedcb09b7755
--- /dev/null
+++ b/src/py/flwr/cli/new/new_test.py
@@ -0,0 +1,101 @@
+# 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 for Flower command line interface `new` command."""
+
+import os
+
+from .new import MlFramework, create_file, load_template, new, render_template
+
+
+def test_load_template() -> None:
+    """Test if load_template returns a string."""
+    # Prepare
+    filename = "app/README.md.tpl"
+
+    # Execute
+    text = load_template(filename)
+
+    # Assert
+    assert isinstance(text, str)
+
+
+def test_render_template() -> None:
+    """Test if a string is correctly substituted."""
+    # Prepare
+    filename = "app/README.md.tpl"
+    data = {"project_name": "FedGPT"}
+
+    # Execute
+    result = render_template(filename, data)
+
+    # Assert
+    assert "# FedGPT" in result
+
+
+def test_create_file(tmp_path: str) -> None:
+    """Test if file with content is created."""
+    # Prepare
+    file_path = os.path.join(tmp_path, "test.txt")
+    content = "Foobar"
+
+    # Execute
+    create_file(file_path, content)
+
+    # Assert
+    with open(file_path, encoding="utf-8") as f:
+        text = f.read()
+
+    assert text == "Foobar"
+
+
+def test_new(tmp_path: str) -> None:
+    """Test if project is created for framework."""
+    # Prepare
+    project_name = "FedGPT"
+    framework = MlFramework.PYTORCH
+    expected_files_top_level = {
+        "requirements.txt",
+        "fedgpt",
+        "README.md",
+        "flower.toml",
+        "pyproject.toml",
+    }
+    expected_files_module = {
+        "__init__.py",
+        "server.py",
+        "client.py",
+        "task.py",
+    }
+
+    # Current directory
+    origin = os.getcwd()
+
+    try:
+        # Change into the temprorary directory
+        os.chdir(tmp_path)
+
+        # Execute
+        new(project_name=project_name, framework=framework)
+
+        # Assert
+        file_list = os.listdir(os.path.join(tmp_path, project_name.lower()))
+        assert set(file_list) == expected_files_top_level
+
+        file_list = os.listdir(
+            os.path.join(tmp_path, project_name.lower(), project_name.lower())
+        )
+        assert set(file_list) == expected_files_module
+    finally:
+        os.chdir(origin)
diff --git a/src/py/flwr/cli/new/templates/__init__.py b/src/py/flwr/cli/new/templates/__init__.py
new file mode 100644
index 000000000000..7a951c2da1a2
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/__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 templates."""
diff --git a/src/py/flwr/cli/new/templates/app/README.md.tpl b/src/py/flwr/cli/new/templates/app/README.md.tpl
new file mode 100644
index 000000000000..516bed0f40c2
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/README.md.tpl
@@ -0,0 +1,47 @@
+# $project_name
+
+## Install dependencies
+
+```bash
+# Using pip
+pip install .
+
+# Or using Poetry
+poetry install
+```
+
+## Run (Simulation Engine)
+
+In the `$project_name` directory, use `flwr run` to run a local simulation:
+
+```bash
+flwr run
+```
+
+## Run (Deployment Engine)
+
+### Start the SuperLink
+
+```bash
+flower-superlink --insecure
+```
+
+### Start the long-running Flower client
+
+In a new terminal window, start the first long-running Flower client:
+
+```bash
+flower-client-app client:app --insecure
+```
+
+In yet another new terminal window, start the second long-running Flower client:
+
+```bash
+flower-client-app client:app --insecure
+```
+
+### Start the ServerApp
+
+```bash
+flower-server-app server:app --insecure
+```
diff --git a/src/py/flwr/cli/new/templates/app/__init__.py b/src/py/flwr/cli/new/templates/app/__init__.py
new file mode 100644
index 000000000000..617628fc9138
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/__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 templates."""
diff --git a/src/py/flwr/cli/new/templates/app/code/__init__.py b/src/py/flwr/cli/new/templates/app/code/__init__.py
new file mode 100644
index 000000000000..7f1a0e9f4fa2
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/__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 templates."""
diff --git a/src/py/flwr/cli/new/templates/app/code/__init__.py.tpl b/src/py/flwr/cli/new/templates/app/code/__init__.py.tpl
new file mode 100644
index 000000000000..57998c81efb8
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/__init__.py.tpl
@@ -0,0 +1 @@
+"""$project_name."""
diff --git a/src/py/flwr/cli/new/templates/app/code/client.numpy.py.tpl b/src/py/flwr/cli/new/templates/app/code/client.numpy.py.tpl
new file mode 100644
index 000000000000..232c305fc2a9
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/client.numpy.py.tpl
@@ -0,0 +1,23 @@
+"""$project_name: A Flower / NumPy app."""
+
+from flwr.client import NumPyClient, ClientApp
+import numpy as np
+
+
+class FlowerClient(NumPyClient):
+    def get_parameters(self, config):
+        return [np.ones((1, 1))]
+
+    def fit(self, parameters, config):
+        return ([np.ones((1, 1))], 1, {})
+
+    def evaluate(self, parameters, config):
+        return float(0.0), 1, {"accuracy": float(1.0)}
+
+
+def client_fn(cid: str):
+    return FlowerClient().to_client()
+
+
+# Flower ClientApp
+app = ClientApp(client_fn=client_fn)
diff --git a/src/py/flwr/cli/new/templates/app/code/client.pytorch.py.tpl b/src/py/flwr/cli/new/templates/app/code/client.pytorch.py.tpl
new file mode 100644
index 000000000000..7137a7791683
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/client.pytorch.py.tpl
@@ -0,0 +1,46 @@
+"""$project_name: A Flower / PyTorch app."""
+
+from flwr.client import NumPyClient, ClientApp
+
+from $project_name.task import (
+    Net,
+    DEVICE,
+    load_data,
+    get_weights,
+    set_weights,
+    train,
+    test,
+)
+
+
+# Define Flower Client and client_fn
+class FlowerClient(NumPyClient):
+    def __init__(self, net, trainloader, valloader):
+        self.net = net
+        self.trainloader = trainloader
+        self.valloader = valloader
+
+    def fit(self, parameters, config):
+        set_weights(self.net, parameters)
+        results = train(self.net, self.trainloader, self.valloader, 1, DEVICE)
+        return get_weights(self.net), len(self.trainloader.dataset), results
+
+    def evaluate(self, parameters, config):
+        set_weights(self.net, parameters)
+        loss, accuracy = test(self.net, self.valloader)
+        return loss, len(self.valloader.dataset), {"accuracy": accuracy}
+
+
+def client_fn(cid):
+    # Load model and data
+    net = Net().to(DEVICE)
+    trainloader, valloader = load_data(int(cid), 2)
+
+    # Return Client instance
+    return FlowerClient(net, trainloader, valloader).to_client()
+
+
+# Flower ClientApp
+app = ClientApp(
+    client_fn,
+)
diff --git a/src/py/flwr/cli/new/templates/app/code/client.tensorflow.py.tpl b/src/py/flwr/cli/new/templates/app/code/client.tensorflow.py.tpl
new file mode 100644
index 000000000000..cc00f8ff0b8c
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/client.tensorflow.py.tpl
@@ -0,0 +1 @@
+"""$project_name: A Flower / TensorFlow app."""
diff --git a/src/py/flwr/cli/new/templates/app/code/server.numpy.py.tpl b/src/py/flwr/cli/new/templates/app/code/server.numpy.py.tpl
new file mode 100644
index 000000000000..03f95ae35cfd
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/server.numpy.py.tpl
@@ -0,0 +1,12 @@
+"""$project_name: A Flower / NumPy app."""
+
+import flwr as fl
+
+# Configure the strategy
+strategy = fl.server.strategy.FedAvg()
+
+# Flower ServerApp
+app = fl.server.ServerApp(
+    config=fl.server.ServerConfig(num_rounds=1),
+    strategy=strategy,
+)
diff --git a/src/py/flwr/cli/new/templates/app/code/server.pytorch.py.tpl b/src/py/flwr/cli/new/templates/app/code/server.pytorch.py.tpl
new file mode 100644
index 000000000000..cb04c052b429
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/server.pytorch.py.tpl
@@ -0,0 +1,28 @@
+"""$project_name: A Flower / PyTorch app."""
+
+from flwr.common import ndarrays_to_parameters
+from flwr.server import ServerApp, ServerConfig
+from flwr.server.strategy import FedAvg
+
+from $project_name.task import Net, get_weights
+
+
+# Initialize model parameters
+ndarrays = get_weights(Net())
+parameters = ndarrays_to_parameters(ndarrays)
+
+
+# Define strategy
+strategy = FedAvg(
+    fraction_fit=1.0,
+    fraction_evaluate=1.0,
+    min_available_clients=2,
+    initial_parameters=parameters,
+)
+
+
+# Create ServerApp
+app = ServerApp(
+    config=ServerConfig(num_rounds=3),
+    strategy=strategy,
+)
diff --git a/src/py/flwr/cli/new/templates/app/code/server.tensorflow.py.tpl b/src/py/flwr/cli/new/templates/app/code/server.tensorflow.py.tpl
new file mode 100644
index 000000000000..cc00f8ff0b8c
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/server.tensorflow.py.tpl
@@ -0,0 +1 @@
+"""$project_name: A Flower / TensorFlow app."""
diff --git a/src/py/flwr/cli/new/templates/app/code/task.pytorch.py.tpl b/src/py/flwr/cli/new/templates/app/code/task.pytorch.py.tpl
new file mode 100644
index 000000000000..85460564b6ef
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/code/task.pytorch.py.tpl
@@ -0,0 +1,106 @@
+"""$project_name: A Flower / PyTorch app."""
+
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import DataLoader
+from torchvision.datasets import CIFAR10
+from torchvision.transforms import Compose, Normalize, ToTensor
+from flwr_datasets import FederatedDataset
+
+DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+
+class Net(nn.Module):
+    """Model (simple CNN adapted from 'PyTorch: A 60 Minute Blitz')"""
+
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(3, 6, 5)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1 = nn.Linear(16 * 5 * 5, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = self.pool(F.relu(self.conv1(x)))
+        x = self.pool(F.relu(self.conv2(x)))
+        x = x.view(-1, 16 * 5 * 5)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        return self.fc3(x)
+
+
+def load_data(partition_id, num_partitions):
+    """Load partition CIFAR10 data."""
+    fds = FederatedDataset(dataset="cifar10", partitioners={"train": num_partitions})
+    partition = fds.load_partition(partition_id)
+    # Divide data on each node: 80% train, 20% test
+    partition_train_test = partition.train_test_split(test_size=0.2)
+    pytorch_transforms = Compose(
+        [ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
+    )
+
+    def apply_transforms(batch):
+        """Apply transforms to the partition from FederatedDataset."""
+        batch["img"] = [pytorch_transforms(img) for img in batch["img"]]
+        return batch
+
+    partition_train_test = partition_train_test.with_transform(apply_transforms)
+    trainloader = DataLoader(partition_train_test["train"], batch_size=32, shuffle=True)
+    testloader = DataLoader(partition_train_test["test"], batch_size=32)
+    return trainloader, testloader
+
+
+def train(net, trainloader, valloader, epochs, device):
+    """Train the model on the training set."""
+    net.to(device)  # move model to GPU if available
+    criterion = torch.nn.CrossEntropyLoss().to(device)
+    optimizer = torch.optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
+    net.train()
+    for _ in range(epochs):
+        for batch in trainloader:
+            images = batch["img"]
+            labels = batch["label"]
+            optimizer.zero_grad()
+            criterion(net(images.to(DEVICE)), labels.to(DEVICE)).backward()
+            optimizer.step()
+
+    train_loss, train_acc = test(net, trainloader)
+    val_loss, val_acc = test(net, valloader)
+
+    results = {
+        "train_loss": train_loss,
+        "train_accuracy": train_acc,
+        "val_loss": val_loss,
+        "val_accuracy": val_acc,
+    }
+    return results
+
+
+def test(net, testloader):
+    """Validate the model on the test set."""
+    criterion = torch.nn.CrossEntropyLoss()
+    correct, loss = 0, 0.0
+    with torch.no_grad():
+        for batch in testloader:
+            images = batch["img"].to(DEVICE)
+            labels = batch["label"].to(DEVICE)
+            outputs = net(images)
+            loss += criterion(outputs, labels).item()
+            correct += (torch.max(outputs.data, 1)[1] == labels).sum().item()
+    accuracy = correct / len(testloader.dataset)
+    return loss, accuracy
+
+
+def get_weights(net):
+    return [val.cpu().numpy() for _, val in net.state_dict().items()]
+
+
+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)
diff --git a/src/py/flwr/cli/new/templates/app/flower.toml.tpl b/src/py/flwr/cli/new/templates/app/flower.toml.tpl
new file mode 100644
index 000000000000..07a6ffaf9e49
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/flower.toml.tpl
@@ -0,0 +1,13 @@
+[project]
+name = "$project_name"
+version = "1.0.0"
+description = ""
+license = "Apache-2.0"
+authors = [
+    "The Flower Authors <hello@flower.ai>",
+]
+readme = "README.md"
+
+[flower.components]
+serverapp = "$project_name.server:app"
+clientapp = "$project_name.client:app"
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
new file mode 100644
index 000000000000..15d8211a1a25
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/pyproject.numpy.toml.tpl
@@ -0,0 +1,19 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "$project_name"
+version = "1.0.0"
+description = ""
+license = "Apache-2.0"
+authors = [
+    "The Flower Authors <hello@flower.ai>",
+]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.9"
+# Mandatory dependencies
+numpy = "^1.21.0"
+flwr = { version = "^1.8.0", extras = ["simulation"] }
diff --git a/src/py/flwr/cli/new/templates/app/pyproject.pytorch.toml.tpl b/src/py/flwr/cli/new/templates/app/pyproject.pytorch.toml.tpl
new file mode 100644
index 000000000000..da0e15b903f8
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/pyproject.pytorch.toml.tpl
@@ -0,0 +1,21 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "$project_name"
+version = "1.0.0"
+description = ""
+license = "Apache-2.0"
+authors = [
+    "The Flower Authors <hello@flower.ai>",
+]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = "^3.9"
+# Mandatory dependencies
+flwr-nightly = { version = "1.8.0.dev20240313", extras = ["simulation"] }
+flwr-datasets = { version = "0.0.2", extras = ["vision"] }
+torch = "2.2.1"
+torchvision = "0.17.1"
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
new file mode 100644
index 000000000000..f7383a78b7d5
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/pyproject.tensorflow.toml.tpl
@@ -0,0 +1,21 @@
+[build-system]
+requires = ["poetry-core>=1.4.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry]
+name = "$project_name"
+version = "1.0.0"
+description = ""
+license = "Apache-2.0"
+authors = [
+    "The Flower Authors <hello@flower.ai>",
+]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.9,<3.11"
+# Mandatory dependencies
+flwr = { version = "^1.8.0", extras = ["simulation"] }
+flwr-datasets = { version = "^0.0.2", extras = ["vision"] }
+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\"" }
diff --git a/src/py/flwr/cli/new/templates/app/requirements.numpy.txt.tpl b/src/py/flwr/cli/new/templates/app/requirements.numpy.txt.tpl
new file mode 100644
index 000000000000..4b460798e96f
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/requirements.numpy.txt.tpl
@@ -0,0 +1,2 @@
+flwr>=1.8, <2.0
+numpy>=1.21.0
diff --git a/src/py/flwr/cli/new/templates/app/requirements.pytorch.txt.tpl b/src/py/flwr/cli/new/templates/app/requirements.pytorch.txt.tpl
new file mode 100644
index 000000000000..ddb8a814447b
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/requirements.pytorch.txt.tpl
@@ -0,0 +1,4 @@
+flwr-nightly[simulation]==1.8.0.dev20240313
+flwr-datasets[vision]==0.0.2
+torch==2.2.1
+torchvision==0.17.1
diff --git a/src/py/flwr/cli/new/templates/app/requirements.tensorflow.txt.tpl b/src/py/flwr/cli/new/templates/app/requirements.tensorflow.txt.tpl
new file mode 100644
index 000000000000..b6fb49a4bbcb
--- /dev/null
+++ b/src/py/flwr/cli/new/templates/app/requirements.tensorflow.txt.tpl
@@ -0,0 +1,4 @@
+flwr>=1.8, <2.0
+flwr-datasets[vision]>=0.0.2, <1.0.0
+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"
diff --git a/src/py/flwr/cli/run/__init__.py b/src/py/flwr/cli/run/__init__.py
new file mode 100644
index 000000000000..43523c215d3e
--- /dev/null
+++ b/src/py/flwr/cli/run/__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 command line interface `run` command."""
+
+from .run import run as run
+
+__all__ = [
+    "run",
+]
diff --git a/src/py/flwr/cli/run/run.py b/src/py/flwr/cli/run/run.py
new file mode 100644
index 000000000000..98b5da1843a6
--- /dev/null
+++ b/src/py/flwr/cli/run/run.py
@@ -0,0 +1,68 @@
+# 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 `run` command."""
+
+import sys
+
+import typer
+
+from flwr.cli import flower_toml
+from flwr.simulation.run_simulation import _run_simulation
+
+
+def run() -> None:
+    """Run Flower project."""
+    typer.secho("Loading project configuration... ", fg=typer.colors.BLUE)
+
+    config, errors, warnings = flower_toml.load_and_validate_with_defaults()
+
+    if config is None:
+        typer.secho(
+            "Project configuration could not be loaded.\nflower.toml is invalid:\n"
+            + "\n".join([f"- {line}" for line in errors]),
+            fg=typer.colors.RED,
+            bold=True,
+        )
+        sys.exit()
+
+    if warnings:
+        typer.secho(
+            "Project configuration is missing the following "
+            "recommended properties:\n" + "\n".join([f"- {line}" for line in warnings]),
+            fg=typer.colors.RED,
+            bold=True,
+        )
+
+    typer.secho("Success", fg=typer.colors.GREEN)
+
+    server_app_ref = config["flower"]["components"]["serverapp"]
+    client_app_ref = config["flower"]["components"]["clientapp"]
+    engine = config["flower"]["engine"]["name"]
+
+    if engine == "simulation":
+        num_supernodes = config["flower"]["engine"]["simulation"]["supernode"]["num"]
+
+        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,
+        )
+    else:
+        typer.secho(
+            f"Engine '{engine}' is not yet supported in `flwr run`",
+            fg=typer.colors.RED,
+            bold=True,
+        )
diff --git a/src/py/flwr/cli/utils.py b/src/py/flwr/cli/utils.py
new file mode 100644
index 000000000000..4e86f0c3b8c8
--- /dev/null
+++ b/src/py/flwr/cli/utils.py
@@ -0,0 +1,67 @@
+# 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 utils."""
+
+from typing import List, cast
+
+import typer
+
+
+def prompt_text(text: str) -> str:
+    """Ask user to enter text input."""
+    while True:
+        result = typer.prompt(
+            typer.style(f"\n💬 {text}", fg=typer.colors.MAGENTA, bold=True)
+        )
+        if len(result) > 0:
+            break
+        print(typer.style("❌ Invalid entry", fg=typer.colors.RED, bold=True))
+
+    return cast(str, result)
+
+
+def prompt_options(text: str, options: List[str]) -> str:
+    """Ask user to select one of the given options and return the selected item."""
+    # Turn options into a list with index as in " [ 0] quickstart-pytorch"
+    options_formatted = [
+        " [ "
+        + typer.style(index, fg=typer.colors.GREEN, bold=True)
+        + "]"
+        + f" {typer.style(name, fg=typer.colors.WHITE, bold=True)}"
+        for index, name in enumerate(options)
+    ]
+
+    while True:
+        index = typer.prompt(
+            "\n"
+            + typer.style(f"💬 {text}", fg=typer.colors.MAGENTA, bold=True)
+            + "\n\n"
+            + "\n".join(options_formatted)
+            + "\n\n\n"
+        )
+        try:
+            options[int(index)]  # pylint: disable=expression-not-assigned
+            break
+        except IndexError:
+            print(typer.style("❌ Index out of range", fg=typer.colors.RED, bold=True))
+            continue
+        except ValueError:
+            print(
+                typer.style("❌ Please choose a number", fg=typer.colors.RED, bold=True)
+            )
+            continue
+
+    result = options[int(index)]
+    return result
diff --git a/src/py/flwr/client/__init__.py b/src/py/flwr/client/__init__.py
index 13540a76cc25..a721fb584164 100644
--- a/src/py/flwr/client/__init__.py
+++ b/src/py/flwr/client/__init__.py
@@ -15,18 +15,20 @@
 """Flower client."""
 
 
-from .app import run_client as run_client
+from .app import run_client_app as run_client_app
 from .app import start_client as start_client
 from .app import start_numpy_client as start_numpy_client
 from .client import Client as Client
+from .client_app import ClientApp as ClientApp
 from .numpy_client import NumPyClient as NumPyClient
 from .typing import ClientFn as ClientFn
 
 __all__ = [
     "Client",
+    "ClientApp",
     "ClientFn",
     "NumPyClient",
-    "run_client",
+    "run_client_app",
     "start_client",
     "start_numpy_client",
 ]
diff --git a/src/py/flwr/client/app.py b/src/py/flwr/client/app.py
index 3448e18e20c5..c8287afc0fd0 100644
--- a/src/py/flwr/client/app.py
+++ b/src/py/flwr/client/app.py
@@ -18,14 +18,16 @@
 import argparse
 import sys
 import time
-from logging import INFO, WARN
+from logging import DEBUG, INFO, WARN
 from pathlib import Path
-from typing import Callable, ContextManager, Optional, Tuple, Union
+from typing import Callable, ContextManager, Optional, Tuple, Type, Union
+
+from grpc import RpcError
 
 from flwr.client.client import Client
-from flwr.client.flower import Flower
-from flwr.client.typing import Bwd, ClientFn, Fwd
-from flwr.common import GRPC_MAX_MESSAGE_LENGTH, EventType, event
+from flwr.client.client_app import ClientApp, LoadClientAppError
+from flwr.client.typing import ClientFn
+from flwr.common import GRPC_MAX_MESSAGE_LENGTH, EventType, Message, event
 from flwr.common.address import parse_address
 from flwr.common.constant import (
     MISSING_EXTRA_REST,
@@ -34,10 +36,11 @@
     TRANSPORT_TYPE_REST,
     TRANSPORT_TYPES,
 )
-from flwr.common.logger import log, warn_experimental_feature
-from flwr.proto.task_pb2 import TaskIns, TaskRes
+from flwr.common.exit_handlers import register_exit_handlers
+from flwr.common.logger import log, warn_deprecated_feature, warn_experimental_feature
+from flwr.common.object_ref import load_app, validate
+from flwr.common.retry_invoker import RetryInvoker, exponential
 
-from .flower import load_flower_callable
 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
@@ -45,13 +48,13 @@
 from .numpy_client import NumPyClient
 
 
-def run_client() -> None:
-    """Run Flower client."""
-    event(EventType.RUN_CLIENT_ENTER)
+def run_client_app() -> None:
+    """Run Flower client app."""
+    event(EventType.RUN_CLIENT_APP_ENTER)
 
     log(INFO, "Long-running Flower client starting")
 
-    args = _parse_args_client().parse_args()
+    args = _parse_args_run_client_app().parse_args()
 
     # Obtain certificates
     if args.insecure:
@@ -62,7 +65,12 @@ def run_client() -> None:
                 "the '--root-certificates' option when running in insecure mode, "
                 "or omit '--insecure' to use HTTPS."
             )
-        log(WARN, "Option `--insecure` was set. Starting insecure HTTP client.")
+        log(
+            WARN,
+            "Option `--insecure` was set. "
+            "Starting insecure HTTP client connected to %s.",
+            args.server,
+        )
         root_certificates = None
     else:
         # Load the certificates if provided, or load the system certificates
@@ -71,42 +79,72 @@ def run_client() -> None:
             root_certificates = None
         else:
             root_certificates = Path(cert_path).read_bytes()
+        log(
+            DEBUG,
+            "Starting secure HTTPS client connected to %s "
+            "with the following certificates: %s.",
+            args.server,
+            cert_path,
+        )
 
-    print(args.root_certificates)
-    print(args.server)
-    print(args.callable_dir)
-    print(args.callable)
+    log(
+        DEBUG,
+        "Flower will load ClientApp `%s`",
+        getattr(args, "client-app"),
+    )
+
+    client_app_dir = args.dir
+    if client_app_dir is not None:
+        sys.path.insert(0, client_app_dir)
+
+    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
 
-    callable_dir = args.callable_dir
-    if callable_dir is not None:
-        sys.path.insert(0, callable_dir)
+    def _load() -> ClientApp:
+        client_app = load_app(app_ref, LoadClientAppError)
 
-    def _load() -> Flower:
-        flower: Flower = load_flower_callable(args.callable)
-        return flower
+        if not isinstance(client_app, ClientApp):
+            raise LoadClientAppError(
+                f"Attribute {app_ref} is not of type {ClientApp}",
+            ) from None
+
+        return client_app
 
     _start_client_internal(
         server_address=args.server,
-        load_flower_callable_fn=_load,
-        transport="grpc-rere",  # Only
+        load_client_app_fn=_load,
+        transport="rest" if args.rest else "grpc-rere",
         root_certificates=root_certificates,
         insecure=args.insecure,
+        max_retries=args.max_retries,
+        max_wait_time=args.max_wait_time,
     )
-    event(EventType.RUN_CLIENT_LEAVE)
+    register_exit_handlers(event_type=EventType.RUN_CLIENT_APP_LEAVE)
 
 
-def _parse_args_client() -> argparse.ArgumentParser:
-    """Parse command line arguments."""
+def _parse_args_run_client_app() -> argparse.ArgumentParser:
+    """Parse flower-client-app command line arguments."""
     parser = argparse.ArgumentParser(
-        description="Start a long-running Flower client",
+        description="Start a Flower client app",
     )
 
+    parser.add_argument(
+        "client-app",
+        help="For example: `client:app` or `project.package.module:wrapper.app`",
+    )
     parser.add_argument(
         "--insecure",
         action="store_true",
         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(
+        "--rest",
+        action="store_true",
+        help="Use REST as a transport layer for the client.",
+    )
     parser.add_argument(
         "--root-certificates",
         metavar="ROOT_CERT",
@@ -120,13 +158,26 @@ def _parse_args_client() -> argparse.ArgumentParser:
         help="Server address",
     )
     parser.add_argument(
-        "--callable",
-        help="For example: `client:flower` or `project.package.module:wrapper.flower`",
+        "--max-retries",
+        type=int,
+        default=None,
+        help="The maximum number of times the client will try to connect to the"
+        "server before giving up in case of a connection error. By default,"
+        "it is set to None, meaning there is no limit to the number of tries.",
     )
     parser.add_argument(
-        "--callable-dir",
+        "--max-wait-time",
+        type=float,
+        default=None,
+        help="The maximum duration before the client stops trying to"
+        "connect to the server in case of connection error. By default, it"
+        "is set to None, meaning there is no limit to the total time.",
+    )
+    parser.add_argument(
+        "--dir",
         default="",
-        help="Add specified directory to the PYTHONPATH and load callable from there."
+        help="Add specified directory to the PYTHONPATH and load Flower "
+        "app from there."
         " Default: current working directory.",
     )
 
@@ -137,10 +188,12 @@ def _check_actionable_client(
     client: Optional[Client], client_fn: Optional[ClientFn]
 ) -> None:
     if client_fn is None and client is None:
-        raise Exception("Both `client_fn` and `client` are `None`, but one is required")
+        raise ValueError(
+            "Both `client_fn` and `client` are `None`, but one is required"
+        )
 
     if client_fn is not None and client is not None:
-        raise Exception(
+        raise ValueError(
             "Both `client_fn` and `client` are provided, but only one is allowed"
         )
 
@@ -149,6 +202,7 @@ def _check_actionable_client(
 # pylint: disable=too-many-branches
 # pylint: disable=too-many-locals
 # pylint: disable=too-many-statements
+# pylint: disable=too-many-arguments
 def start_client(
     *,
     server_address: str,
@@ -158,6 +212,8 @@ def start_client(
     root_certificates: Optional[Union[bytes, str]] = None,
     insecure: Optional[bool] = None,
     transport: Optional[str] = None,
+    max_retries: Optional[int] = None,
+    max_wait_time: Optional[float] = None,
 ) -> None:
     """Start a Flower client node which connects to a Flower server.
 
@@ -191,6 +247,14 @@ class `flwr.client.Client` (default: None)
         - 'grpc-bidi': gRPC, bidirectional streaming
         - 'grpc-rere': gRPC, request-response (experimental)
         - 'rest': HTTP (experimental)
+    max_retries: Optional[int] (default: None)
+        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.
+    max_wait_time: Optional[float] (default: None)
+        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.
 
     Examples
     --------
@@ -225,13 +289,15 @@ class `flwr.client.Client` (default: None)
     event(EventType.START_CLIENT_ENTER)
     _start_client_internal(
         server_address=server_address,
-        load_flower_callable_fn=None,
+        load_client_app_fn=None,
         client_fn=client_fn,
         client=client,
         grpc_max_message_length=grpc_max_message_length,
         root_certificates=root_certificates,
         insecure=insecure,
         transport=transport,
+        max_retries=max_retries,
+        max_wait_time=max_wait_time,
     )
     event(EventType.START_CLIENT_LEAVE)
 
@@ -243,13 +309,15 @@ class `flwr.client.Client` (default: None)
 def _start_client_internal(
     *,
     server_address: str,
-    load_flower_callable_fn: Optional[Callable[[], Flower]] = None,
+    load_client_app_fn: Optional[Callable[[], ClientApp]] = None,
     client_fn: Optional[ClientFn] = None,
     client: Optional[Client] = None,
     grpc_max_message_length: int = GRPC_MAX_MESSAGE_LENGTH,
     root_certificates: Optional[Union[bytes, str]] = None,
     insecure: Optional[bool] = None,
     transport: Optional[str] = None,
+    max_retries: Optional[int] = None,
+    max_wait_time: Optional[float] = None,
 ) -> None:
     """Start a Flower client node which connects to a Flower server.
 
@@ -259,8 +327,8 @@ def _start_client_internal(
         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"`.
-    load_flower_callable_fn : Optional[Callable[[], Flower]] (default: None)
-        A function that can be used to load a `Flower` callable instance.
+    load_client_app_fn : Optional[Callable[[], ClientApp]] (default: None)
+        A function that can be used to load a `ClientApp` instance.
     client_fn : Optional[ClientFn]
         A callable that instantiates a Client. (default: None)
     client : Optional[flwr.client.Client]
@@ -277,7 +345,7 @@ class `flwr.client.Client` (default: None)
         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.
-    insecure : bool (default: True)
+    insecure : Optional[bool] (default: None)
         Starts an insecure gRPC connection when True. Enables HTTPS connection
         when False, using system certificates if `root_certificates` is None.
     transport : Optional[str] (default: None)
@@ -285,11 +353,19 @@ class `flwr.client.Client` (default: None)
         - 'grpc-bidi': gRPC, bidirectional streaming
         - 'grpc-rere': gRPC, request-response (experimental)
         - 'rest': HTTP (experimental)
+    max_retries: Optional[int] (default: None)
+        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.
+    max_wait_time: Optional[float] (default: None)
+        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.
     """
     if insecure is None:
         insecure = root_certificates is None
 
-    if load_flower_callable_fn is None:
+    if load_client_app_fn is None:
         _check_actionable_client(client, client_fn)
 
         if client_fn is None:
@@ -298,25 +374,63 @@ def single_client_factory(
                 cid: str,  # pylint: disable=unused-argument
             ) -> Client:
                 if client is None:  # Added this to keep mypy happy
-                    raise Exception(
+                    raise ValueError(
                         "Both `client_fn` and `client` are `None`, but one is required"
                     )
                 return client  # Always return the same instance
 
             client_fn = single_client_factory
 
-        def _load_app() -> Flower:
-            return Flower(client_fn=client_fn)
+        def _load_client_app() -> ClientApp:
+            return ClientApp(client_fn=client_fn)
 
-        load_flower_callable_fn = _load_app
+        load_client_app_fn = _load_client_app
     else:
-        warn_experimental_feature("`load_flower_callable_fn`")
+        warn_experimental_feature("`load_client_app_fn`")
 
-    # At this point, only `load_flower_callable_fn` should be used
+    # At this point, only `load_client_app_fn` should be used
     # Both `client` and `client_fn` must not be used directly
 
     # Initialize connection context manager
-    connection, address = _init_connection(transport, server_address)
+    connection, address, connection_error_type = _init_connection(
+        transport, server_address
+    )
+
+    retry_invoker = RetryInvoker(
+        wait_factory=exponential,
+        recoverable_exceptions=connection_error_type,
+        max_tries=max_retries,
+        max_time=max_wait_time,
+        on_giveup=lambda retry_state: (
+            log(
+                WARN,
+                "Giving up reconnection after %.2f seconds and %s tries.",
+                retry_state.elapsed_time,
+                retry_state.tries,
+            )
+            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,
+            )
+        ),
+    )
 
     node_state = NodeState()
 
@@ -325,6 +439,7 @@ def _load_app() -> Flower:
         with connection(
             address,
             insecure,
+            retry_invoker,
             grpc_max_message_length,
             root_certificates,
         ) as conn:
@@ -336,40 +451,63 @@ def _load_app() -> Flower:
 
             while True:
                 # Receive
-                task_ins = receive()
-                if task_ins is None:
+                message = receive()
+                if message is None:
                     time.sleep(3)  # Wait for 3s before asking again
                     continue
 
+                log(INFO, "")
+                log(
+                    INFO,
+                    "[RUN %s, ROUND %s]",
+                    message.metadata.run_id,
+                    message.metadata.group_id,
+                )
+                log(
+                    INFO,
+                    "Received: %s message %s",
+                    message.metadata.message_type,
+                    message.metadata.message_id,
+                )
+
                 # Handle control message
-                task_res, sleep_duration = handle_control_message(task_ins=task_ins)
-                if task_res:
-                    send(task_res)
+                out_message, sleep_duration = handle_control_message(message)
+                if out_message:
+                    send(out_message)
                     break
 
-                # Register state
-                node_state.register_workloadstate(workload_id=task_ins.workload_id)
+                # 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)
 
-                # Load app
-                app: Flower = load_flower_callable_fn()
+                # Load ClientApp instance
+                client_app: ClientApp = load_client_app_fn()
 
                 # Handle task message
-                fwd_msg: Fwd = Fwd(
-                    task_ins=task_ins,
-                    state=node_state.retrieve_workloadstate(
-                        workload_id=task_ins.workload_id
-                    ),
-                )
-                bwd_msg: Bwd = app(fwd=fwd_msg)
+                out_message = client_app(message=message, context=context)
 
                 # Update node state
-                node_state.update_workloadstate(
-                    workload_id=bwd_msg.task_res.workload_id,
-                    workload_state=bwd_msg.state,
+                node_state.update_context(
+                    run_id=message.metadata.run_id,
+                    context=context,
                 )
 
                 # Send
-                send(bwd_msg.task_res)
+                send(out_message)
+                log(
+                    INFO,
+                    "[RUN %s, ROUND %s]",
+                    out_message.metadata.run_id,
+                    out_message.metadata.group_id,
+                )
+                log(
+                    INFO,
+                    "Sent: %s reply to message %s",
+                    out_message.metadata.message_type,
+                    message.metadata.message_id,
+                )
 
             # Unregister node
             if delete_node is not None:
@@ -398,6 +536,12 @@ def start_numpy_client(
 ) -> None:
     """Start a Flower NumPyClient which connects to a gRPC server.
 
+    Warning
+    -------
+    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.
+
     Parameters
     ----------
     server_address : str
@@ -453,21 +597,22 @@ def start_numpy_client(
     >>>     root_certificates=Path("/crts/root.pem").read_bytes(),
     >>> )
     """
-    # warnings.warn(
-    #     "flwr.client.start_numpy_client() is deprecated and will "
-    #     "be removed in a future version of Flower. Instead, pass "
-    #     "your client to `flwr.client.start_client()` by calling "
-    #     "first the `.to_client()` method as shown below: \n"
-    #     "\tflwr.client.start_client(\n"
-    #     "\t\tserver_address='<IP>:<PORT>',\n"
-    #     "\t\tclient=FlowerClient().to_client()\n"
-    #     "\t)",
-    #     DeprecationWarning,
-    #     stacklevel=2,
-    # )
+    mssg = (
+        "flwr.client.start_numpy_client() is deprecated. \n\tInstead, use "
+        "`flwr.client.start_client()` by ensuring you first call "
+        "the `.to_client()` method as shown below: \n"
+        "\tflwr.client.start_client(\n"
+        "\t\tserver_address='<IP>:<PORT>',\n"
+        "\t\tclient=FlowerClient().to_client(),"
+        " # <-- where FlowerClient is of type flwr.client.NumPyClient object\n"
+        "\t)\n"
+        "\tUsing `start_numpy_client()` is deprecated."
+    )
+
+    warn_deprecated_feature(name=mssg)
 
     # Calling this function is deprecated. A warning is thrown.
-    # We first need to convert either the supplied client to `Client.`
+    # We first need to convert the supplied client to `Client.`
 
     wrp_client = client.to_client()
 
@@ -481,21 +626,20 @@ def start_numpy_client(
     )
 
 
-def _init_connection(
-    transport: Optional[str], server_address: str
-) -> Tuple[
+def _init_connection(transport: Optional[str], server_address: str) -> Tuple[
     Callable[
-        [str, bool, int, Union[bytes, str, None]],
+        [str, bool, RetryInvoker, int, Union[bytes, str, None]],
         ContextManager[
             Tuple[
-                Callable[[], Optional[TaskIns]],
-                Callable[[TaskRes], None],
+                Callable[[], Optional[Message]],
+                Callable[[Message], None],
                 Optional[Callable[[], None]],
                 Optional[Callable[[], None]],
             ]
         ],
     ],
     str,
+    Type[Exception],
 ]:
     # Parse IP address
     parsed_address = parse_address(server_address)
@@ -511,6 +655,8 @@ def _init_connection(
     # Use either gRPC bidirectional streaming or REST request/response
     if transport == TRANSPORT_TYPE_REST:
         try:
+            from requests.exceptions import ConnectionError as RequestsConnectionError
+
             from .rest_client.connection import http_request_response
         except ModuleNotFoundError:
             sys.exit(MISSING_EXTRA_REST)
@@ -519,14 +665,14 @@ def _init_connection(
                 "When using the REST API, please provide `https://` or "
                 "`http://` before the server address (e.g. `http://127.0.0.1:8080`)"
             )
-        connection = http_request_response
+        connection, error_type = http_request_response, RequestsConnectionError
     elif transport == TRANSPORT_TYPE_GRPC_RERE:
-        connection = grpc_request_response
+        connection, error_type = grpc_request_response, RpcError
     elif transport == TRANSPORT_TYPE_GRPC_BIDI:
-        connection = grpc_connection
+        connection, error_type = grpc_connection, RpcError
     else:
         raise ValueError(
             f"Unknown transport type: {transport} (possible: {TRANSPORT_TYPES})"
         )
 
-    return connection, address
+    return connection, address, error_type
diff --git a/src/py/flwr/client/app_test.py b/src/py/flwr/client/app_test.py
index 7ef6410debad..56d6308a0fe2 100644
--- a/src/py/flwr/client/app_test.py
+++ b/src/py/flwr/client/app_test.py
@@ -41,19 +41,19 @@ class PlainClient(Client):
 
     def get_properties(self, ins: GetPropertiesIns) -> GetPropertiesRes:
         """Raise an Exception because this method is not expected to be called."""
-        raise Exception()
+        raise NotImplementedError()
 
     def get_parameters(self, ins: GetParametersIns) -> GetParametersRes:
         """Raise an Exception because this method is not expected to be called."""
-        raise Exception()
+        raise NotImplementedError()
 
     def fit(self, ins: FitIns) -> FitRes:
         """Raise an Exception because this method is not expected to be called."""
-        raise Exception()
+        raise NotImplementedError()
 
     def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
         """Raise an Exception because this method is not expected to be called."""
-        raise Exception()
+        raise NotImplementedError()
 
 
 class NeedsWrappingClient(NumPyClient):
@@ -61,23 +61,23 @@ class NeedsWrappingClient(NumPyClient):
 
     def get_properties(self, config: Config) -> Dict[str, Scalar]:
         """Raise an Exception because this method is not expected to be called."""
-        raise Exception()
+        raise NotImplementedError()
 
     def get_parameters(self, config: Config) -> NDArrays:
         """Raise an Exception because this method is not expected to be called."""
-        raise Exception()
+        raise NotImplementedError()
 
     def fit(
         self, parameters: NDArrays, config: Config
     ) -> Tuple[NDArrays, int, Dict[str, Scalar]]:
         """Raise an Exception because this method is not expected to be called."""
-        raise Exception()
+        raise NotImplementedError()
 
     def evaluate(
         self, parameters: NDArrays, config: Config
     ) -> Tuple[float, int, Dict[str, Scalar]]:
         """Raise an Exception because this method is not expected to be called."""
-        raise Exception()
+        raise NotImplementedError()
 
 
 def test_to_client_with_client() -> None:
diff --git a/src/py/flwr/client/client.py b/src/py/flwr/client/client.py
index 280e0a8ca989..23a3755f3efe 100644
--- a/src/py/flwr/client/client.py
+++ b/src/py/flwr/client/client.py
@@ -19,9 +19,9 @@
 
 from abc import ABC
 
-from flwr.client.workload_state import WorkloadState
 from flwr.common import (
     Code,
+    Context,
     EvaluateIns,
     EvaluateRes,
     FitIns,
@@ -38,7 +38,7 @@
 class Client(ABC):
     """Abstract base class for Flower clients."""
 
-    state: WorkloadState
+    context: Context
 
     def get_properties(self, ins: GetPropertiesIns) -> GetPropertiesRes:
         """Return set of client's properties.
@@ -141,13 +141,13 @@ def evaluate(self, ins: EvaluateIns) -> EvaluateRes:
             metrics={},
         )
 
-    def get_state(self) -> WorkloadState:
-        """Get the workload state from this client."""
-        return self.state
+    def get_context(self) -> Context:
+        """Get the run context from this client."""
+        return self.context
 
-    def set_state(self, state: WorkloadState) -> None:
-        """Apply a workload state to this client."""
-        self.state = state
+    def set_context(self, context: Context) -> None:
+        """Apply a run context to this client."""
+        self.context = context
 
     def to_client(self) -> Client:
         """Return client (itself)."""
diff --git a/src/py/flwr/client/client_app.py b/src/py/flwr/client/client_app.py
new file mode 100644
index 000000000000..ad7a01326991
--- /dev/null
+++ b/src/py/flwr/client/client_app.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 ClientApp."""
+
+
+from typing import Callable, List, Optional
+
+from flwr.client.message_handler.message_handler import (
+    handle_legacy_message_from_msgtype,
+)
+from flwr.client.mod.utils import make_ffn
+from flwr.client.typing import ClientFn, Mod
+from flwr.common import Context, Message, MessageType
+
+from .typing import ClientAppCallable
+
+
+class ClientApp:
+    """Flower ClientApp.
+
+    Examples
+    --------
+    Assuming a typical `Client` implementation named `FlowerClient`, you can wrap it in
+    a `ClientApp` as follows:
+
+    >>> class FlowerClient(NumPyClient):
+    >>>     # ...
+    >>>
+    >>> def client_fn(cid):
+    >>>    return FlowerClient().to_client()
+    >>>
+    >>> app = ClientApp(client_fn)
+
+    If the above code is in a Python module called `client`, it can be started as
+    follows:
+
+    >>> flower-client-app client:app --insecure
+
+    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`.
+    """
+
+    def __init__(
+        self,
+        client_fn: Optional[ClientFn] = None,  # Only for backward compatibility
+        mods: Optional[List[Mod]] = None,
+    ) -> None:
+        self._mods: List[Mod] = mods if mods is not None else []
+
+        # Create wrapper function for `handle`
+        self._call: Optional[ClientAppCallable] = None
+        if client_fn is not None:
+
+            def ffn(
+                message: Message,
+                context: Context,
+            ) -> Message:  # pylint: disable=invalid-name
+                out_message = handle_legacy_message_from_msgtype(
+                    client_fn=client_fn, message=message, context=context
+                )
+                return out_message
+
+            # Wrap mods around the wrapped handle function
+            self._call = make_ffn(ffn, mods if mods is not None else [])
+
+        # Step functions
+        self._train: Optional[ClientAppCallable] = None
+        self._evaluate: Optional[ClientAppCallable] = None
+        self._query: Optional[ClientAppCallable] = None
+
+    def __call__(self, message: Message, context: Context) -> Message:
+        """Execute `ClientApp`."""
+        # Execute message using `client_fn`
+        if self._call:
+            return self._call(message, context)
+
+        # Execute message using a new
+        if message.metadata.message_type == MessageType.TRAIN:
+            if self._train:
+                return self._train(message, context)
+            raise ValueError("No `train` function registered")
+        if message.metadata.message_type == MessageType.EVALUATE:
+            if self._evaluate:
+                return self._evaluate(message, context)
+            raise ValueError("No `evaluate` function registered")
+        if message.metadata.message_type == MessageType.QUERY:
+            if self._query:
+                return self._query(message, context)
+            raise ValueError("No `query` function registered")
+
+        # Message type did not match one of the known message types abvoe
+        raise ValueError(f"Unknown message_type: {message.metadata.message_type}")
+
+    def train(self) -> Callable[[ClientAppCallable], ClientAppCallable]:
+        """Return a decorator that registers the train fn with the client app.
+
+        Examples
+        --------
+        >>> app = ClientApp()
+        >>>
+        >>> @app.train()
+        >>> def train(message: Message, context: Context) -> Message:
+        >>>    print("ClientApp training running")
+        >>>    # Create and return an echo reply message
+        >>>    return message.create_reply(content=message.content(), ttl="")
+        """
+
+        def train_decorator(train_fn: ClientAppCallable) -> ClientAppCallable:
+            """Register the train fn with the ServerApp object."""
+            if self._call:
+                raise _registration_error(MessageType.TRAIN)
+
+            # Register provided function with the ClientApp object
+            # Wrap mods around the wrapped step function
+            self._train = make_ffn(train_fn, self._mods)
+
+            # Return provided function unmodified
+            return train_fn
+
+        return train_decorator
+
+    def evaluate(self) -> Callable[[ClientAppCallable], ClientAppCallable]:
+        """Return a decorator that registers the evaluate fn with the client app.
+
+        Examples
+        --------
+        >>> app = ClientApp()
+        >>>
+        >>> @app.evaluate()
+        >>> def evaluate(message: Message, context: Context) -> Message:
+        >>>    print("ClientApp evaluation running")
+        >>>    # Create and return an echo reply message
+        >>>    return message.create_reply(content=message.content(), ttl="")
+        """
+
+        def evaluate_decorator(evaluate_fn: ClientAppCallable) -> ClientAppCallable:
+            """Register the evaluate fn with the ServerApp object."""
+            if self._call:
+                raise _registration_error(MessageType.EVALUATE)
+
+            # Register provided function with the ClientApp object
+            # Wrap mods around the wrapped step function
+            self._evaluate = make_ffn(evaluate_fn, self._mods)
+
+            # Return provided function unmodified
+            return evaluate_fn
+
+        return evaluate_decorator
+
+    def query(self) -> Callable[[ClientAppCallable], ClientAppCallable]:
+        """Return a decorator that registers the query fn with the client app.
+
+        Examples
+        --------
+        >>> app = ClientApp()
+        >>>
+        >>> @app.query()
+        >>> def query(message: Message, context: Context) -> Message:
+        >>>    print("ClientApp query running")
+        >>>    # Create and return an echo reply message
+        >>>    return message.create_reply(content=message.content(), ttl="")
+        """
+
+        def query_decorator(query_fn: ClientAppCallable) -> ClientAppCallable:
+            """Register the query fn with the ServerApp object."""
+            if self._call:
+                raise _registration_error(MessageType.QUERY)
+
+            # Register provided function with the ClientApp object
+            # Wrap mods around the wrapped step function
+            self._query = make_ffn(query_fn, self._mods)
+
+            # Return provided function unmodified
+            return query_fn
+
+        return query_decorator
+
+
+class LoadClientAppError(Exception):
+    """Error when trying to load `ClientApp`."""
+
+
+def _registration_error(fn_name: str) -> ValueError:
+    return ValueError(
+        f"""Use either `@app.{fn_name}()` or `client_fn`, but not both.
+
+        Use the `ClientApp` with an existing `client_fn`:
+
+        >>> class FlowerClient(NumPyClient):
+        >>>     # ...
+        >>>
+        >>> def client_fn(cid) -> Client:
+        >>>     return FlowerClient().to_client()
+        >>>
+        >>> app = ClientApp()
+        >>>     client_fn=client_fn,
+        >>> )
+
+        Use the `ClientApp` with a custom {fn_name} function:
+
+        >>> app = ClientApp()
+        >>>
+        >>> @app.{fn_name}()
+        >>> def {fn_name}(message: Message, context: Context) -> Message:
+        >>>    print("ClientApp {fn_name} running")
+        >>>    # Create and return an echo reply message
+        >>>    return message.create_reply(
+        >>>        content=message.content(), ttl=""
+        >>>    )
+        """,
+    )
diff --git a/src/py/flwr/client/dpfedavg_numpy_client.py b/src/py/flwr/client/dpfedavg_numpy_client.py
index 41b4d676df43..ab31a289d29b 100644
--- a/src/py/flwr/client/dpfedavg_numpy_client.py
+++ b/src/py/flwr/client/dpfedavg_numpy_client.py
@@ -22,13 +22,20 @@
 
 from flwr.client.numpy_client import NumPyClient
 from flwr.common.dp import add_gaussian_noise, clip_by_l2
+from flwr.common.logger import warn_deprecated_feature
 from flwr.common.typing import Config, NDArrays, Scalar
 
 
 class DPFedAvgNumPyClient(NumPyClient):
-    """Wrapper for configuring a Flower client for DP."""
+    """Wrapper for configuring a Flower client for DP.
+
+    Warning
+    -------
+    This class is deprecated and will be removed in a future release.
+    """
 
     def __init__(self, client: NumPyClient) -> None:
+        warn_deprecated_feature("`DPFedAvgNumPyClient` wrapper")
         super().__init__()
         self.client = client
 
@@ -117,16 +124,16 @@ def fit(
         update = [np.subtract(x, y) for (x, y) in zip(updated_params, original_params)]
 
         if "dpfedavg_clip_norm" not in config:
-            raise Exception("Clipping threshold not supplied by the server.")
+            raise KeyError("Clipping threshold not supplied by the server.")
         if not isinstance(config["dpfedavg_clip_norm"], float):
-            raise Exception("Clipping threshold should be a floating point value.")
+            raise TypeError("Clipping threshold should be a floating point value.")
 
         # Clipping
         update, clipped = clip_by_l2(update, config["dpfedavg_clip_norm"])
 
         if "dpfedavg_noise_stddev" in config:
             if not isinstance(config["dpfedavg_noise_stddev"], float):
-                raise Exception(
+                raise TypeError(
                     "Scale of noise to be added should be a floating point value."
                 )
             # Noising
@@ -138,7 +145,7 @@ def fit(
         # Calculating value of norm indicator bit, required for adaptive clipping
         if "dpfedavg_adaptive_clip_enabled" in config:
             if not isinstance(config["dpfedavg_adaptive_clip_enabled"], bool):
-                raise Exception(
+                raise TypeError(
                     "dpfedavg_adaptive_clip_enabled should be a boolean-valued flag."
                 )
             metrics["dpfedavg_norm_bit"] = not clipped
diff --git a/src/py/flwr/client/flower.py b/src/py/flwr/client/flower.py
deleted file mode 100644
index 10c78ec45b44..000000000000
--- a/src/py/flwr/client/flower.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# 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 callable."""
-
-
-import importlib
-from typing import cast
-
-from flwr.client.message_handler.message_handler import handle
-from flwr.client.typing import Bwd, ClientFn, Fwd
-
-
-class Flower:
-    """Flower callable.
-
-    Examples
-    --------
-    Assuming a typical client implementation in `FlowerClient`, you can wrap it in a
-    Flower callable as follows:
-
-    >>> class FlowerClient(NumPyClient):
-    >>>     # ...
-    >>>
-    >>> def client_fn(cid):
-    >>>    return FlowerClient().to_client()
-    >>>
-    >>> flower = Flower(client_fn)
-
-    If the above code is in a Python module called `client`, it can be started as
-    follows:
-
-    >>> flower-client --callable client:flower
-
-    In this `client:flower` example, `client` refers to the Python module in which the
-    previous code lives in. `flower` refers to the global attribute `flower` that points
-    to an object of type `Flower` (a Flower callable).
-    """
-
-    def __init__(
-        self,
-        client_fn: ClientFn,  # Only for backward compatibility
-    ) -> None:
-        self.client_fn = client_fn
-
-    def __call__(self, fwd: Fwd) -> Bwd:
-        """."""
-        # Execute the task
-        task_res, state_updated = handle(
-            client_fn=self.client_fn,
-            state=fwd.state,
-            task_ins=fwd.task_ins,
-        )
-        return Bwd(
-            task_res=task_res,
-            state=state_updated,
-        )
-
-
-class LoadCallableError(Exception):
-    """."""
-
-
-def load_flower_callable(module_attribute_str: str) -> Flower:
-    """Load the `Flower` object specified in a module attribute string.
-
-    The module/attribute string should have the form <module>:<attribute>. Valid
-    examples include `client:flower` and `project.package.module:wrapper.flower`. It
-    must refer to a module on the PYTHONPATH, the module needs to have the specified
-    attribute, and the attribute must be of type `Flower`.
-    """
-    module_str, _, attributes_str = module_attribute_str.partition(":")
-    if not module_str:
-        raise LoadCallableError(
-            f"Missing module in {module_attribute_str}",
-        ) from None
-    if not attributes_str:
-        raise LoadCallableError(
-            f"Missing attribute in {module_attribute_str}",
-        ) from None
-
-    # Load module
-    try:
-        module = importlib.import_module(module_str)
-    except ModuleNotFoundError:
-        raise LoadCallableError(
-            f"Unable to load module {module_str}",
-        ) from None
-
-    # Recursively load attribute
-    attribute = module
-    try:
-        for attribute_str in attributes_str.split("."):
-            attribute = getattr(attribute, attribute_str)
-    except AttributeError:
-        raise LoadCallableError(
-            f"Unable to load attribute {attributes_str} from module {module_str}",
-        ) from None
-
-    # Check type
-    if not isinstance(attribute, Flower):
-        raise LoadCallableError(
-            f"Attribute {attributes_str} is not of type {Flower}",
-        ) from None
-
-    return cast(Flower, attribute)
diff --git a/src/py/flwr/client/grpc_client/connection.py b/src/py/flwr/client/grpc_client/connection.py
index 335d28e72828..163a58542c9e 100644
--- a/src/py/flwr/client/grpc_client/connection.py
+++ b/src/py/flwr/client/grpc_client/connection.py
@@ -20,15 +20,27 @@
 from logging import DEBUG
 from pathlib import Path
 from queue import Queue
-from typing import Callable, Iterator, Optional, Tuple, Union
-
-from flwr.common import GRPC_MAX_MESSAGE_LENGTH
+from typing import Callable, Iterator, Optional, Tuple, Union, cast
+
+from flwr.common import (
+    GRPC_MAX_MESSAGE_LENGTH,
+    ConfigsRecord,
+    Message,
+    Metadata,
+    RecordSet,
+)
+from flwr.common import recordset_compat as compat
+from flwr.common import serde
+from flwr.common.constant import MessageType, MessageTypeLegacy
 from flwr.common.grpc import create_channel
 from flwr.common.logger import log
-from flwr.proto.node_pb2 import Node
-from flwr.proto.task_pb2 import Task, TaskIns, TaskRes
-from flwr.proto.transport_pb2 import ClientMessage, ServerMessage
-from flwr.proto.transport_pb2_grpc import FlowerServiceStub
+from flwr.common.retry_invoker import RetryInvoker
+from flwr.proto.transport_pb2 import (  # pylint: disable=E0611
+    ClientMessage,
+    Reason,
+    ServerMessage,
+)
+from flwr.proto.transport_pb2_grpc import FlowerServiceStub  # pylint: disable=E0611
 
 # The following flags can be uncommented for debugging. Other possible values:
 # https://github.com/grpc/grpc/blob/master/doc/environment_variables.md
@@ -43,15 +55,16 @@ def on_channel_state_change(channel_connectivity: str) -> None:
 
 
 @contextmanager
-def grpc_connection(
+def grpc_connection(  # pylint: disable=R0915
     server_address: str,
     insecure: bool,
+    retry_invoker: RetryInvoker,  # pylint: disable=unused-argument
     max_message_length: int = GRPC_MAX_MESSAGE_LENGTH,
     root_certificates: Optional[Union[bytes, str]] = None,
 ) -> Iterator[
     Tuple[
-        Callable[[], Optional[TaskIns]],
-        Callable[[TaskRes], None],
+        Callable[[], Optional[Message]],
+        Callable[[Message], None],
         Optional[Callable[[], None]],
         Optional[Callable[[], None]],
     ]
@@ -64,6 +77,11 @@ def grpc_connection(
         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 `"0.0.0.0:8080"` or
         `"[::]: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
+        Unused argument present for compatibilty.
     max_message_length : int
         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
@@ -114,23 +132,94 @@ def grpc_connection(
 
     server_message_iterator: Iterator[ServerMessage] = stub.Join(iter(queue.get, None))
 
-    def receive() -> TaskIns:
-        server_message = next(server_message_iterator)
-        return TaskIns(
-            task_id=str(uuid.uuid4()),
-            group_id="",
-            workload_id=0,
-            task=Task(
-                producer=Node(node_id=0, anonymous=True),
-                consumer=Node(node_id=0, anonymous=True),
-                ancestry=[],
-                legacy_server_message=server_message,
+    def receive() -> Message:
+        # Receive ServerMessage proto
+        proto = next(server_message_iterator)
+
+        # ServerMessage proto --> *Ins --> RecordSet
+        field = proto.WhichOneof("msg")
+        message_type = ""
+        if field == "get_properties_ins":
+            recordset = compat.getpropertiesins_to_recordset(
+                serde.get_properties_ins_from_proto(proto.get_properties_ins)
+            )
+            message_type = MessageTypeLegacy.GET_PROPERTIES
+        elif field == "get_parameters_ins":
+            recordset = compat.getparametersins_to_recordset(
+                serde.get_parameters_ins_from_proto(proto.get_parameters_ins)
+            )
+            message_type = MessageTypeLegacy.GET_PARAMETERS
+        elif field == "fit_ins":
+            recordset = compat.fitins_to_recordset(
+                serde.fit_ins_from_proto(proto.fit_ins), False
+            )
+            message_type = MessageType.TRAIN
+        elif field == "evaluate_ins":
+            recordset = compat.evaluateins_to_recordset(
+                serde.evaluate_ins_from_proto(proto.evaluate_ins), False
+            )
+            message_type = MessageType.EVALUATE
+        elif field == "reconnect_ins":
+            recordset = RecordSet()
+            recordset.configs_records["config"] = ConfigsRecord(
+                {"seconds": proto.reconnect_ins.seconds}
+            )
+            message_type = "reconnect"
+        else:
+            raise ValueError(
+                "Unsupported instruction in ServerMessage, "
+                "cannot deserialize from ProtoBuf"
+            )
+
+        # Construct Message
+        return Message(
+            metadata=Metadata(
+                run_id=0,
+                message_id=str(uuid.uuid4()),
+                src_node_id=0,
+                dst_node_id=0,
+                reply_to_message="",
+                group_id="",
+                ttl="",
+                message_type=message_type,
             ),
+            content=recordset,
         )
 
-    def send(task_res: TaskRes) -> None:
-        msg = task_res.task.legacy_client_message
-        return queue.put(msg, block=False)
+    def send(message: Message) -> None:
+        # Retrieve RecordSet and message_type
+        recordset = message.content
+        message_type = message.metadata.message_type
+
+        # RecordSet --> *Res --> *Res proto -> ClientMessage proto
+        if message_type == MessageTypeLegacy.GET_PROPERTIES:
+            getpropres = compat.recordset_to_getpropertiesres(recordset)
+            msg_proto = ClientMessage(
+                get_properties_res=serde.get_properties_res_to_proto(getpropres)
+            )
+        elif message_type == MessageTypeLegacy.GET_PARAMETERS:
+            getparamres = compat.recordset_to_getparametersres(recordset, False)
+            msg_proto = ClientMessage(
+                get_parameters_res=serde.get_parameters_res_to_proto(getparamres)
+            )
+        elif message_type == MessageType.TRAIN:
+            fitres = compat.recordset_to_fitres(recordset, False)
+            msg_proto = ClientMessage(fit_res=serde.fit_res_to_proto(fitres))
+        elif message_type == MessageType.EVALUATE:
+            evalres = compat.recordset_to_evaluateres(recordset)
+            msg_proto = ClientMessage(evaluate_res=serde.evaluate_res_to_proto(evalres))
+        elif message_type == "reconnect":
+            reason = cast(
+                Reason.ValueType, recordset.configs_records["config"]["reason"]
+            )
+            msg_proto = ClientMessage(
+                disconnect_res=ClientMessage.DisconnectRes(reason=reason)
+            )
+        else:
+            raise ValueError(f"Invalid message type: {message_type}")
+
+        # Send ClientMessage proto
+        return queue.put(msg_proto, block=False)
 
     try:
         # Yield methods
diff --git a/src/py/flwr/client/grpc_client/connection_test.py b/src/py/flwr/client/grpc_client/connection_test.py
index e5944230e5af..b7737f511a2a 100644
--- a/src/py/flwr/client/grpc_client/connection_test.py
+++ b/src/py/flwr/client/grpc_client/connection_test.py
@@ -23,20 +23,53 @@
 
 import grpc
 
-from flwr.proto.task_pb2 import Task, TaskRes
-from flwr.proto.transport_pb2 import ClientMessage, ServerMessage
+from flwr.common import ConfigsRecord, Message, Metadata, RecordSet
+from flwr.common import recordset_compat as compat
+from flwr.common.constant import MessageTypeLegacy
+from flwr.common.retry_invoker import RetryInvoker, exponential
+from flwr.common.typing import Code, GetPropertiesRes, Status
+from flwr.proto.transport_pb2 import (  # pylint: disable=E0611
+    ClientMessage,
+    ServerMessage,
+)
 from flwr.server.client_manager import SimpleClientManager
-from flwr.server.fleet.grpc_bidi.grpc_server import start_grpc_server
+from flwr.server.superlink.fleet.grpc_bidi.grpc_server import start_grpc_server
 
 from .connection import grpc_connection
 
 EXPECTED_NUM_SERVER_MESSAGE = 10
 
-SERVER_MESSAGE = ServerMessage()
+SERVER_MESSAGE = ServerMessage(get_properties_ins=ServerMessage.GetPropertiesIns())
 SERVER_MESSAGE_RECONNECT = ServerMessage(reconnect_ins=ServerMessage.ReconnectIns())
 
-CLIENT_MESSAGE = ClientMessage()
-CLIENT_MESSAGE_DISCONNECT = ClientMessage(disconnect_res=ClientMessage.DisconnectRes())
+MESSAGE_GET_PROPERTIES = Message(
+    metadata=Metadata(
+        run_id=0,
+        message_id="",
+        src_node_id=0,
+        dst_node_id=0,
+        reply_to_message="",
+        group_id="",
+        ttl="",
+        message_type=MessageTypeLegacy.GET_PROPERTIES,
+    ),
+    content=compat.getpropertiesres_to_recordset(
+        GetPropertiesRes(Status(Code.OK, ""), {})
+    ),
+)
+MESSAGE_DISCONNECT = Message(
+    metadata=Metadata(
+        run_id=0,
+        message_id="",
+        src_node_id=0,
+        dst_node_id=0,
+        reply_to_message="",
+        group_id="",
+        ttl="",
+        message_type="reconnect",
+    ),
+    content=RecordSet(configs_records={"config": ConfigsRecord({"reason": 0})}),
+)
 
 
 def unused_tcp_port() -> int:
@@ -72,7 +105,9 @@ def mock_join(  # type: ignore # pylint: disable=invalid-name
 
 
 @patch(
-    "flwr.server.fleet.grpc_bidi.flower_service_servicer.FlowerServiceServicer.Join",
+    # pylint: disable=line-too-long
+    "flwr.server.superlink.fleet.grpc_bidi.flower_service_servicer.FlowerServiceServicer.Join",  # noqa: E501
+    # pylint: enable=line-too-long
     mock_join,
 )
 def test_integration_connection() -> None:
@@ -93,40 +128,30 @@ def test_integration_connection() -> None:
     def run_client() -> int:
         messages_received: int = 0
 
-        with grpc_connection(server_address=f"[::]:{port}", insecure=True) as conn:
+        with grpc_connection(
+            server_address=f"[::]:{port}",
+            insecure=True,
+            retry_invoker=RetryInvoker(
+                wait_factory=exponential,
+                recoverable_exceptions=grpc.RpcError,
+                max_tries=1,
+                max_time=None,
+            ),
+        ) as conn:
             receive, send, _, _ = conn
 
             # Setup processing loop
             while True:
                 # Block until server responds with a message
-                task_ins = receive()
-
-                if task_ins is None:
-                    raise ValueError("Unexpected None value")
-
-                # pylint: disable=no-member
-                if task_ins.HasField("task") and task_ins.task.HasField(
-                    "legacy_server_message"
-                ):
-                    server_message = task_ins.task.legacy_server_message
-                else:
-                    server_message = None
-                # pylint: enable=no-member
-
-                if server_message is None:
-                    raise ValueError("Unexpected None value")
+                message = receive()
 
                 messages_received += 1
-                if server_message.HasField("reconnect_ins"):
-                    task_res = TaskRes(
-                        task=Task(legacy_client_message=CLIENT_MESSAGE_DISCONNECT)
-                    )
-                    send(task_res)
+                if message.metadata.message_type == "reconnect":  # type: ignore
+                    send(MESSAGE_DISCONNECT)
                     break
 
                 # Process server_message and send client_message...
-                task_res = TaskRes(task=Task(legacy_client_message=CLIENT_MESSAGE))
-                send(task_res)
+                send(MESSAGE_GET_PROPERTIES)
 
         return messages_received
 
diff --git a/src/py/flwr/client/grpc_rere_client/connection.py b/src/py/flwr/client/grpc_rere_client/connection.py
index 30d407a52c53..e6e22998b947 100644
--- a/src/py/flwr/client/grpc_rere_client/connection.py
+++ b/src/py/flwr/client/grpc_rere_client/connection.py
@@ -16,31 +16,31 @@
 
 
 from contextlib import contextmanager
+from copy import copy
 from logging import DEBUG, ERROR
 from pathlib import Path
 from typing import Callable, Dict, Iterator, Optional, Tuple, Union, cast
 
-from flwr.client.message_handler.task_handler import (
-    configure_task_res,
-    get_task_ins,
-    validate_task_ins,
-    validate_task_res,
-)
+from flwr.client.message_handler.message_handler import validate_out_message
+from flwr.client.message_handler.task_handler import get_task_ins, validate_task_ins
 from flwr.common import GRPC_MAX_MESSAGE_LENGTH
 from flwr.common.grpc import create_channel
 from flwr.common.logger import log, warn_experimental_feature
-from flwr.proto.fleet_pb2 import (
+from flwr.common.message import Message, Metadata
+from flwr.common.retry_invoker import RetryInvoker
+from flwr.common.serde import message_from_taskins, message_to_taskres
+from flwr.proto.fleet_pb2 import (  # pylint: disable=E0611
     CreateNodeRequest,
     DeleteNodeRequest,
     PullTaskInsRequest,
     PushTaskResRequest,
 )
-from flwr.proto.fleet_pb2_grpc import FleetStub
-from flwr.proto.node_pb2 import Node
-from flwr.proto.task_pb2 import TaskIns, TaskRes
+from flwr.proto.fleet_pb2_grpc import FleetStub  # pylint: disable=E0611
+from flwr.proto.node_pb2 import Node  # pylint: disable=E0611
+from flwr.proto.task_pb2 import TaskIns  # pylint: disable=E0611
 
 KEY_NODE = "node"
-KEY_TASK_INS = "current_task_ins"
+KEY_METADATA = "in_message_metadata"
 
 
 def on_channel_state_change(channel_connectivity: str) -> None:
@@ -52,12 +52,13 @@ def on_channel_state_change(channel_connectivity: str) -> None:
 def grpc_request_response(
     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,
 ) -> Iterator[
     Tuple[
-        Callable[[], Optional[TaskIns]],
-        Callable[[TaskRes], None],
+        Callable[[], Optional[Message]],
+        Callable[[Message], None],
         Optional[Callable[[], None]],
         Optional[Callable[[], None]],
     ]
@@ -73,6 +74,13 @@ def grpc_request_response(
         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)
@@ -101,8 +109,8 @@ def grpc_request_response(
     channel.subscribe(on_channel_state_change)
     stub = FleetStub(channel)
 
-    # Necessary state to link TaskRes to TaskIns
-    state: Dict[str, Optional[TaskIns]] = {KEY_TASK_INS: None}
+    # Necessary state to validate messages to be sent
+    state: Dict[str, Optional[Metadata]] = {KEY_METADATA: None}
 
     # Enable create_node and delete_node to store node
     node_store: Dict[str, Optional[Node]] = {KEY_NODE: None}
@@ -114,7 +122,8 @@ def grpc_request_response(
     def create_node() -> None:
         """Set create_node."""
         create_node_request = CreateNodeRequest()
-        create_node_response = stub.CreateNode(
+        create_node_response = retry_invoker.invoke(
+            stub.CreateNode,
             request=create_node_request,
         )
         node_store[KEY_NODE] = create_node_response.node
@@ -128,11 +137,11 @@ def delete_node() -> None:
         node: Node = cast(Node, node_store[KEY_NODE])
 
         delete_node_request = DeleteNodeRequest(node=node)
-        stub.DeleteNode(request=delete_node_request)
+        retry_invoker.invoke(stub.DeleteNode, request=delete_node_request)
 
         del node_store[KEY_NODE]
 
-    def receive() -> Optional[TaskIns]:
+    def receive() -> Optional[Message]:
         """Receive next task from server."""
         # Get Node
         if node_store[KEY_NODE] is None:
@@ -142,50 +151,53 @@ def receive() -> Optional[TaskIns]:
 
         # Request instructions (task) from server
         request = PullTaskInsRequest(node=node)
-        response = stub.PullTaskIns(request=request)
+        response = retry_invoker.invoke(stub.PullTaskIns, request=request)
 
         # Get the current TaskIns
         task_ins: Optional[TaskIns] = get_task_ins(response)
 
         # Discard the current TaskIns if not valid
-        if task_ins is not None and not validate_task_ins(
-            task_ins, discard_reconnect_ins=True
+        if task_ins is not None and not (
+            task_ins.task.consumer.node_id == node.node_id
+            and validate_task_ins(task_ins)
         ):
             task_ins = None
 
-        # Remember `task_ins` until `task_res` is available
-        state[KEY_TASK_INS] = task_ins
+        # Construct the Message
+        in_message = message_from_taskins(task_ins) if task_ins else None
+
+        # Remember `metadata` of the in message
+        state[KEY_METADATA] = copy(in_message.metadata) if in_message else None
 
-        # Return the TaskIns if available
-        return task_ins
+        # Return the message if available
+        return in_message
 
-    def send(task_res: TaskRes) -> None:
+    def send(message: Message) -> None:
         """Send task result back to server."""
         # Get Node
         if node_store[KEY_NODE] is None:
             log(ERROR, "Node instance missing")
             return
-        node: Node = cast(Node, node_store[KEY_NODE])
 
-        # Get incoming TaskIns
-        if state[KEY_TASK_INS] is None:
-            log(ERROR, "No current TaskIns")
+        # Get incoming message
+        in_metadata = state[KEY_METADATA]
+        if in_metadata is None:
+            log(ERROR, "No current message")
             return
-        task_ins: TaskIns = cast(TaskIns, state[KEY_TASK_INS])
 
-        # Check if fields to be set are not initialized
-        if not validate_task_res(task_res):
-            state[KEY_TASK_INS] = None
-            log(ERROR, "TaskRes has been initialized accidentally")
+        # Validate out message
+        if not validate_out_message(message, in_metadata):
+            log(ERROR, "Invalid out message")
+            return
 
-        # Configure TaskRes
-        task_res = configure_task_res(task_res, task_ins, node)
+        # Construct TaskRes
+        task_res = message_to_taskres(message)
 
         # Serialize ProtoBuf to bytes
         request = PushTaskResRequest(task_res_list=[task_res])
-        _ = stub.PushTaskRes(request)
+        _ = retry_invoker.invoke(stub.PushTaskRes, request)
 
-        state[KEY_TASK_INS] = None
+        state[KEY_METADATA] = None
 
     try:
         # Yield methods
diff --git a/src/py/flwr/client/message_handler/message_handler.py b/src/py/flwr/client/message_handler/message_handler.py
index 0f3070cfb01a..9a5d70b1ac4d 100644
--- a/src/py/flwr/client/message_handler/message_handler.py
+++ b/src/py/flwr/client/message_handler/message_handler.py
@@ -15,25 +15,34 @@
 """Client-side message handler."""
 
 
-from typing import Optional, Tuple
+from logging import WARN
+from typing import Optional, Tuple, cast
 
 from flwr.client.client import (
-    Client,
     maybe_call_evaluate,
     maybe_call_fit,
     maybe_call_get_parameters,
     maybe_call_get_properties,
 )
-from flwr.client.message_handler.task_handler import (
-    get_server_message_from_task_ins,
-    wrap_client_message_in_task_res,
-)
-from flwr.client.secure_aggregation import SecureAggregationHandler
+from flwr.client.numpy_client import NumPyClient
 from flwr.client.typing import ClientFn
-from flwr.client.workload_state import WorkloadState
-from flwr.common import serde
-from flwr.proto.task_pb2 import SecureAggregation, Task, TaskIns, TaskRes
-from flwr.proto.transport_pb2 import ClientMessage, Reason, ServerMessage
+from flwr.common import ConfigsRecord, Context, Message, Metadata, RecordSet, log
+from flwr.common.constant import MessageType, MessageTypeLegacy
+from flwr.common.recordset_compat import (
+    evaluateres_to_recordset,
+    fitres_to_recordset,
+    getparametersres_to_recordset,
+    getpropertiesres_to_recordset,
+    recordset_to_evaluateins,
+    recordset_to_fitins,
+    recordset_to_getparametersins,
+    recordset_to_getpropertiesins,
+)
+from flwr.proto.transport_pb2 import (  # pylint: disable=E0611
+    ClientMessage,
+    Reason,
+    ServerMessage,
+)
 
 
 class UnexpectedServerMessage(Exception):
@@ -44,128 +53,97 @@ class UnknownServerMessage(Exception):
     """Exception indicating that the received message is unknown."""
 
 
-def handle_control_message(task_ins: TaskIns) -> Tuple[Optional[TaskRes], int]:
+def handle_control_message(message: Message) -> Tuple[Optional[Message], int]:
     """Handle control part of the incoming message.
 
     Parameters
     ----------
-    task_ins : TaskIns
-        The task instruction coming from the server, to be processed by the client.
+    message : Message
+        The Message coming from the server, to be processed by the client.
 
     Returns
     -------
+    message : Optional[Message]
+        Message to be sent back to the server. If None, the client should
+        continue to process messages from the server.
     sleep_duration : int
         Number of seconds that the client should disconnect from the server.
-    keep_going : bool
-        Flag that indicates whether the client should continue to process the
-        next message from the server (True) or disconnect and optionally
-        reconnect later (False).
     """
-    server_msg = get_server_message_from_task_ins(task_ins, exclude_reconnect_ins=False)
-
-    # SecAgg message
-    if server_msg is None:
-        return None, 0
-
-    # ReconnectIns message
-    field = server_msg.WhichOneof("msg")
-    if field == "reconnect_ins":
-        disconnect_msg, sleep_duration = _reconnect(server_msg.reconnect_ins)
-        task_res = wrap_client_message_in_task_res(disconnect_msg)
-        return task_res, sleep_duration
+    if message.metadata.message_type == "reconnect":
+        # Retrieve ReconnectIns from recordset
+        recordset = message.content
+        seconds = cast(int, recordset.configs_records["config"]["seconds"])
+        # Construct ReconnectIns and call _reconnect
+        disconnect_msg, sleep_duration = _reconnect(
+            ServerMessage.ReconnectIns(seconds=seconds)
+        )
+        # Store DisconnectRes in recordset
+        reason = cast(int, disconnect_msg.disconnect_res.reason)
+        recordset = RecordSet()
+        recordset.configs_records["config"] = ConfigsRecord({"reason": reason})
+        out_message = message.create_reply(recordset, ttl="")
+        # Return TaskRes and sleep duration
+        return out_message, sleep_duration
 
     # Any other message
     return None, 0
 
 
-def handle(
-    client_fn: ClientFn, state: WorkloadState, task_ins: TaskIns
-) -> Tuple[TaskRes, WorkloadState]:
-    """Handle incoming TaskIns from the server.
-
-    Parameters
-    ----------
-    client_fn : ClientFn
-        A callable that instantiates a Client.
-    state : WorkloadState
-        A dataclass storing the state for the workload being executed by the client.
-    task_ins: TaskIns
-        The task instruction coming from the server, to be processed by the client.
-
-    Returns
-    -------
-    task_res : TaskRes
-        The task response that should be returned to the server.
-    """
-    server_msg = get_server_message_from_task_ins(task_ins, exclude_reconnect_ins=False)
-    if server_msg is None:
-        # Instantiate the client
-        client = client_fn("-1")
-        client.set_state(state)
-        # Secure Aggregation
-        if task_ins.task.HasField("sa") and isinstance(
-            client, SecureAggregationHandler
-        ):
-            # pylint: disable-next=invalid-name
-            named_values = serde.named_values_from_proto(task_ins.task.sa.named_values)
-            res = client.handle_secure_aggregation(named_values)
-            task_res = TaskRes(
-                task_id="",
-                group_id="",
-                workload_id=0,
-                task=Task(
-                    ancestry=[],
-                    sa=SecureAggregation(named_values=serde.named_values_to_proto(res)),
-                ),
-            )
-            return task_res, client.get_state()
-        raise NotImplementedError()
-    client_msg, updated_state = handle_legacy_message(client_fn, state, server_msg)
-    task_res = wrap_client_message_in_task_res(client_msg)
-    return task_res, updated_state
-
-
-def handle_legacy_message(
-    client_fn: ClientFn, state: WorkloadState, server_msg: ServerMessage
-) -> Tuple[ClientMessage, WorkloadState]:
-    """Handle incoming messages from the server.
-
-    Parameters
-    ----------
-    client_fn : ClientFn
-        A callable that instantiates a Client.
-    state : WorkloadState
-        A dataclass storing the state for the workload being executed by the client.
-    server_msg: ServerMessage
-        The message coming from the server, to be processed by the client.
-
-    Returns
-    -------
-    client_msg : ClientMessage
-        The result message that should be returned to the server.
-    """
-    field = server_msg.WhichOneof("msg")
-
-    # Must be handled elsewhere
-    if field == "reconnect_ins":
-        raise UnexpectedServerMessage()
-
-    # Instantiate the client
-    client = client_fn("-1")
-    client.set_state(state)
-    # Execute task
-    message = None
-    if field == "get_properties_ins":
-        message = _get_properties(client, server_msg.get_properties_ins)
-    if field == "get_parameters_ins":
-        message = _get_parameters(client, server_msg.get_parameters_ins)
-    if field == "fit_ins":
-        message = _fit(client, server_msg.fit_ins)
-    if field == "evaluate_ins":
-        message = _evaluate(client, server_msg.evaluate_ins)
-    if message:
-        return message, client.get_state()
-    raise UnknownServerMessage()
+def handle_legacy_message_from_msgtype(
+    client_fn: ClientFn, message: Message, context: Context
+) -> Message:
+    """Handle legacy message in the inner most mod."""
+    client = client_fn(str(message.metadata.partition_id))
+
+    # Check if NumPyClient is returend
+    if isinstance(client, NumPyClient):
+        client = client.to_client()
+        log(
+            WARN,
+            "Deprecation Warning: The `client_fn` function must return an instance "
+            "of `Client`, but an instance of `NumpyClient` was returned. "
+            "Please use `NumPyClient.to_client()` method to convert it to `Client`.",
+        )
+
+    client.set_context(context)
+
+    message_type = message.metadata.message_type
+
+    # Handle GetPropertiesIns
+    if message_type == MessageTypeLegacy.GET_PROPERTIES:
+        get_properties_res = maybe_call_get_properties(
+            client=client,
+            get_properties_ins=recordset_to_getpropertiesins(message.content),
+        )
+        out_recordset = getpropertiesres_to_recordset(get_properties_res)
+    # Handle GetParametersIns
+    elif message_type == MessageTypeLegacy.GET_PARAMETERS:
+        get_parameters_res = maybe_call_get_parameters(
+            client=client,
+            get_parameters_ins=recordset_to_getparametersins(message.content),
+        )
+        out_recordset = getparametersres_to_recordset(
+            get_parameters_res, keep_input=False
+        )
+    # Handle FitIns
+    elif message_type == MessageType.TRAIN:
+        fit_res = maybe_call_fit(
+            client=client,
+            fit_ins=recordset_to_fitins(message.content, keep_input=True),
+        )
+        out_recordset = fitres_to_recordset(fit_res, keep_input=False)
+    # Handle EvaluateIns
+    elif message_type == MessageType.EVALUATE:
+        evaluate_res = maybe_call_evaluate(
+            client=client,
+            evaluate_ins=recordset_to_evaluateins(message.content, keep_input=True),
+        )
+        out_recordset = evaluateres_to_recordset(evaluate_res)
+    else:
+        raise ValueError(f"Invalid message type: {message_type}")
+
+    # Return Message
+    return message.create_reply(out_recordset, ttl="")
 
 
 def _reconnect(
@@ -182,65 +160,18 @@ def _reconnect(
     return ClientMessage(disconnect_res=disconnect_res), sleep_duration
 
 
-def _get_properties(
-    client: Client, get_properties_msg: ServerMessage.GetPropertiesIns
-) -> ClientMessage:
-    # Deserialize `get_properties` instruction
-    get_properties_ins = serde.get_properties_ins_from_proto(get_properties_msg)
-
-    # Request properties
-    get_properties_res = maybe_call_get_properties(
-        client=client,
-        get_properties_ins=get_properties_ins,
-    )
-
-    # Serialize response
-    get_properties_res_proto = serde.get_properties_res_to_proto(get_properties_res)
-    return ClientMessage(get_properties_res=get_properties_res_proto)
-
-
-def _get_parameters(
-    client: Client, get_parameters_msg: ServerMessage.GetParametersIns
-) -> ClientMessage:
-    # Deserialize `get_parameters` instruction
-    get_parameters_ins = serde.get_parameters_ins_from_proto(get_parameters_msg)
-
-    # Request parameters
-    get_parameters_res = maybe_call_get_parameters(
-        client=client,
-        get_parameters_ins=get_parameters_ins,
-    )
-
-    # Serialize response
-    get_parameters_res_proto = serde.get_parameters_res_to_proto(get_parameters_res)
-    return ClientMessage(get_parameters_res=get_parameters_res_proto)
-
-
-def _fit(client: Client, fit_msg: ServerMessage.FitIns) -> ClientMessage:
-    # Deserialize fit instruction
-    fit_ins = serde.fit_ins_from_proto(fit_msg)
-
-    # Perform fit
-    fit_res = maybe_call_fit(
-        client=client,
-        fit_ins=fit_ins,
-    )
-
-    # Serialize fit result
-    fit_res_proto = serde.fit_res_to_proto(fit_res)
-    return ClientMessage(fit_res=fit_res_proto)
-
-
-def _evaluate(client: Client, evaluate_msg: ServerMessage.EvaluateIns) -> ClientMessage:
-    # Deserialize evaluate instruction
-    evaluate_ins = serde.evaluate_ins_from_proto(evaluate_msg)
-
-    # Perform evaluation
-    evaluate_res = maybe_call_evaluate(
-        client=client,
-        evaluate_ins=evaluate_ins,
-    )
-
-    # Serialize evaluate result
-    evaluate_res_proto = serde.evaluate_res_to_proto(evaluate_res)
-    return ClientMessage(evaluate_res=evaluate_res_proto)
+def validate_out_message(out_message: Message, in_message_metadata: Metadata) -> bool:
+    """Validate the out message."""
+    out_meta = out_message.metadata
+    in_meta = in_message_metadata
+    if (  # pylint: disable-next=too-many-boolean-expressions
+        out_meta.run_id == in_meta.run_id
+        and out_meta.message_id == ""  # This will be generated by the server
+        and out_meta.src_node_id == in_meta.dst_node_id
+        and out_meta.dst_node_id == in_meta.src_node_id
+        and out_meta.reply_to_message == in_meta.message_id
+        and out_meta.group_id == in_meta.group_id
+        and out_meta.message_type == in_meta.message_type
+    ):
+        return True
+    return False
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 d7f410d81fc0..eaf16f7dc993 100644
--- a/src/py/flwr/client/message_handler/message_handler_test.py
+++ b/src/py/flwr/client/message_handler/message_handler_test.py
@@ -15,12 +15,16 @@
 """Client-side message handler tests."""
 
 
+import unittest
 import uuid
+from copy import copy
+from typing import List
 
 from flwr.client import Client
 from flwr.client.typing import ClientFn
-from flwr.client.workload_state import WorkloadState
 from flwr.common import (
+    Code,
+    Context,
     EvaluateIns,
     EvaluateRes,
     FitIns,
@@ -29,15 +33,17 @@
     GetParametersRes,
     GetPropertiesIns,
     GetPropertiesRes,
+    Message,
+    Metadata,
     Parameters,
-    serde,
-    typing,
+    RecordSet,
+    Status,
 )
-from flwr.proto.node_pb2 import Node
-from flwr.proto.task_pb2 import Task, TaskIns, TaskRes
-from flwr.proto.transport_pb2 import ClientMessage, Code, ServerMessage, Status
+from flwr.common import recordset_compat as compat
+from flwr.common import typing
+from flwr.common.constant import MessageTypeLegacy
 
-from .message_handler import handle, handle_control_message
+from .message_handler import handle_legacy_message_from_msgtype, validate_out_message
 
 
 class ClientWithoutProps(Client):
@@ -116,137 +122,169 @@ def test_client_without_get_properties() -> None:
     """Test client implementing get_properties."""
     # Prepare
     client = ClientWithoutProps()
-    ins = ServerMessage.GetPropertiesIns()
-
-    task_ins: TaskIns = TaskIns(
-        task_id=str(uuid.uuid4()),
-        group_id="",
-        workload_id=0,
-        task=Task(
-            producer=Node(node_id=0, anonymous=True),
-            consumer=Node(node_id=0, anonymous=True),
-            ancestry=[],
-            legacy_server_message=ServerMessage(get_properties_ins=ins),
+    recordset = compat.getpropertiesins_to_recordset(GetPropertiesIns({}))
+    message = Message(
+        metadata=Metadata(
+            run_id=123,
+            message_id=str(uuid.uuid4()),
+            group_id="some group ID",
+            src_node_id=0,
+            dst_node_id=1123,
+            reply_to_message="",
+            ttl="",
+            message_type=MessageTypeLegacy.GET_PROPERTIES,
         ),
+        content=recordset,
     )
 
     # Execute
-    disconnect_task_res, actual_sleep_duration = handle_control_message(
-        task_ins=task_ins
-    )
-    task_res, _ = handle(
+    actual_msg = handle_legacy_message_from_msgtype(
         client_fn=_get_client_fn(client),
-        state=WorkloadState(state={}),
-        task_ins=task_ins,
+        message=message,
+        context=Context(state=RecordSet()),
     )
 
-    if not task_res.HasField("task"):
-        raise ValueError("Task value not found")
-
-    # pylint: disable=no-member
-    if not task_res.task.HasField("legacy_client_message"):
-        raise ValueError("Unexpected None value")
-    # pylint: enable=no-member
-
-    task_res.MergeFrom(
-        TaskRes(
-            task_id=str(uuid.uuid4()),
-            group_id="",
-            workload_id=0,
-        )
-    )
-    # pylint: disable=no-member
-    task_res.task.MergeFrom(
-        Task(
-            producer=Node(node_id=0, anonymous=True),
-            consumer=Node(node_id=0, anonymous=True),
-            ancestry=[task_ins.task_id],
-        )
-    )
-
-    actual_msg = task_res.task.legacy_client_message
-    # pylint: enable=no-member
-
     # Assert
-    expected_get_properties_res = ClientMessage.GetPropertiesRes(
+    expected_get_properties_res = GetPropertiesRes(
         status=Status(
             code=Code.GET_PROPERTIES_NOT_IMPLEMENTED,
             message="Client does not implement `get_properties`",
-        )
+        ),
+        properties={},
+    )
+    expected_rs = compat.getpropertiesres_to_recordset(expected_get_properties_res)
+    expected_msg = Message(
+        metadata=Metadata(
+            run_id=123,
+            message_id="",
+            group_id="some group ID",
+            src_node_id=1123,
+            dst_node_id=0,
+            reply_to_message=message.metadata.message_id,
+            ttl="",
+            message_type=MessageTypeLegacy.GET_PROPERTIES,
+        ),
+        content=expected_rs,
     )
-    expected_msg = ClientMessage(get_properties_res=expected_get_properties_res)
 
-    assert actual_msg == expected_msg
-    assert not disconnect_task_res
-    assert actual_sleep_duration == 0
+    assert actual_msg.content == expected_msg.content
+    assert actual_msg.metadata == expected_msg.metadata
 
 
 def test_client_with_get_properties() -> None:
     """Test client not implementing get_properties."""
     # Prepare
     client = ClientWithProps()
-    ins = ServerMessage.GetPropertiesIns()
-    task_ins = TaskIns(
-        task_id=str(uuid.uuid4()),
-        group_id="",
-        workload_id=0,
-        task=Task(
-            producer=Node(node_id=0, anonymous=True),
-            consumer=Node(node_id=0, anonymous=True),
-            ancestry=[],
-            legacy_server_message=ServerMessage(get_properties_ins=ins),
+    recordset = compat.getpropertiesins_to_recordset(GetPropertiesIns({}))
+    message = Message(
+        metadata=Metadata(
+            run_id=123,
+            message_id=str(uuid.uuid4()),
+            group_id="some group ID",
+            src_node_id=0,
+            dst_node_id=1123,
+            reply_to_message="",
+            ttl="",
+            message_type=MessageTypeLegacy.GET_PROPERTIES,
         ),
+        content=recordset,
     )
 
     # Execute
-    disconnect_task_res, actual_sleep_duration = handle_control_message(
-        task_ins=task_ins
-    )
-    task_res, _ = handle(
+    actual_msg = handle_legacy_message_from_msgtype(
         client_fn=_get_client_fn(client),
-        state=WorkloadState(state={}),
-        task_ins=task_ins,
-    )
-
-    if not task_res.HasField("task"):
-        raise ValueError("Task value not found")
-
-    # pylint: disable=no-member
-    if not task_res.task.HasField("legacy_client_message"):
-        raise ValueError("Unexpected None value")
-    # pylint: enable=no-member
-
-    task_res.MergeFrom(
-        TaskRes(
-            task_id=str(uuid.uuid4()),
-            group_id="",
-            workload_id=0,
-        )
+        message=message,
+        context=Context(state=RecordSet()),
     )
-    # pylint: disable=no-member
-    task_res.task.MergeFrom(
-        Task(
-            producer=Node(node_id=0, anonymous=True),
-            consumer=Node(node_id=0, anonymous=True),
-            ancestry=[task_ins.task_id],
-        )
-    )
-
-    actual_msg = task_res.task.legacy_client_message
-    # pylint: enable=no-member
 
     # Assert
-    expected_get_properties_res = ClientMessage.GetPropertiesRes(
+    expected_get_properties_res = GetPropertiesRes(
         status=Status(
             code=Code.OK,
             message="Success",
         ),
-        properties=serde.properties_to_proto(
-            properties={"str_prop": "val", "int_prop": 1}
+        properties={"str_prop": "val", "int_prop": 1},
+    )
+    expected_rs = compat.getpropertiesres_to_recordset(expected_get_properties_res)
+    expected_msg = Message(
+        metadata=Metadata(
+            run_id=123,
+            message_id="",
+            group_id="some group ID",
+            src_node_id=1123,
+            dst_node_id=0,
+            reply_to_message=message.metadata.message_id,
+            ttl="",
+            message_type=MessageTypeLegacy.GET_PROPERTIES,
         ),
+        content=expected_rs,
     )
-    expected_msg = ClientMessage(get_properties_res=expected_get_properties_res)
 
-    assert actual_msg == expected_msg
-    assert not disconnect_task_res
-    assert actual_sleep_duration == 0
+    assert actual_msg.content == expected_msg.content
+    assert actual_msg.metadata == expected_msg.metadata
+
+
+class TestMessageValidation(unittest.TestCase):
+    """Test message validation."""
+
+    def setUp(self) -> None:
+        """Set up the message validation."""
+        # Common setup for tests
+        self.in_metadata = Metadata(
+            run_id=123,
+            message_id="qwerty",
+            src_node_id=10,
+            dst_node_id=20,
+            reply_to_message="",
+            group_id="group1",
+            ttl="60",
+            message_type="mock",
+        )
+        self.valid_out_metadata = Metadata(
+            run_id=123,
+            message_id="",
+            src_node_id=20,
+            dst_node_id=10,
+            reply_to_message="qwerty",
+            group_id="group1",
+            ttl="60",
+            message_type="mock",
+        )
+        self.common_content = RecordSet()
+
+    def test_valid_message(self) -> None:
+        """Test a valid message."""
+        # Prepare
+        valid_message = Message(metadata=self.valid_out_metadata, content=RecordSet())
+
+        # Assert
+        self.assertTrue(validate_out_message(valid_message, self.in_metadata))
+
+    def test_invalid_message_run_id(self) -> None:
+        """Test invalid messages."""
+        # Prepare
+        msg = Message(metadata=self.valid_out_metadata, content=RecordSet())
+
+        # Execute
+        invalid_metadata_list: List[Metadata] = []
+        attrs = list(vars(self.valid_out_metadata).keys())
+        for attr in attrs:
+            if attr == "_partition_id":
+                continue
+            if attr == "_ttl":  # Skip configurable ttl
+                continue
+            # Make an invalid metadata
+            invalid_metadata = copy(self.valid_out_metadata)
+            value = getattr(invalid_metadata, attr)
+            if isinstance(value, int):
+                value = 999
+            elif isinstance(value, str):
+                value = "999"
+            setattr(invalid_metadata, attr, value)
+            # Add to list
+            invalid_metadata_list.append(invalid_metadata)
+
+        # Assert
+        for invalid_metadata in invalid_metadata_list:
+            msg._metadata = invalid_metadata  # pylint: disable=protected-access
+            self.assertFalse(validate_out_message(msg, self.in_metadata))
diff --git a/src/py/flwr/client/message_handler/task_handler.py b/src/py/flwr/client/message_handler/task_handler.py
index fc24539998c0..7f515a30fe5a 100644
--- a/src/py/flwr/client/message_handler/task_handler.py
+++ b/src/py/flwr/client/message_handler/task_handler.py
@@ -17,21 +17,17 @@
 
 from typing import Optional
 
-from flwr.proto.fleet_pb2 import PullTaskInsResponse
-from flwr.proto.node_pb2 import Node
-from flwr.proto.task_pb2 import Task, TaskIns, TaskRes
-from flwr.proto.transport_pb2 import ClientMessage, ServerMessage
+from flwr.proto.fleet_pb2 import PullTaskInsResponse  # pylint: disable=E0611
+from flwr.proto.task_pb2 import TaskIns  # pylint: disable=E0611
 
 
-def validate_task_ins(task_ins: TaskIns, discard_reconnect_ins: bool) -> bool:
+def validate_task_ins(task_ins: TaskIns) -> bool:
     """Validate a TaskIns before it entering the message handling process.
 
     Parameters
     ----------
     task_ins: TaskIns
         The task instruction coming from the server.
-    discard_reconnect_ins: bool
-        If True, ReconnectIns will not be considered as valid content.
 
     Returns
     -------
@@ -39,58 +35,8 @@ def validate_task_ins(task_ins: TaskIns, discard_reconnect_ins: bool) -> bool:
         True if the TaskIns is deemed valid and therefore suitable for
         undergoing the message handling process, False otherwise.
     """
-    # Check if the task_ins contains legacy_server_message or sa.
-    # If legacy_server_message is set, check if ServerMessage is one of
-    # {GetPropertiesIns, GetParametersIns, FitIns, EvaluateIns, ReconnectIns*}
-    # Discard ReconnectIns if discard_reconnect_ins is true.
-    if (
-        not task_ins.HasField("task")
-        or (
-            not task_ins.task.HasField("legacy_server_message")
-            and not task_ins.task.HasField("sa")
-        )
-        or (
-            discard_reconnect_ins
-            and task_ins.task.legacy_server_message.WhichOneof("msg") == "reconnect_ins"
-        )
-    ):
+    if not (task_ins.HasField("task") and task_ins.task.HasField("recordset")):
         return False
-
-    return True
-
-
-def validate_task_res(task_res: TaskRes) -> bool:
-    """Validate a TaskRes before filling its fields in the `send()` function.
-
-    Parameters
-    ----------
-    task_res: TaskRes
-        The task response to be sent to the server.
-
-    Returns
-    -------
-    is_valid: bool
-        True if the `task_id`, `group_id`, and `workload_id` fields in TaskRes
-        and the `producer`, `consumer`, and `ancestry` fields in its sub-message Task
-        are not initialized accidentally elsewhere,
-        False otherwise.
-    """
-    # Retrieve initialized fields in TaskRes and Task
-    initialized_fields_in_task_res = {field.name for field, _ in task_res.ListFields()}
-    initialized_fields_in_task = {field.name for field, _ in task_res.task.ListFields()}
-
-    # Check if certain fields are already initialized
-    # pylint: disable-next=too-many-boolean-expressions
-    if (
-        "task_id" in initialized_fields_in_task_res
-        or "group_id" in initialized_fields_in_task_res
-        or "workload_id" in initialized_fields_in_task_res
-        or "producer" in initialized_fields_in_task
-        or "consumer" in initialized_fields_in_task
-        or "ancestry" in initialized_fields_in_task
-    ):
-        return False
-
     return True
 
 
@@ -106,61 +52,3 @@ def get_task_ins(
     task_ins: TaskIns = pull_task_ins_response.task_ins_list[0]
 
     return task_ins
-
-
-def get_server_message_from_task_ins(
-    task_ins: TaskIns, exclude_reconnect_ins: bool
-) -> Optional[ServerMessage]:
-    """Get ServerMessage from TaskIns, if available."""
-    # Return the message if it is in
-    # {GetPropertiesIns, GetParametersIns, FitIns, EvaluateIns}
-    # Return the message if it is ReconnectIns and exclude_reconnect_ins is False.
-    if not validate_task_ins(
-        task_ins, discard_reconnect_ins=exclude_reconnect_ins
-    ) or not task_ins.task.HasField("legacy_server_message"):
-        return None
-
-    return task_ins.task.legacy_server_message
-
-
-def wrap_client_message_in_task_res(client_message: ClientMessage) -> TaskRes:
-    """Wrap ClientMessage in TaskRes."""
-    # Instantiate a TaskRes, only filling client_message field.
-    return TaskRes(
-        task_id="",
-        group_id="",
-        workload_id=0,
-        task=Task(ancestry=[], legacy_client_message=client_message),
-    )
-
-
-def configure_task_res(
-    task_res: TaskRes, ref_task_ins: TaskIns, producer: Node
-) -> TaskRes:
-    """Set the metadata of a TaskRes.
-
-    Fill `group_id` and `workload_id` in TaskRes
-    and `producer`, `consumer`, and `ancestry` in Task in TaskRes.
-
-    `producer` in Task in TaskRes will remain unchanged/unset.
-
-    Note that protobuf API `protobuf.message.MergeFrom(other_msg)`
-    does NOT always overwrite fields that are set in `other_msg`.
-    Please refer to:
-    https://googleapis.dev/python/protobuf/latest/google/protobuf/message.html
-    """
-    task_res = TaskRes(
-        task_id="",  # This will be generated by the server
-        group_id=ref_task_ins.group_id,
-        workload_id=ref_task_ins.workload_id,
-        task=task_res.task,
-    )
-    # pylint: disable-next=no-member
-    task_res.task.MergeFrom(
-        Task(
-            producer=producer,
-            consumer=ref_task_ins.task.producer,
-            ancestry=[ref_task_ins.task_id],
-        )
-    )
-    return task_res
diff --git a/src/py/flwr/client/message_handler/task_handler_test.py b/src/py/flwr/client/message_handler/task_handler_test.py
index 21f3a2ead98a..c8b9e14737ff 100644
--- a/src/py/flwr/client/message_handler/task_handler_test.py
+++ b/src/py/flwr/client/message_handler/task_handler_test.py
@@ -15,100 +15,31 @@
 """Tests for module task_handler."""
 
 
-from flwr.client.message_handler.task_handler import (
-    get_server_message_from_task_ins,
-    get_task_ins,
-    validate_task_ins,
-    validate_task_res,
-    wrap_client_message_in_task_res,
-)
-from flwr.proto.fleet_pb2 import PullTaskInsResponse
-from flwr.proto.task_pb2 import SecureAggregation, Task, TaskIns, TaskRes
-from flwr.proto.transport_pb2 import ClientMessage, ServerMessage
+from flwr.client.message_handler.task_handler import get_task_ins, validate_task_ins
+from flwr.common import RecordSet, serde
+from flwr.proto.fleet_pb2 import PullTaskInsResponse  # pylint: disable=E0611
+from flwr.proto.task_pb2 import Task, TaskIns  # pylint: disable=E0611
 
 
 def test_validate_task_ins_no_task() -> None:
     """Test validate_task_ins."""
     task_ins = TaskIns(task=None)
 
-    assert not validate_task_ins(task_ins, discard_reconnect_ins=True)
-    assert not validate_task_ins(task_ins, discard_reconnect_ins=False)
+    assert not validate_task_ins(task_ins)
 
 
 def test_validate_task_ins_no_content() -> None:
     """Test validate_task_ins."""
-    task_ins = TaskIns(task=Task(legacy_server_message=None, sa=None))
+    task_ins = TaskIns(task=Task(recordset=None))
 
-    assert not validate_task_ins(task_ins, discard_reconnect_ins=True)
-    assert not validate_task_ins(task_ins, discard_reconnect_ins=False)
+    assert not validate_task_ins(task_ins)
 
 
-def test_validate_task_ins_with_reconnect_ins() -> None:
+def test_validate_task_ins_valid() -> None:
     """Test validate_task_ins."""
-    task_ins = TaskIns(
-        task=Task(
-            legacy_server_message=ServerMessage(
-                reconnect_ins=ServerMessage.ReconnectIns(seconds=3)
-            )
-        )
-    )
-
-    assert not validate_task_ins(task_ins, discard_reconnect_ins=True)
-    assert validate_task_ins(task_ins, discard_reconnect_ins=False)
-
-
-def test_validate_task_ins_valid_legacy_server_message() -> None:
-    """Test validate_task_ins."""
-    task_ins = TaskIns(
-        task=Task(
-            legacy_server_message=ServerMessage(
-                get_properties_ins=ServerMessage.GetPropertiesIns()
-            )
-        )
-    )
-
-    assert validate_task_ins(task_ins, discard_reconnect_ins=True)
-    assert validate_task_ins(task_ins, discard_reconnect_ins=False)
-
-
-def test_validate_task_ins_valid_sa() -> None:
-    """Test validate_task_ins."""
-    task_ins = TaskIns(task=Task(sa=SecureAggregation()))
-
-    assert validate_task_ins(task_ins, discard_reconnect_ins=True)
-    assert validate_task_ins(task_ins, discard_reconnect_ins=False)
-
-
-def test_validate_task_res() -> None:
-    """Test validate_task_res."""
-    task_res = TaskRes(task=Task())
-    assert validate_task_res(task_res)
-
-    task_res.task_id = "123"
-    assert not validate_task_res(task_res)
-
-    task_res.Clear()
-    task_res.group_id = "123"
-    assert not validate_task_res(task_res)
-
-    task_res.Clear()
-    task_res.workload_id = 61016
-    assert not validate_task_res(task_res)
-
-    task_res.Clear()
-    # pylint: disable-next=no-member
-    task_res.task.producer.node_id = 0
-    assert not validate_task_res(task_res)
-
-    task_res.Clear()
-    # pylint: disable-next=no-member
-    task_res.task.consumer.node_id = 0
-    assert not validate_task_res(task_res)
+    task_ins = TaskIns(task=Task(recordset=serde.recordset_to_proto(RecordSet())))
 
-    task_res.Clear()
-    # pylint: disable-next=no-member
-    task_res.task.ancestry.append("123")
-    assert not validate_task_res(task_res)
+    assert validate_task_ins(task_ins)
 
 
 def test_get_task_ins_empty_response() -> None:
@@ -134,61 +65,3 @@ def test_get_task_ins_multiple_ins() -> None:
     )
     actual_task_ins = get_task_ins(res)
     assert actual_task_ins == expected_task_ins
-
-
-def test_get_server_message_from_task_ins_invalid() -> None:
-    """Test get_server_message_from_task_ins."""
-    task_ins = TaskIns(task=Task(legacy_server_message=None))
-    msg_t = get_server_message_from_task_ins(task_ins, exclude_reconnect_ins=True)
-    msg_f = get_server_message_from_task_ins(task_ins, exclude_reconnect_ins=False)
-
-    assert msg_t is None
-    assert msg_f is None
-
-
-def test_get_server_message_from_task_ins_reconnect_ins() -> None:
-    """Test get_server_message_from_task_ins."""
-    expected_server_message = ServerMessage(
-        reconnect_ins=ServerMessage.ReconnectIns(seconds=3)
-    )
-    task_ins = TaskIns(task=Task(legacy_server_message=expected_server_message))
-    msg_t = get_server_message_from_task_ins(task_ins, exclude_reconnect_ins=True)
-    msg_f = get_server_message_from_task_ins(task_ins, exclude_reconnect_ins=False)
-
-    assert msg_t is None
-    assert msg_f == expected_server_message
-
-
-def test_get_server_message_from_task_ins_sa() -> None:
-    """Test get_server_message_from_task_ins."""
-    task_ins = TaskIns(task=Task(sa=SecureAggregation()))
-    msg_t = get_server_message_from_task_ins(task_ins, exclude_reconnect_ins=True)
-    msg_f = get_server_message_from_task_ins(task_ins, exclude_reconnect_ins=False)
-
-    assert msg_t is None
-    assert msg_f is None
-
-
-def test_get_server_message_from_task_ins_valid_legacy_server_message() -> None:
-    """Test get_server_message_from_task_ins."""
-    expected_server_message = ServerMessage(
-        get_properties_ins=ServerMessage.GetPropertiesIns()
-    )
-    task_ins = TaskIns(task=Task(legacy_server_message=expected_server_message))
-    msg_t = get_server_message_from_task_ins(task_ins, exclude_reconnect_ins=True)
-    msg_f = get_server_message_from_task_ins(task_ins, exclude_reconnect_ins=False)
-
-    assert msg_t == expected_server_message
-    assert msg_f == expected_server_message
-
-
-def test_wrap_client_message_in_task_res() -> None:
-    """Test wrap_client_message_in_task_res."""
-    expected_client_message = ClientMessage(
-        get_properties_res=ClientMessage.GetPropertiesRes()
-    )
-    task_res = wrap_client_message_in_task_res(expected_client_message)
-
-    assert validate_task_res(task_res)
-    # pylint: disable-next=no-member
-    assert task_res.task.legacy_client_message == expected_client_message
diff --git a/src/py/flwr/client/mod/__init__.py b/src/py/flwr/client/mod/__init__.py
new file mode 100644
index 000000000000..1cd79fa944fe
--- /dev/null
+++ b/src/py/flwr/client/mod/__init__.py
@@ -0,0 +1,33 @@
+# 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.
+# ==============================================================================
+"""Mods."""
+
+
+from .centraldp_mods import adaptiveclipping_mod, fixedclipping_mod
+from .comms_mods import message_size_mod, parameters_size_mod
+from .localdp_mod import LocalDpMod
+from .secure_aggregation import secagg_mod, secaggplus_mod
+from .utils import make_ffn
+
+__all__ = [
+    "adaptiveclipping_mod",
+    "fixedclipping_mod",
+    "LocalDpMod",
+    "make_ffn",
+    "secagg_mod",
+    "secaggplus_mod",
+    "message_size_mod",
+    "parameters_size_mod",
+]
diff --git a/src/py/flwr/client/mod/centraldp_mods.py b/src/py/flwr/client/mod/centraldp_mods.py
new file mode 100644
index 000000000000..4f4a595e8d9c
--- /dev/null
+++ b/src/py/flwr/client/mod/centraldp_mods.py
@@ -0,0 +1,157 @@
+# 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.
+# ==============================================================================
+"""Clipping modifiers for central DP with client-side clipping."""
+
+
+from logging import INFO
+
+from flwr.client.typing import ClientAppCallable
+from flwr.common import ndarrays_to_parameters, parameters_to_ndarrays
+from flwr.common import recordset_compat as compat
+from flwr.common.constant import MessageType
+from flwr.common.context import Context
+from flwr.common.differential_privacy import (
+    compute_adaptive_clip_model_update,
+    compute_clip_model_update,
+)
+from flwr.common.differential_privacy_constants import KEY_CLIPPING_NORM, KEY_NORM_BIT
+from flwr.common.logger import log
+from flwr.common.message import Message
+
+
+def fixedclipping_mod(
+    msg: Message, ctxt: Context, call_next: ClientAppCallable
+) -> Message:
+    """Client-side fixed clipping modifier.
+
+    This mod needs to be used with the DifferentialPrivacyClientSideFixedClipping
+    server-side strategy wrapper.
+
+    The wrapper sends the clipping_norm value to the client.
+
+    This mod clips the client model updates before sending them to the server.
+
+    It operates on messages of type `MessageType.TRAIN`.
+
+    Notes
+    -----
+    Consider the order of mods when using multiple.
+
+    Typically, fixedclipping_mod should be the last to operate on params.
+    """
+    if msg.metadata.message_type != MessageType.TRAIN:
+        return call_next(msg, ctxt)
+    fit_ins = compat.recordset_to_fitins(msg.content, keep_input=True)
+    if KEY_CLIPPING_NORM not in fit_ins.config:
+        raise KeyError(
+            f"The {KEY_CLIPPING_NORM} value is not supplied by the "
+            f"DifferentialPrivacyClientSideFixedClipping wrapper at"
+            f" the server side."
+        )
+
+    clipping_norm = float(fit_ins.config[KEY_CLIPPING_NORM])
+    server_to_client_params = parameters_to_ndarrays(fit_ins.parameters)
+
+    # Call inner app
+    out_msg = call_next(msg, ctxt)
+
+    # Check if the msg has error
+    if out_msg.has_error():
+        return out_msg
+
+    fit_res = compat.recordset_to_fitres(out_msg.content, keep_input=True)
+
+    client_to_server_params = parameters_to_ndarrays(fit_res.parameters)
+
+    # Clip the client update
+    compute_clip_model_update(
+        client_to_server_params,
+        server_to_client_params,
+        clipping_norm,
+    )
+
+    log(INFO, "fixedclipping_mod: parameters are clipped by value: %s.", clipping_norm)
+
+    fit_res.parameters = ndarrays_to_parameters(client_to_server_params)
+    out_msg.content = compat.fitres_to_recordset(fit_res, keep_input=True)
+    return out_msg
+
+
+def adaptiveclipping_mod(
+    msg: Message, ctxt: Context, call_next: ClientAppCallable
+) -> Message:
+    """Client-side adaptive clipping modifier.
+
+    This mod needs to be used with the DifferentialPrivacyClientSideAdaptiveClipping
+    server-side strategy wrapper.
+
+    The wrapper sends the clipping_norm value to the client.
+
+    This mod clips the client model updates before sending them to the server.
+
+    It also sends KEY_NORM_BIT to the server for computing the new clipping value.
+
+    It operates on messages of type `MessageType.TRAIN`.
+
+    Notes
+    -----
+    Consider the order of mods when using multiple.
+
+    Typically, adaptiveclipping_mod should be the last to operate on params.
+    """
+    if msg.metadata.message_type != MessageType.TRAIN:
+        return call_next(msg, ctxt)
+
+    fit_ins = compat.recordset_to_fitins(msg.content, keep_input=True)
+
+    if KEY_CLIPPING_NORM not in fit_ins.config:
+        raise KeyError(
+            f"The {KEY_CLIPPING_NORM} value is not supplied by the "
+            f"DifferentialPrivacyClientSideFixedClipping wrapper at"
+            f" the server side."
+        )
+    if not isinstance(fit_ins.config[KEY_CLIPPING_NORM], float):
+        raise ValueError(f"{KEY_CLIPPING_NORM} should be a float value.")
+    clipping_norm = float(fit_ins.config[KEY_CLIPPING_NORM])
+    server_to_client_params = parameters_to_ndarrays(fit_ins.parameters)
+
+    # Call inner app
+    out_msg = call_next(msg, ctxt)
+
+    # Check if the msg has error
+    if out_msg.has_error():
+        return out_msg
+
+    fit_res = compat.recordset_to_fitres(out_msg.content, keep_input=True)
+
+    client_to_server_params = parameters_to_ndarrays(fit_res.parameters)
+
+    # Clip the client update
+    norm_bit = compute_adaptive_clip_model_update(
+        client_to_server_params,
+        server_to_client_params,
+        clipping_norm,
+    )
+    log(
+        INFO,
+        "adaptiveclipping_mod: parameters are clipped by value: %s.",
+        clipping_norm,
+    )
+
+    fit_res.parameters = ndarrays_to_parameters(client_to_server_params)
+
+    fit_res.metrics[KEY_NORM_BIT] = norm_bit
+    out_msg.content = compat.fitres_to_recordset(fit_res, keep_input=True)
+    return out_msg
diff --git a/src/py/flwr/client/mod/comms_mods.py b/src/py/flwr/client/mod/comms_mods.py
new file mode 100644
index 000000000000..102d2f477262
--- /dev/null
+++ b/src/py/flwr/client/mod/comms_mods.py
@@ -0,0 +1,79 @@
+# 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.
+# ==============================================================================
+"""Mods that report statistics about message communication."""
+
+from logging import INFO
+
+import numpy as np
+
+from flwr.client.typing import ClientAppCallable
+from flwr.common.context import Context
+from flwr.common.logger import log
+from flwr.common.message import Message
+
+
+def message_size_mod(
+    msg: Message, ctxt: Context, call_next: ClientAppCallable
+) -> Message:
+    """Message size mod.
+
+    This mod logs the size in Bytes of the message being transmited.
+    """
+    message_size_in_bytes = 0
+
+    for p_record in msg.content.parameters_records.values():
+        message_size_in_bytes += p_record.count_bytes()
+
+    for c_record in msg.content.configs_records.values():
+        message_size_in_bytes += c_record.count_bytes()
+
+    for m_record in msg.content.metrics_records.values():
+        message_size_in_bytes += m_record.count_bytes()
+
+    log(INFO, "Message size: %i Bytes", message_size_in_bytes)
+
+    return call_next(msg, ctxt)
+
+
+def parameters_size_mod(
+    msg: Message, ctxt: Context, call_next: ClientAppCallable
+) -> Message:
+    """Parameters size mod.
+
+    This mod logs the number of parameters transmitted in the message as well as their
+    size in Bytes.
+    """
+    model_size_stats = {}
+    parameters_size_in_bytes = 0
+    for record_name, p_record in msg.content.parameters_records.items():
+        p_record_bytes = p_record.count_bytes()
+        parameters_size_in_bytes += p_record_bytes
+        parameter_count = 0
+        for array in p_record.values():
+            parameter_count += (
+                int(np.prod(array.shape)) if array.shape else array.numpy().size
+            )
+
+        model_size_stats[f"{record_name}"] = {
+            "parameters": parameter_count,
+            "bytes": p_record_bytes,
+        }
+
+    if model_size_stats:
+        log(INFO, model_size_stats)
+
+    log(INFO, "Total parameters transmited: %i Bytes", parameters_size_in_bytes)
+
+    return call_next(msg, ctxt)
diff --git a/src/py/flwr/client/mod/localdp_mod.py b/src/py/flwr/client/mod/localdp_mod.py
new file mode 100644
index 000000000000..3b0311a612b9
--- /dev/null
+++ b/src/py/flwr/client/mod/localdp_mod.py
@@ -0,0 +1,148 @@
+# 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.
+# ==============================================================================
+"""Local DP modifier."""
+
+
+from logging import INFO
+
+import numpy as np
+
+from flwr.client.typing import ClientAppCallable
+from flwr.common import ndarrays_to_parameters, parameters_to_ndarrays
+from flwr.common import recordset_compat as compat
+from flwr.common.constant import MessageType
+from flwr.common.context import Context
+from flwr.common.differential_privacy import (
+    add_localdp_gaussian_noise_to_params,
+    compute_clip_model_update,
+)
+from flwr.common.logger import log
+from flwr.common.message import Message
+
+
+class LocalDpMod:
+    """Modifier for local differential privacy.
+
+    This mod clips the client model updates and
+    adds noise to the params before sending them to the server.
+
+    It operates on messages of type `MessageType.TRAIN`.
+
+    Parameters
+    ----------
+    clipping_norm : float
+        The value of the clipping norm.
+    sensitivity : float
+        The sensitivity of the client model.
+    epsilon : float
+        The privacy budget.
+        Smaller value of epsilon indicates a higher level of privacy protection.
+    delta : float
+        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.
+
+    Examples
+    --------
+    Create an instance of the local DP mod and add it to the client-side mods:
+
+    >>> local_dp_mod = LocalDpMod( ... )
+    >>> app = fl.client.ClientApp(
+    >>>     client_fn=client_fn, mods=[local_dp_mod]
+    >>> )
+    """
+
+    def __init__(
+        self, clipping_norm: float, sensitivity: float, epsilon: float, delta: float
+    ) -> None:
+        if clipping_norm <= 0:
+            raise ValueError("The clipping norm should be a positive value.")
+
+        if sensitivity < 0:
+            raise ValueError("The sensitivity should be a non-negative value.")
+
+        if epsilon < 0:
+            raise ValueError("Epsilon should be a non-negative value.")
+
+        if delta < 0:
+            raise ValueError("Delta should be a non-negative value.")
+
+        self.clipping_norm = clipping_norm
+        self.sensitivity = sensitivity
+        self.epsilon = epsilon
+        self.delta = delta
+
+    def __call__(
+        self, msg: Message, ctxt: Context, call_next: ClientAppCallable
+    ) -> Message:
+        """Perform local DP on the client model parameters.
+
+        Parameters
+        ----------
+        msg : Message
+            The message received from the server.
+        ctxt : Context
+            The context of the client.
+        call_next : ClientAppCallable
+            The callable to call the next middleware in the chain.
+
+        Returns
+        -------
+        Message
+            The modified message to be sent back to the server.
+        """
+        if msg.metadata.message_type != MessageType.TRAIN:
+            return call_next(msg, ctxt)
+
+        fit_ins = compat.recordset_to_fitins(msg.content, keep_input=True)
+        server_to_client_params = parameters_to_ndarrays(fit_ins.parameters)
+
+        # Call inner app
+        out_msg = call_next(msg, ctxt)
+
+        # Check if the msg has error
+        if out_msg.has_error():
+            return out_msg
+
+        fit_res = compat.recordset_to_fitres(out_msg.content, keep_input=True)
+
+        client_to_server_params = parameters_to_ndarrays(fit_res.parameters)
+
+        # Clip the client update
+        compute_clip_model_update(
+            client_to_server_params,
+            server_to_client_params,
+            self.clipping_norm,
+        )
+        log(
+            INFO, "LocalDpMod: parameters are clipped by value: %s.", self.clipping_norm
+        )
+
+        fit_res.parameters = ndarrays_to_parameters(client_to_server_params)
+
+        # Add noise to model params
+        add_localdp_gaussian_noise_to_params(
+            fit_res.parameters, self.sensitivity, self.epsilon, self.delta
+        )
+        log(
+            INFO,
+            "LocalDpMod: local DP noise with "
+            "standard deviation: %s added to parameters.",
+            self.sensitivity * np.sqrt(2 * np.log(1.25 / self.delta)) / self.epsilon,
+        )
+
+        out_msg.content = compat.fitres_to_recordset(fit_res, keep_input=True)
+        return out_msg
diff --git a/src/py/flwr/client/workload_state.py b/src/py/flwr/client/mod/secure_aggregation/__init__.py
similarity index 77%
rename from src/py/flwr/client/workload_state.py
rename to src/py/flwr/client/mod/secure_aggregation/__init__.py
index 42ae2a925f47..8892d8c03935 100644
--- a/src/py/flwr/client/workload_state.py
+++ b/src/py/flwr/client/mod/secure_aggregation/__init__.py
@@ -12,14 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Workload state."""
+"""Secure Aggregation mods."""
 
-from dataclasses import dataclass
-from typing import Dict
 
+from .secagg_mod import secagg_mod
+from .secaggplus_mod import secaggplus_mod
 
-@dataclass
-class WorkloadState:
-    """State of a workload executed by a client node."""
-
-    state: Dict[str, str]
+__all__ = [
+    "secagg_mod",
+    "secaggplus_mod",
+]
diff --git a/src/py/flwr/client/mod/secure_aggregation/secagg_mod.py b/src/py/flwr/client/mod/secure_aggregation/secagg_mod.py
new file mode 100644
index 000000000000..d87af59a4e6e
--- /dev/null
+++ b/src/py/flwr/client/mod/secure_aggregation/secagg_mod.py
@@ -0,0 +1,30 @@
+# 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.
+# ==============================================================================
+"""Modifier for the SecAgg protocol."""
+
+
+from flwr.client.typing import ClientAppCallable
+from flwr.common import Context, Message
+
+from .secaggplus_mod import secaggplus_mod
+
+
+def secagg_mod(
+    msg: Message,
+    ctxt: Context,
+    call_next: ClientAppCallable,
+) -> Message:
+    """Handle incoming message and return results, following the SecAgg protocol."""
+    return secaggplus_mod(msg, ctxt, call_next)
diff --git a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
new file mode 100644
index 000000000000..989d5f6e1361
--- /dev/null
+++ b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod.py
@@ -0,0 +1,518 @@
+# 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.
+# ==============================================================================
+"""Modifier for the SecAgg+ protocol."""
+
+
+import os
+from dataclasses import dataclass, field
+from logging import DEBUG, WARNING
+from typing import Any, Dict, List, Tuple, cast
+
+from flwr.client.typing import ClientAppCallable
+from flwr.common import (
+    ConfigsRecord,
+    Context,
+    Message,
+    Parameters,
+    RecordSet,
+    ndarray_to_bytes,
+    parameters_to_ndarrays,
+)
+from flwr.common import recordset_compat as compat
+from flwr.common.constant import MessageType
+from flwr.common.logger import log
+from flwr.common.secure_aggregation.crypto.shamir import create_shares
+from flwr.common.secure_aggregation.crypto.symmetric_encryption import (
+    bytes_to_private_key,
+    bytes_to_public_key,
+    decrypt,
+    encrypt,
+    generate_key_pairs,
+    generate_shared_key,
+    private_key_to_bytes,
+    public_key_to_bytes,
+)
+from flwr.common.secure_aggregation.ndarrays_arithmetic import (
+    factor_combine,
+    parameters_addition,
+    parameters_mod,
+    parameters_multiply,
+    parameters_subtraction,
+)
+from flwr.common.secure_aggregation.quantization import quantize
+from flwr.common.secure_aggregation.secaggplus_constants import (
+    RECORD_KEY_CONFIGS,
+    RECORD_KEY_STATE,
+    Key,
+    Stage,
+)
+from flwr.common.secure_aggregation.secaggplus_utils import (
+    pseudo_rand_gen,
+    share_keys_plaintext_concat,
+    share_keys_plaintext_separate,
+)
+from flwr.common.typing import ConfigsRecordValues
+
+
+@dataclass
+# pylint: disable-next=too-many-instance-attributes
+class SecAggPlusState:
+    """State of the SecAgg+ protocol."""
+
+    current_stage: str = Stage.UNMASK
+
+    nid: int = 0
+    sample_num: int = 0
+    share_num: int = 0
+    threshold: int = 0
+    clipping_range: float = 0.0
+    target_range: int = 0
+    mod_range: int = 0
+    max_weight: float = 0.0
+
+    # Secret key (sk) and public key (pk)
+    sk1: bytes = b""
+    pk1: bytes = b""
+    sk2: bytes = b""
+    pk2: bytes = b""
+
+    # Random seed for generating the private mask
+    rd_seed: bytes = b""
+
+    rd_seed_share_dict: Dict[int, bytes] = field(default_factory=dict)
+    sk1_share_dict: Dict[int, bytes] = field(default_factory=dict)
+    # The dict of the shared secrets from sk2
+    ss2_dict: Dict[int, bytes] = field(default_factory=dict)
+    public_keys_dict: Dict[int, Tuple[bytes, bytes]] = field(default_factory=dict)
+
+    def __init__(self, **kwargs: ConfigsRecordValues) -> None:
+        for k, v in kwargs.items():
+            if k.endswith(":V"):
+                continue
+            new_v: Any = v
+            if k.endswith(":K"):
+                k = k[:-2]
+                keys = cast(List[int], v)
+                values = cast(List[bytes], kwargs[f"{k}:V"])
+                if len(values) > len(keys):
+                    updated_values = [
+                        tuple(values[i : i + 2]) for i in range(0, len(values), 2)
+                    ]
+                    new_v = dict(zip(keys, updated_values))
+                else:
+                    new_v = dict(zip(keys, values))
+            self.__setattr__(k, new_v)
+
+    def to_dict(self) -> Dict[str, ConfigsRecordValues]:
+        """Convert the state to a dictionary."""
+        ret = vars(self)
+        for k in list(ret.keys()):
+            if isinstance(ret[k], dict):
+                # Replace dict with two lists
+                v = cast(Dict[str, Any], ret.pop(k))
+                ret[f"{k}:K"] = list(v.keys())
+                if k == "public_keys_dict":
+                    v_list: List[bytes] = []
+                    for b1_b2 in cast(List[Tuple[bytes, bytes]], v.values()):
+                        v_list.extend(b1_b2)
+                    ret[f"{k}:V"] = v_list
+                else:
+                    ret[f"{k}:V"] = list(v.values())
+        return ret
+
+
+def secaggplus_mod(
+    msg: Message,
+    ctxt: Context,
+    call_next: ClientAppCallable,
+) -> Message:
+    """Handle incoming message and return results, following the SecAgg+ protocol."""
+    # Ignore non-fit messages
+    if msg.metadata.message_type != MessageType.TRAIN:
+        return call_next(msg, ctxt)
+
+    # Retrieve local state
+    if RECORD_KEY_STATE not in ctxt.state.configs_records:
+        ctxt.state.configs_records[RECORD_KEY_STATE] = ConfigsRecord({})
+    state_dict = ctxt.state.configs_records[RECORD_KEY_STATE]
+    state = SecAggPlusState(**state_dict)
+
+    # Retrieve incoming configs
+    configs = msg.content.configs_records[RECORD_KEY_CONFIGS]
+
+    # Check the validity of the next stage
+    check_stage(state.current_stage, configs)
+
+    # Update the current stage
+    state.current_stage = cast(str, configs.pop(Key.STAGE))
+
+    # Check the validity of the configs based on the current stage
+    check_configs(state.current_stage, configs)
+
+    # Execute
+    out_content = RecordSet()
+    if state.current_stage == Stage.SETUP:
+        state.nid = msg.metadata.dst_node_id
+        res = _setup(state, configs)
+    elif state.current_stage == Stage.SHARE_KEYS:
+        res = _share_keys(state, configs)
+    elif state.current_stage == Stage.COLLECT_MASKED_VECTORS:
+        out_msg = call_next(msg, ctxt)
+        out_content = out_msg.content
+        fitres = compat.recordset_to_fitres(out_content, keep_input=True)
+        res = _collect_masked_vectors(
+            state, configs, fitres.num_examples, fitres.parameters
+        )
+        for p_record in out_content.parameters_records.values():
+            p_record.clear()
+    elif state.current_stage == Stage.UNMASK:
+        res = _unmask(state, configs)
+    else:
+        raise ValueError(f"Unknown SecAgg/SecAgg+ stage: {state.current_stage}")
+
+    # Save state
+    ctxt.state.configs_records[RECORD_KEY_STATE] = ConfigsRecord(state.to_dict())
+
+    # Return message
+    out_content.configs_records[RECORD_KEY_CONFIGS] = ConfigsRecord(res, False)
+    return msg.create_reply(out_content, ttl="")
+
+
+def check_stage(current_stage: str, configs: ConfigsRecord) -> None:
+    """Check the validity of the next stage."""
+    # Check the existence of Config.STAGE
+    if Key.STAGE not in configs:
+        raise KeyError(
+            f"The required key '{Key.STAGE}' is missing from the ConfigsRecord."
+        )
+
+    # Check the value type of the Config.STAGE
+    next_stage = configs[Key.STAGE]
+    if not isinstance(next_stage, str):
+        raise TypeError(
+            f"The value for the key '{Key.STAGE}' must be of type {str}, "
+            f"but got {type(next_stage)} instead."
+        )
+
+    # Check the validity of the next stage
+    if next_stage == Stage.SETUP:
+        if current_stage != Stage.UNMASK:
+            log(WARNING, "Restart from the setup stage")
+    # If stage is not "setup",
+    # the stage from configs should be the expected next stage
+    else:
+        stages = Stage.all()
+        expected_next_stage = stages[(stages.index(current_stage) + 1) % len(stages)]
+        if next_stage != expected_next_stage:
+            raise ValueError(
+                "Abort secure aggregation: "
+                f"expect {expected_next_stage} stage, but receive {next_stage} stage"
+            )
+
+
+# pylint: disable-next=too-many-branches
+def check_configs(stage: str, configs: ConfigsRecord) -> None:
+    """Check the validity of the configs."""
+    # Check configs for the setup stage
+    if stage == Stage.SETUP:
+        key_type_pairs = [
+            (Key.SAMPLE_NUMBER, int),
+            (Key.SHARE_NUMBER, int),
+            (Key.THRESHOLD, int),
+            (Key.CLIPPING_RANGE, float),
+            (Key.TARGET_RANGE, int),
+            (Key.MOD_RANGE, int),
+        ]
+        for key, expected_type in key_type_pairs:
+            if key not in configs:
+                raise KeyError(
+                    f"Stage {Stage.SETUP}: the required key '{key}' is "
+                    "missing from the ConfigsRecord."
+                )
+            # Bool is a subclass of int in Python,
+            # so `isinstance(v, int)` will return True even if v is a boolean.
+            # pylint: disable-next=unidiomatic-typecheck
+            if type(configs[key]) is not expected_type:
+                raise TypeError(
+                    f"Stage {Stage.SETUP}: The value for the key '{key}' "
+                    f"must be of type {expected_type}, "
+                    f"but got {type(configs[key])} instead."
+                )
+    elif stage == Stage.SHARE_KEYS:
+        for key, value in configs.items():
+            if (
+                not isinstance(value, list)
+                or len(value) != 2
+                or not isinstance(value[0], bytes)
+                or not isinstance(value[1], bytes)
+            ):
+                raise TypeError(
+                    f"Stage {Stage.SHARE_KEYS}: "
+                    f"the value for the key '{key}' must be a list of two bytes."
+                )
+    elif stage == Stage.COLLECT_MASKED_VECTORS:
+        key_type_pairs = [
+            (Key.CIPHERTEXT_LIST, bytes),
+            (Key.SOURCE_LIST, int),
+        ]
+        for key, expected_type in key_type_pairs:
+            if key not in configs:
+                raise KeyError(
+                    f"Stage {Stage.COLLECT_MASKED_VECTORS}: "
+                    f"the required key '{key}' is "
+                    "missing from the ConfigsRecord."
+                )
+            if not isinstance(configs[key], list) or any(
+                elm
+                for elm in cast(List[Any], configs[key])
+                # pylint: disable-next=unidiomatic-typecheck
+                if type(elm) is not expected_type
+            ):
+                raise TypeError(
+                    f"Stage {Stage.COLLECT_MASKED_VECTORS}: "
+                    f"the value for the key '{key}' "
+                    f"must be of type List[{expected_type.__name__}]"
+                )
+    elif stage == Stage.UNMASK:
+        key_type_pairs = [
+            (Key.ACTIVE_NODE_ID_LIST, int),
+            (Key.DEAD_NODE_ID_LIST, int),
+        ]
+        for key, expected_type in key_type_pairs:
+            if key not in configs:
+                raise KeyError(
+                    f"Stage {Stage.UNMASK}: "
+                    f"the required key '{key}' is "
+                    "missing from the ConfigsRecord."
+                )
+            if not isinstance(configs[key], list) or any(
+                elm
+                for elm in cast(List[Any], configs[key])
+                # pylint: disable-next=unidiomatic-typecheck
+                if type(elm) is not expected_type
+            ):
+                raise TypeError(
+                    f"Stage {Stage.UNMASK}: "
+                    f"the value for the key '{key}' "
+                    f"must be of type List[{expected_type.__name__}]"
+                )
+    else:
+        raise ValueError(f"Unknown secagg stage: {stage}")
+
+
+def _setup(
+    state: SecAggPlusState, configs: ConfigsRecord
+) -> Dict[str, ConfigsRecordValues]:
+    # Assigning parameter values to object fields
+    sec_agg_param_dict = configs
+    state.sample_num = cast(int, sec_agg_param_dict[Key.SAMPLE_NUMBER])
+    log(DEBUG, "Node %d: starting stage 0...", state.nid)
+
+    state.share_num = cast(int, sec_agg_param_dict[Key.SHARE_NUMBER])
+    state.threshold = cast(int, sec_agg_param_dict[Key.THRESHOLD])
+    state.clipping_range = cast(float, sec_agg_param_dict[Key.CLIPPING_RANGE])
+    state.target_range = cast(int, sec_agg_param_dict[Key.TARGET_RANGE])
+    state.mod_range = cast(int, sec_agg_param_dict[Key.MOD_RANGE])
+    state.max_weight = cast(float, sec_agg_param_dict[Key.MAX_WEIGHT])
+
+    # Dictionaries containing node IDs as keys
+    # and their respective secret shares as values.
+    state.rd_seed_share_dict = {}
+    state.sk1_share_dict = {}
+    # Dictionary containing node IDs as keys
+    # and their respective shared secrets (with this client) as values.
+    state.ss2_dict = {}
+
+    # Create 2 sets private public key pairs
+    # One for creating pairwise masks
+    # One for encrypting message to distribute shares
+    sk1, pk1 = generate_key_pairs()
+    sk2, pk2 = generate_key_pairs()
+
+    state.sk1, state.pk1 = private_key_to_bytes(sk1), public_key_to_bytes(pk1)
+    state.sk2, state.pk2 = private_key_to_bytes(sk2), public_key_to_bytes(pk2)
+    log(DEBUG, "Node %d: stage 0 completes. uploading public keys...", state.nid)
+    return {Key.PUBLIC_KEY_1: state.pk1, Key.PUBLIC_KEY_2: state.pk2}
+
+
+# pylint: disable-next=too-many-locals
+def _share_keys(
+    state: SecAggPlusState, configs: ConfigsRecord
+) -> Dict[str, ConfigsRecordValues]:
+    named_bytes_tuples = cast(Dict[str, Tuple[bytes, bytes]], configs)
+    key_dict = {int(sid): (pk1, pk2) for sid, (pk1, pk2) in named_bytes_tuples.items()}
+    log(DEBUG, "Node %d: starting stage 1...", state.nid)
+    state.public_keys_dict = key_dict
+
+    # Check if the size is larger than threshold
+    if len(state.public_keys_dict) < state.threshold:
+        raise ValueError("Available neighbours number smaller than threshold")
+
+    # Check if all public keys are unique
+    pk_list: List[bytes] = []
+    for pk1, pk2 in state.public_keys_dict.values():
+        pk_list.append(pk1)
+        pk_list.append(pk2)
+    if len(set(pk_list)) != len(pk_list):
+        raise ValueError("Some public keys are identical")
+
+    # Check if public keys of this client are correct in the dictionary
+    if (
+        state.public_keys_dict[state.nid][0] != state.pk1
+        or state.public_keys_dict[state.nid][1] != state.pk2
+    ):
+        raise ValueError(
+            "Own public keys are displayed in dict incorrectly, should not happen!"
+        )
+
+    # Generate the private mask seed
+    state.rd_seed = os.urandom(32)
+
+    # Create shares for the private mask seed and the first private key
+    b_shares = create_shares(state.rd_seed, state.threshold, state.share_num)
+    sk1_shares = create_shares(state.sk1, state.threshold, state.share_num)
+
+    srcs, dsts, ciphertexts = [], [], []
+
+    # Distribute shares
+    for idx, (nid, (_, pk2)) in enumerate(state.public_keys_dict.items()):
+        if nid == state.nid:
+            state.rd_seed_share_dict[state.nid] = b_shares[idx]
+            state.sk1_share_dict[state.nid] = sk1_shares[idx]
+        else:
+            shared_key = generate_shared_key(
+                bytes_to_private_key(state.sk2),
+                bytes_to_public_key(pk2),
+            )
+            state.ss2_dict[nid] = shared_key
+            plaintext = share_keys_plaintext_concat(
+                state.nid, nid, b_shares[idx], sk1_shares[idx]
+            )
+            ciphertext = encrypt(shared_key, plaintext)
+            srcs.append(state.nid)
+            dsts.append(nid)
+            ciphertexts.append(ciphertext)
+
+    log(DEBUG, "Node %d: stage 1 completes. uploading key shares...", state.nid)
+    return {Key.DESTINATION_LIST: dsts, Key.CIPHERTEXT_LIST: ciphertexts}
+
+
+# pylint: disable-next=too-many-locals
+def _collect_masked_vectors(
+    state: SecAggPlusState,
+    configs: ConfigsRecord,
+    num_examples: int,
+    updated_parameters: Parameters,
+) -> Dict[str, ConfigsRecordValues]:
+    log(DEBUG, "Node %d: starting stage 2...", state.nid)
+    available_clients: List[int] = []
+    ciphertexts = cast(List[bytes], configs[Key.CIPHERTEXT_LIST])
+    srcs = cast(List[int], configs[Key.SOURCE_LIST])
+    if len(ciphertexts) + 1 < state.threshold:
+        raise ValueError("Not enough available neighbour clients.")
+
+    # Decrypt ciphertexts, verify their sources, and store shares.
+    for src, ciphertext in zip(srcs, ciphertexts):
+        shared_key = state.ss2_dict[src]
+        plaintext = decrypt(shared_key, ciphertext)
+        actual_src, dst, rd_seed_share, sk1_share = share_keys_plaintext_separate(
+            plaintext
+        )
+        available_clients.append(src)
+        if src != actual_src:
+            raise ValueError(
+                f"Node {state.nid}: received ciphertext "
+                f"from {actual_src} instead of {src}."
+            )
+        if dst != state.nid:
+            raise ValueError(
+                f"Node {state.nid}: received an encrypted message"
+                f"for Node {dst} from Node {src}."
+            )
+        state.rd_seed_share_dict[src] = rd_seed_share
+        state.sk1_share_dict[src] = sk1_share
+
+    # Fit
+    ratio = num_examples / state.max_weight
+    if ratio > 1:
+        log(
+            WARNING,
+            "Potential overflow warning: the provided weight (%s) exceeds the specified"
+            " max_weight (%s). This may lead to overflow issues.",
+            num_examples,
+            state.max_weight,
+        )
+    q_ratio = round(ratio * state.target_range)
+    dq_ratio = q_ratio / state.target_range
+
+    parameters = parameters_to_ndarrays(updated_parameters)
+    parameters = parameters_multiply(parameters, dq_ratio)
+
+    # Quantize parameter update (vector)
+    quantized_parameters = quantize(
+        parameters, state.clipping_range, state.target_range
+    )
+
+    quantized_parameters = factor_combine(q_ratio, quantized_parameters)
+
+    dimensions_list: List[Tuple[int, ...]] = [a.shape for a in quantized_parameters]
+
+    # Add private mask
+    private_mask = pseudo_rand_gen(state.rd_seed, state.mod_range, dimensions_list)
+    quantized_parameters = parameters_addition(quantized_parameters, private_mask)
+
+    for node_id in available_clients:
+        # Add pairwise masks
+        shared_key = generate_shared_key(
+            bytes_to_private_key(state.sk1),
+            bytes_to_public_key(state.public_keys_dict[node_id][0]),
+        )
+        pairwise_mask = pseudo_rand_gen(shared_key, state.mod_range, dimensions_list)
+        if state.nid > node_id:
+            quantized_parameters = parameters_addition(
+                quantized_parameters, pairwise_mask
+            )
+        else:
+            quantized_parameters = parameters_subtraction(
+                quantized_parameters, pairwise_mask
+            )
+
+    # Take mod of final weight update vector and return to server
+    quantized_parameters = parameters_mod(quantized_parameters, state.mod_range)
+    log(DEBUG, "Node %d: stage 2 completed, uploading masked parameters...", state.nid)
+    return {
+        Key.MASKED_PARAMETERS: [ndarray_to_bytes(arr) for arr in quantized_parameters]
+    }
+
+
+def _unmask(
+    state: SecAggPlusState, configs: ConfigsRecord
+) -> Dict[str, ConfigsRecordValues]:
+    log(DEBUG, "Node %d: starting stage 3...", state.nid)
+
+    active_nids = cast(List[int], configs[Key.ACTIVE_NODE_ID_LIST])
+    dead_nids = cast(List[int], configs[Key.DEAD_NODE_ID_LIST])
+    # Send private mask seed share for every avaliable client (including itself)
+    # Send first private key share for building pairwise mask for every dropped client
+    if len(active_nids) < state.threshold:
+        raise ValueError("Available neighbours number smaller than threshold")
+
+    all_nids, shares = [], []
+    all_nids = active_nids + dead_nids
+    shares += [state.rd_seed_share_dict[nid] for nid in active_nids]
+    shares += [state.sk1_share_dict[nid] for nid in dead_nids]
+
+    log(DEBUG, "Node %d: stage 3 completes. uploading key shares...", state.nid)
+    return {Key.NODE_ID_LIST: all_nids, Key.SHARE_LIST: shares}
diff --git a/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
new file mode 100644
index 000000000000..db5ed67c02a4
--- /dev/null
+++ b/src/py/flwr/client/mod/secure_aggregation/secaggplus_mod_test.py
@@ -0,0 +1,316 @@
+# 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.
+# ==============================================================================
+"""The SecAgg+ protocol modifier tests."""
+
+import unittest
+from itertools import product
+from typing import Callable, Dict, List
+
+from flwr.client.mod import make_ffn
+from flwr.common import ConfigsRecord, Context, Message, Metadata, RecordSet
+from flwr.common.constant import MessageType
+from flwr.common.secure_aggregation.secaggplus_constants import (
+    RECORD_KEY_CONFIGS,
+    RECORD_KEY_STATE,
+    Key,
+    Stage,
+)
+from flwr.common.typing import ConfigsRecordValues
+
+from .secaggplus_mod import SecAggPlusState, check_configs, secaggplus_mod
+
+
+def get_test_handler(
+    ctxt: Context,
+) -> Callable[[Dict[str, ConfigsRecordValues]], ConfigsRecord]:
+    """."""
+
+    def empty_ffn(_msg: Message, _2: Context) -> Message:
+        return _msg.create_reply(RecordSet(), ttl="")
+
+    app = make_ffn(empty_ffn, [secaggplus_mod])
+
+    def func(configs: Dict[str, ConfigsRecordValues]) -> ConfigsRecord:
+        in_msg = Message(
+            metadata=Metadata(
+                run_id=0,
+                message_id="",
+                src_node_id=0,
+                dst_node_id=123,
+                reply_to_message="",
+                group_id="",
+                ttl="",
+                message_type=MessageType.TRAIN,
+            ),
+            content=RecordSet(
+                configs_records={RECORD_KEY_CONFIGS: ConfigsRecord(configs)}
+            ),
+        )
+        out_msg = app(in_msg, ctxt)
+        return out_msg.content.configs_records[RECORD_KEY_CONFIGS]
+
+    return func
+
+
+def _make_ctxt() -> Context:
+    cfg = ConfigsRecord(SecAggPlusState().to_dict())
+    return Context(RecordSet(configs_records={RECORD_KEY_STATE: cfg}))
+
+
+def _make_set_state_fn(
+    ctxt: Context,
+) -> Callable[[str], None]:
+    def set_stage(stage: str) -> None:
+        state_dict = ctxt.state.configs_records[RECORD_KEY_STATE]
+        state = SecAggPlusState(**state_dict)
+        state.current_stage = stage
+        ctxt.state.configs_records[RECORD_KEY_STATE] = ConfigsRecord(state.to_dict())
+
+    return set_stage
+
+
+class TestSecAggPlusHandler(unittest.TestCase):
+    """Test the SecAgg+ protocol handler."""
+
+    def test_stage_transition(self) -> None:
+        """Test stage transition."""
+        ctxt = _make_ctxt()
+        handler = get_test_handler(ctxt)
+        set_stage = _make_set_state_fn(ctxt)
+
+        assert Stage.all() == (
+            Stage.SETUP,
+            Stage.SHARE_KEYS,
+            Stage.COLLECT_MASKED_VECTORS,
+            Stage.UNMASK,
+        )
+
+        valid_transitions = {
+            # From one stage to the next stage
+            (Stage.UNMASK, Stage.SETUP),
+            (Stage.SETUP, Stage.SHARE_KEYS),
+            (Stage.SHARE_KEYS, Stage.COLLECT_MASKED_VECTORS),
+            (Stage.COLLECT_MASKED_VECTORS, Stage.UNMASK),
+            # From any stage to the initial stage
+            # Such transitions will log a warning.
+            (Stage.SETUP, Stage.SETUP),
+            (Stage.SHARE_KEYS, Stage.SETUP),
+            (Stage.COLLECT_MASKED_VECTORS, Stage.SETUP),
+        }
+
+        invalid_transitions = set(product(Stage.all(), Stage.all())).difference(
+            valid_transitions
+        )
+
+        # Test valid transitions
+        # If the next stage is valid, the function should update the current stage
+        # and then raise KeyError or other exceptions when trying to execute SA.
+        for current_stage, next_stage in valid_transitions:
+            set_stage(current_stage)
+
+            with self.assertRaises(KeyError):
+                handler({Key.STAGE: next_stage})
+
+        # Test invalid transitions
+        # If the next stage is invalid, the function should raise ValueError
+        for current_stage, next_stage in invalid_transitions:
+            set_stage(current_stage)
+
+            with self.assertRaises(ValueError):
+                handler({Key.STAGE: next_stage})
+
+    def test_stage_setup_check(self) -> None:
+        """Test content checking for the setup stage."""
+        ctxt = _make_ctxt()
+        handler = get_test_handler(ctxt)
+        set_stage = _make_set_state_fn(ctxt)
+
+        valid_key_type_pairs = [
+            (Key.SAMPLE_NUMBER, int),
+            (Key.SHARE_NUMBER, int),
+            (Key.THRESHOLD, int),
+            (Key.CLIPPING_RANGE, float),
+            (Key.TARGET_RANGE, int),
+            (Key.MOD_RANGE, int),
+        ]
+
+        type_to_test_value: Dict[type, ConfigsRecordValues] = {
+            int: 10,
+            bool: True,
+            float: 1.0,
+            str: "test",
+            bytes: b"test",
+        }
+
+        valid_configs: Dict[str, ConfigsRecordValues] = {
+            key: type_to_test_value[value_type]
+            for key, value_type in valid_key_type_pairs
+        }
+
+        # Test valid configs
+        try:
+            check_configs(Stage.SETUP, ConfigsRecord(valid_configs))
+        # pylint: disable-next=broad-except
+        except Exception as exc:
+            self.fail(f"check_configs() raised {type(exc)} unexpectedly!")
+
+        # Set the stage
+        valid_configs[Key.STAGE] = Stage.SETUP
+
+        # Test invalid configs
+        for key, value_type in valid_key_type_pairs:
+            invalid_configs = valid_configs.copy()
+
+            # Test wrong value type for the key
+            for other_type, other_value in type_to_test_value.items():
+                if other_type == value_type:
+                    continue
+                invalid_configs[key] = other_value
+
+                set_stage(Stage.UNMASK)
+                with self.assertRaises(TypeError):
+                    handler(invalid_configs.copy())
+
+            # Test missing key
+            invalid_configs.pop(key)
+
+            set_stage(Stage.UNMASK)
+            with self.assertRaises(KeyError):
+                handler(invalid_configs.copy())
+
+    def test_stage_share_keys_check(self) -> None:
+        """Test content checking for the share keys stage."""
+        ctxt = _make_ctxt()
+        handler = get_test_handler(ctxt)
+        set_stage = _make_set_state_fn(ctxt)
+
+        valid_configs: Dict[str, ConfigsRecordValues] = {
+            "1": [b"public key 1", b"public key 2"],
+            "2": [b"public key 1", b"public key 2"],
+            "3": [b"public key 1", b"public key 2"],
+        }
+
+        # Test valid configs
+        try:
+            check_configs(Stage.SHARE_KEYS, ConfigsRecord(valid_configs))
+        # pylint: disable-next=broad-except
+        except Exception as exc:
+            self.fail(f"check_configs() raised {type(exc)} unexpectedly!")
+
+        # Set the stage
+        valid_configs[Key.STAGE] = Stage.SHARE_KEYS
+
+        # Test invalid configs
+        invalid_values: List[ConfigsRecordValues] = [
+            b"public key 1",
+            [b"public key 1"],
+            [b"public key 1", b"public key 2", b"public key 3"],
+        ]
+
+        for value in invalid_values:
+            invalid_configs = valid_configs.copy()
+            invalid_configs["1"] = value
+
+            set_stage(Stage.SETUP)
+            with self.assertRaises(TypeError):
+                handler(invalid_configs.copy())
+
+    def test_stage_collect_masked_vectors_check(self) -> None:
+        """Test content checking for the collect masked vectors stage."""
+        ctxt = _make_ctxt()
+        handler = get_test_handler(ctxt)
+        set_stage = _make_set_state_fn(ctxt)
+
+        valid_configs: Dict[str, ConfigsRecordValues] = {
+            Key.CIPHERTEXT_LIST: [b"ctxt!", b"ctxt@", b"ctxt#", b"ctxt?"],
+            Key.SOURCE_LIST: [32, 51324, 32324123, -3],
+        }
+
+        # Test valid configs
+        try:
+            check_configs(Stage.COLLECT_MASKED_VECTORS, ConfigsRecord(valid_configs))
+        # pylint: disable-next=broad-except
+        except Exception as exc:
+            self.fail(f"check_configs() raised {type(exc)} unexpectedly!")
+
+        # Set the stage
+        valid_configs[Key.STAGE] = Stage.COLLECT_MASKED_VECTORS
+
+        # Test invalid configs
+        # Test missing keys
+        for key in list(valid_configs.keys()):
+            if key == Key.STAGE:
+                continue
+            invalid_configs = valid_configs.copy()
+            invalid_configs.pop(key)
+
+            set_stage(Stage.SHARE_KEYS)
+            with self.assertRaises(KeyError):
+                handler(invalid_configs)
+
+        # Test wrong value type for the key
+        for key in valid_configs:
+            if key == Key.STAGE:
+                continue
+            invalid_configs = valid_configs.copy()
+            invalid_configs[key] = [3.1415926]
+
+            set_stage(Stage.SHARE_KEYS)
+            with self.assertRaises(TypeError):
+                handler(invalid_configs)
+
+    def test_stage_unmask_check(self) -> None:
+        """Test content checking for the unmasking stage."""
+        ctxt = _make_ctxt()
+        handler = get_test_handler(ctxt)
+        set_stage = _make_set_state_fn(ctxt)
+
+        valid_configs: Dict[str, ConfigsRecordValues] = {
+            Key.ACTIVE_NODE_ID_LIST: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            Key.DEAD_NODE_ID_LIST: [32, 51324, 32324123, -3],
+        }
+
+        # Test valid configs
+        try:
+            check_configs(Stage.UNMASK, ConfigsRecord(valid_configs))
+        # pylint: disable-next=broad-except
+        except Exception as exc:
+            self.fail(f"check_configs() raised {type(exc)} unexpectedly!")
+
+        # Set the stage
+        valid_configs[Key.STAGE] = Stage.UNMASK
+
+        # Test invalid configs
+        # Test missing keys
+        for key in list(valid_configs.keys()):
+            if key == Key.STAGE:
+                continue
+            invalid_configs = valid_configs.copy()
+            invalid_configs.pop(key)
+
+            set_stage(Stage.COLLECT_MASKED_VECTORS)
+            with self.assertRaises(KeyError):
+                handler(invalid_configs)
+
+        # Test wrong value type for the key
+        for key in valid_configs:
+            if key == Key.STAGE:
+                continue
+            invalid_configs = valid_configs.copy()
+            invalid_configs[key] = [True, False, True, False]
+
+            set_stage(Stage.COLLECT_MASKED_VECTORS)
+            with self.assertRaises(TypeError):
+                handler(invalid_configs)
diff --git a/src/py/flwr/client/mod/utils.py b/src/py/flwr/client/mod/utils.py
new file mode 100644
index 000000000000..4c3c32944f01
--- /dev/null
+++ b/src/py/flwr/client/mod/utils.py
@@ -0,0 +1,36 @@
+# 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.
+# ==============================================================================
+"""Utility functions for mods."""
+
+
+from typing import List
+
+from flwr.client.typing import ClientAppCallable, Mod
+from flwr.common import Context, Message
+
+
+def make_ffn(ffn: ClientAppCallable, mods: List[Mod]) -> ClientAppCallable:
+    """."""
+
+    def wrap_ffn(_ffn: ClientAppCallable, _mod: Mod) -> ClientAppCallable:
+        def new_ffn(message: Message, context: Context) -> Message:
+            return _mod(message, context, _ffn)
+
+        return new_ffn
+
+    for mod in reversed(mods):
+        ffn = wrap_ffn(ffn, mod)
+
+    return ffn
diff --git a/src/py/flwr/client/mod/utils_test.py b/src/py/flwr/client/mod/utils_test.py
new file mode 100644
index 000000000000..e588b8b53b3b
--- /dev/null
+++ b/src/py/flwr/client/mod/utils_test.py
@@ -0,0 +1,154 @@
+# 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.
+# ==============================================================================
+"""Tests for the utility functions."""
+
+
+import unittest
+from typing import List, cast
+
+from flwr.client.typing import ClientAppCallable, Mod
+from flwr.common import (
+    ConfigsRecord,
+    Context,
+    Message,
+    Metadata,
+    MetricsRecord,
+    RecordSet,
+)
+
+from .utils import make_ffn
+
+METRIC = "context"
+COUNTER = "counter"
+
+
+def _increment_context_counter(context: Context) -> None:
+    # Read from context
+    current_counter = cast(int, context.state.metrics_records[METRIC][COUNTER])
+    # update and override context
+    current_counter += 1
+    context.state.metrics_records[METRIC] = MetricsRecord({COUNTER: current_counter})
+
+
+def make_mock_mod(name: str, footprint: List[str]) -> Mod:
+    """Make a mock mod."""
+
+    def mod(message: Message, context: Context, app: ClientAppCallable) -> Message:
+        footprint.append(name)
+        # add empty ConfigRecord to in_message for this mod
+        message.content.configs_records[name] = ConfigsRecord()
+        _increment_context_counter(context)
+        out_message: Message = app(message, context)
+        footprint.append(name)
+        _increment_context_counter(context)
+        # add empty ConfigRegcord to out_message for this mod
+        out_message.content.configs_records[name] = ConfigsRecord()
+        return out_message
+
+    return mod
+
+
+def make_mock_app(name: str, footprint: List[str]) -> ClientAppCallable:
+    """Make a mock app."""
+
+    def app(message: Message, context: Context) -> Message:
+        footprint.append(name)
+        message.content.configs_records[name] = ConfigsRecord()
+        out_message = Message(metadata=message.metadata, content=RecordSet())
+        out_message.content.configs_records[name] = ConfigsRecord()
+        print(context)
+        return out_message
+
+    return app
+
+
+def _get_dummy_flower_message() -> Message:
+    return Message(
+        content=RecordSet(),
+        metadata=Metadata(
+            run_id=0,
+            message_id="",
+            group_id="",
+            src_node_id=0,
+            dst_node_id=0,
+            reply_to_message="",
+            ttl="",
+            message_type="mock",
+        ),
+    )
+
+
+class TestMakeApp(unittest.TestCase):
+    """Tests for the `make_app` function."""
+
+    def test_multiple_mods(self) -> None:
+        """Test if multiple mods are called in the correct order."""
+        # Prepare
+        footprint: List[str] = []
+        mock_app = make_mock_app("app", footprint)
+        mock_mod_names = [f"mod{i}" for i in range(1, 15)]
+        mock_mods = [make_mock_mod(name, footprint) for name in mock_mod_names]
+
+        state = RecordSet()
+        state.metrics_records[METRIC] = MetricsRecord({COUNTER: 0.0})
+        context = Context(state=state)
+        message = _get_dummy_flower_message()
+
+        # Execute
+        wrapped_app = make_ffn(mock_app, mock_mods)
+        out_message = wrapped_app(message, context)
+
+        # Assert
+        trace = mock_mod_names + ["app"]
+        self.assertEqual(footprint, trace + list(reversed(mock_mod_names)))
+        # pylint: disable-next=no-member
+        self.assertEqual(
+            "".join(message.content.configs_records.keys()), "".join(trace)
+        )
+        self.assertEqual(
+            "".join(out_message.content.configs_records.keys()),
+            "".join(reversed(trace)),
+        )
+        self.assertEqual(state.metrics_records[METRIC][COUNTER], 2 * len(mock_mods))
+
+    def test_filter(self) -> None:
+        """Test if a mod can filter incoming TaskIns."""
+        # Prepare
+        footprint: List[str] = []
+        mock_app = make_mock_app("app", footprint)
+        context = Context(state=RecordSet())
+        message = _get_dummy_flower_message()
+
+        def filter_mod(
+            message: Message,
+            _1: Context,
+            _2: ClientAppCallable,
+        ) -> Message:
+            footprint.append("filter")
+            message.content.configs_records["filter"] = ConfigsRecord()
+            out_message = Message(metadata=message.metadata, content=RecordSet())
+            out_message.content.configs_records["filter"] = ConfigsRecord()
+            # Skip calling app
+            return out_message
+
+        # Execute
+        wrapped_app = make_ffn(mock_app, [filter_mod])
+        out_message = wrapped_app(message, context)
+
+        # Assert
+        self.assertEqual(footprint, ["filter"])
+        # pylint: disable-next=no-member
+        self.assertEqual(list(message.content.configs_records.keys())[0], "filter")
+        self.assertEqual(list(out_message.content.configs_records.keys())[0], "filter")
diff --git a/src/py/flwr/client/node_state.py b/src/py/flwr/client/node_state.py
index ee4f70dc4dca..71681b783419 100644
--- a/src/py/flwr/client/node_state.py
+++ b/src/py/flwr/client/node_state.py
@@ -17,34 +17,32 @@
 
 from typing import Any, Dict
 
-from flwr.client.workload_state import WorkloadState
+from flwr.common import Context, RecordSet
 
 
 class NodeState:
-    """State of a node where client nodes execute workloads."""
+    """State of a node where client nodes execute runs."""
 
     def __init__(self) -> None:
         self._meta: Dict[str, Any] = {}  # holds metadata about the node
-        self.workload_states: Dict[int, WorkloadState] = {}
+        self.run_contexts: Dict[int, Context] = {}
 
-    def register_workloadstate(self, workload_id: int) -> None:
-        """Register new workload state for this node."""
-        if workload_id not in self.workload_states:
-            self.workload_states[workload_id] = WorkloadState({})
+    def register_context(self, run_id: int) -> None:
+        """Register new run context for this node."""
+        if run_id not in self.run_contexts:
+            self.run_contexts[run_id] = Context(state=RecordSet())
 
-    def retrieve_workloadstate(self, workload_id: int) -> WorkloadState:
-        """Get workload state given a workload_id."""
-        if workload_id in self.workload_states:
-            return self.workload_states[workload_id]
+    def retrieve_context(self, run_id: int) -> Context:
+        """Get run context given a run_id."""
+        if run_id in self.run_contexts:
+            return self.run_contexts[run_id]
 
         raise RuntimeError(
-            f"WorkloadState for workload_id={workload_id} doesn't exist."
-            " A workload must be registered before it can be retrieved or updated "
+            f"Context for run_id={run_id} doesn't exist."
+            " A run context must be registered before it can be retrieved or updated "
             " by a client."
         )
 
-    def update_workloadstate(
-        self, workload_id: int, workload_state: WorkloadState
-    ) -> None:
-        """Update workload state."""
-        self.workload_states[workload_id] = workload_state
+    def update_context(self, run_id: int, context: Context) -> None:
+        """Update run context."""
+        self.run_contexts[run_id] = context
diff --git a/src/py/flwr/client/node_state_tests.py b/src/py/flwr/client/node_state_tests.py
index d9f9ae7db3b0..193f52661579 100644
--- a/src/py/flwr/client/node_state_tests.py
+++ b/src/py/flwr/client/node_state_tests.py
@@ -15,45 +15,51 @@
 """Node state tests."""
 
 
+from typing import cast
+
 from flwr.client.node_state import NodeState
-from flwr.client.workload_state import WorkloadState
-from flwr.proto.task_pb2 import TaskIns
+from flwr.common import ConfigsRecord, Context
+from flwr.proto.task_pb2 import TaskIns  # pylint: disable=E0611
+
 
+def _run_dummy_task(context: Context) -> Context:
+    counter_value: str = "1"
+    if "counter" in context.state.configs_records.keys():
+        counter_value = cast(str, context.state.configs_records["counter"]["count"])
+        counter_value += "1"
 
-def _run_dummy_task(state: WorkloadState) -> WorkloadState:
-    if "counter" in state.state:
-        state.state["counter"] += "1"
-    else:
-        state.state["counter"] = "1"
+    context.state.configs_records["counter"] = ConfigsRecord({"count": counter_value})
 
-    return state
+    return context
 
 
-def test_multiworkload_in_node_state() -> None:
+def test_multirun_in_node_state() -> None:
     """Test basic NodeState logic."""
     # Tasks to perform
-    tasks = [TaskIns(workload_id=w_id) for w_id in [0, 1, 1, 2, 3, 2, 1, 5]]
-    # the "tasks" is to count how many times each workload is executed
+    tasks = [TaskIns(run_id=run_id) for run_id in [0, 1, 1, 2, 3, 2, 1, 5]]
+    # the "tasks" is to count how many times each run is executed
     expected_values = {0: "1", 1: "1" * 3, 2: "1" * 2, 3: "1", 5: "1"}
 
     # NodeState
     node_state = NodeState()
 
     for task in tasks:
-        w_id = task.workload_id
+        run_id = task.run_id
 
         # Register
-        node_state.register_workloadstate(workload_id=w_id)
+        node_state.register_context(run_id=run_id)
 
-        # Get workload state
-        state = node_state.retrieve_workloadstate(workload_id=w_id)
+        # Get run state
+        context = node_state.retrieve_context(run_id=run_id)
 
         # Run "task"
-        updated_state = _run_dummy_task(state)
+        updated_state = _run_dummy_task(context)
 
-        # Update workload state
-        node_state.update_workloadstate(workload_id=w_id, workload_state=updated_state)
+        # Update run state
+        node_state.update_context(run_id=run_id, context=updated_state)
 
     # Verify values
-    for w_id, state in node_state.workload_states.items():
-        assert state.state["counter"] == expected_values[w_id]
+    for run_id, context in node_state.run_contexts.items():
+        assert (
+            context.state.configs_records["counter"]["count"] == expected_values[run_id]
+        )
diff --git a/src/py/flwr/client/numpy_client.py b/src/py/flwr/client/numpy_client.py
index 8b0893ea30aa..0247958d88a9 100644
--- a/src/py/flwr/client/numpy_client.py
+++ b/src/py/flwr/client/numpy_client.py
@@ -19,9 +19,9 @@
 from typing import Callable, Dict, Tuple
 
 from flwr.client.client import Client
-from flwr.client.workload_state import WorkloadState
 from flwr.common import (
     Config,
+    Context,
     NDArrays,
     Scalar,
     ndarrays_to_parameters,
@@ -70,7 +70,7 @@
 class NumPyClient(ABC):
     """Abstract base class for Flower clients using NumPy."""
 
-    state: WorkloadState
+    context: Context
 
     def get_properties(self, config: Config) -> Dict[str, Scalar]:
         """Return a client's set of properties.
@@ -174,13 +174,13 @@ def evaluate(
         _ = (self, parameters, config)
         return 0.0, 0, {}
 
-    def get_state(self) -> WorkloadState:
-        """Get the workload state from this client."""
-        return self.state
+    def get_context(self) -> Context:
+        """Get the run context from this client."""
+        return self.context
 
-    def set_state(self, state: WorkloadState) -> None:
-        """Apply a workload state to this client."""
-        self.state = state
+    def set_context(self, context: Context) -> None:
+        """Apply a run context to this client."""
+        self.context = context
 
     def to_client(self) -> Client:
         """Convert to object to Client type and return it."""
@@ -242,7 +242,7 @@ def _fit(self: Client, ins: FitIns) -> FitRes:
         and isinstance(results[1], int)
         and isinstance(results[2], dict)
     ):
-        raise Exception(EXCEPTION_MESSAGE_WRONG_RETURN_TYPE_FIT)
+        raise TypeError(EXCEPTION_MESSAGE_WRONG_RETURN_TYPE_FIT)
 
     # Return FitRes
     parameters_prime, num_examples, metrics = results
@@ -266,7 +266,7 @@ def _evaluate(self: Client, ins: EvaluateIns) -> EvaluateRes:
         and isinstance(results[1], int)
         and isinstance(results[2], dict)
     ):
-        raise Exception(EXCEPTION_MESSAGE_WRONG_RETURN_TYPE_EVALUATE)
+        raise TypeError(EXCEPTION_MESSAGE_WRONG_RETURN_TYPE_EVALUATE)
 
     # Return EvaluateRes
     loss, num_examples, metrics = results
@@ -278,21 +278,21 @@ def _evaluate(self: Client, ins: EvaluateIns) -> EvaluateRes:
     )
 
 
-def _get_state(self: Client) -> WorkloadState:
-    """Return state of underlying NumPyClient."""
-    return self.numpy_client.get_state()  # type: ignore
+def _get_context(self: Client) -> Context:
+    """Return context of underlying NumPyClient."""
+    return self.numpy_client.get_context()  # type: ignore
 
 
-def _set_state(self: Client, state: WorkloadState) -> None:
-    """Apply state to underlying NumPyClient."""
-    self.numpy_client.set_state(state)  # type: ignore
+def _set_context(self: Client, context: Context) -> None:
+    """Apply context to underlying NumPyClient."""
+    self.numpy_client.set_context(context)  # type: ignore
 
 
 def _wrap_numpy_client(client: NumPyClient) -> Client:
     member_dict: Dict[str, Callable] = {  # type: ignore
         "__init__": _constructor,
-        "get_state": _get_state,
-        "set_state": _set_state,
+        "get_context": _get_context,
+        "set_context": _set_context,
     }
 
     # Add wrapper type methods (if overridden)
diff --git a/src/py/flwr/client/rest_client/connection.py b/src/py/flwr/client/rest_client/connection.py
index d22b246dbd61..d2cc71ba3b3f 100644
--- a/src/py/flwr/client/rest_client/connection.py
+++ b/src/py/flwr/client/rest_client/connection.py
@@ -17,19 +17,19 @@
 
 import sys
 from contextlib import contextmanager
+from copy import copy
 from logging import ERROR, INFO, WARN
 from typing import Callable, Dict, Iterator, Optional, Tuple, Union, cast
 
-from flwr.client.message_handler.task_handler import (
-    configure_task_res,
-    get_task_ins,
-    validate_task_ins,
-    validate_task_res,
-)
+from flwr.client.message_handler.message_handler import validate_out_message
+from flwr.client.message_handler.task_handler import get_task_ins, validate_task_ins
 from flwr.common import GRPC_MAX_MESSAGE_LENGTH
 from flwr.common.constant import MISSING_EXTRA_REST
 from flwr.common.logger import log
-from flwr.proto.fleet_pb2 import (
+from flwr.common.message import Message, Metadata
+from flwr.common.retry_invoker import RetryInvoker
+from flwr.common.serde import message_from_taskins, message_to_taskres
+from flwr.proto.fleet_pb2 import (  # pylint: disable=E0611
     CreateNodeRequest,
     CreateNodeResponse,
     DeleteNodeRequest,
@@ -38,8 +38,8 @@
     PushTaskResRequest,
     PushTaskResResponse,
 )
-from flwr.proto.node_pb2 import Node
-from flwr.proto.task_pb2 import TaskIns, TaskRes
+from flwr.proto.node_pb2 import Node  # pylint: disable=E0611
+from flwr.proto.task_pb2 import TaskIns  # pylint: disable=E0611
 
 try:
     import requests
@@ -48,7 +48,7 @@
 
 
 KEY_NODE = "node"
-KEY_TASK_INS = "current_task_ins"
+KEY_METADATA = "in_message_metadata"
 
 
 PATH_CREATE_NODE: str = "api/v0/fleet/create-node"
@@ -62,14 +62,15 @@
 def http_request_response(
     server_address: str,
     insecure: bool,  # pylint: disable=unused-argument
+    retry_invoker: RetryInvoker,
     max_message_length: int = GRPC_MAX_MESSAGE_LENGTH,  # pylint: disable=W0613
     root_certificates: Optional[
         Union[bytes, str]
     ] = None,  # pylint: disable=unused-argument
 ) -> Iterator[
     Tuple[
-        Callable[[], Optional[TaskIns]],
-        Callable[[TaskRes], None],
+        Callable[[], Optional[Message]],
+        Callable[[Message], None],
         Optional[Callable[[], None]],
         Optional[Callable[[], None]],
     ]
@@ -85,6 +86,12 @@ def http_request_response(
         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
+        Unused argument present for compatibilty.
+    retry_invoker: RetryInvoker
+        `RetryInvoker` object that will try to reconnect the client to the server
+        after REST connection 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)
@@ -120,8 +127,8 @@ def http_request_response(
             "must be provided as a string path to the client.",
         )
 
-    # Necessary state to link TaskRes to TaskIns
-    state: Dict[str, Optional[TaskIns]] = {KEY_TASK_INS: None}
+    # Necessary state to validate messages to be sent
+    state: Dict[str, Optional[Metadata]] = {KEY_METADATA: None}
 
     # Enable create_node and delete_node to store node
     node_store: Dict[str, Optional[Node]] = {KEY_NODE: None}
@@ -135,7 +142,8 @@ def create_node() -> None:
         create_node_req_proto = CreateNodeRequest()
         create_node_req_bytes: bytes = create_node_req_proto.SerializeToString()
 
-        res = requests.post(
+        res = retry_invoker.invoke(
+            requests.post,
             url=f"{base_url}/{PATH_CREATE_NODE}",
             headers={
                 "Accept": "application/protobuf",
@@ -143,6 +151,7 @@ def create_node() -> None:
             },
             data=create_node_req_bytes,
             verify=verify,
+            timeout=None,
         )
 
         # Check status code and headers
@@ -177,7 +186,8 @@ def delete_node() -> None:
         node: Node = cast(Node, node_store[KEY_NODE])
         delete_node_req_proto = DeleteNodeRequest(node=node)
         delete_node_req_req_bytes: bytes = delete_node_req_proto.SerializeToString()
-        res = requests.post(
+        res = retry_invoker.invoke(
+            requests.post,
             url=f"{base_url}/{PATH_DELETE_NODE}",
             headers={
                 "Accept": "application/protobuf",
@@ -185,6 +195,7 @@ def delete_node() -> None:
             },
             data=delete_node_req_req_bytes,
             verify=verify,
+            timeout=None,
         )
 
         # Check status code and headers
@@ -204,7 +215,7 @@ def delete_node() -> None:
                 PATH_PULL_TASK_INS,
             )
 
-    def receive() -> Optional[TaskIns]:
+    def receive() -> Optional[Message]:
         """Receive next task from server."""
         # Get Node
         if node_store[KEY_NODE] is None:
@@ -217,7 +228,8 @@ def receive() -> Optional[TaskIns]:
         pull_task_ins_req_bytes: bytes = pull_task_ins_req_proto.SerializeToString()
 
         # Request instructions (task) from server
-        res = requests.post(
+        res = retry_invoker.invoke(
+            requests.post,
             url=f"{base_url}/{PATH_PULL_TASK_INS}",
             headers={
                 "Accept": "application/protobuf",
@@ -225,6 +237,7 @@ def receive() -> Optional[TaskIns]:
             },
             data=pull_task_ins_req_bytes,
             verify=verify,
+            timeout=None,
         )
 
         # Check status code and headers
@@ -253,40 +266,41 @@ def receive() -> Optional[TaskIns]:
         task_ins: Optional[TaskIns] = get_task_ins(pull_task_ins_response_proto)
 
         # Discard the current TaskIns if not valid
-        if task_ins is not None and not validate_task_ins(
-            task_ins, discard_reconnect_ins=True
+        if task_ins is not None and not (
+            task_ins.task.consumer.node_id == node.node_id
+            and validate_task_ins(task_ins)
         ):
             task_ins = None
 
-        # Remember `task_ins` until `task_res` is available
-        state[KEY_TASK_INS] = task_ins
-
-        # Return the TaskIns if available
+        # Return the Message if available
+        message = None
+        state[KEY_METADATA] = None
         if task_ins is not None:
+            message = message_from_taskins(task_ins)
+            state[KEY_METADATA] = copy(message.metadata)
             log(INFO, "[Node] POST /%s: success", PATH_PULL_TASK_INS)
-        return task_ins
+        return message
 
-    def send(task_res: TaskRes) -> None:
+    def send(message: Message) -> None:
         """Send task result back to server."""
         # Get Node
         if node_store[KEY_NODE] is None:
             log(ERROR, "Node instance missing")
             return
-        node: Node = cast(Node, node_store[KEY_NODE])
 
-        if state[KEY_TASK_INS] is None:
-            log(ERROR, "No current TaskIns")
+        # Get incoming message
+        in_metadata = state[KEY_METADATA]
+        if in_metadata is None:
+            log(ERROR, "No current message")
             return
 
-        task_ins: TaskIns = cast(TaskIns, state[KEY_TASK_INS])
-
-        # Check if fields to be set are not initialized
-        if not validate_task_res(task_res):
-            state[KEY_TASK_INS] = None
-            log(ERROR, "TaskRes has been initialized accidentally")
+        # Validate out message
+        if not validate_out_message(message, in_metadata):
+            log(ERROR, "Invalid out message")
+            return
 
-        # Configure TaskRes
-        task_res = configure_task_res(task_res, task_ins, node)
+        # Construct TaskRes
+        task_res = message_to_taskres(message)
 
         # Serialize ProtoBuf to bytes
         push_task_res_request_proto = PushTaskResRequest(task_res_list=[task_res])
@@ -295,7 +309,8 @@ def send(task_res: TaskRes) -> None:
         )
 
         # Send ClientMessage to server
-        res = requests.post(
+        res = retry_invoker.invoke(
+            requests.post,
             url=f"{base_url}/{PATH_PUSH_TASK_RES}",
             headers={
                 "Accept": "application/protobuf",
@@ -303,9 +318,10 @@ def send(task_res: TaskRes) -> None:
             },
             data=push_task_res_request_bytes,
             verify=verify,
+            timeout=None,
         )
 
-        state[KEY_TASK_INS] = None
+        state[KEY_METADATA] = None
 
         # Check status code and headers
         if res.status_code != 200:
diff --git a/src/py/flwr/client/secure_aggregation/handler.py b/src/py/flwr/client/secure_aggregation/handler.py
deleted file mode 100644
index 487ed842c93f..000000000000
--- a/src/py/flwr/client/secure_aggregation/handler.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# Copyright 2020 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.
-# ==============================================================================
-"""Message Handler for Secure Aggregation (abstract base class)."""
-
-
-from abc import ABC, abstractmethod
-from typing import Dict
-
-from flwr.common.typing import Value
-
-
-class SecureAggregationHandler(ABC):
-    """Abstract base class for secure aggregation message handlers."""
-
-    @abstractmethod
-    def handle_secure_aggregation(
-        self, named_values: Dict[str, Value]
-    ) -> Dict[str, Value]:
-        """Handle incoming Secure Aggregation message and return results.
-
-        Parameters
-        ----------
-        named_values : Dict[str, Value]
-            The named values retrieved from the SecureAggregation sub-message
-            of Task message in the server's TaskIns.
-
-        Returns
-        -------
-        Dict[str, Value]
-            The final/intermediate results of the Secure Aggregation protocol.
-        """
diff --git a/src/py/flwr/client/secure_aggregation/secaggplus_handler.py b/src/py/flwr/client/secure_aggregation/secaggplus_handler.py
deleted file mode 100644
index efbb00a9d916..000000000000
--- a/src/py/flwr/client/secure_aggregation/secaggplus_handler.py
+++ /dev/null
@@ -1,488 +0,0 @@
-# Copyright 2020 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.
-# ==============================================================================
-"""Message handler for the SecAgg+ protocol."""
-
-
-import os
-from dataclasses import dataclass, field
-from logging import ERROR, INFO, WARNING
-from typing import Any, Dict, List, Optional, Tuple, Union, cast
-
-from flwr.client.client import Client
-from flwr.client.numpy_client import NumPyClient
-from flwr.common import (
-    bytes_to_ndarray,
-    ndarray_to_bytes,
-    ndarrays_to_parameters,
-    parameters_to_ndarrays,
-)
-from flwr.common.logger import log
-from flwr.common.secure_aggregation.crypto.shamir import create_shares
-from flwr.common.secure_aggregation.crypto.symmetric_encryption import (
-    bytes_to_private_key,
-    bytes_to_public_key,
-    decrypt,
-    encrypt,
-    generate_key_pairs,
-    generate_shared_key,
-    private_key_to_bytes,
-    public_key_to_bytes,
-)
-from flwr.common.secure_aggregation.ndarrays_arithmetic import (
-    factor_combine,
-    parameters_addition,
-    parameters_mod,
-    parameters_multiply,
-    parameters_subtraction,
-)
-from flwr.common.secure_aggregation.quantization import quantize
-from flwr.common.secure_aggregation.secaggplus_constants import (
-    KEY_ACTIVE_SECURE_ID_LIST,
-    KEY_CIPHERTEXT_LIST,
-    KEY_CLIPPING_RANGE,
-    KEY_DEAD_SECURE_ID_LIST,
-    KEY_DESTINATION_LIST,
-    KEY_MASKED_PARAMETERS,
-    KEY_MOD_RANGE,
-    KEY_PARAMETERS,
-    KEY_PUBLIC_KEY_1,
-    KEY_PUBLIC_KEY_2,
-    KEY_SAMPLE_NUMBER,
-    KEY_SECURE_ID,
-    KEY_SECURE_ID_LIST,
-    KEY_SHARE_LIST,
-    KEY_SHARE_NUMBER,
-    KEY_SOURCE_LIST,
-    KEY_STAGE,
-    KEY_TARGET_RANGE,
-    KEY_THRESHOLD,
-    STAGE_COLLECT_MASKED_INPUT,
-    STAGE_SETUP,
-    STAGE_SHARE_KEYS,
-    STAGE_UNMASK,
-    STAGES,
-)
-from flwr.common.secure_aggregation.secaggplus_utils import (
-    pseudo_rand_gen,
-    share_keys_plaintext_concat,
-    share_keys_plaintext_separate,
-)
-from flwr.common.typing import FitIns, Value
-
-from .handler import SecureAggregationHandler
-
-
-@dataclass
-# pylint: disable-next=too-many-instance-attributes
-class SecAggPlusState:
-    """State of the SecAgg+ protocol."""
-
-    sid: int = 0
-    sample_num: int = 0
-    share_num: int = 0
-    threshold: int = 0
-    clipping_range: float = 0.0
-    target_range: int = 0
-    mod_range: int = 0
-
-    # Secret key (sk) and public key (pk)
-    sk1: bytes = b""
-    pk1: bytes = b""
-    sk2: bytes = b""
-    pk2: bytes = b""
-
-    # Random seed for generating the private mask
-    rd_seed: bytes = b""
-
-    rd_seed_share_dict: Dict[int, bytes] = field(default_factory=dict)
-    sk1_share_dict: Dict[int, bytes] = field(default_factory=dict)
-    # The dict of the shared secrets from sk2
-    ss2_dict: Dict[int, bytes] = field(default_factory=dict)
-    public_keys_dict: Dict[int, Tuple[bytes, bytes]] = field(default_factory=dict)
-
-    client: Optional[Union[Client, NumPyClient]] = None
-
-
-class SecAggPlusHandler(SecureAggregationHandler):
-    """Message handler for the SecAgg+ protocol."""
-
-    _shared_state = SecAggPlusState()
-    _current_stage = STAGE_UNMASK
-
-    def handle_secure_aggregation(
-        self, named_values: Dict[str, Value]
-    ) -> Dict[str, Value]:
-        """Handle incoming message and return results, following the SecAgg+ protocol.
-
-        Parameters
-        ----------
-        named_values : Dict[str, Value]
-            The named values retrieved from the SecureAggregation sub-message
-            of Task message in the server's TaskIns.
-
-        Returns
-        -------
-        Dict[str, Value]
-            The final/intermediate results of the SecAgg+ protocol.
-        """
-        # Check if self is a client
-        if not isinstance(self, (Client, NumPyClient)):
-            raise TypeError(
-                "The subclass of SecAggPlusHandler must be "
-                "the subclass of Client or NumPyClient."
-            )
-
-        # Check the validity of the next stage
-        check_stage(self._current_stage, named_values)
-
-        # Update the current stage
-        self._current_stage = cast(str, named_values.pop(KEY_STAGE))
-
-        # Check the validity of the `named_values` based on the current stage
-        check_named_values(self._current_stage, named_values)
-
-        # Execute
-        if self._current_stage == STAGE_SETUP:
-            self._shared_state = SecAggPlusState(client=self)
-            return _setup(self._shared_state, named_values)
-        if self._current_stage == STAGE_SHARE_KEYS:
-            return _share_keys(self._shared_state, named_values)
-        if self._current_stage == STAGE_COLLECT_MASKED_INPUT:
-            return _collect_masked_input(self._shared_state, named_values)
-        if self._current_stage == STAGE_UNMASK:
-            return _unmask(self._shared_state, named_values)
-        raise ValueError(f"Unknown secagg stage: {self._current_stage}")
-
-
-def check_stage(current_stage: str, named_values: Dict[str, Value]) -> None:
-    """Check the validity of the next stage."""
-    # Check the existence of KEY_STAGE
-    if KEY_STAGE not in named_values:
-        raise KeyError(
-            f"The required key '{KEY_STAGE}' is missing from the input `named_values`."
-        )
-
-    # Check the value type of the KEY_STAGE
-    next_stage = named_values[KEY_STAGE]
-    if not isinstance(next_stage, str):
-        raise TypeError(
-            f"The value for the key '{KEY_STAGE}' must be of type {str}, "
-            f"but got {type(next_stage)} instead."
-        )
-
-    # Check the validity of the next stage
-    if next_stage == STAGE_SETUP:
-        if current_stage != STAGE_UNMASK:
-            log(WARNING, "Restart from the setup stage")
-    # If stage is not "setup",
-    # the stage from `named_values` should be the expected next stage
-    else:
-        expected_next_stage = STAGES[(STAGES.index(current_stage) + 1) % len(STAGES)]
-        if next_stage != expected_next_stage:
-            raise ValueError(
-                "Abort secure aggregation: "
-                f"expect {expected_next_stage} stage, but receive {next_stage} stage"
-            )
-
-
-# pylint: disable-next=too-many-branches
-def check_named_values(stage: str, named_values: Dict[str, Value]) -> None:
-    """Check the validity of the input `named_values`."""
-    # Check `named_values` for the setup stage
-    if stage == STAGE_SETUP:
-        key_type_pairs = [
-            (KEY_SAMPLE_NUMBER, int),
-            (KEY_SECURE_ID, int),
-            (KEY_SHARE_NUMBER, int),
-            (KEY_THRESHOLD, int),
-            (KEY_CLIPPING_RANGE, float),
-            (KEY_TARGET_RANGE, int),
-            (KEY_MOD_RANGE, int),
-        ]
-        for key, expected_type in key_type_pairs:
-            if key not in named_values:
-                raise KeyError(
-                    f"Stage {STAGE_SETUP}: the required key '{key}' is "
-                    "missing from the input `named_values`."
-                )
-            # Bool is a subclass of int in Python,
-            # so `isinstance(v, int)` will return True even if v is a boolean.
-            # pylint: disable-next=unidiomatic-typecheck
-            if type(named_values[key]) is not expected_type:
-                raise TypeError(
-                    f"Stage {STAGE_SETUP}: The value for the key '{key}' "
-                    f"must be of type {expected_type}, "
-                    f"but got {type(named_values[key])} instead."
-                )
-    elif stage == STAGE_SHARE_KEYS:
-        for key, value in named_values.items():
-            if (
-                not isinstance(value, list)
-                or len(value) != 2
-                or not isinstance(value[0], bytes)
-                or not isinstance(value[1], bytes)
-            ):
-                raise TypeError(
-                    f"Stage {STAGE_SHARE_KEYS}: "
-                    f"the value for the key '{key}' must be a list of two bytes."
-                )
-    elif stage == STAGE_COLLECT_MASKED_INPUT:
-        key_type_pairs = [
-            (KEY_CIPHERTEXT_LIST, bytes),
-            (KEY_SOURCE_LIST, int),
-            (KEY_PARAMETERS, bytes),
-        ]
-        for key, expected_type in key_type_pairs:
-            if key not in named_values:
-                raise KeyError(
-                    f"Stage {STAGE_COLLECT_MASKED_INPUT}: "
-                    f"the required key '{key}' is "
-                    "missing from the input `named_values`."
-                )
-            if not isinstance(named_values[key], list) or any(
-                elm
-                for elm in cast(List[Any], named_values[key])
-                # pylint: disable-next=unidiomatic-typecheck
-                if type(elm) is not expected_type
-            ):
-                raise TypeError(
-                    f"Stage {STAGE_COLLECT_MASKED_INPUT}: "
-                    f"the value for the key '{key}' "
-                    f"must be of type List[{expected_type.__name__}]"
-                )
-    elif stage == STAGE_UNMASK:
-        key_type_pairs = [
-            (KEY_ACTIVE_SECURE_ID_LIST, int),
-            (KEY_DEAD_SECURE_ID_LIST, int),
-        ]
-        for key, expected_type in key_type_pairs:
-            if key not in named_values:
-                raise KeyError(
-                    f"Stage {STAGE_UNMASK}: "
-                    f"the required key '{key}' is "
-                    "missing from the input `named_values`."
-                )
-            if not isinstance(named_values[key], list) or any(
-                elm
-                for elm in cast(List[Any], named_values[key])
-                # pylint: disable-next=unidiomatic-typecheck
-                if type(elm) is not expected_type
-            ):
-                raise TypeError(
-                    f"Stage {STAGE_UNMASK}: "
-                    f"the value for the key '{key}' "
-                    f"must be of type List[{expected_type.__name__}]"
-                )
-    else:
-        raise ValueError(f"Unknown secagg stage: {stage}")
-
-
-def _setup(state: SecAggPlusState, named_values: Dict[str, Value]) -> Dict[str, Value]:
-    # Assigning parameter values to object fields
-    sec_agg_param_dict = named_values
-    state.sample_num = cast(int, sec_agg_param_dict[KEY_SAMPLE_NUMBER])
-    state.sid = cast(int, sec_agg_param_dict[KEY_SECURE_ID])
-    log(INFO, "Client %d: starting stage 0...", state.sid)
-
-    state.share_num = cast(int, sec_agg_param_dict[KEY_SHARE_NUMBER])
-    state.threshold = cast(int, sec_agg_param_dict[KEY_THRESHOLD])
-    state.clipping_range = cast(float, sec_agg_param_dict[KEY_CLIPPING_RANGE])
-    state.target_range = cast(int, sec_agg_param_dict[KEY_TARGET_RANGE])
-    state.mod_range = cast(int, sec_agg_param_dict[KEY_MOD_RANGE])
-
-    # Dictionaries containing client secure IDs as keys
-    # and their respective secret shares as values.
-    state.rd_seed_share_dict = {}
-    state.sk1_share_dict = {}
-    # Dictionary containing client secure IDs as keys
-    # and their respective shared secrets (with this client) as values.
-    state.ss2_dict = {}
-
-    # Create 2 sets private public key pairs
-    # One for creating pairwise masks
-    # One for encrypting message to distribute shares
-    sk1, pk1 = generate_key_pairs()
-    sk2, pk2 = generate_key_pairs()
-
-    state.sk1, state.pk1 = private_key_to_bytes(sk1), public_key_to_bytes(pk1)
-    state.sk2, state.pk2 = private_key_to_bytes(sk2), public_key_to_bytes(pk2)
-    log(INFO, "Client %d: stage 0 completes. uploading public keys...", state.sid)
-    return {KEY_PUBLIC_KEY_1: state.pk1, KEY_PUBLIC_KEY_2: state.pk2}
-
-
-# pylint: disable-next=too-many-locals
-def _share_keys(
-    state: SecAggPlusState, named_values: Dict[str, Value]
-) -> Dict[str, Value]:
-    named_bytes_tuples = cast(Dict[str, Tuple[bytes, bytes]], named_values)
-    key_dict = {int(sid): (pk1, pk2) for sid, (pk1, pk2) in named_bytes_tuples.items()}
-    log(INFO, "Client %d: starting stage 1...", state.sid)
-    state.public_keys_dict = key_dict
-
-    # Check if the size is larger than threshold
-    if len(state.public_keys_dict) < state.threshold:
-        raise Exception("Available neighbours number smaller than threshold")
-
-    # Check if all public keys are unique
-    pk_list: List[bytes] = []
-    for pk1, pk2 in state.public_keys_dict.values():
-        pk_list.append(pk1)
-        pk_list.append(pk2)
-    if len(set(pk_list)) != len(pk_list):
-        raise Exception("Some public keys are identical")
-
-    # Check if public keys of this client are correct in the dictionary
-    if (
-        state.public_keys_dict[state.sid][0] != state.pk1
-        or state.public_keys_dict[state.sid][1] != state.pk2
-    ):
-        raise Exception(
-            "Own public keys are displayed in dict incorrectly, should not happen!"
-        )
-
-    # Generate the private mask seed
-    state.rd_seed = os.urandom(32)
-
-    # Create shares for the private mask seed and the first private key
-    b_shares = create_shares(state.rd_seed, state.threshold, state.share_num)
-    sk1_shares = create_shares(state.sk1, state.threshold, state.share_num)
-
-    srcs, dsts, ciphertexts = [], [], []
-
-    # Distribute shares
-    for idx, (sid, (_, pk2)) in enumerate(state.public_keys_dict.items()):
-        if sid == state.sid:
-            state.rd_seed_share_dict[state.sid] = b_shares[idx]
-            state.sk1_share_dict[state.sid] = sk1_shares[idx]
-        else:
-            shared_key = generate_shared_key(
-                bytes_to_private_key(state.sk2),
-                bytes_to_public_key(pk2),
-            )
-            state.ss2_dict[sid] = shared_key
-            plaintext = share_keys_plaintext_concat(
-                state.sid, sid, b_shares[idx], sk1_shares[idx]
-            )
-            ciphertext = encrypt(shared_key, plaintext)
-            srcs.append(state.sid)
-            dsts.append(sid)
-            ciphertexts.append(ciphertext)
-
-    log(INFO, "Client %d: stage 1 completes. uploading key shares...", state.sid)
-    return {KEY_DESTINATION_LIST: dsts, KEY_CIPHERTEXT_LIST: ciphertexts}
-
-
-# pylint: disable-next=too-many-locals
-def _collect_masked_input(
-    state: SecAggPlusState, named_values: Dict[str, Value]
-) -> Dict[str, Value]:
-    log(INFO, "Client %d: starting stage 2...", state.sid)
-    available_clients: List[int] = []
-    ciphertexts = cast(List[bytes], named_values[KEY_CIPHERTEXT_LIST])
-    srcs = cast(List[int], named_values[KEY_SOURCE_LIST])
-    if len(ciphertexts) + 1 < state.threshold:
-        raise Exception("Not enough available neighbour clients.")
-
-    # Decrypt ciphertexts, verify their sources, and store shares.
-    for src, ciphertext in zip(srcs, ciphertexts):
-        shared_key = state.ss2_dict[src]
-        plaintext = decrypt(shared_key, ciphertext)
-        actual_src, dst, rd_seed_share, sk1_share = share_keys_plaintext_separate(
-            plaintext
-        )
-        available_clients.append(src)
-        if src != actual_src:
-            raise ValueError(
-                f"Client {state.sid}: received ciphertext "
-                f"from {actual_src} instead of {src}."
-            )
-        if dst != state.sid:
-            ValueError(
-                f"Client {state.sid}: received an encrypted message"
-                f"for Client {dst} from Client {src}."
-            )
-        state.rd_seed_share_dict[src] = rd_seed_share
-        state.sk1_share_dict[src] = sk1_share
-
-    # Fit client
-    parameters_bytes = cast(List[bytes], named_values[KEY_PARAMETERS])
-    parameters = [bytes_to_ndarray(w) for w in parameters_bytes]
-    if isinstance(state.client, Client):
-        fit_res = state.client.fit(
-            FitIns(parameters=ndarrays_to_parameters(parameters), config={})
-        )
-        parameters_factor = fit_res.num_examples
-        parameters = parameters_to_ndarrays(fit_res.parameters)
-    elif isinstance(state.client, NumPyClient):
-        parameters, parameters_factor, _ = state.client.fit(parameters, {})
-    else:
-        log(ERROR, "Client %d: fit function is missing.", state.sid)
-
-    # Quantize parameter update (vector)
-    quantized_parameters = quantize(
-        parameters, state.clipping_range, state.target_range
-    )
-
-    quantized_parameters = parameters_multiply(quantized_parameters, parameters_factor)
-    quantized_parameters = factor_combine(parameters_factor, quantized_parameters)
-
-    dimensions_list: List[Tuple[int, ...]] = [a.shape for a in quantized_parameters]
-
-    # Add private mask
-    private_mask = pseudo_rand_gen(state.rd_seed, state.mod_range, dimensions_list)
-    quantized_parameters = parameters_addition(quantized_parameters, private_mask)
-
-    for client_id in available_clients:
-        # Add pairwise masks
-        shared_key = generate_shared_key(
-            bytes_to_private_key(state.sk1),
-            bytes_to_public_key(state.public_keys_dict[client_id][0]),
-        )
-        pairwise_mask = pseudo_rand_gen(shared_key, state.mod_range, dimensions_list)
-        if state.sid > client_id:
-            quantized_parameters = parameters_addition(
-                quantized_parameters, pairwise_mask
-            )
-        else:
-            quantized_parameters = parameters_subtraction(
-                quantized_parameters, pairwise_mask
-            )
-
-    # Take mod of final weight update vector and return to server
-    quantized_parameters = parameters_mod(quantized_parameters, state.mod_range)
-    log(INFO, "Client %d: stage 2 completes. uploading masked parameters...", state.sid)
-    return {
-        KEY_MASKED_PARAMETERS: [ndarray_to_bytes(arr) for arr in quantized_parameters]
-    }
-
-
-def _unmask(state: SecAggPlusState, named_values: Dict[str, Value]) -> Dict[str, Value]:
-    log(INFO, "Client %d: starting stage 3...", state.sid)
-
-    active_sids = cast(List[int], named_values[KEY_ACTIVE_SECURE_ID_LIST])
-    dead_sids = cast(List[int], named_values[KEY_DEAD_SECURE_ID_LIST])
-    # Send private mask seed share for every avaliable client (including itclient)
-    # Send first private key share for building pairwise mask for every dropped client
-    if len(active_sids) < state.threshold:
-        raise Exception("Available neighbours number smaller than threshold")
-
-    sids, shares = [], []
-    sids += active_sids
-    shares += [state.rd_seed_share_dict[sid] for sid in active_sids]
-    sids += dead_sids
-    shares += [state.sk1_share_dict[sid] for sid in dead_sids]
-
-    log(INFO, "Client %d: stage 3 completes. uploading key shares...", state.sid)
-    return {KEY_SECURE_ID_LIST: sids, KEY_SHARE_LIST: shares}
diff --git a/src/py/flwr/client/secure_aggregation/secaggplus_handler_test.py b/src/py/flwr/client/secure_aggregation/secaggplus_handler_test.py
deleted file mode 100644
index 9693a46af989..000000000000
--- a/src/py/flwr/client/secure_aggregation/secaggplus_handler_test.py
+++ /dev/null
@@ -1,287 +0,0 @@
-# Copyright 2020 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.
-# ==============================================================================
-"""The SecAgg+ protocol handler tests."""
-
-import unittest
-from itertools import product
-from typing import Any, Dict, List, cast
-
-from flwr.client import NumPyClient
-from flwr.common.secure_aggregation.secaggplus_constants import (
-    KEY_ACTIVE_SECURE_ID_LIST,
-    KEY_CIPHERTEXT_LIST,
-    KEY_CLIPPING_RANGE,
-    KEY_DEAD_SECURE_ID_LIST,
-    KEY_MOD_RANGE,
-    KEY_PARAMETERS,
-    KEY_SAMPLE_NUMBER,
-    KEY_SECURE_ID,
-    KEY_SHARE_NUMBER,
-    KEY_SOURCE_LIST,
-    KEY_STAGE,
-    KEY_TARGET_RANGE,
-    KEY_THRESHOLD,
-    STAGE_COLLECT_MASKED_INPUT,
-    STAGE_SETUP,
-    STAGE_SHARE_KEYS,
-    STAGE_UNMASK,
-    STAGES,
-)
-from flwr.common.typing import Value
-
-from .secaggplus_handler import SecAggPlusHandler, check_named_values
-
-
-class EmptyFlowerNumPyClient(NumPyClient, SecAggPlusHandler):
-    """Empty NumPyClient."""
-
-
-class TestSecAggPlusHandler(unittest.TestCase):
-    """Test the SecAgg+ protocol handler."""
-
-    def test_invalid_handler(self) -> None:
-        """Test invalid handler."""
-        handler = SecAggPlusHandler()
-
-        with self.assertRaises(TypeError):
-            handler.handle_secure_aggregation({})
-
-    def test_stage_transition(self) -> None:
-        """Test stage transition."""
-        handler = EmptyFlowerNumPyClient()
-
-        assert STAGES == (
-            STAGE_SETUP,
-            STAGE_SHARE_KEYS,
-            STAGE_COLLECT_MASKED_INPUT,
-            STAGE_UNMASK,
-        )
-
-        valid_transitions = {
-            # From one stage to the next stage
-            (STAGE_UNMASK, STAGE_SETUP),
-            (STAGE_SETUP, STAGE_SHARE_KEYS),
-            (STAGE_SHARE_KEYS, STAGE_COLLECT_MASKED_INPUT),
-            (STAGE_COLLECT_MASKED_INPUT, STAGE_UNMASK),
-            # From any stage to the initial stage
-            # Such transitions will log a warning.
-            (STAGE_SETUP, STAGE_SETUP),
-            (STAGE_SHARE_KEYS, STAGE_SETUP),
-            (STAGE_COLLECT_MASKED_INPUT, STAGE_SETUP),
-        }
-
-        invalid_transitions = set(product(STAGES, STAGES)).difference(valid_transitions)
-
-        # Test valid transitions
-        # If the next stage is valid, the function should update the current stage
-        # and then raise KeyError or other exceptions when trying to execute SA.
-        for current_stage, next_stage in valid_transitions:
-            # pylint: disable-next=protected-access
-            handler._current_stage = current_stage
-
-            with self.assertRaises(KeyError):
-                handler.handle_secure_aggregation({KEY_STAGE: next_stage})
-            # pylint: disable-next=protected-access
-            assert handler._current_stage == next_stage
-
-        # Test invalid transitions
-        # If the next stage is invalid, the function should raise ValueError
-        for current_stage, next_stage in invalid_transitions:
-            # pylint: disable-next=protected-access
-            handler._current_stage = current_stage
-
-            with self.assertRaises(ValueError):
-                handler.handle_secure_aggregation({KEY_STAGE: next_stage})
-            # pylint: disable-next=protected-access
-            assert handler._current_stage == current_stage
-
-    def test_stage_setup_check(self) -> None:
-        """Test content checking for the setup stage."""
-        handler = EmptyFlowerNumPyClient()
-
-        valid_key_type_pairs = [
-            (KEY_SAMPLE_NUMBER, int),
-            (KEY_SECURE_ID, int),
-            (KEY_SHARE_NUMBER, int),
-            (KEY_THRESHOLD, int),
-            (KEY_CLIPPING_RANGE, float),
-            (KEY_TARGET_RANGE, int),
-            (KEY_MOD_RANGE, int),
-        ]
-
-        type_to_test_value: Dict[type, Value] = {
-            int: 10,
-            bool: True,
-            float: 1.0,
-            str: "test",
-            bytes: b"test",
-        }
-
-        valid_named_values: Dict[str, Value] = {
-            key: type_to_test_value[value_type]
-            for key, value_type in valid_key_type_pairs
-        }
-
-        # Test valid `named_values`
-        try:
-            check_named_values(STAGE_SETUP, valid_named_values.copy())
-        # pylint: disable-next=broad-except
-        except Exception as exc:
-            self.fail(f"check_named_values() raised {type(exc)} unexpectedly!")
-
-        # Set the stage
-        valid_named_values[KEY_STAGE] = STAGE_SETUP
-
-        # Test invalid `named_values`
-        for key, value_type in valid_key_type_pairs:
-            invalid_named_values = valid_named_values.copy()
-
-            # Test wrong value type for the key
-            for other_type, other_value in type_to_test_value.items():
-                if other_type == value_type:
-                    continue
-                invalid_named_values[key] = other_value
-                # pylint: disable-next=protected-access
-                handler._current_stage = STAGE_UNMASK
-                with self.assertRaises(TypeError):
-                    handler.handle_secure_aggregation(invalid_named_values.copy())
-
-            # Test missing key
-            invalid_named_values.pop(key)
-            # pylint: disable-next=protected-access
-            handler._current_stage = STAGE_UNMASK
-            with self.assertRaises(KeyError):
-                handler.handle_secure_aggregation(invalid_named_values.copy())
-
-    def test_stage_share_keys_check(self) -> None:
-        """Test content checking for the share keys stage."""
-        handler = EmptyFlowerNumPyClient()
-
-        valid_named_values: Dict[str, Value] = {
-            "1": [b"public key 1", b"public key 2"],
-            "2": [b"public key 1", b"public key 2"],
-            "3": [b"public key 1", b"public key 2"],
-        }
-
-        # Test valid `named_values`
-        try:
-            check_named_values(STAGE_SHARE_KEYS, valid_named_values.copy())
-        # pylint: disable-next=broad-except
-        except Exception as exc:
-            self.fail(f"check_named_values() raised {type(exc)} unexpectedly!")
-
-        # Set the stage
-        valid_named_values[KEY_STAGE] = STAGE_SHARE_KEYS
-
-        # Test invalid `named_values`
-        invalid_values: List[Value] = [
-            b"public key 1",
-            [b"public key 1"],
-            [b"public key 1", b"public key 2", b"public key 3"],
-        ]
-
-        for value in invalid_values:
-            invalid_named_values = valid_named_values.copy()
-            invalid_named_values["1"] = value
-
-            # pylint: disable-next=protected-access
-            handler._current_stage = STAGE_SETUP
-            with self.assertRaises(TypeError):
-                handler.handle_secure_aggregation(invalid_named_values.copy())
-
-    def test_stage_collect_masked_input_check(self) -> None:
-        """Test content checking for the collect masked input stage."""
-        handler = EmptyFlowerNumPyClient()
-
-        valid_named_values: Dict[str, Value] = {
-            KEY_CIPHERTEXT_LIST: [b"ctxt!", b"ctxt@", b"ctxt#", b"ctxt?"],
-            KEY_SOURCE_LIST: [32, 51324, 32324123, -3],
-            KEY_PARAMETERS: [b"params1", b"params2"],
-        }
-
-        # Test valid `named_values`
-        try:
-            check_named_values(STAGE_COLLECT_MASKED_INPUT, valid_named_values.copy())
-        # pylint: disable-next=broad-except
-        except Exception as exc:
-            self.fail(f"check_named_values() raised {type(exc)} unexpectedly!")
-
-        # Set the stage
-        valid_named_values[KEY_STAGE] = STAGE_COLLECT_MASKED_INPUT
-
-        # Test invalid `named_values`
-        # Test missing keys
-        for key in list(valid_named_values.keys()):
-            if key == KEY_STAGE:
-                continue
-            invalid_named_values = valid_named_values.copy()
-            invalid_named_values.pop(key)
-            # pylint: disable-next=protected-access
-            handler._current_stage = STAGE_SHARE_KEYS
-            with self.assertRaises(KeyError):
-                handler.handle_secure_aggregation(invalid_named_values)
-
-        # Test wrong value type for the key
-        for key in valid_named_values:
-            if key == KEY_STAGE:
-                continue
-            invalid_named_values = valid_named_values.copy()
-            cast(List[Any], invalid_named_values[key]).append(3.1415926)
-            # pylint: disable-next=protected-access
-            handler._current_stage = STAGE_SHARE_KEYS
-            with self.assertRaises(TypeError):
-                handler.handle_secure_aggregation(invalid_named_values)
-
-    def test_stage_unmask_check(self) -> None:
-        """Test content checking for the unmasking stage."""
-        handler = EmptyFlowerNumPyClient()
-
-        valid_named_values: Dict[str, Value] = {
-            KEY_ACTIVE_SECURE_ID_LIST: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
-            KEY_DEAD_SECURE_ID_LIST: [32, 51324, 32324123, -3],
-        }
-
-        # Test valid `named_values`
-        try:
-            check_named_values(STAGE_UNMASK, valid_named_values.copy())
-        # pylint: disable-next=broad-except
-        except Exception as exc:
-            self.fail(f"check_named_values() raised {type(exc)} unexpectedly!")
-
-        # Set the stage
-        valid_named_values[KEY_STAGE] = STAGE_UNMASK
-
-        # Test invalid `named_values`
-        # Test missing keys
-        for key in list(valid_named_values.keys()):
-            if key == KEY_STAGE:
-                continue
-            invalid_named_values = valid_named_values.copy()
-            invalid_named_values.pop(key)
-            # pylint: disable-next=protected-access
-            handler._current_stage = STAGE_COLLECT_MASKED_INPUT
-            with self.assertRaises(KeyError):
-                handler.handle_secure_aggregation(invalid_named_values)
-
-        # Test wrong value type for the key
-        for key in valid_named_values:
-            if key == KEY_STAGE:
-                continue
-            invalid_named_values = valid_named_values.copy()
-            cast(List[Any], invalid_named_values[key]).append(True)
-            # pylint: disable-next=protected-access
-            handler._current_stage = STAGE_COLLECT_MASKED_INPUT
-            with self.assertRaises(TypeError):
-                handler.handle_secure_aggregation(invalid_named_values)
diff --git a/src/py/flwr/client/typing.py b/src/py/flwr/client/typing.py
index 2c1f7506592c..956ac7a15c05 100644
--- a/src/py/flwr/client/typing.py
+++ b/src/py/flwr/client/typing.py
@@ -14,30 +14,15 @@
 # ==============================================================================
 """Custom types for Flower clients."""
 
-from dataclasses import dataclass
+
 from typing import Callable
 
-from flwr.client.workload_state import WorkloadState
-from flwr.proto.task_pb2 import TaskIns, TaskRes
+from flwr.common import Context, Message
 
 from .client import Client as Client
 
-
-@dataclass
-class Fwd:
-    """."""
-
-    task_ins: TaskIns
-    state: WorkloadState
-
-
-@dataclass
-class Bwd:
-    """."""
-
-    task_res: TaskRes
-    state: WorkloadState
-
-
-FlowerCallable = Callable[[Fwd], Bwd]
+# Compatibility
 ClientFn = Callable[[str], Client]
+
+ClientAppCallable = Callable[[Message, Context], Message]
+Mod = Callable[[Message, Context, ClientAppCallable], Message]
diff --git a/src/py/flwr/common/__init__.py b/src/py/flwr/common/__init__.py
index 2f45de45dfc3..9f9ff7ebc68a 100644
--- a/src/py/flwr/common/__init__.py
+++ b/src/py/flwr/common/__init__.py
@@ -15,14 +15,26 @@
 """Common components shared between server and client."""
 
 
+from .constant import MessageType as MessageType
+from .constant import MessageTypeLegacy as MessageTypeLegacy
+from .context import Context as Context
 from .date import now as now
 from .grpc import GRPC_MAX_MESSAGE_LENGTH
 from .logger import configure as configure
 from .logger import log as log
+from .message import Error as Error
+from .message import Message as Message
+from .message import Metadata as Metadata
 from .parameter import bytes_to_ndarray as bytes_to_ndarray
 from .parameter import ndarray_to_bytes as ndarray_to_bytes
 from .parameter import ndarrays_to_parameters as ndarrays_to_parameters
 from .parameter import parameters_to_ndarrays as parameters_to_ndarrays
+from .record import Array as Array
+from .record import ConfigsRecord as ConfigsRecord
+from .record import MetricsRecord as MetricsRecord
+from .record import ParametersRecord as ParametersRecord
+from .record import RecordSet as RecordSet
+from .record import array_from_numpy as array_from_numpy
 from .telemetry import EventType as EventType
 from .telemetry import event as event
 from .typing import ClientMessage as ClientMessage
@@ -49,11 +61,15 @@
 from .typing import Status as Status
 
 __all__ = [
+    "Array",
+    "array_from_numpy",
     "bytes_to_ndarray",
     "ClientMessage",
     "Code",
     "Config",
+    "ConfigsRecord",
     "configure",
+    "Context",
     "DisconnectRes",
     "EvaluateIns",
     "EvaluateRes",
@@ -61,14 +77,20 @@
     "EventType",
     "FitIns",
     "FitRes",
+    "Error",
     "GetParametersIns",
     "GetParametersRes",
     "GetPropertiesIns",
     "GetPropertiesRes",
     "GRPC_MAX_MESSAGE_LENGTH",
     "log",
+    "Message",
+    "MessageType",
+    "MessageTypeLegacy",
+    "Metadata",
     "Metrics",
     "MetricsAggregationFn",
+    "MetricsRecord",
     "ndarray_to_bytes",
     "now",
     "NDArray",
@@ -76,8 +98,10 @@
     "ndarrays_to_parameters",
     "Parameters",
     "parameters_to_ndarrays",
+    "ParametersRecord",
     "Properties",
     "ReconnectIns",
+    "RecordSet",
     "Scalar",
     "ServerMessage",
     "Status",
diff --git a/src/py/flwr/common/address_test.py b/src/py/flwr/common/address_test.py
index c12dd5fd289e..420b89871d69 100644
--- a/src/py/flwr/common/address_test.py
+++ b/src/py/flwr/common/address_test.py
@@ -109,10 +109,10 @@ def test_domain_correct() -> None:
     """Test if a correct domain address is correctly parsed."""
     # Prepare
     addresses = [
-        ("flower.dev:123", ("flower.dev", 123, None)),
-        ("sub.flower.dev:123", ("sub.flower.dev", 123, None)),
-        ("sub2.sub1.flower.dev:123", ("sub2.sub1.flower.dev", 123, None)),
-        ("s5.s4.s3.s2.s1.flower.dev:123", ("s5.s4.s3.s2.s1.flower.dev", 123, None)),
+        ("flower.ai:123", ("flower.ai", 123, None)),
+        ("sub.flower.ai:123", ("sub.flower.ai", 123, None)),
+        ("sub2.sub1.flower.ai:123", ("sub2.sub1.flower.ai", 123, None)),
+        ("s5.s4.s3.s2.s1.flower.ai:123", ("s5.s4.s3.s2.s1.flower.ai", 123, None)),
         ("localhost:123", ("localhost", 123, None)),
         ("https://localhost:123", ("https://localhost", 123, None)),
         ("http://localhost:123", ("http://localhost", 123, None)),
@@ -130,8 +130,8 @@ def test_domain_incorrect() -> None:
     """Test if an incorrect domain address returns None."""
     # Prepare
     addresses = [
-        "flower.dev",
-        "flower.dev:65536",
+        "flower.ai",
+        "flower.ai:65536",
     ]
 
     for address in addresses:
diff --git a/src/py/flwr/common/constant.py b/src/py/flwr/common/constant.py
index 49802f2815be..7d30a10f5881 100644
--- a/src/py/flwr/common/constant.py
+++ b/src/py/flwr/common/constant.py
@@ -15,6 +15,8 @@
 """Flower constants."""
 
 
+from __future__ import annotations
+
 MISSING_EXTRA_REST = """
 Extra dependencies required for using the REST-based Fleet API are missing.
 
@@ -26,8 +28,43 @@
 TRANSPORT_TYPE_GRPC_BIDI = "grpc-bidi"
 TRANSPORT_TYPE_GRPC_RERE = "grpc-rere"
 TRANSPORT_TYPE_REST = "rest"
+TRANSPORT_TYPE_VCE = "vce"
 TRANSPORT_TYPES = [
     TRANSPORT_TYPE_GRPC_BIDI,
     TRANSPORT_TYPE_GRPC_RERE,
     TRANSPORT_TYPE_REST,
+    TRANSPORT_TYPE_VCE,
 ]
+
+
+class MessageType:
+    """Message type."""
+
+    TRAIN = "train"
+    EVALUATE = "evaluate"
+    QUERY = "query"
+
+    def __new__(cls) -> MessageType:
+        """Prevent instantiation."""
+        raise TypeError(f"{cls.__name__} cannot be instantiated.")
+
+
+class MessageTypeLegacy:
+    """Legacy message type."""
+
+    GET_PROPERTIES = "get_properties"
+    GET_PARAMETERS = "get_parameters"
+
+    def __new__(cls) -> MessageTypeLegacy:
+        """Prevent instantiation."""
+        raise TypeError(f"{cls.__name__} cannot be instantiated.")
+
+
+class SType:
+    """Serialisation type."""
+
+    NUMPY = "numpy.ndarray"
+
+    def __new__(cls) -> SType:
+        """Prevent instantiation."""
+        raise TypeError(f"{cls.__name__} cannot be instantiated.")
diff --git a/src/py/flwr/common/context.py b/src/py/flwr/common/context.py
new file mode 100644
index 000000000000..b6349307d150
--- /dev/null
+++ b/src/py/flwr/common/context.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.
+# ==============================================================================
+"""Context."""
+
+
+from dataclasses import dataclass
+
+from .record import RecordSet
+
+
+@dataclass
+class Context:
+    """State of your run.
+
+    Parameters
+    ----------
+    state : RecordSet
+        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)
+    """
+
+    state: RecordSet
diff --git a/src/py/flwr/common/differential_privacy.py b/src/py/flwr/common/differential_privacy.py
new file mode 100644
index 000000000000..85dc198ef8a0
--- /dev/null
+++ b/src/py/flwr/common/differential_privacy.py
@@ -0,0 +1,176 @@
+# 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.
+# ==============================================================================
+"""Utility functions for differential privacy."""
+
+
+from logging import WARNING
+from typing import Optional, Tuple
+
+import numpy as np
+
+from flwr.common import (
+    NDArrays,
+    Parameters,
+    ndarrays_to_parameters,
+    parameters_to_ndarrays,
+)
+from flwr.common.logger import log
+
+
+def get_norm(input_arrays: NDArrays) -> float:
+    """Compute the L2 norm of the flattened input."""
+    array_norms = [np.linalg.norm(array.flat) for array in input_arrays]
+    # pylint: disable=consider-using-generator
+    return float(np.sqrt(sum([norm**2 for norm in array_norms])))
+
+
+def add_gaussian_noise_inplace(input_arrays: NDArrays, std_dev: float) -> None:
+    """Add Gaussian noise to each element of the input arrays."""
+    for array in input_arrays:
+        array += np.random.normal(0, std_dev, array.shape)
+
+
+def clip_inputs_inplace(input_arrays: NDArrays, clipping_norm: float) -> None:
+    """Clip model update based on the clipping norm in-place.
+
+    FlatClip method of the paper: https://arxiv.org/abs/1710.06963
+    """
+    input_norm = get_norm(input_arrays)
+    scaling_factor = min(1, clipping_norm / input_norm)
+    for array in input_arrays:
+        array *= scaling_factor
+
+
+def compute_stdv(
+    noise_multiplier: float, clipping_norm: float, num_sampled_clients: int
+) -> float:
+    """Compute standard deviation for noise addition.
+
+    Paper: https://arxiv.org/abs/1710.06963
+    """
+    return float((noise_multiplier * clipping_norm) / num_sampled_clients)
+
+
+def compute_clip_model_update(
+    param1: NDArrays, param2: NDArrays, clipping_norm: float
+) -> None:
+    """Compute model update (param1 - param2) and clip it.
+
+    Then add the clipped value to param1."""
+    model_update = [np.subtract(x, y) for (x, y) in zip(param1, param2)]
+    clip_inputs_inplace(model_update, clipping_norm)
+
+    for i, _ in enumerate(param2):
+        param1[i] = param2[i] + model_update[i]
+
+
+def adaptive_clip_inputs_inplace(input_arrays: NDArrays, clipping_norm: float) -> bool:
+    """Clip model update based on the clipping norm in-place.
+
+    It returns true if scaling_factor < 1 which is used for norm_bit
+    FlatClip method of the paper: https://arxiv.org/abs/1710.06963
+    """
+    input_norm = get_norm(input_arrays)
+    scaling_factor = min(1, clipping_norm / input_norm)
+    for array in input_arrays:
+        array *= scaling_factor
+    return scaling_factor < 1
+
+
+def compute_adaptive_clip_model_update(
+    param1: NDArrays, param2: NDArrays, clipping_norm: float
+) -> bool:
+    """Compute model update, clip it, then add the clipped value to param1.
+
+    model update = param1 - param2
+    Return the norm_bit
+    """
+    model_update = [np.subtract(x, y) for (x, y) in zip(param1, param2)]
+    norm_bit = adaptive_clip_inputs_inplace(model_update, clipping_norm)
+
+    for i, _ in enumerate(param2):
+        param1[i] = param2[i] + model_update[i]
+
+    return norm_bit
+
+
+def add_gaussian_noise_to_params(
+    model_params: Parameters,
+    noise_multiplier: float,
+    clipping_norm: float,
+    num_sampled_clients: int,
+) -> Parameters:
+    """Add gaussian noise to model parameters."""
+    model_params_ndarrays = parameters_to_ndarrays(model_params)
+    add_gaussian_noise_inplace(
+        model_params_ndarrays,
+        compute_stdv(noise_multiplier, clipping_norm, num_sampled_clients),
+    )
+    return ndarrays_to_parameters(model_params_ndarrays)
+
+
+def compute_adaptive_noise_params(
+    noise_multiplier: float,
+    num_sampled_clients: float,
+    clipped_count_stddev: Optional[float],
+) -> Tuple[float, float]:
+    """Compute noising parameters for the adaptive clipping.
+
+    Paper: https://arxiv.org/abs/1905.03871
+    """
+    if noise_multiplier > 0:
+        if clipped_count_stddev is None:
+            clipped_count_stddev = num_sampled_clients / 20
+        if noise_multiplier >= 2 * clipped_count_stddev:
+            raise ValueError(
+                f"If not specified, `clipped_count_stddev` is set to "
+                f"`num_sampled_clients`/20 by default. This value "
+                f"({num_sampled_clients / 20}) is too low to achieve the "
+                f"desired effective `noise_multiplier` ({noise_multiplier}). "
+                f"Consider increasing `clipped_count_stddev` or decreasing "
+                f"`noise_multiplier`."
+            )
+        noise_multiplier_value = (
+            noise_multiplier ** (-2) - (2 * clipped_count_stddev) ** (-2)
+        ) ** -0.5
+
+        adding_noise = noise_multiplier_value / noise_multiplier
+        if adding_noise >= 2:
+            log(
+                WARNING,
+                "A significant amount of noise (%s) has to be "
+                "added. Consider increasing `clipped_count_stddev` or "
+                "`num_sampled_clients`.",
+                adding_noise,
+            )
+
+    else:
+        if clipped_count_stddev is None:
+            clipped_count_stddev = 0.0
+        noise_multiplier_value = 0.0
+
+    return clipped_count_stddev, noise_multiplier_value
+
+
+def add_localdp_gaussian_noise_to_params(
+    model_params: Parameters, sensitivity: float, epsilon: float, delta: float
+) -> Parameters:
+    """Add local DP gaussian noise to model parameters."""
+    model_params_ndarrays = parameters_to_ndarrays(model_params)
+    add_gaussian_noise_inplace(
+        model_params_ndarrays,
+        sensitivity * np.sqrt(2 * np.log(1.25 / delta)) / epsilon,
+    )
+    return ndarrays_to_parameters(model_params_ndarrays)
diff --git a/src/py/flwr/common/differential_privacy_constants.py b/src/py/flwr/common/differential_privacy_constants.py
new file mode 100644
index 000000000000..415759dfdf0b
--- /dev/null
+++ b/src/py/flwr/common/differential_privacy_constants.py
@@ -0,0 +1,25 @@
+# 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 differential privacy."""
+
+
+KEY_CLIPPING_NORM = "clipping_norm"
+KEY_NORM_BIT = "norm_bit"
+CLIENTS_DISCREPANCY_WARNING = (
+    "The number of clients returning parameters (%s)"
+    " differs from the number of sampled clients (%s)."
+    " This could impact the differential privacy guarantees,"
+    " potentially leading to privacy leakage or inadequate noise calibration."
+)
diff --git a/src/py/flwr/common/differential_privacy_test.py b/src/py/flwr/common/differential_privacy_test.py
new file mode 100644
index 000000000000..ea2e193df2f3
--- /dev/null
+++ b/src/py/flwr/common/differential_privacy_test.py
@@ -0,0 +1,209 @@
+# 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.
+# ==============================================================================
+"""Differential Privacy (DP) utility functions tests."""
+
+
+import numpy as np
+
+from .differential_privacy import (
+    add_gaussian_noise_inplace,
+    clip_inputs_inplace,
+    compute_adaptive_noise_params,
+    compute_clip_model_update,
+    compute_stdv,
+    get_norm,
+)
+
+
+def test_add_gaussian_noise_inplace() -> None:
+    """Test add_gaussian_noise_inplace function."""
+    # Prepare
+    update = [np.array([[1.0, 2.0], [3.0, 4.0]]), np.array([[5.0, 6.0], [7.0, 8.0]])]
+    std_dev = 0.1
+
+    # Execute
+    add_gaussian_noise_inplace(update, std_dev)
+
+    # Assert
+    # Check that the shape of the result is the same as the input
+    for layer in update:
+        assert layer.shape == (2, 2)
+
+    # Check that the values have been changed and are not equal to the original update
+    for layer in update:
+        assert not np.array_equal(
+            layer, [[1.0, 2.0], [3.0, 4.0]]
+        ) and not np.array_equal(layer, [[5.0, 6.0], [7.0, 8.0]])
+
+    # Check that the noise has been added
+    for layer in update:
+        noise_added = (
+            layer - np.array([[1.0, 2.0], [3.0, 4.0]])
+            if np.array_equal(layer, [[1.0, 2.0], [3.0, 4.0]])
+            else layer - np.array([[5.0, 6.0], [7.0, 8.0]])
+        )
+        assert np.any(np.abs(noise_added) > 0)
+
+
+def test_get_norm() -> None:
+    """Test get_norm function."""
+    # Prepare
+    update = [np.array([[1, 2], [3, 4]]), np.array([[5, 6], [7, 8]])]
+
+    # Execute
+    result = get_norm(update)
+
+    expected = float(
+        np.linalg.norm(np.concatenate([sub_update.flatten() for sub_update in update]))
+    )
+
+    # Assert
+    assert expected == result
+
+
+def test_clip_inputs_inplace() -> None:
+    """Test clip_inputs_inplace function."""
+    # Prepare
+    updates = [
+        np.array([[1.5, -0.5], [2.0, -1.0]]),
+        np.array([0.5, -0.5]),
+        np.array([[-0.5, 1.5], [-1.0, 2.0]]),
+        np.array([-0.5, 0.5]),
+    ]
+    clipping_norm = 1.5
+
+    original_updates = [np.copy(update) for update in updates]
+
+    # Execute
+    clip_inputs_inplace(updates, clipping_norm)
+
+    # Assert
+    for updated, original_update in zip(updates, original_updates):
+        clip_norm = np.linalg.norm(original_update)
+        assert np.all(updated <= clip_norm) and np.all(updated >= -clip_norm)
+
+
+def test_compute_stdv() -> None:
+    """Test compute_stdv function."""
+    # Prepare
+    noise_multiplier = 1.0
+    clipping_norm = 0.5
+    num_sampled_clients = 10
+
+    # Execute
+    stdv = compute_stdv(noise_multiplier, clipping_norm, num_sampled_clients)
+
+    # Assert
+    expected_stdv = float((noise_multiplier * clipping_norm) / num_sampled_clients)
+    assert stdv == expected_stdv
+
+
+def test_compute_clip_model_update() -> None:
+    """Test compute_clip_model_update function."""
+    # Prepare
+    param1 = [
+        np.array([0.5, 1.5, 2.5]),
+        np.array([3.5, 4.5, 5.5]),
+        np.array([6.5, 7.5, 8.5]),
+    ]
+    param2 = [
+        np.array([1.0, 2.0, 3.0]),
+        np.array([4.0, 5.0, 6.0]),
+        np.array([7.0, 8.0, 9.0]),
+    ]
+    clipping_norm = 4
+
+    expected_result = [
+        np.array([0.5, 1.5, 2.5]),
+        np.array([3.5, 4.5, 5.5]),
+        np.array([6.5, 7.5, 8.5]),
+    ]
+
+    # Execute
+    compute_clip_model_update(param1, param2, clipping_norm)
+
+    # Assert
+    for i, param in enumerate(param1):
+        np.testing.assert_array_almost_equal(param, expected_result[i])
+
+
+def test_compute_adaptive_noise_params() -> None:
+    """Test compute_adaptive_noise_params function."""
+    # Test valid input with positive noise_multiplier
+    noise_multiplier = 1.0
+    num_sampled_clients = 100.0
+    clipped_count_stddev = None
+    result = compute_adaptive_noise_params(
+        noise_multiplier, num_sampled_clients, clipped_count_stddev
+    )
+
+    # Assert
+    assert isinstance(result, tuple)
+    assert len(result) == 2
+    assert result[0] > 0.0
+    assert result[1] > 0.0
+
+    # Test valid input with zero noise_multiplier
+    noise_multiplier = 0.0
+    num_sampled_clients = 50.0
+    clipped_count_stddev = None
+    result = compute_adaptive_noise_params(
+        noise_multiplier, num_sampled_clients, clipped_count_stddev
+    )
+
+    # Assert
+    assert isinstance(result, tuple)
+    assert len(result) == 2
+    assert result[0] == 0.0
+    assert result[1] == 0.0
+
+    # Test valid input with specified clipped_count_stddev
+    noise_multiplier = 3.0
+    num_sampled_clients = 200.0
+    clipped_count_stddev = 5.0
+    result = compute_adaptive_noise_params(
+        noise_multiplier, num_sampled_clients, clipped_count_stddev
+    )
+
+    # Assert
+    assert isinstance(result, tuple)
+    assert len(result) == 2
+    assert result[0] == clipped_count_stddev
+    assert result[1] > 0.0
+
+    # Test invalid input with noise_multiplier >= 2 * clipped_count_stddev
+    noise_multiplier = 10.0
+    num_sampled_clients = 100.0
+    clipped_count_stddev = None
+    try:
+        compute_adaptive_noise_params(
+            noise_multiplier, num_sampled_clients, clipped_count_stddev
+        )
+    except ValueError:
+        pass
+    else:
+        raise AssertionError("Expected ValueError not raised.")
+
+    # Test intermediate calculation
+    noise_multiplier = 3.0
+    num_sampled_clients = 200.0
+    clipped_count_stddev = 5.0
+    result = compute_adaptive_noise_params(
+        noise_multiplier, num_sampled_clients, clipped_count_stddev
+    )
+    temp_value = (noise_multiplier ** (-2) - (2 * clipped_count_stddev) ** (-2)) ** -0.5
+
+    # Assert
+    assert np.isclose(result[1], temp_value, rtol=1e-6)
diff --git a/src/py/flwr/common/dp.py b/src/py/flwr/common/dp.py
index 5030ad34805b..83a72b8ce749 100644
--- a/src/py/flwr/common/dp.py
+++ b/src/py/flwr/common/dp.py
@@ -19,11 +19,13 @@
 
 import numpy as np
 
+from flwr.common.logger import warn_deprecated_feature
 from flwr.common.typing import NDArrays
 
 
 # Calculates the L2-norm of a potentially ragged array
 def _get_update_norm(update: NDArrays) -> float:
+    warn_deprecated_feature("`_get_update_norm` method")
     flattened_update = update[0]
     for i in range(1, len(update)):
         flattened_update = np.append(flattened_update, update[i])
@@ -32,6 +34,7 @@ def _get_update_norm(update: NDArrays) -> float:
 
 def add_gaussian_noise(update: NDArrays, std_dev: float) -> NDArrays:
     """Add iid Gaussian noise to each floating point value in the update."""
+    warn_deprecated_feature("`add_gaussian_noise` method")
     update_noised = [
         layer + np.random.normal(0, std_dev, layer.shape) for layer in update
     ]
@@ -40,6 +43,7 @@ def add_gaussian_noise(update: NDArrays, std_dev: float) -> NDArrays:
 
 def clip_by_l2(update: NDArrays, threshold: float) -> Tuple[NDArrays, bool]:
     """Scales the update so thats its L2 norm is upper-bound to threshold."""
+    warn_deprecated_feature("`clip_by_l2` method")
     update_norm = _get_update_norm(update)
     scaling_factor = min(1, threshold / update_norm)
     update_clipped: NDArrays = [layer * scaling_factor for layer in update]
diff --git a/src/py/flwr/common/exit_handlers.py b/src/py/flwr/common/exit_handlers.py
new file mode 100644
index 000000000000..30750c28a450
--- /dev/null
+++ b/src/py/flwr/common/exit_handlers.py
@@ -0,0 +1,87 @@
+# 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.
+# ==============================================================================
+"""Common function to register exit handlers for server and client."""
+
+
+import sys
+from signal import SIGINT, SIGTERM, signal
+from threading import Thread
+from types import FrameType
+from typing import List, Optional
+
+from grpc import Server
+
+from flwr.common.telemetry import EventType, event
+
+
+def register_exit_handlers(
+    event_type: EventType,
+    grpc_servers: Optional[List[Server]] = None,
+    bckg_threads: Optional[List[Thread]] = None,
+) -> None:
+    """Register exit handlers for `SIGINT` and `SIGTERM` signals.
+
+    Parameters
+    ----------
+    event_type : EventType
+        The telemetry event that should be logged before exit.
+    grpc_servers: Optional[List[Server]] (default: None)
+        An otpional list of gRPC servers that need to be gracefully
+        terminated before exiting.
+    bckg_threads: Optional[List[Thread]] (default: None)
+        An optional list of threads that need to be gracefully
+        terminated before exiting.
+    """
+    default_handlers = {
+        SIGINT: None,
+        SIGTERM: None,
+    }
+
+    def graceful_exit_handler(  # type: ignore
+        signalnum,
+        frame: FrameType,  # pylint: disable=unused-argument
+    ) -> None:
+        """Exit handler to be registered with `signal.signal`.
+
+        When called will reset signal handler to original signal handler from
+        default_handlers.
+        """
+        # Reset to default handler
+        signal(signalnum, default_handlers[signalnum])
+
+        event_res = event(event_type=event_type)
+
+        if grpc_servers is not None:
+            for grpc_server in grpc_servers:
+                grpc_server.stop(grace=1)
+
+        if bckg_threads is not None:
+            for bckg_thread in bckg_threads:
+                bckg_thread.join()
+
+        # Ensure event has happend
+        event_res.result()
+
+        # Setup things for graceful exit
+        sys.exit(0)
+
+    default_handlers[SIGINT] = signal(  # type: ignore
+        SIGINT,
+        graceful_exit_handler,  # type: ignore
+    )
+    default_handlers[SIGTERM] = signal(  # type: ignore
+        SIGTERM,
+        graceful_exit_handler,  # type: ignore
+    )
diff --git a/src/py/flwr/common/grpc.py b/src/py/flwr/common/grpc.py
index 9d0543ea8c75..7d0eba078ab0 100644
--- a/src/py/flwr/common/grpc.py
+++ b/src/py/flwr/common/grpc.py
@@ -15,7 +15,7 @@
 """Utility functions for gRPC."""
 
 
-from logging import INFO
+from logging import DEBUG
 from typing import Optional
 
 import grpc
@@ -49,12 +49,12 @@ def create_channel(
 
     if insecure:
         channel = grpc.insecure_channel(server_address, options=channel_options)
-        log(INFO, "Opened insecure gRPC connection (no certificates were passed)")
+        log(DEBUG, "Opened insecure gRPC connection (no certificates were passed)")
     else:
         ssl_channel_credentials = grpc.ssl_channel_credentials(root_certificates)
         channel = grpc.secure_channel(
             server_address, ssl_channel_credentials, options=channel_options
         )
-        log(INFO, "Opened secure gRPC connection using certificates")
+        log(DEBUG, "Opened secure gRPC connection using certificates")
 
     return channel
diff --git a/src/py/flwr/common/logger.py b/src/py/flwr/common/logger.py
index 29d1562a86d3..2bc41773ed61 100644
--- a/src/py/flwr/common/logger.py
+++ b/src/py/flwr/common/logger.py
@@ -18,21 +18,86 @@
 import logging
 from logging import WARN, LogRecord
 from logging.handlers import HTTPHandler
-from typing import Any, Dict, Optional, Tuple
+from typing import TYPE_CHECKING, Any, Dict, Optional, TextIO, Tuple
 
 # Create logger
 LOGGER_NAME = "flwr"
 FLOWER_LOGGER = logging.getLogger(LOGGER_NAME)
 FLOWER_LOGGER.setLevel(logging.DEBUG)
 
-DEFAULT_FORMATTER = logging.Formatter(
-    "%(levelname)s %(name)s %(asctime)s | %(filename)s:%(lineno)d | %(message)s"
-)
+LOG_COLORS = {
+    "DEBUG": "\033[94m",  # Blue
+    "INFO": "\033[92m",  # Green
+    "WARNING": "\033[93m",  # Yellow
+    "ERROR": "\033[91m",  # Red
+    "CRITICAL": "\033[95m",  # Magenta
+    "RESET": "\033[0m",  # Reset to default
+}
+
+if TYPE_CHECKING:
+    StreamHandler = logging.StreamHandler[Any]
+else:
+    StreamHandler = logging.StreamHandler
+
+
+class ConsoleHandler(StreamHandler):
+    """Console handler that allows configurable formatting."""
+
+    def __init__(
+        self,
+        timestamps: bool = False,
+        json: bool = False,
+        colored: bool = True,
+        stream: Optional[TextIO] = None,
+    ) -> None:
+        super().__init__(stream)
+        self.timestamps = timestamps
+        self.json = json
+        self.colored = colored
+
+    def emit(self, record: LogRecord) -> None:
+        """Emit a record."""
+        if self.json:
+            record.message = record.getMessage().replace("\t", "").strip()
+
+            # Check if the message is empty
+            if not record.message:
+                return
+
+        super().emit(record)
+
+    def format(self, record: LogRecord) -> str:
+        """Format function that adds colors to log level."""
+        seperator = " " * (8 - len(record.levelname))
+        if self.json:
+            log_fmt = "{lvl='%(levelname)s', time='%(asctime)s', msg='%(message)s'}"
+        else:
+            log_fmt = (
+                f"{LOG_COLORS[record.levelname] if self.colored else ''}"
+                f"%(levelname)s {'%(asctime)s' if self.timestamps else ''}"
+                f"{LOG_COLORS['RESET'] if self.colored else ''}"
+                f": {seperator} %(message)s"
+            )
+        formatter = logging.Formatter(log_fmt)
+        return formatter.format(record)
+
+
+def update_console_handler(level: int, timestamps: bool, colored: bool) -> None:
+    """Update the logging handler."""
+    for handler in logging.getLogger(LOGGER_NAME).handlers:
+        if isinstance(handler, ConsoleHandler):
+            handler.setLevel(level)
+            handler.timestamps = timestamps
+            handler.colored = colored
+
 
 # Configure console logger
-console_handler = logging.StreamHandler()
-console_handler.setLevel(logging.DEBUG)
-console_handler.setFormatter(DEFAULT_FORMATTER)
+console_handler = ConsoleHandler(
+    timestamps=False,
+    json=False,
+    colored=True,
+)
+console_handler.setLevel(logging.INFO)
 FLOWER_LOGGER.addHandler(console_handler)
 
 
@@ -103,11 +168,23 @@ def warn_experimental_feature(name: str) -> None:
     """Warn the user when they use an experimental feature."""
     log(
         WARN,
-        """
-        EXPERIMENTAL FEATURE: %s
+        """EXPERIMENTAL FEATURE: %s
+
+            This is an experimental feature. It could change significantly or be removed
+            entirely in future versions of Flower.
+        """,
+        name,
+    )
+
+
+def warn_deprecated_feature(name: str) -> None:
+    """Warn the user when they use a deprecated feature."""
+    log(
+        WARN,
+        """DEPRECATED FEATURE: %s
 
-        This is an experimental feature. It could change significantly or be removed
-        entirely in future versions of Flower.
+            This is a deprecated feature. It will be removed
+            entirely in future versions of Flower.
         """,
         name,
     )
diff --git a/src/py/flwr/common/message.py b/src/py/flwr/common/message.py
new file mode 100644
index 000000000000..88cf750f1a94
--- /dev/null
+++ b/src/py/flwr/common/message.py
@@ -0,0 +1,323 @@
+# 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.
+# ==============================================================================
+"""Message."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from .record import RecordSet
+
+
+@dataclass
+class Metadata:  # pylint: disable=too-many-instance-attributes
+    """A dataclass holding metadata associated with the current message.
+
+    Parameters
+    ----------
+    run_id : int
+        An identifier for the current run.
+    message_id : str
+        An identifier for the current message.
+    src_node_id : int
+        An identifier for the node sending this message.
+    dst_node_id : int
+        An identifier for the node receiving this message.
+    reply_to_message : str
+        An identifier for the message this message replies to.
+    group_id : str
+        An identifier for grouping messages. In some settings,
+        this is used as the FL round.
+    ttl : str
+        Time-to-live for this message.
+    message_type : str
+        A string that encodes the action to be executed on
+        the receiving end.
+    partition_id : Optional[int]
+        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.
+    """
+
+    _run_id: int
+    _message_id: str
+    _src_node_id: int
+    _dst_node_id: int
+    _reply_to_message: str
+    _group_id: str
+    _ttl: str
+    _message_type: str
+    _partition_id: int | None
+
+    def __init__(  # pylint: disable=too-many-arguments
+        self,
+        run_id: int,
+        message_id: str,
+        src_node_id: int,
+        dst_node_id: int,
+        reply_to_message: str,
+        group_id: str,
+        ttl: str,
+        message_type: str,
+        partition_id: int | None = None,
+    ) -> None:
+        self._run_id = run_id
+        self._message_id = message_id
+        self._src_node_id = src_node_id
+        self._dst_node_id = dst_node_id
+        self._reply_to_message = reply_to_message
+        self._group_id = group_id
+        self._ttl = ttl
+        self._message_type = message_type
+        self._partition_id = partition_id
+
+    @property
+    def run_id(self) -> int:
+        """An identifier for the current run."""
+        return self._run_id
+
+    @property
+    def message_id(self) -> str:
+        """An identifier for the current message."""
+        return self._message_id
+
+    @property
+    def src_node_id(self) -> int:
+        """An identifier for the node sending this message."""
+        return self._src_node_id
+
+    @property
+    def reply_to_message(self) -> str:
+        """An identifier for the message this message replies to."""
+        return self._reply_to_message
+
+    @property
+    def dst_node_id(self) -> int:
+        """An identifier for the node receiving this message."""
+        return self._dst_node_id
+
+    @dst_node_id.setter
+    def dst_node_id(self, value: int) -> None:
+        """Set dst_node_id."""
+        self._dst_node_id = value
+
+    @property
+    def group_id(self) -> str:
+        """An identifier for grouping messages."""
+        return self._group_id
+
+    @group_id.setter
+    def group_id(self, value: str) -> None:
+        """Set group_id."""
+        self._group_id = value
+
+    @property
+    def ttl(self) -> str:
+        """Time-to-live for this message."""
+        return self._ttl
+
+    @ttl.setter
+    def ttl(self, value: str) -> None:
+        """Set ttl."""
+        self._ttl = value
+
+    @property
+    def message_type(self) -> str:
+        """A string that encodes the action to be executed on the receiving end."""
+        return self._message_type
+
+    @message_type.setter
+    def message_type(self, value: str) -> None:
+        """Set message_type."""
+        self._message_type = value
+
+    @property
+    def partition_id(self) -> int | None:
+        """An identifier telling which data partition a ClientApp should use."""
+        return self._partition_id
+
+    @partition_id.setter
+    def partition_id(self, value: int) -> None:
+        """Set patition_id."""
+        self._partition_id = value
+
+
+@dataclass
+class Error:
+    """A dataclass that stores information about an error that occurred.
+
+    Parameters
+    ----------
+    code : int
+        An identifier for the error.
+    reason : Optional[str]
+        A reason for why the error arose (e.g. an exception stack-trace)
+    """
+
+    _code: int
+    _reason: str | None = None
+
+    def __init__(self, code: int, reason: str | None = None) -> None:
+        self._code = code
+        self._reason = reason
+
+    @property
+    def code(self) -> int:
+        """Error code."""
+        return self._code
+
+    @property
+    def reason(self) -> str | None:
+        """Reason reported about the error."""
+        return self._reason
+
+
+@dataclass
+class Message:
+    """State of your application from the viewpoint of the entity using it.
+
+    Parameters
+    ----------
+    metadata : Metadata
+        A dataclass including information about the message to be executed.
+    content : Optional[RecordSet]
+        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.
+    error : Optional[Error]
+        A dataclass that captures information about an error that took place
+        when processing another message.
+    """
+
+    _metadata: Metadata
+    _content: RecordSet | None = None
+    _error: Error | None = None
+
+    def __init__(
+        self,
+        metadata: Metadata,
+        content: RecordSet | None = None,
+        error: Error | None = None,
+    ) -> None:
+        self._metadata = metadata
+
+        if not (content is None) ^ (error is None):
+            raise ValueError("Either `content` or `error` must be set, but not both.")
+
+        self._content = content
+        self._error = error
+
+    @property
+    def metadata(self) -> Metadata:
+        """A dataclass including information about the message to be executed."""
+        return self._metadata
+
+    @property
+    def content(self) -> RecordSet:
+        """The content of this message."""
+        if self._content is None:
+            raise ValueError(
+                "Message content is None. Use <message>.has_content() "
+                "to check if a message has content."
+            )
+        return self._content
+
+    @content.setter
+    def content(self, value: RecordSet) -> None:
+        """Set content."""
+        if self._error is None:
+            self._content = value
+        else:
+            raise ValueError("A message with an error set cannot have content.")
+
+    @property
+    def error(self) -> Error:
+        """Error captured by this message."""
+        if self._error is None:
+            raise ValueError(
+                "Message error is None. Use <message>.has_error() "
+                "to check first if a message carries an error."
+            )
+        return self._error
+
+    @error.setter
+    def error(self, value: Error) -> None:
+        """Set error."""
+        if self.has_content():
+            raise ValueError("A message with content set cannot carry an error.")
+        self._error = value
+
+    def has_content(self) -> bool:
+        """Return True if message has content, else False."""
+        return self._content is not None
+
+    def has_error(self) -> bool:
+        """Return True if message has an error, else False."""
+        return self._error is not None
+
+    def _create_reply_metadata(self, ttl: str) -> Metadata:
+        """Construct metadata for a reply message."""
+        return Metadata(
+            run_id=self.metadata.run_id,
+            message_id="",
+            src_node_id=self.metadata.dst_node_id,
+            dst_node_id=self.metadata.src_node_id,
+            reply_to_message=self.metadata.message_id,
+            group_id=self.metadata.group_id,
+            ttl=ttl,
+            message_type=self.metadata.message_type,
+            partition_id=self.metadata.partition_id,
+        )
+
+    def create_error_reply(
+        self,
+        error: Error,
+        ttl: str,
+    ) -> Message:
+        """Construct a reply message indicating an error happened.
+
+        Parameters
+        ----------
+        error : Error
+            The error that was encountered.
+        ttl : str
+            Time-to-live for this message.
+        """
+        # Create reply with error
+        message = Message(metadata=self._create_reply_metadata(ttl), error=error)
+        return message
+
+    def create_reply(self, content: RecordSet, ttl: str) -> Message:
+        """Create a reply to this message with specified content and TTL.
+
+        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.
+
+        Parameters
+        ----------
+        content : RecordSet
+            The content for the reply message.
+        ttl : str
+            Time-to-live for this message.
+
+        Returns
+        -------
+        Message
+            A new `Message` instance representing the reply.
+        """
+        return Message(
+            metadata=self._create_reply_metadata(ttl),
+            content=content,
+        )
diff --git a/src/py/flwr/common/message_test.py b/src/py/flwr/common/message_test.py
new file mode 100644
index 000000000000..ba628bb3235a
--- /dev/null
+++ b/src/py/flwr/common/message_test.py
@@ -0,0 +1,109 @@
+# Copyright 2020 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.
+# ==============================================================================
+"""Message tests."""
+
+
+from contextlib import ExitStack
+from typing import Any, Callable
+
+import pytest
+
+# pylint: enable=E0611
+from . import RecordSet
+from .message import Error, Message
+from .serde_test import RecordMaker
+
+
+@pytest.mark.parametrize(
+    "content_fn, error_fn, context",
+    [
+        (
+            lambda maker: maker.recordset(1, 1, 1),
+            None,
+            None,
+        ),  # check when only content is set
+        (None, lambda code: Error(code=code), None),  # check when only error is set
+        (
+            lambda maker: maker.recordset(1, 1, 1),
+            lambda code: Error(code=code),
+            pytest.raises(ValueError),
+        ),  # check when both are set (ERROR)
+        (None, None, pytest.raises(ValueError)),  # check when neither is set (ERROR)
+    ],
+)
+def test_message_creation(
+    content_fn: Callable[
+        [
+            RecordMaker,
+        ],
+        RecordSet,
+    ],
+    error_fn: Callable[[int], Error],
+    context: Any,
+) -> None:
+    """Test Message creation attempting to pass content and/or error."""
+    # Prepare
+    maker = RecordMaker(state=2)
+    metadata = maker.metadata()
+
+    with ExitStack() as stack:
+        if context:
+            stack.enter_context(context)
+
+        _ = Message(
+            metadata=metadata,
+            content=None if content_fn is None else content_fn(maker),
+            error=None if error_fn is None else error_fn(0),
+        )
+
+
+def create_message_with_content() -> Message:
+    """Create a Message with content."""
+    maker = RecordMaker(state=2)
+    metadata = maker.metadata()
+    return Message(metadata=metadata, content=RecordSet())
+
+
+def create_message_with_error() -> Message:
+    """Create a Message with error."""
+    maker = RecordMaker(state=2)
+    metadata = maker.metadata()
+    return Message(metadata=metadata, error=Error(code=1))
+
+
+@pytest.mark.parametrize(
+    "message_creation_fn",
+    [
+        create_message_with_content,
+        create_message_with_error,
+    ],
+)
+def test_altering_message(
+    message_creation_fn: Callable[
+        [],
+        Message,
+    ],
+) -> None:
+    """Test that a message with content doesn't allow setting an error.
+
+    And viceversa.
+    """
+    message = message_creation_fn()
+
+    with pytest.raises(ValueError):
+        if message.has_content():
+            message.error = Error(code=123)
+        if message.has_error():
+            message.content = RecordSet()
diff --git a/src/py/flwr/common/object_ref.py b/src/py/flwr/common/object_ref.py
new file mode 100644
index 000000000000..4660f07e24a4
--- /dev/null
+++ b/src/py/flwr/common/object_ref.py
@@ -0,0 +1,140 @@
+# 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.
+# ==============================================================================
+"""Helper functions to load objects from a reference."""
+
+
+import ast
+import importlib
+from importlib.util import find_spec
+from typing import Any, Optional, Tuple, Type
+
+OBJECT_REF_HELP_STR = """
+\n\nThe object reference string should have the form <module>:<attribute>. Valid
+examples include `client:app` and `project.package.module:wrapper.app`. It must
+refer to a module on the PYTHONPATH and the module needs to have the specified
+attribute.
+"""
+
+
+def validate(
+    module_attribute_str: str,
+) -> Tuple[bool, Optional[str]]:
+    """Validate object reference.
+
+    The object reference string should have the form <module>:<attribute>. Valid
+    examples include `client:app` and `project.package.module:wrapper.app`. It must
+    refer to a module on the PYTHONPATH and the module needs to have the specified
+    attribute.
+
+    Returns
+    -------
+    Tuple[bool, Optional[str]]
+        A boolean indicating whether an object reference is valid and
+        the reason why it might not be.
+    """
+    module_str, _, attributes_str = module_attribute_str.partition(":")
+    if not module_str:
+        return (
+            False,
+            f"Missing module in {module_attribute_str}{OBJECT_REF_HELP_STR}",
+        )
+    if not attributes_str:
+        return (
+            False,
+            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}",
+            )
+        return (True, None)
+
+    return (
+        False,
+        f"Unable to load module {module_str}{OBJECT_REF_HELP_STR}",
+    )
+
+
+def load_app(
+    module_attribute_str: str,
+    error_type: Type[Exception],
+) -> Any:
+    """Return the object specified in a module attribute string.
+
+    The module/attribute string should have the form <module>:<attribute>. Valid
+    examples include `client:app` and `project.package.module:wrapper.app`. It must
+    refer to a module on the PYTHONPATH, the module needs to have the specified
+    attribute.
+    """
+    valid, error_msg = validate(module_attribute_str)
+    if not valid and error_msg:
+        raise error_type(error_msg) from None
+
+    module_str, _, attributes_str = module_attribute_str.partition(":")
+
+    try:
+        module = importlib.import_module(module_str)
+    except ModuleNotFoundError:
+        raise error_type(
+            f"Unable to load module {module_str}{OBJECT_REF_HELP_STR}",
+        ) from None
+
+    # Recursively load attribute
+    attribute = module
+    try:
+        for attribute_str in attributes_str.split("."):
+            attribute = getattr(attribute, attribute_str)
+    except AttributeError:
+        raise error_type(
+            f"Unable to load attribute {attributes_str} from module {module_str}"
+            f"{OBJECT_REF_HELP_STR}",
+        ) from None
+
+    return attribute
+
+
+def _find_attribute_in_module(file_path: str, attribute_name: str) -> bool:
+    """Check if attribute_name exists in module's abstract symbolic tree."""
+    with open(file_path, encoding="utf-8") as file:
+        node = ast.parse(file.read(), filename=file_path)
+
+    for n in ast.walk(node):
+        if isinstance(n, ast.Assign):
+            for target in n.targets:
+                if isinstance(target, ast.Name) and target.id == attribute_name:
+                    return True
+                if _is_module_in_all(attribute_name, target, n):
+                    return True
+    return False
+
+
+def _is_module_in_all(attribute_name: str, target: ast.expr, n: ast.Assign) -> bool:
+    """Now check if attribute_name is in __all__."""
+    if isinstance(target, ast.Name) and target.id == "__all__":
+        if isinstance(n.value, ast.List):
+            for elt in n.value.elts:
+                if isinstance(elt, ast.Str) and elt.s == attribute_name:
+                    return True
+        elif isinstance(n.value, ast.Tuple):
+            for elt in n.value.elts:
+                if isinstance(elt, ast.Str) and elt.s == attribute_name:
+                    return True
+    return False
diff --git a/src/py/flwr/common/object_ref_test.py b/src/py/flwr/common/object_ref_test.py
new file mode 100644
index 000000000000..f4513a4319ab
--- /dev/null
+++ b/src/py/flwr/common/object_ref_test.py
@@ -0,0 +1,46 @@
+# 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 validation function of object refs."""
+
+from .object_ref import OBJECT_REF_HELP_STR, validate
+
+
+def test_validate_object_reference() -> None:
+    """Test that validate_object_reference succeeds correctly."""
+    # Prepare
+    ref = "flwr.cli.run:run"
+
+    # Execute
+    is_valid, error = validate(ref)
+
+    # Assert
+    assert is_valid
+    assert error is None
+
+
+def test_validate_object_reference_fails() -> None:
+    """Test that validate_object_reference fails correctly."""
+    # Prepare
+    ref = "flwr.cli.run:runa"
+
+    # Execute
+    is_valid, error = validate(ref)
+
+    # Assert
+    assert not is_valid
+    assert (
+        error
+        == f"Unable to find attribute runa in module flwr.cli.run{OBJECT_REF_HELP_STR}"
+    )
diff --git a/src/py/flwr/common/pyproject.py b/src/py/flwr/common/pyproject.py
new file mode 100644
index 000000000000..66585e422397
--- /dev/null
+++ b/src/py/flwr/common/pyproject.py
@@ -0,0 +1,41 @@
+# 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.
+# ==============================================================================
+"""Validates the project's name property."""
+
+import re
+
+
+def validate_project_name(name: str) -> bool:
+    """Validate the project name against PEP 621 and PEP 503 specifications.
+
+    Conventions at a glance:
+    - Must be lowercase
+    - Must not contain special characters
+    - Must use hyphens(recommended) or underscores. No spaces.
+    - Recommended to be no more than 40 characters long (But it can be)
+
+    Parameters
+    ----------
+    name : str
+        The project name to validate.
+
+    Returns
+    -------
+    bool
+        True if the name is valid, False otherwise.
+    """
+    if not name or len(name) > 40 or not re.match(r"^[a-z0-9-_]+$", name):
+        return False
+    return True
diff --git a/src/py/flwr/common/pyproject_test.py b/src/py/flwr/common/pyproject_test.py
new file mode 100644
index 000000000000..88a945054b83
--- /dev/null
+++ b/src/py/flwr/common/pyproject_test.py
@@ -0,0 +1,108 @@
+# 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 function that validates name property."""
+
+from .pyproject import validate_project_name
+
+
+# Happy Flow
+def test_valid_name_with_lower_case() -> None:
+    """Test a valid single-word project name with all lower case."""
+    # Prepare
+    name = "myproject"
+    expected = True
+    # Execute
+    actual = validate_project_name(name)
+    # Assert
+    assert actual == expected, f"Expected {name} to be valid"
+
+
+def test_valid_name_with_dashes() -> None:
+    """Test a valid project name with hyphens inbetween."""
+    # Prepare
+    name = "valid-project-name"
+    expected = True
+    # Execute
+    actual = validate_project_name(name)
+    # Assert
+    assert actual == expected, f"Expected {name} to be valid"
+
+
+def test_valid_name_with_underscores() -> None:
+    """Test a valid project name with underscores inbetween."""
+    # Prepare
+    name = "valid_project_name"
+    expected = True
+    # Execute
+    actual = validate_project_name(name)
+    # Assert
+    assert actual == expected, f"Expected {name} to be valid"
+
+
+def test_invalid_name_with_upper_letters() -> None:
+    """Tests a project name with Spaces and Uppercase letter."""
+    # Prepare
+    name = "Invalid Project Name"
+    expected = False
+    # Execute
+    actual = validate_project_name(name)
+    # Assert
+    assert actual == expected, "Upper Case and Spaces are not allowed"
+
+
+def test_name_with_spaces() -> None:
+    """Tests a project name with spaces inbetween."""
+    # Prepare
+    name = "name with spaces"
+    expected = False
+    # Execute
+    actual = validate_project_name(name)
+    # Assert
+    assert actual == expected, "Spaces are not allowed"
+
+
+def test_empty_name() -> None:
+    """Tests use-case for an empty project name."""
+    # Prepare
+    name = ""
+    expected = False
+    # Execute
+    actual = validate_project_name(name)
+    # Assert
+    assert actual == expected, "Empty name is not valid"
+
+
+def test_long_name() -> None:
+    """Tests for long project names."""
+    # Prepare
+    name = "a" * 41
+    expected = False
+    # Execute
+    actual = validate_project_name(name)
+    # Assert
+    # It can be more than 40 but generally
+    # it is recommended not to be more than 40
+    assert actual == expected, "Name longer than 40 characters is not recommended"
+
+
+def test_name_with_special_characters() -> None:
+    """Tests for project names with special characters."""
+    # Prepare
+    name = "name!@#"
+    expected = False
+    # Execute
+    actual = validate_project_name(name)
+    # Assert
+    assert actual == expected, "Special characters are not allowed"
diff --git a/src/py/flwr/common/record/__init__.py b/src/py/flwr/common/record/__init__.py
new file mode 100644
index 000000000000..60bc54b8552a
--- /dev/null
+++ b/src/py/flwr/common/record/__init__.py
@@ -0,0 +1,30 @@
+# 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.
+# ==============================================================================
+"""Record APIs."""
+
+from .configsrecord import ConfigsRecord
+from .conversion_utils import array_from_numpy
+from .metricsrecord import MetricsRecord
+from .parametersrecord import Array, ParametersRecord
+from .recordset import RecordSet
+
+__all__ = [
+    "Array",
+    "array_from_numpy",
+    "ConfigsRecord",
+    "MetricsRecord",
+    "ParametersRecord",
+    "RecordSet",
+]
diff --git a/src/py/flwr/common/record/configsrecord.py b/src/py/flwr/common/record/configsrecord.py
new file mode 100644
index 000000000000..471c85f0b961
--- /dev/null
+++ b/src/py/flwr/common/record/configsrecord.py
@@ -0,0 +1,123 @@
+# 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.
+# ==============================================================================
+"""ConfigsRecord."""
+
+
+from typing import Dict, List, Optional, get_args
+
+from flwr.common.typing import ConfigsRecordValues, ConfigsScalar
+
+from .typeddict import TypedDict
+
+
+def _check_key(key: str) -> None:
+    """Check if key is of expected type."""
+    if not isinstance(key, str):
+        raise TypeError(f"Key must be of type `str` but `{type(key)}` was passed.")
+
+
+def _check_value(value: ConfigsRecordValues) -> None:
+    def is_valid(__v: ConfigsScalar) -> None:
+        """Check if value is of expected type."""
+        if not isinstance(__v, get_args(ConfigsScalar)):
+            raise TypeError(
+                "Not all values are of valid type."
+                f" Expected `{ConfigsRecordValues}` but `{type(__v)}` was passed."
+            )
+
+    if isinstance(value, list):
+        # If your lists are large (e.g. 1M+ elements) this will be slow
+        # 1s to check 10M element list on a M2 Pro
+        # In such settings, you'd be better of treating such config as
+        # an array and pass it to a ParametersRecord.
+        # Empty lists are valid
+        if len(value) > 0:
+            is_valid(value[0])
+            # all elements in the list must be of the same valid type
+            # this is needed for protobuf
+            value_type = type(value[0])
+            if not all(isinstance(v, value_type) for v in value):
+                raise TypeError(
+                    "All values in a list must be of the same valid type. "
+                    f"One of {ConfigsScalar}."
+                )
+    else:
+        is_valid(value)
+
+
+class ConfigsRecord(TypedDict[str, ConfigsRecordValues]):
+    """Configs record."""
+
+    def __init__(
+        self,
+        configs_dict: Optional[Dict[str, ConfigsRecordValues]] = None,
+        keep_input: bool = True,
+    ) -> None:
+        """Construct a ConfigsRecord object.
+
+        Parameters
+        ----------
+        configs_dict : Optional[Dict[str, ConfigsRecordValues]]
+            A dictionary that stores basic types (i.e. `str`, `int`, `float`, `bytes` as
+            defined in `ConfigsScalar`) and lists of such types (see
+            `ConfigsScalarList`).
+        keep_input : bool (default: True)
+            A boolean indicating whether config passed should be deleted from the input
+            dictionary immediately after adding them to the record. When set
+            to True, the data is duplicated in memory. If memory is a concern, set
+            it to False.
+        """
+        super().__init__(_check_key, _check_value)
+        if configs_dict:
+            for k in list(configs_dict.keys()):
+                self[k] = configs_dict[k]
+                if not keep_input:
+                    del configs_dict[k]
+
+    def count_bytes(self) -> int:
+        """Return number of Bytes stored in this object.
+
+        This function counts booleans as occupying 1 Byte.
+        """
+
+        def get_var_bytes(value: ConfigsScalar) -> int:
+            """Return Bytes of value passed."""
+            if isinstance(value, bool):
+                var_bytes = 1
+            elif isinstance(value, (int, float)):
+                var_bytes = (
+                    8  # the profobufing represents int/floats in ConfigRecords as 64bit
+                )
+            if isinstance(value, (str, bytes)):
+                var_bytes = len(value)
+            return var_bytes
+
+        num_bytes = 0
+
+        for k, v in self.items():
+            if isinstance(v, List):
+                if isinstance(v[0], (bytes, str)):
+                    # not all str are of equal length necessarily
+                    # for both the footprint of each element is 1 Byte
+                    num_bytes += int(sum(len(s) for s in v))  # type: ignore
+                else:
+                    num_bytes += get_var_bytes(v[0]) * len(v)
+            else:
+                num_bytes += get_var_bytes(v)
+
+            # We also count the bytes footprint of the keys
+            num_bytes += len(k)
+
+        return num_bytes
diff --git a/src/py/flwr/common/record/conversion_utils.py b/src/py/flwr/common/record/conversion_utils.py
new file mode 100644
index 000000000000..7cc0b04283e9
--- /dev/null
+++ b/src/py/flwr/common/record/conversion_utils.py
@@ -0,0 +1,40 @@
+# 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.
+# ==============================================================================
+"""Conversion utility functions for Records."""
+
+
+from io import BytesIO
+
+import numpy as np
+
+from ..constant import SType
+from ..typing import NDArray
+from .parametersrecord import Array
+
+
+def array_from_numpy(ndarray: NDArray) -> Array:
+    """Create Array from NumPy ndarray."""
+    buffer = BytesIO()
+    # WARNING: NEVER set allow_pickle to true.
+    # Reason: loading pickled data can execute arbitrary code
+    # Source: https://numpy.org/doc/stable/reference/generated/numpy.save.html
+    np.save(buffer, ndarray, allow_pickle=False)
+    data = buffer.getvalue()
+    return Array(
+        dtype=str(ndarray.dtype),
+        shape=list(ndarray.shape),
+        stype=SType.NUMPY,
+        data=data,
+    )
diff --git a/src/py/flwr/common/record/conversion_utils_test.py b/src/py/flwr/common/record/conversion_utils_test.py
new file mode 100644
index 000000000000..84be37fda4a3
--- /dev/null
+++ b/src/py/flwr/common/record/conversion_utils_test.py
@@ -0,0 +1,44 @@
+# 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.
+# ==============================================================================
+"""Unit tests for conversion utility functions."""
+
+
+import unittest
+from io import BytesIO
+
+import numpy as np
+
+from ..constant import SType
+from .conversion_utils import array_from_numpy
+
+
+class TestArrayFromNumpy(unittest.TestCase):
+    """Unit tests for array_from_numpy."""
+
+    def test_array_from_numpy(self) -> None:
+        """Test the array_from_numpy function."""
+        # Prepare
+        original_array = np.array([1, 2, 3], dtype=np.float32)
+
+        # Execute
+        array_instance = array_from_numpy(original_array)
+        buffer = BytesIO(array_instance.data)
+        deserialized_array = np.load(buffer, allow_pickle=False)
+
+        # Assert
+        self.assertEqual(array_instance.dtype, str(original_array.dtype))
+        self.assertEqual(array_instance.shape, list(original_array.shape))
+        self.assertEqual(array_instance.stype, SType.NUMPY)
+        np.testing.assert_array_equal(deserialized_array, original_array)
diff --git a/src/py/flwr/common/record/metricsrecord.py b/src/py/flwr/common/record/metricsrecord.py
new file mode 100644
index 000000000000..2b6e584be390
--- /dev/null
+++ b/src/py/flwr/common/record/metricsrecord.py
@@ -0,0 +1,102 @@
+# 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.
+# ==============================================================================
+"""MetricsRecord."""
+
+
+from typing import Dict, List, Optional, get_args
+
+from flwr.common.typing import MetricsRecordValues, MetricsScalar
+
+from .typeddict import TypedDict
+
+
+def _check_key(key: str) -> None:
+    """Check if key is of expected type."""
+    if not isinstance(key, str):
+        raise TypeError(f"Key must be of type `str` but `{type(key)}` was passed.")
+
+
+def _check_value(value: MetricsRecordValues) -> None:
+    def is_valid(__v: MetricsScalar) -> None:
+        """Check if value is of expected type."""
+        if not isinstance(__v, get_args(MetricsScalar)) or isinstance(__v, bool):
+            raise TypeError(
+                "Not all values are of valid type."
+                f" Expected `{MetricsRecordValues}` but `{type(__v)}` was passed."
+            )
+
+    if isinstance(value, list):
+        # If your lists are large (e.g. 1M+ elements) this will be slow
+        # 1s to check 10M element list on a M2 Pro
+        # In such settings, you'd be better of treating such metric as
+        # an array and pass it to a ParametersRecord.
+        # Empty lists are valid
+        if len(value) > 0:
+            is_valid(value[0])
+            # all elements in the list must be of the same valid type
+            # this is needed for protobuf
+            value_type = type(value[0])
+            if not all(isinstance(v, value_type) for v in value):
+                raise TypeError(
+                    "All values in a list must be of the same valid type. "
+                    f"One of {MetricsScalar}."
+                )
+    else:
+        is_valid(value)
+
+
+class MetricsRecord(TypedDict[str, MetricsRecordValues]):
+    """Metrics record."""
+
+    def __init__(
+        self,
+        metrics_dict: Optional[Dict[str, MetricsRecordValues]] = None,
+        keep_input: bool = True,
+    ):
+        """Construct a MetricsRecord object.
+
+        Parameters
+        ----------
+        metrics_dict : Optional[Dict[str, MetricsRecordValues]]
+            A dictionary that stores basic types (i.e. `int`, `float` as defined
+            in `MetricsScalar`) and list of such types (see `MetricsScalarList`).
+        keep_input : bool (default: True)
+            A boolean indicating whether metrics should be deleted from the input
+            dictionary immediately after adding them to the record. When set
+            to True, the data is duplicated in memory. If memory is a concern, set
+            it to False.
+        """
+        super().__init__(_check_key, _check_value)
+        if metrics_dict:
+            for k in list(metrics_dict.keys()):
+                self[k] = metrics_dict[k]
+                if not keep_input:
+                    del metrics_dict[k]
+
+    def count_bytes(self) -> int:
+        """Return number of Bytes stored in this object."""
+        num_bytes = 0
+
+        for k, v in self.items():
+            if isinstance(v, List):
+                # both int and float normally take 4 bytes
+                # But MetricRecords are mapped to 64bit int/float
+                # during protobuffing
+                num_bytes += 8 * len(v)
+            else:
+                num_bytes += 8
+            # We also count the bytes footprint of the keys
+            num_bytes += len(k)
+        return num_bytes
diff --git a/src/py/flwr/common/record/parametersrecord.py b/src/py/flwr/common/record/parametersrecord.py
new file mode 100644
index 000000000000..a4a71f751f97
--- /dev/null
+++ b/src/py/flwr/common/record/parametersrecord.py
@@ -0,0 +1,136 @@
+# 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.
+# ==============================================================================
+"""ParametersRecord and Array."""
+
+from dataclasses import dataclass
+from io import BytesIO
+from typing import List, Optional, OrderedDict, cast
+
+import numpy as np
+
+from ..constant import SType
+from ..typing import NDArray
+from .typeddict import TypedDict
+
+
+@dataclass
+class Array:
+    """Array type.
+
+    A dataclass containing serialized data from an array-like or tensor-like object
+    along with some metadata about it.
+
+    Parameters
+    ----------
+    dtype : str
+        A string representing the data type of the serialised object (e.g. `np.float32`)
+
+    shape : List[int]
+        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.
+
+    stype : str
+        A string indicating the type of serialisation mechanism used to generate the
+        bytes in `data` from an array-like or tensor-like object.
+
+    data: bytes
+        A buffer of bytes containing the data.
+    """
+
+    dtype: str
+    shape: List[int]
+    stype: str
+    data: bytes
+
+    def numpy(self) -> NDArray:
+        """Return the array as a NumPy array."""
+        if self.stype != SType.NUMPY:
+            raise TypeError(
+                f"Unsupported serialization type for numpy conversion: '{self.stype}'"
+            )
+        bytes_io = BytesIO(self.data)
+        # WARNING: NEVER set allow_pickle to true.
+        # Reason: loading pickled data can execute arbitrary code
+        # Source: https://numpy.org/doc/stable/reference/generated/numpy.load.html
+        ndarray_deserialized = np.load(bytes_io, allow_pickle=False)
+        return cast(NDArray, ndarray_deserialized)
+
+
+def _check_key(key: str) -> None:
+    """Check if key is of expected type."""
+    if not isinstance(key, str):
+        raise TypeError(f"Key must be of type `str` but `{type(key)}` was passed.")
+
+
+def _check_value(value: Array) -> None:
+    if not isinstance(value, Array):
+        raise TypeError(
+            f"Value must be of type `{Array}` but `{type(value)}` was passed."
+        )
+
+
+@dataclass
+class ParametersRecord(TypedDict[str, Array]):
+    """Parameters record.
+
+    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.
+    """
+
+    def __init__(
+        self,
+        array_dict: Optional[OrderedDict[str, Array]] = None,
+        keep_input: bool = False,
+    ) -> None:
+        """Construct a ParametersRecord object.
+
+        Parameters
+        ----------
+        array_dict : Optional[OrderedDict[str, Array]]
+            A dictionary that stores serialized array-like or tensor-like objects.
+        keep_input : bool (default: False)
+            A boolean indicating whether parameters should be deleted from the input
+            dictionary immediately after adding them to the record. If False, the
+            dictionary passed to `set_parameters()` will be empty once exiting from that
+            function. This is the desired behaviour when working with very large
+            models/tensors/arrays. However, if you plan to continue working with your
+            parameters after adding it to the record, set this flag to True. When set
+            to True, the data is duplicated in memory.
+        """
+        super().__init__(_check_key, _check_value)
+        if array_dict:
+            for k in list(array_dict.keys()):
+                self[k] = array_dict[k]
+                if not keep_input:
+                    del array_dict[k]
+
+    def count_bytes(self) -> int:
+        """Return number of Bytes stored in this object.
+
+        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.
+        """
+        num_bytes = 0
+
+        for k, v in self.items():
+            num_bytes += len(v.data)
+
+            # We also count the bytes footprint of the keys
+            num_bytes += len(k)
+
+        return num_bytes
diff --git a/src/py/flwr/common/record/parametersrecord_test.py b/src/py/flwr/common/record/parametersrecord_test.py
new file mode 100644
index 000000000000..e840e5e266e4
--- /dev/null
+++ b/src/py/flwr/common/record/parametersrecord_test.py
@@ -0,0 +1,101 @@
+# 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.
+# ==============================================================================
+"""Unit tests for ParametersRecord and Array."""
+
+import unittest
+from collections import OrderedDict
+from io import BytesIO
+from typing import List
+
+import numpy as np
+import pytest
+
+from flwr.common import ndarray_to_bytes
+
+from ..constant import SType
+from ..typing import NDArray
+from .parametersrecord import Array, ParametersRecord
+
+
+def _get_buffer_from_ndarray(array: NDArray) -> bytes:
+    """Return a bytes buffer froma given NumPy array."""
+    buffer = BytesIO()
+    np.save(buffer, array, allow_pickle=False)
+    return buffer.getvalue()
+
+
+class TestArray(unittest.TestCase):
+    """Unit tests for Array."""
+
+    def test_numpy_conversion_valid(self) -> None:
+        """Test the numpy method with valid Array instance."""
+        # Prepare
+        original_array = np.array([1, 2, 3], dtype=np.float32)
+
+        buffer = _get_buffer_from_ndarray(original_array)
+
+        # Execute
+        array_instance = Array(
+            dtype=str(original_array.dtype),
+            shape=list(original_array.shape),
+            stype=SType.NUMPY,
+            data=buffer,
+        )
+        converted_array = array_instance.numpy()
+
+        # Assert
+        np.testing.assert_array_equal(converted_array, original_array)
+
+    def test_numpy_conversion_invalid(self) -> None:
+        """Test the numpy method with invalid Array instance."""
+        # Prepare
+        array_instance = Array(
+            dtype="float32",
+            shape=[3],
+            stype="invalid_stype",  # Non-numpy stype
+            data=b"",
+        )
+
+        # Execute and assert
+        with self.assertRaises(TypeError):
+            array_instance.numpy()
+
+
+@pytest.mark.parametrize(
+    "shape, dtype",
+    [
+        ([100], "float32"),
+        ([31, 31], "int8"),
+        ([31, 153], "bool_"),  # bool_ is represented as a whole Byte in NumPy
+    ],
+)
+def test_count_bytes(shape: List[int], dtype: str) -> None:
+    """Test bytes in a ParametersRecord are computed correctly."""
+    original_array = np.random.randn(*shape).astype(np.dtype(dtype))
+
+    buff = ndarray_to_bytes(original_array)
+
+    buffer = _get_buffer_from_ndarray(original_array)
+
+    array_instance = Array(
+        dtype=str(original_array.dtype),
+        shape=list(original_array.shape),
+        stype=SType.NUMPY,
+        data=buffer,
+    )
+    key_name = "data"
+    p_record = ParametersRecord(OrderedDict({key_name: array_instance}))
+
+    assert len(buff) + len(key_name) == p_record.count_bytes()
diff --git a/src/py/flwr/common/record/recordset.py b/src/py/flwr/common/record/recordset.py
new file mode 100644
index 000000000000..d8ef44ab15c2
--- /dev/null
+++ b/src/py/flwr/common/record/recordset.py
@@ -0,0 +1,79 @@
+# 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.
+# ==============================================================================
+"""RecordSet."""
+
+
+from dataclasses import dataclass
+from typing import Callable, Dict, Optional, Type, TypeVar
+
+from .configsrecord import ConfigsRecord
+from .metricsrecord import MetricsRecord
+from .parametersrecord import ParametersRecord
+from .typeddict import TypedDict
+
+T = TypeVar("T")
+
+
+@dataclass
+class RecordSet:
+    """RecordSet stores groups of parameters, metrics and configs."""
+
+    _parameters_records: TypedDict[str, ParametersRecord]
+    _metrics_records: TypedDict[str, MetricsRecord]
+    _configs_records: TypedDict[str, ConfigsRecord]
+
+    def __init__(
+        self,
+        parameters_records: Optional[Dict[str, ParametersRecord]] = None,
+        metrics_records: Optional[Dict[str, MetricsRecord]] = None,
+        configs_records: Optional[Dict[str, ConfigsRecord]] = None,
+    ) -> None:
+        def _get_check_fn(__t: Type[T]) -> Callable[[T], None]:
+            def _check_fn(__v: T) -> None:
+                if not isinstance(__v, __t):
+                    raise TypeError(f"Expected `{__t}`, but `{type(__v)}` was passed.")
+
+            return _check_fn
+
+        self._parameters_records = TypedDict[str, ParametersRecord](
+            _get_check_fn(str), _get_check_fn(ParametersRecord)
+        )
+        self._metrics_records = TypedDict[str, MetricsRecord](
+            _get_check_fn(str), _get_check_fn(MetricsRecord)
+        )
+        self._configs_records = TypedDict[str, ConfigsRecord](
+            _get_check_fn(str), _get_check_fn(ConfigsRecord)
+        )
+        if parameters_records is not None:
+            self._parameters_records.update(parameters_records)
+        if metrics_records is not None:
+            self._metrics_records.update(metrics_records)
+        if configs_records is not None:
+            self._configs_records.update(configs_records)
+
+    @property
+    def parameters_records(self) -> TypedDict[str, ParametersRecord]:
+        """Dictionary holding ParametersRecord instances."""
+        return self._parameters_records
+
+    @property
+    def metrics_records(self) -> TypedDict[str, MetricsRecord]:
+        """Dictionary holding MetricsRecord instances."""
+        return self._metrics_records
+
+    @property
+    def configs_records(self) -> TypedDict[str, ConfigsRecord]:
+        """Dictionary holding ConfigsRecord instances."""
+        return self._configs_records
diff --git a/src/py/flwr/common/record/recordset_test.py b/src/py/flwr/common/record/recordset_test.py
new file mode 100644
index 000000000000..0e0b149881be
--- /dev/null
+++ b/src/py/flwr/common/record/recordset_test.py
@@ -0,0 +1,400 @@
+# 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.
+# ==============================================================================
+"""RecordSet tests."""
+
+from copy import deepcopy
+from typing import Callable, Dict, List, OrderedDict, Type, Union
+
+import numpy as np
+import pytest
+
+from flwr.common.parameter import ndarrays_to_parameters, parameters_to_ndarrays
+from flwr.common.recordset_compat import (
+    parameters_to_parametersrecord,
+    parametersrecord_to_parameters,
+)
+from flwr.common.typing import (
+    ConfigsRecordValues,
+    MetricsRecordValues,
+    NDArray,
+    NDArrays,
+    Parameters,
+)
+
+from . import Array, ConfigsRecord, MetricsRecord, ParametersRecord
+
+
+def get_ndarrays() -> NDArrays:
+    """Return list of NumPy arrays."""
+    arr1 = np.array([[1.0, 2.0], [3.0, 4], [5.0, 6.0]])
+    arr2 = np.eye(2, 7, 3)
+
+    return [arr1, arr2]
+
+
+def ndarray_to_array(ndarray: NDArray) -> Array:
+    """Represent NumPy ndarray as Array."""
+    return Array(
+        data=ndarray.tobytes(),
+        dtype=str(ndarray.dtype),
+        stype="numpy.ndarray.tobytes",
+        shape=list(ndarray.shape),
+    )
+
+
+def test_ndarray_to_array() -> None:
+    """Test creation of Array object from NumPy ndarray."""
+    shape = (2, 7, 9)
+    arr = np.eye(*shape)
+
+    array = ndarray_to_array(arr)
+
+    arr_ = np.frombuffer(buffer=array.data, dtype=array.dtype).reshape(array.shape)
+
+    assert np.array_equal(arr, arr_)
+
+
+def test_parameters_to_array_and_back() -> None:
+    """Test conversion between legacy Parameters and Array."""
+    ndarrays = get_ndarrays()
+
+    # Array represents a single array, unlike parameters, which represent a
+    # list of arrays
+    ndarray = ndarrays[0]
+
+    parameters = ndarrays_to_parameters([ndarray])
+
+    array = Array(
+        data=parameters.tensors[0], dtype="", stype=parameters.tensor_type, shape=[]
+    )
+
+    parameters = Parameters(tensors=[array.data], tensor_type=array.stype)
+
+    ndarray_ = parameters_to_ndarrays(parameters=parameters)[0]
+
+    assert np.array_equal(ndarray, ndarray_)
+
+
+@pytest.mark.parametrize(
+    "keep_input, validate_freed_fn",
+    [
+        (False, lambda x, x_copy, y: len(x.tensors) == 0),  # check tensors were freed
+        (True, lambda x, x_copy, y: x.tensors == y.tensors),  # check they are equal
+    ],
+)
+def test_parameters_to_parametersrecord_and_back(
+    keep_input: bool,
+    validate_freed_fn: Callable[[Parameters, Parameters, Parameters], bool],
+) -> None:
+    """Test conversion between legacy Parameters and ParametersRecords."""
+    ndarrays = get_ndarrays()
+
+    parameters = ndarrays_to_parameters(ndarrays)
+    parameters_copy = deepcopy(parameters)
+
+    params_record = parameters_to_parametersrecord(
+        parameters=parameters, keep_input=keep_input
+    )
+
+    parameters_ = parametersrecord_to_parameters(params_record, keep_input=keep_input)
+
+    ndarrays_ = parameters_to_ndarrays(parameters=parameters_)
+
+    # Validate returned NDArrays match those at the beginning
+    for arr, arr_ in zip(ndarrays, ndarrays_):
+        assert np.array_equal(arr, arr_), "no"
+
+    # Validate initial Parameters object has been handled according to `keep_input`
+    assert validate_freed_fn(parameters, parameters_copy, parameters_)
+
+
+def test_set_parameters_while_keeping_intputs() -> None:
+    """Tests keep_input functionality in ParametersRecord."""
+    # Adding parameters to a record that doesn't erase entries in the input `array_dict`
+    array_dict = OrderedDict(
+        {str(i): ndarray_to_array(ndarray) for i, ndarray in enumerate(get_ndarrays())}
+    )
+    p_record = ParametersRecord(array_dict, keep_input=True)
+
+    # Creating a second parametersrecord passing the same `array_dict` (not erased)
+    p_record_2 = ParametersRecord(array_dict)
+    assert p_record == p_record_2
+
+    # Now it should be empty (the second ParametersRecord wasn't flagged to keep it)
+    assert len(array_dict) == 0
+
+
+def test_set_parameters_with_correct_types() -> None:
+    """Test adding dictionary of Arrays to ParametersRecord."""
+    p_record = ParametersRecord()
+    array_dict = OrderedDict(
+        {str(i): ndarray_to_array(ndarray) for i, ndarray in enumerate(get_ndarrays())}
+    )
+    p_record.update(array_dict)
+
+
+@pytest.mark.parametrize(
+    "key_type, value_fn",
+    [
+        (str, lambda x: x),  # correct key, incorrect value
+        (str, lambda x: x.tolist()),  # correct key, incorrect value
+        (int, ndarray_to_array),  # incorrect key, correct value
+        (int, lambda x: x),  # incorrect key, incorrect value
+        (int, lambda x: x.tolist()),  # incorrect key, incorrect value
+    ],
+)
+def test_set_parameters_with_incorrect_types(
+    key_type: Type[Union[int, str]],
+    value_fn: Callable[[NDArray], Union[NDArray, List[float]]],
+) -> None:
+    """Test adding dictionary of unsupported types to ParametersRecord."""
+    p_record = ParametersRecord()
+
+    array_dict = {
+        key_type(i): value_fn(ndarray) for i, ndarray in enumerate(get_ndarrays())
+    }
+
+    with pytest.raises(TypeError):
+        p_record.update(array_dict)
+
+
+@pytest.mark.parametrize(
+    "key_type, value_fn",
+    [
+        (str, lambda x: int(x.flatten()[0])),  # str: int
+        (str, lambda x: float(x.flatten()[0])),  # str: float
+        (str, lambda x: x.flatten().astype("int").tolist()),  # str: List[int]
+        (str, lambda x: x.flatten().astype("float").tolist()),  # str: List[float]
+        (str, lambda x: []),  # str: empty list
+    ],
+)
+def test_set_metrics_to_metricsrecord_with_correct_types(
+    key_type: Type[str],
+    value_fn: Callable[[NDArray], MetricsRecordValues],
+) -> None:
+    """Test adding metrics of various types to a MetricsRecord."""
+    m_record = MetricsRecord()
+
+    labels = [1, 2.0]
+    arrays = get_ndarrays()
+
+    my_metrics = OrderedDict(
+        {key_type(label): value_fn(arr) for label, arr in zip(labels, arrays)}
+    )
+
+    # Add metric
+    m_record.update(my_metrics)
+
+    # Check metrics are actually added
+    assert my_metrics == m_record
+
+
+@pytest.mark.parametrize(
+    "key_type, value_fn",
+    [
+        (str, lambda x: str(x.flatten()[0])),  # str: str  (supported: unsupported)
+        (str, lambda x: bool(x.flatten()[0])),  # str: bool  (supported: unsupported)
+        (
+            str,
+            lambda x: x.flatten().astype("str").tolist(),
+        ),  # str: List[str] (supported: unsupported)
+        (str, lambda x: x),  # str: NDArray (supported: unsupported)
+        (
+            str,
+            lambda x: {str(v): v for v in x.flatten()},
+        ),  # str: dict[str: float] (supported: unsupported)
+        (
+            str,
+            lambda x: [{str(v): v for v in x.flatten()}],
+        ),  # str: List[dict[str: float]] (supported: unsupported)
+        (
+            str,
+            lambda x: [1, 2.0, 3.0, 4],
+        ),  # str: List[mixing valid types] (supported: unsupported)
+        (
+            int,
+            lambda x: x.flatten().tolist(),
+        ),  # int: List[str] (unsupported: supported)
+        (
+            float,
+            lambda x: x.flatten().tolist(),
+        ),  # float: List[int] (unsupported: supported)
+    ],
+)
+def test_set_metrics_to_metricsrecord_with_incorrect_types(
+    key_type: Type[Union[str, int, float, bool]],
+    value_fn: Callable[[NDArray], Union[NDArray, Dict[str, NDArray], List[float]]],
+) -> None:
+    """Test adding metrics of various unsupported types to a MetricsRecord."""
+    m_record = MetricsRecord()
+
+    labels = [1, 2.0]
+    arrays = get_ndarrays()
+
+    my_metrics = OrderedDict(
+        {key_type(label): value_fn(arr) for label, arr in zip(labels, arrays)}
+    )
+
+    with pytest.raises(TypeError):
+        m_record.update(my_metrics)
+
+
+@pytest.mark.parametrize(
+    "keep_input",
+    [
+        (True),
+        (False),
+    ],
+)
+def test_set_metrics_to_metricsrecord_with_and_without_keeping_input(
+    keep_input: bool,
+) -> None:
+    """Test keep_input functionality for MetricsRecord."""
+    # constructing a valid input
+    labels = [1, 2.0]
+    arrays = get_ndarrays()
+    my_metrics = OrderedDict(
+        {str(label): arr.flatten().tolist() for label, arr in zip(labels, arrays)}
+    )
+
+    my_metrics_copy = my_metrics.copy()
+
+    # Add metric
+    m_record = MetricsRecord(my_metrics, keep_input=keep_input)
+
+    # Check metrics are actually added
+    # Check that input dict has been emptied when enabled such behaviour
+    if keep_input:
+        assert my_metrics == m_record
+    else:
+        assert my_metrics_copy == m_record
+        assert len(my_metrics) == 0
+
+
+@pytest.mark.parametrize(
+    "key_type, value_fn",
+    [
+        (str, lambda x: str(x.flatten()[0])),  # str: str
+        (str, lambda x: int(x.flatten()[0])),  # str: int
+        (str, lambda x: float(x.flatten()[0])),  # str: float
+        (str, lambda x: bool(x.flatten()[0])),  # str: bool
+        (str, lambda x: x.flatten().tobytes()),  # str: bytes
+        (str, lambda x: x.flatten().astype("str").tolist()),  # str: List[str]
+        (str, lambda x: x.flatten().astype("int").tolist()),  # str: List[int]
+        (str, lambda x: x.flatten().astype("float").tolist()),  # str: List[float]
+        (str, lambda x: x.flatten().astype("bool").tolist()),  # str: List[bool]
+        (str, lambda x: [x.flatten().tobytes()]),  # str: List[bytes]
+        (str, lambda x: []),  # str: emptyt list
+    ],
+)
+def test_set_configs_to_configsrecord_with_correct_types(
+    key_type: Type[str],
+    value_fn: Callable[[NDArray], ConfigsRecordValues],
+) -> None:
+    """Test adding configs of various types to a ConfigsRecord."""
+    labels = [1, 2.0]
+    arrays = get_ndarrays()
+
+    my_configs = OrderedDict(
+        {key_type(label): value_fn(arr) for label, arr in zip(labels, arrays)}
+    )
+
+    c_record = ConfigsRecord(my_configs)
+
+    # check values are actually there
+    assert c_record == my_configs
+
+
+@pytest.mark.parametrize(
+    "key_type, value_fn",
+    [
+        (str, lambda x: x),  # str: NDArray (supported: unsupported)
+        (
+            str,
+            lambda x: {str(v): v for v in x.flatten()},
+        ),  # str: dict[str: float] (supported: unsupported)
+        (
+            str,
+            lambda x: [{str(v): v for v in x.flatten()}],
+        ),  # str: List[dict[str: float]] (supported: unsupported)
+        (
+            str,
+            lambda x: [1, 2.0, 3.0, 4],
+        ),  # str: List[mixing valid types] (supported: unsupported)
+        (
+            int,
+            lambda x: x.flatten().tolist(),
+        ),  # int: List[str] (unsupported: supported)
+        (
+            float,
+            lambda x: x.flatten().tolist(),
+        ),  # float: List[int] (unsupported: supported)
+    ],
+)
+def test_set_configs_to_configsrecord_with_incorrect_types(
+    key_type: Type[Union[str, int, float]],
+    value_fn: Callable[[NDArray], Union[NDArray, Dict[str, NDArray], List[float]]],
+) -> None:
+    """Test adding configs of various unsupported types to a ConfigsRecord."""
+    c_record = ConfigsRecord()
+
+    labels = [1, 2.0]
+    arrays = get_ndarrays()
+
+    my_configs = OrderedDict(
+        {key_type(label): value_fn(arr) for label, arr in zip(labels, arrays)}
+    )
+
+    with pytest.raises(TypeError):
+        c_record.update(my_configs)
+
+
+def test_count_bytes_metricsrecord() -> None:
+    """Test counting bytes in MetricsRecord."""
+    data = {"a": 1, "b": 2.0, "c": [1, 2, 3], "d": [1.0, 2.0, 3.0, 4.0, 5.0]}
+    bytes_in_dict = 8 + 8 + 3 * 8 + 5 * 8
+    bytes_in_dict += 4  # represnting the keys
+
+    m_record = MetricsRecord()
+    m_record.update(OrderedDict(data))
+    record_bytest_count = m_record.count_bytes()
+    assert bytes_in_dict == record_bytest_count
+
+
+def test_count_bytes_configsrecord() -> None:
+    """Test counting bytes in ConfigsRecord."""
+    data = {"a": 1, "b": 2.0, "c": [1, 2, 3], "d": [1.0, 2.0, 3.0, 4.0, 5.0]}
+    bytes_in_dict = 8 + 8 + 3 * 8 + 5 * 8
+    bytes_in_dict += 4  # represnting the keys
+
+    to_add = {
+        "aa": True,
+        "bb": "False",
+        "cc": bytes(9),
+        "dd": [True, False, False],
+        "ee": ["True", "False"],
+        "ff": [bytes(1), bytes(13), bytes(51)],
+    }
+    data = {**data, **to_add}
+    bytes_in_dict += 1 + 5 + 9 + 3 + (4 + 5) + (1 + 13 + 51)
+    bytes_in_dict += 12  # represnting the keys
+
+    bytes_in_dict = int(bytes_in_dict)
+
+    c_record = ConfigsRecord()
+    c_record.update(OrderedDict(data))
+
+    record_bytest_count = c_record.count_bytes()
+    assert bytes_in_dict == record_bytest_count
diff --git a/src/py/flwr/common/record/typeddict.py b/src/py/flwr/common/record/typeddict.py
new file mode 100644
index 000000000000..23d70dc4f7e8
--- /dev/null
+++ b/src/py/flwr/common/record/typeddict.py
@@ -0,0 +1,113 @@
+# 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.
+# ==============================================================================
+"""Typed dict base class for *Records."""
+
+
+from typing import Any, Callable, Dict, Generic, Iterator, Tuple, TypeVar, cast
+
+K = TypeVar("K")  # Key type
+V = TypeVar("V")  # Value type
+
+
+class TypedDict(Generic[K, V]):
+    """Typed dictionary."""
+
+    def __init__(
+        self, check_key_fn: Callable[[K], None], check_value_fn: Callable[[V], None]
+    ):
+        self._data: Dict[K, V] = {}
+        self._check_key_fn = check_key_fn
+        self._check_value_fn = check_value_fn
+
+    def __setitem__(self, key: K, value: V) -> None:
+        """Set the given key to the given value after type checking."""
+        # Check the types of key and value
+        self._check_key_fn(key)
+        self._check_value_fn(value)
+        # Set key-value pair
+        self._data[key] = value
+
+    def __delitem__(self, key: K) -> None:
+        """Remove the item with the specified key."""
+        del self._data[key]
+
+    def __getitem__(self, item: K) -> V:
+        """Return the value for the specified key."""
+        return self._data[item]
+
+    def __iter__(self) -> Iterator[K]:
+        """Yield an iterator over the keys of the dictionary."""
+        return iter(self._data)
+
+    def __repr__(self) -> str:
+        """Return a string representation of the dictionary."""
+        return self._data.__repr__()
+
+    def __len__(self) -> int:
+        """Return the number of items in the dictionary."""
+        return len(self._data)
+
+    def __contains__(self, key: K) -> bool:
+        """Check if the dictionary contains the specified key."""
+        return key in self._data
+
+    def __eq__(self, other: object) -> bool:
+        """Compare this instance to another dictionary or TypedDict."""
+        if isinstance(other, TypedDict):
+            return self._data == other._data
+        if isinstance(other, dict):
+            return self._data == other
+        return NotImplemented
+
+    def items(self) -> Iterator[Tuple[K, V]]:
+        """R.items() -> a set-like object providing a view on R's items."""
+        return cast(Iterator[Tuple[K, V]], self._data.items())
+
+    def keys(self) -> Iterator[K]:
+        """R.keys() -> a set-like object providing a view on R's keys."""
+        return cast(Iterator[K], self._data.keys())
+
+    def values(self) -> Iterator[V]:
+        """R.values() -> an object providing a view on R's values."""
+        return cast(Iterator[V], self._data.values())
+
+    def update(self, *args: Any, **kwargs: Any) -> None:
+        """R.update([E, ]**F) -> None.
+
+        Update R from dict/iterable E and F.
+        """
+        for key, value in dict(*args, **kwargs).items():
+            self[key] = value
+
+    def pop(self, key: K) -> V:
+        """R.pop(k[,d]) -> v, remove specified key and return the corresponding value.
+
+        If key is not found, d is returned if given, otherwise KeyError is raised.
+        """
+        return self._data.pop(key)
+
+    def get(self, key: K, default: V) -> V:
+        """R.get(k[,d]) -> R[k] if k in R, else d.
+
+        d defaults to None.
+        """
+        return self._data.get(key, default)
+
+    def clear(self) -> None:
+        """R.clear() -> None.
+
+        Remove all items from R.
+        """
+        self._data.clear()
diff --git a/src/py/flwr/common/recordset_compat.py b/src/py/flwr/common/recordset_compat.py
new file mode 100644
index 000000000000..394ea1353bab
--- /dev/null
+++ b/src/py/flwr/common/recordset_compat.py
@@ -0,0 +1,399 @@
+# 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.
+# ==============================================================================
+"""RecordSet utilities."""
+
+
+from typing import Dict, Mapping, OrderedDict, Tuple, Union, cast, get_args
+
+from . import Array, ConfigsRecord, MetricsRecord, ParametersRecord, RecordSet
+from .typing import (
+    Code,
+    ConfigsRecordValues,
+    EvaluateIns,
+    EvaluateRes,
+    FitIns,
+    FitRes,
+    GetParametersIns,
+    GetParametersRes,
+    GetPropertiesIns,
+    GetPropertiesRes,
+    MetricsRecordValues,
+    Parameters,
+    Scalar,
+    Status,
+)
+
+
+def parametersrecord_to_parameters(
+    record: ParametersRecord, keep_input: bool
+) -> Parameters:
+    """Convert ParameterRecord to legacy Parameters.
+
+    Warnings
+    --------
+    Because `Arrays` in `ParametersRecord` encode more information of the
+    array-like or tensor-like data (e.g their datatype, shape) than `Parameters` it
+    might not be possible to reconstruct such data structures from `Parameters` objects
+    alone. Additional information or metadata must be provided from elsewhere.
+
+    Parameters
+    ----------
+    record : ParametersRecord
+        The record to be conveted into Parameters.
+    keep_input : bool
+        A boolean indicating whether entries in the record should be deleted from the
+        input dictionary immediately after adding them to the record.
+    """
+    parameters = Parameters(tensors=[], tensor_type="")
+
+    for key in list(record.keys()):
+        parameters.tensors.append(record[key].data)
+
+        if not parameters.tensor_type:
+            # Setting from first array in record. Recall the warning in the docstrings
+            # of this function.
+            parameters.tensor_type = record[key].stype
+
+        if not keep_input:
+            del record[key]
+
+    return parameters
+
+
+def parameters_to_parametersrecord(
+    parameters: Parameters, keep_input: bool
+) -> ParametersRecord:
+    """Convert legacy Parameters into a single ParametersRecord.
+
+    Because there is no concept of names in the legacy Parameters, arbitrary keys will
+    be used when constructing the ParametersRecord. Similarly, the shape and data type
+    won't be recorded in the Array objects.
+
+    Parameters
+    ----------
+    parameters : Parameters
+        Parameters object to be represented as a ParametersRecord.
+    keep_input : bool
+        A boolean indicating whether parameters should be deleted from the input
+        Parameters object (i.e. a list of serialized NumPy arrays) immediately after
+        adding them to the record.
+    """
+    tensor_type = parameters.tensor_type
+
+    num_arrays = len(parameters.tensors)
+    ordered_dict = OrderedDict()
+    for idx in range(num_arrays):
+        if keep_input:
+            tensor = parameters.tensors[idx]
+        else:
+            tensor = parameters.tensors.pop(0)
+        ordered_dict[str(idx)] = Array(
+            data=tensor, dtype="", stype=tensor_type, shape=[]
+        )
+
+    return ParametersRecord(ordered_dict, keep_input=keep_input)
+
+
+def _check_mapping_from_recordscalartype_to_scalar(
+    record_data: Mapping[str, Union[ConfigsRecordValues, MetricsRecordValues]]
+) -> Dict[str, Scalar]:
+    """Check mapping `common.*RecordValues` into `common.Scalar` is possible."""
+    for value in record_data.values():
+        if not isinstance(value, get_args(Scalar)):
+            raise TypeError(
+                "There is not a 1:1 mapping between `common.Scalar` types and those "
+                "supported in `common.ConfigsRecordValues` or "
+                "`common.ConfigsRecordValues`. Consider casting your values to a type "
+                "supported by the `common.RecordSet` infrastructure. "
+                f"You used type: {type(value)}"
+            )
+    return cast(Dict[str, Scalar], record_data)
+
+
+def _recordset_to_fit_or_evaluate_ins_components(
+    recordset: RecordSet,
+    ins_str: str,
+    keep_input: bool,
+) -> Tuple[Parameters, Dict[str, Scalar]]:
+    """Derive Fit/Evaluate Ins from a RecordSet."""
+    # get Array and construct Parameters
+    parameters_record = recordset.parameters_records[f"{ins_str}.parameters"]
+
+    parameters = parametersrecord_to_parameters(
+        parameters_record, keep_input=keep_input
+    )
+
+    # get config dict
+    config_record = recordset.configs_records[f"{ins_str}.config"]
+    # pylint: disable-next=protected-access
+    config_dict = _check_mapping_from_recordscalartype_to_scalar(config_record._data)
+
+    return parameters, config_dict
+
+
+def _fit_or_evaluate_ins_to_recordset(
+    ins: Union[FitIns, EvaluateIns], keep_input: bool
+) -> RecordSet:
+    recordset = RecordSet()
+
+    ins_str = "fitins" if isinstance(ins, FitIns) else "evaluateins"
+    parametersrecord = parameters_to_parametersrecord(ins.parameters, keep_input)
+    recordset.parameters_records[f"{ins_str}.parameters"] = parametersrecord
+
+    recordset.configs_records[f"{ins_str}.config"] = ConfigsRecord(
+        ins.config  # type: ignore
+    )
+
+    return recordset
+
+
+def _embed_status_into_recordset(
+    res_str: str, status: Status, recordset: RecordSet
+) -> RecordSet:
+    status_dict: Dict[str, ConfigsRecordValues] = {
+        "code": int(status.code.value),
+        "message": status.message,
+    }
+    # we add it to a `ConfigsRecord`` because the `status.message`` is a string
+    # and `str` values aren't supported in `MetricsRecords`
+    recordset.configs_records[f"{res_str}.status"] = ConfigsRecord(status_dict)
+    return recordset
+
+
+def _extract_status_from_recordset(res_str: str, recordset: RecordSet) -> Status:
+    status = recordset.configs_records[f"{res_str}.status"]
+    code = cast(int, status["code"])
+    return Status(code=Code(code), message=str(status["message"]))
+
+
+def recordset_to_fitins(recordset: RecordSet, keep_input: bool) -> FitIns:
+    """Derive FitIns from a RecordSet object."""
+    parameters, config = _recordset_to_fit_or_evaluate_ins_components(
+        recordset,
+        ins_str="fitins",
+        keep_input=keep_input,
+    )
+
+    return FitIns(parameters=parameters, config=config)
+
+
+def fitins_to_recordset(fitins: FitIns, keep_input: bool) -> RecordSet:
+    """Construct a RecordSet from a FitIns object."""
+    return _fit_or_evaluate_ins_to_recordset(fitins, keep_input)
+
+
+def recordset_to_fitres(recordset: RecordSet, keep_input: bool) -> FitRes:
+    """Derive FitRes from a RecordSet object."""
+    ins_str = "fitres"
+    parameters = parametersrecord_to_parameters(
+        recordset.parameters_records[f"{ins_str}.parameters"], keep_input=keep_input
+    )
+
+    num_examples = cast(
+        int, recordset.metrics_records[f"{ins_str}.num_examples"]["num_examples"]
+    )
+    configs_record = recordset.configs_records[f"{ins_str}.metrics"]
+    # pylint: disable-next=protected-access
+    metrics = _check_mapping_from_recordscalartype_to_scalar(configs_record._data)
+    status = _extract_status_from_recordset(ins_str, recordset)
+
+    return FitRes(
+        status=status, parameters=parameters, num_examples=num_examples, metrics=metrics
+    )
+
+
+def fitres_to_recordset(fitres: FitRes, keep_input: bool) -> RecordSet:
+    """Construct a RecordSet from a FitRes object."""
+    recordset = RecordSet()
+
+    res_str = "fitres"
+
+    recordset.configs_records[f"{res_str}.metrics"] = ConfigsRecord(
+        fitres.metrics  # type: ignore
+    )
+    recordset.metrics_records[f"{res_str}.num_examples"] = MetricsRecord(
+        {"num_examples": fitres.num_examples},
+    )
+    recordset.parameters_records[f"{res_str}.parameters"] = (
+        parameters_to_parametersrecord(
+            fitres.parameters,
+            keep_input,
+        )
+    )
+
+    # status
+    recordset = _embed_status_into_recordset(res_str, fitres.status, recordset)
+
+    return recordset
+
+
+def recordset_to_evaluateins(recordset: RecordSet, keep_input: bool) -> EvaluateIns:
+    """Derive EvaluateIns from a RecordSet object."""
+    parameters, config = _recordset_to_fit_or_evaluate_ins_components(
+        recordset,
+        ins_str="evaluateins",
+        keep_input=keep_input,
+    )
+
+    return EvaluateIns(parameters=parameters, config=config)
+
+
+def evaluateins_to_recordset(evaluateins: EvaluateIns, keep_input: bool) -> RecordSet:
+    """Construct a RecordSet from a EvaluateIns object."""
+    return _fit_or_evaluate_ins_to_recordset(evaluateins, keep_input)
+
+
+def recordset_to_evaluateres(recordset: RecordSet) -> EvaluateRes:
+    """Derive EvaluateRes from a RecordSet object."""
+    ins_str = "evaluateres"
+
+    loss = cast(int, recordset.metrics_records[f"{ins_str}.loss"]["loss"])
+
+    num_examples = cast(
+        int, recordset.metrics_records[f"{ins_str}.num_examples"]["num_examples"]
+    )
+    configs_record = recordset.configs_records[f"{ins_str}.metrics"]
+
+    # pylint: disable-next=protected-access
+    metrics = _check_mapping_from_recordscalartype_to_scalar(configs_record._data)
+    status = _extract_status_from_recordset(ins_str, recordset)
+
+    return EvaluateRes(
+        status=status, loss=loss, num_examples=num_examples, metrics=metrics
+    )
+
+
+def evaluateres_to_recordset(evaluateres: EvaluateRes) -> RecordSet:
+    """Construct a RecordSet from a EvaluateRes object."""
+    recordset = RecordSet()
+
+    res_str = "evaluateres"
+    # loss
+    recordset.metrics_records[f"{res_str}.loss"] = MetricsRecord(
+        {"loss": evaluateres.loss},
+    )
+
+    # num_examples
+    recordset.metrics_records[f"{res_str}.num_examples"] = MetricsRecord(
+        {"num_examples": evaluateres.num_examples},
+    )
+
+    # metrics
+    recordset.configs_records[f"{res_str}.metrics"] = ConfigsRecord(
+        evaluateres.metrics,  # type: ignore
+    )
+
+    # status
+    recordset = _embed_status_into_recordset(
+        f"{res_str}", evaluateres.status, recordset
+    )
+
+    return recordset
+
+
+def recordset_to_getparametersins(recordset: RecordSet) -> GetParametersIns:
+    """Derive GetParametersIns from a RecordSet object."""
+    config_record = recordset.configs_records["getparametersins.config"]
+    # pylint: disable-next=protected-access
+    config_dict = _check_mapping_from_recordscalartype_to_scalar(config_record._data)
+
+    return GetParametersIns(config=config_dict)
+
+
+def getparametersins_to_recordset(getparameters_ins: GetParametersIns) -> RecordSet:
+    """Construct a RecordSet from a GetParametersIns object."""
+    recordset = RecordSet()
+
+    recordset.configs_records["getparametersins.config"] = ConfigsRecord(
+        getparameters_ins.config,  # type: ignore
+    )
+    return recordset
+
+
+def getparametersres_to_recordset(
+    getparametersres: GetParametersRes, keep_input: bool
+) -> RecordSet:
+    """Construct a RecordSet from a GetParametersRes object."""
+    recordset = RecordSet()
+    res_str = "getparametersres"
+    parameters_record = parameters_to_parametersrecord(
+        getparametersres.parameters, keep_input=keep_input
+    )
+    recordset.parameters_records[f"{res_str}.parameters"] = parameters_record
+
+    # status
+    recordset = _embed_status_into_recordset(
+        res_str, getparametersres.status, recordset
+    )
+
+    return recordset
+
+
+def recordset_to_getparametersres(
+    recordset: RecordSet, keep_input: bool
+) -> GetParametersRes:
+    """Derive GetParametersRes from a RecordSet object."""
+    res_str = "getparametersres"
+    parameters = parametersrecord_to_parameters(
+        recordset.parameters_records[f"{res_str}.parameters"], keep_input=keep_input
+    )
+
+    status = _extract_status_from_recordset(res_str, recordset)
+    return GetParametersRes(status=status, parameters=parameters)
+
+
+def recordset_to_getpropertiesins(recordset: RecordSet) -> GetPropertiesIns:
+    """Derive GetPropertiesIns from a RecordSet object."""
+    config_record = recordset.configs_records["getpropertiesins.config"]
+    # pylint: disable-next=protected-access
+    config_dict = _check_mapping_from_recordscalartype_to_scalar(config_record._data)
+
+    return GetPropertiesIns(config=config_dict)
+
+
+def getpropertiesins_to_recordset(getpropertiesins: GetPropertiesIns) -> RecordSet:
+    """Construct a RecordSet from a GetPropertiesRes object."""
+    recordset = RecordSet()
+    recordset.configs_records["getpropertiesins.config"] = ConfigsRecord(
+        getpropertiesins.config,  # type: ignore
+    )
+    return recordset
+
+
+def recordset_to_getpropertiesres(recordset: RecordSet) -> GetPropertiesRes:
+    """Derive GetPropertiesRes from a RecordSet object."""
+    res_str = "getpropertiesres"
+    config_record = recordset.configs_records[f"{res_str}.properties"]
+    # pylint: disable-next=protected-access
+    properties = _check_mapping_from_recordscalartype_to_scalar(config_record._data)
+
+    status = _extract_status_from_recordset(res_str, recordset=recordset)
+
+    return GetPropertiesRes(status=status, properties=properties)
+
+
+def getpropertiesres_to_recordset(getpropertiesres: GetPropertiesRes) -> RecordSet:
+    """Construct a RecordSet from a GetPropertiesRes object."""
+    recordset = RecordSet()
+    res_str = "getpropertiesres"
+    recordset.configs_records[f"{res_str}.properties"] = ConfigsRecord(
+        getpropertiesres.properties,  # type: ignore
+    )
+    # status
+    recordset = _embed_status_into_recordset(
+        res_str, getpropertiesres.status, recordset
+    )
+
+    return recordset
diff --git a/src/py/flwr/common/recordset_compat_test.py b/src/py/flwr/common/recordset_compat_test.py
new file mode 100644
index 000000000000..288326dc9e83
--- /dev/null
+++ b/src/py/flwr/common/recordset_compat_test.py
@@ -0,0 +1,303 @@
+# 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.
+# ==============================================================================
+"""RecordSet from legacy messages tests."""
+
+from copy import deepcopy
+from typing import Callable, Dict
+
+import numpy as np
+import pytest
+
+from .parameter import ndarrays_to_parameters
+from .recordset_compat import (
+    evaluateins_to_recordset,
+    evaluateres_to_recordset,
+    fitins_to_recordset,
+    fitres_to_recordset,
+    getparametersins_to_recordset,
+    getparametersres_to_recordset,
+    getpropertiesins_to_recordset,
+    getpropertiesres_to_recordset,
+    recordset_to_evaluateins,
+    recordset_to_evaluateres,
+    recordset_to_fitins,
+    recordset_to_fitres,
+    recordset_to_getparametersins,
+    recordset_to_getparametersres,
+    recordset_to_getpropertiesins,
+    recordset_to_getpropertiesres,
+)
+from .typing import (
+    Code,
+    EvaluateIns,
+    EvaluateRes,
+    FitIns,
+    FitRes,
+    GetParametersIns,
+    GetParametersRes,
+    GetPropertiesIns,
+    GetPropertiesRes,
+    NDArrays,
+    Scalar,
+    Status,
+)
+
+
+def get_ndarrays() -> NDArrays:
+    """Return list of NumPy arrays."""
+    arr1 = np.array([[1.0, 2.0], [3.0, 4], [5.0, 6.0]])
+    arr2 = np.eye(2, 7, 3)
+
+    return [arr1, arr2]
+
+
+##################################################
+#  Testing conversion: *Ins --> RecordSet --> *Ins
+#  Testing conversion: *Res <-- RecordSet <-- *Res
+##################################################
+
+
+def _get_valid_fitins() -> FitIns:
+    arrays = get_ndarrays()
+    return FitIns(parameters=ndarrays_to_parameters(arrays), config={"a": 1.0, "b": 0})
+
+
+def _get_valid_fitres() -> FitRes:
+    """Returnn Valid parameters but potentially invalid config."""
+    arrays = get_ndarrays()
+    metrics: Dict[str, Scalar] = {"a": 1.0, "b": 0}
+    return FitRes(
+        parameters=ndarrays_to_parameters(arrays),
+        num_examples=1,
+        status=Status(code=Code(0), message=""),
+        metrics=metrics,
+    )
+
+
+def _get_valid_evaluateins() -> EvaluateIns:
+    fit_ins = _get_valid_fitins()
+    return EvaluateIns(parameters=fit_ins.parameters, config=fit_ins.config)
+
+
+def _get_valid_evaluateres() -> EvaluateRes:
+    """Return potentially invalid config."""
+    metrics: Dict[str, Scalar] = {"a": 1.0, "b": 0}
+    return EvaluateRes(
+        num_examples=1,
+        loss=0.1,
+        status=Status(code=Code(0), message=""),
+        metrics=metrics,
+    )
+
+
+def _get_valid_getparametersins() -> GetParametersIns:
+    config_dict: Dict[str, Scalar] = {
+        "a": 1.0,
+        "b": 3,
+        "c": True,
+    }  # valid since both Ins/Res communicate over ConfigsRecord
+
+    return GetParametersIns(config_dict)
+
+
+def _get_valid_getparametersres() -> GetParametersRes:
+    arrays = get_ndarrays()
+    return GetParametersRes(
+        status=Status(code=Code(0), message=""),
+        parameters=ndarrays_to_parameters(arrays),
+    )
+
+
+def _get_valid_getpropertiesins() -> GetPropertiesIns:
+    getparamsins = _get_valid_getparametersins()
+    return GetPropertiesIns(config=getparamsins.config)
+
+
+def _get_valid_getpropertiesres() -> GetPropertiesRes:
+    config_dict: Dict[str, Scalar] = {
+        "a": 1.0,
+        "b": 3,
+        "c": True,
+    }  # valid since both Ins/Res communicate over ConfigsRecord
+
+    return GetPropertiesRes(
+        status=Status(code=Code(0), message=""), properties=config_dict
+    )
+
+
+@pytest.mark.parametrize(
+    "keep_input, validate_freed_fn",
+    [
+        (
+            False,
+            lambda x, x_copy, y: len(x.parameters.tensors) == 0 and x_copy == y,
+        ),  # check tensors were freed
+        (
+            True,
+            lambda x, x_copy, y: x == y,
+        ),
+    ],
+)
+def test_fitins_to_recordset_and_back(
+    keep_input: bool, validate_freed_fn: Callable[[FitIns, FitIns, FitIns], bool]
+) -> None:
+    """Test conversion FitIns --> RecordSet --> FitIns."""
+    fitins = _get_valid_fitins()
+
+    fitins_copy = deepcopy(fitins)
+
+    recordset = fitins_to_recordset(fitins, keep_input=keep_input)
+
+    fitins_ = recordset_to_fitins(recordset, keep_input=keep_input)
+
+    assert validate_freed_fn(fitins, fitins_copy, fitins_)
+
+
+@pytest.mark.parametrize(
+    "keep_input, validate_freed_fn",
+    [
+        (
+            False,
+            lambda x, x_copy, y: len(x.parameters.tensors) == 0 and x_copy == y,
+        ),  # check tensors were freed
+        (
+            True,
+            lambda x, x_copy, y: x == y,
+        ),
+    ],
+)
+def test_fitres_to_recordset_and_back(
+    keep_input: bool, validate_freed_fn: Callable[[FitRes, FitRes, FitRes], bool]
+) -> None:
+    """Test conversion FitRes --> RecordSet --> FitRes."""
+    fitres = _get_valid_fitres()
+
+    fitres_copy = deepcopy(fitres)
+
+    recordset = fitres_to_recordset(fitres, keep_input=keep_input)
+    fitres_ = recordset_to_fitres(recordset, keep_input=keep_input)
+
+    assert validate_freed_fn(fitres, fitres_copy, fitres_)
+
+
+@pytest.mark.parametrize(
+    "keep_input, validate_freed_fn",
+    [
+        (
+            False,
+            lambda x, x_copy, y: len(x.parameters.tensors) == 0 and x_copy == y,
+        ),  # check tensors were freed
+        (
+            True,
+            lambda x, x_copy, y: x == y,
+        ),
+    ],
+)
+def test_evaluateins_to_recordset_and_back(
+    keep_input: bool,
+    validate_freed_fn: Callable[[EvaluateIns, EvaluateIns, EvaluateIns], bool],
+) -> None:
+    """Test conversion EvaluateIns --> RecordSet --> EvaluateIns."""
+    evaluateins = _get_valid_evaluateins()
+
+    evaluateins_copy = deepcopy(evaluateins)
+
+    recordset = evaluateins_to_recordset(evaluateins, keep_input=keep_input)
+
+    evaluateins_ = recordset_to_evaluateins(recordset, keep_input=keep_input)
+
+    assert validate_freed_fn(evaluateins, evaluateins_copy, evaluateins_)
+
+
+def test_evaluateres_to_recordset_and_back() -> None:
+    """Test conversion EvaluateRes --> RecordSet --> EvaluateRes."""
+    evaluateres = _get_valid_evaluateres()
+
+    evaluateres_copy = deepcopy(evaluateres)
+
+    recordset = evaluateres_to_recordset(evaluateres)
+    evaluateres_ = recordset_to_evaluateres(recordset)
+
+    assert evaluateres_copy == evaluateres_
+
+
+def test_get_properties_ins_to_recordset_and_back() -> None:
+    """Test conversion GetPropertiesIns --> RecordSet --> GetPropertiesIns."""
+    getproperties_ins = _get_valid_getpropertiesins()
+
+    getproperties_ins_copy = deepcopy(getproperties_ins)
+
+    recordset = getpropertiesins_to_recordset(getproperties_ins)
+    getproperties_ins_ = recordset_to_getpropertiesins(recordset)
+
+    assert getproperties_ins_copy == getproperties_ins_
+
+
+def test_get_properties_res_to_recordset_and_back() -> None:
+    """Test conversion GetPropertiesRes --> RecordSet --> GetPropertiesRes."""
+    getproperties_res = _get_valid_getpropertiesres()
+
+    getproperties_res_copy = deepcopy(getproperties_res)
+
+    recordset = getpropertiesres_to_recordset(getproperties_res)
+    getproperties_res_ = recordset_to_getpropertiesres(recordset)
+
+    assert getproperties_res_copy == getproperties_res_
+
+
+def test_get_parameters_ins_to_recordset_and_back() -> None:
+    """Test conversion GetParametersIns --> RecordSet --> GetParametersIns."""
+    getparameters_ins = _get_valid_getparametersins()
+
+    getparameters_ins_copy = deepcopy(getparameters_ins)
+
+    recordset = getparametersins_to_recordset(getparameters_ins)
+    getparameters_ins_ = recordset_to_getparametersins(recordset)
+
+    assert getparameters_ins_copy == getparameters_ins_
+
+
+@pytest.mark.parametrize(
+    "keep_input, validate_freed_fn",
+    [
+        (
+            False,
+            lambda x, x_copy, y: len(x.parameters.tensors) == 0 and x_copy == y,
+        ),  # check tensors were freed
+        (
+            True,
+            lambda x, x_copy, y: x == y,
+        ),
+    ],
+)
+def test_get_parameters_res_to_recordset_and_back(
+    keep_input: bool,
+    validate_freed_fn: Callable[
+        [GetParametersRes, GetParametersRes, GetParametersRes], bool
+    ],
+) -> None:
+    """Test conversion GetParametersRes --> RecordSet --> GetParametersRes."""
+    getparameteres_res = _get_valid_getparametersres()
+
+    getparameters_res_copy = deepcopy(getparameteres_res)
+
+    recordset = getparametersres_to_recordset(getparameteres_res, keep_input=keep_input)
+    getparameteres_res_ = recordset_to_getparametersres(
+        recordset, keep_input=keep_input
+    )
+
+    assert validate_freed_fn(
+        getparameteres_res, getparameters_res_copy, getparameteres_res_
+    )
diff --git a/src/py/flwr/common/retry_invoker.py b/src/py/flwr/common/retry_invoker.py
index a60fff57e7bf..5441e766983a 100644
--- a/src/py/flwr/common/retry_invoker.py
+++ b/src/py/flwr/common/retry_invoker.py
@@ -156,6 +156,7 @@ class RetryInvoker:
     >>> invoker.invoke(my_func, arg1, arg2, kw1=kwarg1)
     """
 
+    # pylint: disable-next=too-many-arguments
     def __init__(
         self,
         wait_factory: Callable[[], Generator[float, None, None]],
diff --git a/src/py/flwr/common/secure_aggregation/ndarrays_arithmetic.py b/src/py/flwr/common/secure_aggregation/ndarrays_arithmetic.py
index 57afa56b7a08..e926a9531bea 100644
--- a/src/py/flwr/common/secure_aggregation/ndarrays_arithmetic.py
+++ b/src/py/flwr/common/secure_aggregation/ndarrays_arithmetic.py
@@ -15,7 +15,7 @@
 """Utility functions for performing operations on Numpy NDArrays."""
 
 
-from typing import Any, List, Tuple
+from typing import Any, List, Tuple, Union
 
 import numpy as np
 from numpy.typing import DTypeLike, NDArray
@@ -68,14 +68,14 @@ def parameters_mod(parameters: List[NDArray[Any]], divisor: int) -> List[NDArray
 
 
 def parameters_multiply(
-    parameters: List[NDArray[Any]], multiplier: int
+    parameters: List[NDArray[Any]], multiplier: Union[int, float]
 ) -> List[NDArray[Any]]:
-    """Multiply parameters by an integer multiplier."""
+    """Multiply parameters by an integer/float multiplier."""
     return [parameters[idx] * multiplier for idx in range(len(parameters))]
 
 
 def parameters_divide(
-    parameters: List[NDArray[Any]], divisor: int
+    parameters: List[NDArray[Any]], divisor: Union[int, float]
 ) -> List[NDArray[Any]]:
-    """Divide weight by an integer divisor."""
+    """Divide weight by an integer/float divisor."""
     return [parameters[idx] / divisor for idx in range(len(parameters))]
diff --git a/src/py/flwr/common/secure_aggregation/secaggplus_constants.py b/src/py/flwr/common/secure_aggregation/secaggplus_constants.py
index 8dd21a6016f1..8a15908c13c5 100644
--- a/src/py/flwr/common/secure_aggregation/secaggplus_constants.py
+++ b/src/py/flwr/common/secure_aggregation/secaggplus_constants.py
@@ -15,30 +15,55 @@
 """Constants for the SecAgg/SecAgg+ protocol."""
 
 
-# Names of stages
-STAGE_SETUP = "setup"
-STAGE_SHARE_KEYS = "share_keys"
-STAGE_COLLECT_MASKED_INPUT = "collect_masked_input"
-STAGE_UNMASK = "unmask"
-STAGES = (STAGE_SETUP, STAGE_SHARE_KEYS, STAGE_COLLECT_MASKED_INPUT, STAGE_UNMASK)
-
-# All valid keys in received/replied `named_values` dictionaries
-KEY_STAGE = "stage"
-KEY_SAMPLE_NUMBER = "sample_num"
-KEY_SECURE_ID = "secure_id"
-KEY_SHARE_NUMBER = "share_num"
-KEY_THRESHOLD = "threshold"
-KEY_CLIPPING_RANGE = "clipping_range"
-KEY_TARGET_RANGE = "target_range"
-KEY_MOD_RANGE = "mod_range"
-KEY_PUBLIC_KEY_1 = "pk1"
-KEY_PUBLIC_KEY_2 = "pk2"
-KEY_DESTINATION_LIST = "dsts"
-KEY_CIPHERTEXT_LIST = "ctxts"
-KEY_SOURCE_LIST = "srcs"
-KEY_PARAMETERS = "params"
-KEY_MASKED_PARAMETERS = "masked_params"
-KEY_ACTIVE_SECURE_ID_LIST = "active_sids"
-KEY_DEAD_SECURE_ID_LIST = "dead_sids"
-KEY_SECURE_ID_LIST = "sids"
-KEY_SHARE_LIST = "shares"
+from __future__ import annotations
+
+RECORD_KEY_STATE = "secaggplus_state"
+RECORD_KEY_CONFIGS = "secaggplus_configs"
+RATIO_QUANTIZATION_RANGE = 1073741824  # 1 << 30
+
+
+class Stage:
+    """Stages for the SecAgg+ protocol."""
+
+    SETUP = "setup"
+    SHARE_KEYS = "share_keys"
+    COLLECT_MASKED_VECTORS = "collect_masked_vectors"
+    UNMASK = "unmask"
+    _stages = (SETUP, SHARE_KEYS, COLLECT_MASKED_VECTORS, UNMASK)
+
+    @classmethod
+    def all(cls) -> tuple[str, str, str, str]:
+        """Return all stages."""
+        return cls._stages
+
+    def __new__(cls) -> Stage:
+        """Prevent instantiation."""
+        raise TypeError(f"{cls.__name__} cannot be instantiated.")
+
+
+class Key:
+    """Keys for the configs in the ConfigsRecord."""
+
+    STAGE = "stage"
+    SAMPLE_NUMBER = "sample_num"
+    SHARE_NUMBER = "share_num"
+    THRESHOLD = "threshold"
+    CLIPPING_RANGE = "clipping_range"
+    TARGET_RANGE = "target_range"
+    MOD_RANGE = "mod_range"
+    MAX_WEIGHT = "max_weight"
+    PUBLIC_KEY_1 = "pk1"
+    PUBLIC_KEY_2 = "pk2"
+    DESTINATION_LIST = "dsts"
+    CIPHERTEXT_LIST = "ctxts"
+    SOURCE_LIST = "srcs"
+    PARAMETERS = "params"
+    MASKED_PARAMETERS = "masked_params"
+    ACTIVE_NODE_ID_LIST = "active_nids"
+    DEAD_NODE_ID_LIST = "dead_nids"
+    NODE_ID_LIST = "nids"
+    SHARE_LIST = "shares"
+
+    def __new__(cls) -> Key:
+        """Prevent instantiation."""
+        raise TypeError(f"{cls.__name__} cannot be instantiated.")
diff --git a/src/py/flwr/common/secure_aggregation/secaggplus_utils.py b/src/py/flwr/common/secure_aggregation/secaggplus_utils.py
index def677e9d5d9..c373573477b9 100644
--- a/src/py/flwr/common/secure_aggregation/secaggplus_utils.py
+++ b/src/py/flwr/common/secure_aggregation/secaggplus_utils.py
@@ -23,16 +23,16 @@
 
 
 def share_keys_plaintext_concat(
-    source: int, destination: int, b_share: bytes, sk_share: bytes
+    src_node_id: int, dst_node_id: int, b_share: bytes, sk_share: bytes
 ) -> bytes:
     """Combine arguments to bytes.
 
     Parameters
     ----------
-    source : int
-        the secure ID of the source.
-    destination : int
-        the secure ID of the destination.
+    src_node_id : int
+        the node ID of the source.
+    dst_node_id : int
+        the node ID of the destination.
     b_share : bytes
         the private key share of the source sent to the destination.
     sk_share : bytes
@@ -45,8 +45,8 @@ def share_keys_plaintext_concat(
     """
     return b"".join(
         [
-            int.to_bytes(source, 4, "little"),
-            int.to_bytes(destination, 4, "little"),
+            int.to_bytes(src_node_id, 8, "little", signed=True),
+            int.to_bytes(dst_node_id, 8, "little", signed=True),
             int.to_bytes(len(b_share), 4, "little"),
             b_share,
             sk_share,
@@ -64,21 +64,21 @@ def share_keys_plaintext_separate(plaintext: bytes) -> Tuple[int, int, bytes, by
 
     Returns
     -------
-    source : int
-        the secure ID of the source.
-    destination : int
-        the secure ID of the destination.
+    src_node_id : int
+        the node ID of the source.
+    dst_node_id : int
+        the node ID of the destination.
     b_share : bytes
         the private key share of the source sent to the destination.
     sk_share : bytes
         the secret key share of the source sent to the destination.
     """
     src, dst, mark = (
-        int.from_bytes(plaintext[:4], "little"),
-        int.from_bytes(plaintext[4:8], "little"),
-        int.from_bytes(plaintext[8:12], "little"),
+        int.from_bytes(plaintext[:8], "little", signed=True),
+        int.from_bytes(plaintext[8:16], "little", signed=True),
+        int.from_bytes(plaintext[16:20], "little"),
     )
-    ret = (src, dst, plaintext[12 : 12 + mark], plaintext[12 + mark :])
+    ret = (src, dst, plaintext[20 : 20 + mark], plaintext[20 + mark :])
     return ret
 
 
diff --git a/src/py/flwr/common/serde.py b/src/py/flwr/common/serde.py
index c8c73e87e04a..6c7a077d2f9f 100644
--- a/src/py/flwr/common/serde.py
+++ b/src/py/flwr/common/serde.py
@@ -15,9 +15,24 @@
 """ProtoBuf serialization and deserialization."""
 
 
-from typing import Any, Dict, List, MutableMapping, cast
-
-from flwr.proto.task_pb2 import Value
+from typing import Any, Dict, List, MutableMapping, OrderedDict, Type, TypeVar, cast
+
+from google.protobuf.message import Message as GrpcMessage
+
+# pylint: disable=E0611
+from flwr.proto.error_pb2 import Error as ProtoError
+from flwr.proto.node_pb2 import Node
+from flwr.proto.recordset_pb2 import Array as ProtoArray
+from flwr.proto.recordset_pb2 import BoolList, BytesList
+from flwr.proto.recordset_pb2 import ConfigsRecord as ProtoConfigsRecord
+from flwr.proto.recordset_pb2 import ConfigsRecordValue as ProtoConfigsRecordValue
+from flwr.proto.recordset_pb2 import DoubleList
+from flwr.proto.recordset_pb2 import MetricsRecord as ProtoMetricsRecord
+from flwr.proto.recordset_pb2 import MetricsRecordValue as ProtoMetricsRecordValue
+from flwr.proto.recordset_pb2 import ParametersRecord as ProtoParametersRecord
+from flwr.proto.recordset_pb2 import RecordSet as ProtoRecordSet
+from flwr.proto.recordset_pb2 import Sint64List, StringList
+from flwr.proto.task_pb2 import Task, TaskIns, TaskRes
 from flwr.proto.transport_pb2 import (
     ClientMessage,
     Code,
@@ -28,139 +43,10 @@
     Status,
 )
 
-from . import typing
-
-#  === ServerMessage message ===
-
-
-def server_message_to_proto(server_message: typing.ServerMessage) -> ServerMessage:
-    """Serialize `ServerMessage` to ProtoBuf."""
-    if server_message.get_properties_ins is not None:
-        return ServerMessage(
-            get_properties_ins=get_properties_ins_to_proto(
-                server_message.get_properties_ins,
-            )
-        )
-    if server_message.get_parameters_ins is not None:
-        return ServerMessage(
-            get_parameters_ins=get_parameters_ins_to_proto(
-                server_message.get_parameters_ins,
-            )
-        )
-    if server_message.fit_ins is not None:
-        return ServerMessage(
-            fit_ins=fit_ins_to_proto(
-                server_message.fit_ins,
-            )
-        )
-    if server_message.evaluate_ins is not None:
-        return ServerMessage(
-            evaluate_ins=evaluate_ins_to_proto(
-                server_message.evaluate_ins,
-            )
-        )
-    raise Exception("No instruction set in ServerMessage, cannot serialize to ProtoBuf")
-
-
-def server_message_from_proto(
-    server_message_proto: ServerMessage,
-) -> typing.ServerMessage:
-    """Deserialize `ServerMessage` from ProtoBuf."""
-    field = server_message_proto.WhichOneof("msg")
-    if field == "get_properties_ins":
-        return typing.ServerMessage(
-            get_properties_ins=get_properties_ins_from_proto(
-                server_message_proto.get_properties_ins,
-            )
-        )
-    if field == "get_parameters_ins":
-        return typing.ServerMessage(
-            get_parameters_ins=get_parameters_ins_from_proto(
-                server_message_proto.get_parameters_ins,
-            )
-        )
-    if field == "fit_ins":
-        return typing.ServerMessage(
-            fit_ins=fit_ins_from_proto(
-                server_message_proto.fit_ins,
-            )
-        )
-    if field == "evaluate_ins":
-        return typing.ServerMessage(
-            evaluate_ins=evaluate_ins_from_proto(
-                server_message_proto.evaluate_ins,
-            )
-        )
-    raise Exception(
-        "Unsupported instruction in ServerMessage, cannot deserialize from ProtoBuf"
-    )
-
-
-#  === ClientMessage message ===
-
-
-def client_message_to_proto(client_message: typing.ClientMessage) -> ClientMessage:
-    """Serialize `ClientMessage` to ProtoBuf."""
-    if client_message.get_properties_res is not None:
-        return ClientMessage(
-            get_properties_res=get_properties_res_to_proto(
-                client_message.get_properties_res,
-            )
-        )
-    if client_message.get_parameters_res is not None:
-        return ClientMessage(
-            get_parameters_res=get_parameters_res_to_proto(
-                client_message.get_parameters_res,
-            )
-        )
-    if client_message.fit_res is not None:
-        return ClientMessage(
-            fit_res=fit_res_to_proto(
-                client_message.fit_res,
-            )
-        )
-    if client_message.evaluate_res is not None:
-        return ClientMessage(
-            evaluate_res=evaluate_res_to_proto(
-                client_message.evaluate_res,
-            )
-        )
-    raise Exception("No instruction set in ClientMessage, cannot serialize to ProtoBuf")
-
-
-def client_message_from_proto(
-    client_message_proto: ClientMessage,
-) -> typing.ClientMessage:
-    """Deserialize `ClientMessage` from ProtoBuf."""
-    field = client_message_proto.WhichOneof("msg")
-    if field == "get_properties_res":
-        return typing.ClientMessage(
-            get_properties_res=get_properties_res_from_proto(
-                client_message_proto.get_properties_res,
-            )
-        )
-    if field == "get_parameters_res":
-        return typing.ClientMessage(
-            get_parameters_res=get_parameters_res_from_proto(
-                client_message_proto.get_parameters_res,
-            )
-        )
-    if field == "fit_res":
-        return typing.ClientMessage(
-            fit_res=fit_res_from_proto(
-                client_message_proto.fit_res,
-            )
-        )
-    if field == "evaluate_res":
-        return typing.ClientMessage(
-            evaluate_res=evaluate_res_from_proto(
-                client_message_proto.evaluate_res,
-            )
-        )
-    raise Exception(
-        "Unsupported instruction in ClientMessage, cannot deserialize from ProtoBuf"
-    )
-
+# pylint: enable=E0611
+from . import Array, ConfigsRecord, MetricsRecord, ParametersRecord, RecordSet, typing
+from .message import Error, Message, Metadata
+from .record.typeddict import TypedDict
 
 #  === Parameters message ===
 
@@ -186,26 +72,9 @@ def reconnect_ins_to_proto(ins: typing.ReconnectIns) -> ServerMessage.ReconnectI
     return ServerMessage.ReconnectIns()
 
 
-def reconnect_ins_from_proto(msg: ServerMessage.ReconnectIns) -> typing.ReconnectIns:
-    """Deserialize `ReconnectIns` from ProtoBuf."""
-    return typing.ReconnectIns(seconds=msg.seconds)
-
-
 # === DisconnectRes message ===
 
 
-def disconnect_res_to_proto(res: typing.DisconnectRes) -> ClientMessage.DisconnectRes:
-    """Serialize `DisconnectRes` to ProtoBuf."""
-    reason_proto = Reason.UNKNOWN
-    if res.reason == "RECONNECT":
-        reason_proto = Reason.RECONNECT
-    elif res.reason == "POWER_DISCONNECTED":
-        reason_proto = Reason.POWER_DISCONNECTED
-    elif res.reason == "WIFI_UNAVAILABLE":
-        reason_proto = Reason.WIFI_UNAVAILABLE
-    return ClientMessage.DisconnectRes(reason=reason_proto)
-
-
 def disconnect_res_from_proto(msg: ClientMessage.DisconnectRes) -> typing.DisconnectRes:
     """Deserialize `DisconnectRes` from ProtoBuf."""
     if msg.reason == Reason.RECONNECT:
@@ -474,7 +343,7 @@ def scalar_to_proto(scalar: typing.Scalar) -> Scalar:
     if isinstance(scalar, str):
         return Scalar(string=scalar)
 
-    raise Exception(
+    raise ValueError(
         f"Accepted types: {bool, bytes, float, int, str} (but not {type(scalar)})"
     )
 
@@ -486,86 +355,313 @@ def scalar_from_proto(scalar_msg: Scalar) -> typing.Scalar:
     return cast(typing.Scalar, scalar)
 
 
-# === Value messages ===
+# === Record messages ===
 
 
-_python_type_to_field_name = {
+_type_to_field = {
     float: "double",
     int: "sint64",
     bool: "bool",
     str: "string",
     bytes: "bytes",
 }
+_list_type_to_class_and_field = {
+    float: (DoubleList, "double_list"),
+    int: (Sint64List, "sint64_list"),
+    bool: (BoolList, "bool_list"),
+    str: (StringList, "string_list"),
+    bytes: (BytesList, "bytes_list"),
+}
+T = TypeVar("T")
 
 
-_python_list_type_to_message_and_field_name = {
-    float: (Value.DoubleList, "double_list"),
-    int: (Value.Sint64List, "sint64_list"),
-    bool: (Value.BoolList, "bool_list"),
-    str: (Value.StringList, "string_list"),
-    bytes: (Value.BytesList, "bytes_list"),
-}
+def _record_value_to_proto(
+    value: Any, allowed_types: List[type], proto_class: Type[T]
+) -> T:
+    """Serialize `*RecordValue` to ProtoBuf.
+
+    Note: `bool` MUST be put in the front of allowd_types if it exists.
+    """
+    arg = {}
+    for t in allowed_types:
+        # Single element
+        # Note: `isinstance(False, int) == True`.
+        if isinstance(value, t):
+            arg[_type_to_field[t]] = value
+            return proto_class(**arg)
+        # List
+        if isinstance(value, list) and all(isinstance(item, t) for item in value):
+            list_class, field_name = _list_type_to_class_and_field[t]
+            arg[field_name] = list_class(vals=value)
+            return proto_class(**arg)
+    # Invalid types
+    raise TypeError(
+        f"The type of the following value is not allowed "
+        f"in '{proto_class.__name__}':\n{value}"
+    )
 
 
-def _check_value(value: typing.Value) -> None:
-    if isinstance(value, tuple(_python_type_to_field_name.keys())):
-        return
-    if isinstance(value, list):
-        if len(value) > 0 and isinstance(
-            value[0], tuple(_python_type_to_field_name.keys())
-        ):
-            data_type = type(value[0])
-            for element in value:
-                if isinstance(element, data_type):
-                    continue
-                raise Exception(
-                    f"Inconsistent type: the types of elements in the list must "
-                    f"be the same (expected {data_type}, but got {type(element)})."
-                )
+def _record_value_from_proto(value_proto: GrpcMessage) -> Any:
+    """Deserialize `*RecordValue` from ProtoBuf."""
+    value_field = cast(str, value_proto.WhichOneof("value"))
+    if value_field.endswith("list"):
+        value = list(getattr(value_proto, value_field).vals)
     else:
-        raise TypeError(
-            f"Accepted types: {bool, bytes, float, int, str} or "
-            f"list of these types."
+        value = getattr(value_proto, value_field)
+    return value
+
+
+def _record_value_dict_to_proto(
+    value_dict: TypedDict[str, Any],
+    allowed_types: List[type],
+    value_proto_class: Type[T],
+) -> Dict[str, T]:
+    """Serialize the record value dict to ProtoBuf.
+
+    Note: `bool` MUST be put in the front of allowd_types if it exists.
+    """
+    # Move bool to the front
+    if bool in allowed_types and allowed_types[0] != bool:
+        allowed_types.remove(bool)
+        allowed_types.insert(0, bool)
+
+    def proto(_v: Any) -> T:
+        return _record_value_to_proto(_v, allowed_types, value_proto_class)
+
+    return {k: proto(v) for k, v in value_dict.items()}
+
+
+def _record_value_dict_from_proto(
+    value_dict_proto: MutableMapping[str, Any]
+) -> Dict[str, Any]:
+    """Deserialize the record value dict from ProtoBuf."""
+    return {k: _record_value_from_proto(v) for k, v in value_dict_proto.items()}
+
+
+def array_to_proto(array: Array) -> ProtoArray:
+    """Serialize Array to ProtoBuf."""
+    return ProtoArray(**vars(array))
+
+
+def array_from_proto(array_proto: ProtoArray) -> Array:
+    """Deserialize Array from ProtoBuf."""
+    return Array(
+        dtype=array_proto.dtype,
+        shape=list(array_proto.shape),
+        stype=array_proto.stype,
+        data=array_proto.data,
+    )
+
+
+def parameters_record_to_proto(record: ParametersRecord) -> ProtoParametersRecord:
+    """Serialize ParametersRecord to ProtoBuf."""
+    return ProtoParametersRecord(
+        data_keys=record.keys(),
+        data_values=map(array_to_proto, record.values()),
+    )
+
+
+def parameters_record_from_proto(
+    record_proto: ProtoParametersRecord,
+) -> ParametersRecord:
+    """Deserialize ParametersRecord from ProtoBuf."""
+    return ParametersRecord(
+        array_dict=OrderedDict(
+            zip(record_proto.data_keys, map(array_from_proto, record_proto.data_values))
+        ),
+        keep_input=False,
+    )
+
+
+def metrics_record_to_proto(record: MetricsRecord) -> ProtoMetricsRecord:
+    """Serialize MetricsRecord to ProtoBuf."""
+    return ProtoMetricsRecord(
+        data=_record_value_dict_to_proto(record, [float, int], ProtoMetricsRecordValue)
+    )
+
+
+def metrics_record_from_proto(record_proto: ProtoMetricsRecord) -> MetricsRecord:
+    """Deserialize MetricsRecord from ProtoBuf."""
+    return MetricsRecord(
+        metrics_dict=cast(
+            Dict[str, typing.MetricsRecordValues],
+            _record_value_dict_from_proto(record_proto.data),
+        ),
+        keep_input=False,
+    )
+
+
+def configs_record_to_proto(record: ConfigsRecord) -> ProtoConfigsRecord:
+    """Serialize ConfigsRecord to ProtoBuf."""
+    return ProtoConfigsRecord(
+        data=_record_value_dict_to_proto(
+            record,
+            [bool, int, float, str, bytes],
+            ProtoConfigsRecordValue,
         )
+    )
 
 
-def value_to_proto(value: typing.Value) -> Value:
-    """Serialize `Value` to ProtoBuf."""
-    _check_value(value)
+def configs_record_from_proto(record_proto: ProtoConfigsRecord) -> ConfigsRecord:
+    """Deserialize ConfigsRecord from ProtoBuf."""
+    return ConfigsRecord(
+        configs_dict=cast(
+            Dict[str, typing.ConfigsRecordValues],
+            _record_value_dict_from_proto(record_proto.data),
+        ),
+        keep_input=False,
+    )
 
-    arg = {}
-    if isinstance(value, list):
-        msg_class, field_name = _python_list_type_to_message_and_field_name[
-            type(value[0]) if len(value) > 0 else int
-        ]
-        arg[field_name] = msg_class(vals=value)
-    else:
-        arg[_python_type_to_field_name[type(value)]] = value
-    return Value(**arg)
 
+# === Error message ===
+
+
+def error_to_proto(error: Error) -> ProtoError:
+    """Serialize Error to ProtoBuf."""
+    reason = error.reason if error.reason else ""
+    return ProtoError(code=error.code, reason=reason)
+
+
+def error_from_proto(error_proto: ProtoError) -> Error:
+    """Deserialize Error from ProtoBuf."""
+    reason = error_proto.reason if len(error_proto.reason) > 0 else None
+    return Error(code=error_proto.code, reason=reason)
+
+
+# === RecordSet message ===
+
+
+def recordset_to_proto(recordset: RecordSet) -> ProtoRecordSet:
+    """Serialize RecordSet to ProtoBuf."""
+    return ProtoRecordSet(
+        parameters={
+            k: parameters_record_to_proto(v)
+            for k, v in recordset.parameters_records.items()
+        },
+        metrics={
+            k: metrics_record_to_proto(v) for k, v in recordset.metrics_records.items()
+        },
+        configs={
+            k: configs_record_to_proto(v) for k, v in recordset.configs_records.items()
+        },
+    )
+
+
+def recordset_from_proto(recordset_proto: ProtoRecordSet) -> RecordSet:
+    """Deserialize RecordSet from ProtoBuf."""
+    return RecordSet(
+        parameters_records={
+            k: parameters_record_from_proto(v)
+            for k, v in recordset_proto.parameters.items()
+        },
+        metrics_records={
+            k: metrics_record_from_proto(v) for k, v in recordset_proto.metrics.items()
+        },
+        configs_records={
+            k: configs_record_from_proto(v) for k, v in recordset_proto.configs.items()
+        },
+    )
 
-def value_from_proto(value_msg: Value) -> typing.Value:
-    """Deserialize `Value` from ProtoBuf."""
-    value_field = cast(str, value_msg.WhichOneof("value"))
-    if value_field.endswith("list"):
-        value = list(getattr(value_msg, value_field).vals)
-    else:
-        value = getattr(value_msg, value_field)
-    return cast(typing.Value, value)
 
+# === Message ===
+
+
+def message_to_taskins(message: Message) -> TaskIns:
+    """Create a TaskIns from the Message."""
+    md = message.metadata
+    return TaskIns(
+        group_id=md.group_id,
+        run_id=md.run_id,
+        task=Task(
+            producer=Node(node_id=0, anonymous=True),  # Assume driver node
+            consumer=Node(node_id=md.dst_node_id, anonymous=False),
+            ttl=md.ttl,
+            ancestry=[md.reply_to_message] if md.reply_to_message != "" else [],
+            task_type=md.message_type,
+            recordset=(
+                recordset_to_proto(message.content) if message.has_content() else None
+            ),
+            error=error_to_proto(message.error) if message.has_error() else None,
+        ),
+    )
 
-# === Named Values ===
 
+def message_from_taskins(taskins: TaskIns) -> Message:
+    """Create a Message from the TaskIns."""
+    # Retrieve the Metadata
+    metadata = Metadata(
+        run_id=taskins.run_id,
+        message_id=taskins.task_id,
+        src_node_id=taskins.task.producer.node_id,
+        dst_node_id=taskins.task.consumer.node_id,
+        reply_to_message=taskins.task.ancestry[0] if taskins.task.ancestry else "",
+        group_id=taskins.group_id,
+        ttl=taskins.task.ttl,
+        message_type=taskins.task.task_type,
+    )
 
-def named_values_to_proto(
-    named_values: Dict[str, typing.Value],
-) -> Dict[str, Value]:
-    """Serialize named values to ProtoBuf."""
-    return {name: value_to_proto(value) for name, value in named_values.items()}
+    # Construct Message
+    return Message(
+        metadata=metadata,
+        content=(
+            recordset_from_proto(taskins.task.recordset)
+            if taskins.task.HasField("recordset")
+            else None
+        ),
+        error=(
+            error_from_proto(taskins.task.error)
+            if taskins.task.HasField("error")
+            else None
+        ),
+    )
 
 
-def named_values_from_proto(
-    named_values_proto: MutableMapping[str, Value]
-) -> Dict[str, typing.Value]:
-    """Deserialize named values from ProtoBuf."""
-    return {name: value_from_proto(value) for name, value in named_values_proto.items()}
+def message_to_taskres(message: Message) -> TaskRes:
+    """Create a TaskRes from the Message."""
+    md = message.metadata
+    return TaskRes(
+        task_id="",  # This will be generated by the server
+        group_id=md.group_id,
+        run_id=md.run_id,
+        task=Task(
+            producer=Node(node_id=md.src_node_id, anonymous=False),
+            consumer=Node(node_id=0, anonymous=True),  # Assume driver node
+            ttl=md.ttl,
+            ancestry=[md.reply_to_message] if md.reply_to_message != "" else [],
+            task_type=md.message_type,
+            recordset=(
+                recordset_to_proto(message.content) if message.has_content() else None
+            ),
+            error=error_to_proto(message.error) if message.has_error() else None,
+        ),
+    )
+
+
+def message_from_taskres(taskres: TaskRes) -> Message:
+    """Create a Message from the TaskIns."""
+    # Retrieve the MetaData
+    metadata = Metadata(
+        run_id=taskres.run_id,
+        message_id=taskres.task_id,
+        src_node_id=taskres.task.producer.node_id,
+        dst_node_id=taskres.task.consumer.node_id,
+        reply_to_message=taskres.task.ancestry[0] if taskres.task.ancestry else "",
+        group_id=taskres.group_id,
+        ttl=taskres.task.ttl,
+        message_type=taskres.task.task_type,
+    )
+
+    # Construct the Message
+    return Message(
+        metadata=metadata,
+        content=(
+            recordset_from_proto(taskres.task.recordset)
+            if taskres.task.HasField("recordset")
+            else None
+        ),
+        error=(
+            error_from_proto(taskres.task.error)
+            if taskres.task.HasField("error")
+            else None
+        ),
+    )
diff --git a/src/py/flwr/common/serde_test.py b/src/py/flwr/common/serde_test.py
index ba07890f4658..8596e5d2f330 100644
--- a/src/py/flwr/common/serde_test.py
+++ b/src/py/flwr/common/serde_test.py
@@ -14,21 +14,42 @@
 # ==============================================================================
 """(De-)serialization tests."""
 
+import random
+import string
+from typing import Any, Callable, Optional, OrderedDict, Type, TypeVar, Union, cast
 
-from typing import Dict, Union, cast
+import pytest
 
-from flwr.common import typing
+# pylint: disable=E0611
 from flwr.proto import transport_pb2 as pb2
+from flwr.proto.recordset_pb2 import Array as ProtoArray
+from flwr.proto.recordset_pb2 import ConfigsRecord as ProtoConfigsRecord
+from flwr.proto.recordset_pb2 import MetricsRecord as ProtoMetricsRecord
+from flwr.proto.recordset_pb2 import ParametersRecord as ProtoParametersRecord
+from flwr.proto.recordset_pb2 import RecordSet as ProtoRecordSet
 
+# pylint: enable=E0611
+from . import Array, ConfigsRecord, MetricsRecord, ParametersRecord, RecordSet, typing
+from .message import Error, Message, Metadata
 from .serde import (
-    named_values_from_proto,
-    named_values_to_proto,
+    array_from_proto,
+    array_to_proto,
+    configs_record_from_proto,
+    configs_record_to_proto,
+    message_from_taskins,
+    message_from_taskres,
+    message_to_taskins,
+    message_to_taskres,
+    metrics_record_from_proto,
+    metrics_record_to_proto,
+    parameters_record_from_proto,
+    parameters_record_to_proto,
+    recordset_from_proto,
+    recordset_to_proto,
     scalar_from_proto,
     scalar_to_proto,
     status_from_proto,
     status_to_proto,
-    value_from_proto,
-    value_to_proto,
 )
 
 
@@ -50,8 +71,8 @@ def test_serialisation_deserialisation() -> None:
 def test_status_to_proto() -> None:
     """Test status message (de-)serialization."""
     # Prepare
-    code_msg = pb2.Code.OK
-    status_msg = pb2.Status(code=code_msg, message="Success")
+    code_msg = pb2.Code.OK  # pylint: disable=E1101
+    status_msg = pb2.Status(code=code_msg, message="Success")  # pylint: disable=E1101
 
     code = typing.Code.OK
     status = typing.Status(code=code, message="Success")
@@ -66,8 +87,8 @@ def test_status_to_proto() -> None:
 def test_status_from_proto() -> None:
     """Test status message (de-)serialization."""
     # Prepare
-    code_msg = pb2.Code.OK
-    status_msg = pb2.Status(code=code_msg, message="Success")
+    code_msg = pb2.Code.OK  # pylint: disable=E1101
+    status_msg = pb2.Status(code=code_msg, message="Success")  # pylint: disable=E1101
 
     code = typing.Code.OK
     status = typing.Status(code=code, message="Success")
@@ -79,81 +100,290 @@ def test_status_from_proto() -> None:
     assert actual_status == status
 
 
-def test_value_serialization_deserialization() -> None:
-    """Test if values are identical after (de-)serialization."""
-    # Prepare
-    values = [
-        # boolean scalar and list
-        True,
-        [True, False, False, True],
-        # bytes scalar and list
-        b"test \x01\x02\x03 !@#$%^&*()",
-        [b"\x0a\x0b", b"\x0c\x0d\x0e", b"\x0f"],
-        # float scalar and list
-        3.14,
-        [2.714, -0.012],
-        # integer scalar and list
-        23,
-        [123456],
-        # string scalar and list
-        "abcdefghijklmnopqrstuvwxy",
-        ["456hgdhfd", "1234567890123456789012345678901", "I'm a string."],
-        # empty list
-        [],
-    ]
-
-    for value in values:
-        # Execute
-        serialized = value_to_proto(cast(typing.Value, value))
-        deserialized = value_from_proto(serialized)
+T = TypeVar("T")
 
-        # Assert
-        if isinstance(value, list):
-            assert isinstance(deserialized, list)
-            assert len(value) == len(deserialized)
-            for elm1, elm2 in zip(value, deserialized):
-                assert elm1 == elm2
+
+class RecordMaker:
+    """A record maker based on a seeded random number generator."""
+
+    def __init__(self, state: int = 42) -> None:
+        self.rng = random.Random(state)
+
+    def randbytes(self, n: int) -> bytes:
+        """Create a bytes."""
+        return self.rng.getrandbits(n * 8).to_bytes(n, "little")
+
+    def get_str(self, length: Optional[int] = None) -> str:
+        """Create a string."""
+        char_pool = (
+            string.ascii_letters + string.digits + " !@#$%^&*()_-+=[]|;':,./<>?{}"
+        )
+        if length is None:
+            length = self.rng.randint(1, 10)
+        return "".join(self.rng.choices(char_pool, k=length))
+
+    def get_value(self, dtype: Type[T]) -> T:
+        """Create a value of a given type."""
+        ret: Any = None
+        if dtype == bool:
+            ret = self.rng.random() < 0.5
+        elif dtype == str:
+            ret = self.get_str(self.rng.randint(10, 100))
+        elif dtype == int:
+            ret = self.rng.randint(-1 << 30, 1 << 30)
+        elif dtype == float:
+            ret = (self.rng.random() - 0.5) * (2.0 ** self.rng.randint(0, 50))
+        elif dtype == bytes:
+            ret = self.randbytes(self.rng.randint(10, 100))
         else:
-            assert value == deserialized
+            raise NotImplementedError(f"Unsupported dtype: {dtype}")
+        return cast(T, ret)
 
+    def array(self) -> Array:
+        """Create a Array."""
+        dtypes = ("float", "int")
+        stypes = ("torch", "tf", "numpy")
+        max_shape_size = 100
+        max_shape_dim = 10
+        min_max_bytes_size = (10, 1000)
 
-def test_named_values_serialization_deserialization() -> None:
-    """Test if named values is identical after (de-)serialization."""
+        dtype = self.rng.choice(dtypes)
+        shape = [
+            self.rng.randint(1, max_shape_size)
+            for _ in range(self.rng.randint(1, max_shape_dim))
+        ]
+        stype = self.rng.choice(stypes)
+        data = self.randbytes(self.rng.randint(*min_max_bytes_size))
+        return Array(dtype=dtype, shape=shape, stype=stype, data=data)
+
+    def parameters_record(self) -> ParametersRecord:
+        """Create a ParametersRecord."""
+        num_arrays = self.rng.randint(1, 5)
+        arrays = OrderedDict(
+            [(self.get_str(), self.array()) for i in range(num_arrays)]
+        )
+        return ParametersRecord(arrays, keep_input=False)
+
+    def metrics_record(self) -> MetricsRecord:
+        """Create a MetricsRecord."""
+        num_entries = self.rng.randint(1, 5)
+        types = (float, int)
+        return MetricsRecord(
+            metrics_dict={
+                self.get_str(): self.get_value(self.rng.choice(types))
+                for _ in range(num_entries)
+            },
+            keep_input=False,
+        )
+
+    def configs_record(self) -> ConfigsRecord:
+        """Create a ConfigsRecord."""
+        num_entries = self.rng.randint(1, 5)
+        types = (str, int, float, bytes, bool)
+        return ConfigsRecord(
+            configs_dict={
+                self.get_str(): self.get_value(self.rng.choice(types))
+                for _ in range(num_entries)
+            },
+            keep_input=False,
+        )
+
+    def recordset(
+        self,
+        num_params_records: int,
+        num_metrics_records: int,
+        num_configs_records: int,
+    ) -> RecordSet:
+        """Create a RecordSet."""
+        return RecordSet(
+            parameters_records={
+                self.get_str(): self.parameters_record()
+                for _ in range(num_params_records)
+            },
+            metrics_records={
+                self.get_str(): self.metrics_record()
+                for _ in range(num_metrics_records)
+            },
+            configs_records={
+                self.get_str(): self.configs_record()
+                for _ in range(num_configs_records)
+            },
+        )
+
+    def metadata(self) -> Metadata:
+        """Create a Metadata."""
+        return Metadata(
+            run_id=self.rng.randint(0, 1 << 30),
+            message_id=self.get_str(64),
+            group_id=self.get_str(30),
+            src_node_id=self.rng.randint(0, 1 << 63),
+            dst_node_id=self.rng.randint(0, 1 << 63),
+            reply_to_message=self.get_str(64),
+            ttl=self.get_str(10),
+            message_type=self.get_str(10),
+        )
+
+
+def test_array_serialization_deserialization() -> None:
+    """Test serialization and deserialization of Array."""
     # Prepare
-    values = [
-        # boolean scalar and list
-        True,
-        [True, False, False, True],
-        # bytes scalar and list
-        b"test \x01\x02\x03 !@#$%^&*()",
-        [b"\x0a\x0b", b"\x0c\x0d\x0e", b"\x0f"],
-        # float scalar and list
-        3.14,
-        [2.714, -0.012],
-        # integer scalar and list
-        23,
-        [123456],
-        # string scalar and list
-        "abcdefghijklmnopqrstuvwxy",
-        ["456hgdhfd", "1234567890123456789012345678901", "I'm a string."],
-        # empty list
-        [],
-    ]
-    named_values = {f"value {i}": value for i, value in enumerate(values)}
+    maker = RecordMaker()
+    original = maker.array()
 
     # Execute
-    serialized = named_values_to_proto(cast(Dict[str, typing.Value], named_values))
-    deserialized = named_values_from_proto(serialized)
+    proto = array_to_proto(original)
+    deserialized = array_from_proto(proto)
 
     # Assert
-    assert len(named_values) == len(deserialized)
-    for name in named_values:
-        expected = named_values[name]
-        actual = deserialized[name]
-        if isinstance(expected, list):
-            assert isinstance(actual, list)
-            assert len(expected) == len(actual)
-            for elm1, elm2 in zip(expected, actual):
-                assert elm1 == elm2
-        else:
-            assert expected == actual
+    assert isinstance(proto, ProtoArray)
+    assert original == deserialized
+
+
+def test_parameters_record_serialization_deserialization() -> None:
+    """Test serialization and deserialization of ParametersRecord."""
+    # Prepare
+    maker = RecordMaker()
+    original = maker.parameters_record()
+
+    # Execute
+    proto = parameters_record_to_proto(original)
+    deserialized = parameters_record_from_proto(proto)
+
+    # Assert
+    assert isinstance(proto, ProtoParametersRecord)
+    assert original == deserialized
+
+
+def test_metrics_record_serialization_deserialization() -> None:
+    """Test serialization and deserialization of MetricsRecord."""
+    # Prepare
+    maker = RecordMaker()
+    original = maker.metrics_record()
+
+    # Execute
+    proto = metrics_record_to_proto(original)
+    deserialized = metrics_record_from_proto(proto)
+
+    # Assert
+    assert isinstance(proto, ProtoMetricsRecord)
+    assert original == deserialized
+
+
+def test_configs_record_serialization_deserialization() -> None:
+    """Test serialization and deserialization of ConfigsRecord."""
+    # Prepare
+    maker = RecordMaker()
+    original = maker.configs_record()
+
+    # Execute
+    proto = configs_record_to_proto(original)
+    deserialized = configs_record_from_proto(proto)
+
+    # Assert
+    assert isinstance(proto, ProtoConfigsRecord)
+    assert original == deserialized
+
+
+def test_recordset_serialization_deserialization() -> None:
+    """Test serialization and deserialization of RecordSet."""
+    # Prepare
+    maker = RecordMaker(state=0)
+    original = maker.recordset(2, 2, 1)
+
+    # Execute
+    proto = recordset_to_proto(original)
+    deserialized = recordset_from_proto(proto)
+
+    # Assert
+    assert isinstance(proto, ProtoRecordSet)
+    assert original == deserialized
+
+
+@pytest.mark.parametrize(
+    "content_fn, error_fn",
+    [
+        (
+            lambda maker: maker.recordset(1, 1, 1),
+            None,
+        ),  # check when only content is set
+        (None, lambda code: Error(code=code)),  # check when only error is set
+    ],
+)
+def test_message_to_and_from_taskins(
+    content_fn: Callable[
+        [
+            RecordMaker,
+        ],
+        RecordSet,
+    ],
+    error_fn: Callable[[int], Error],
+) -> None:
+    """Test Message to and from TaskIns."""
+    # Prepare
+
+    maker = RecordMaker(state=1)
+    metadata = maker.metadata()
+    # pylint: disable-next=protected-access
+    metadata._src_node_id = 0  # Assume driver node
+
+    original = Message(
+        metadata=metadata,
+        content=None if content_fn is None else content_fn(maker),
+        error=None if error_fn is None else error_fn(0),
+    )
+
+    # Execute
+    taskins = message_to_taskins(original)
+    taskins.task_id = metadata.message_id
+    deserialized = message_from_taskins(taskins)
+
+    # Assert
+    if original.has_content():
+        assert original.content == deserialized.content
+    if original.has_error():
+        assert original.error == deserialized.error
+    assert metadata == deserialized.metadata
+
+
+@pytest.mark.parametrize(
+    "content_fn, error_fn",
+    [
+        (
+            lambda maker: maker.recordset(1, 1, 1),
+            None,
+        ),  # check when only content is set
+        (None, lambda code: Error(code=code)),  # check when only error is set
+    ],
+)
+def test_message_to_and_from_taskres(
+    content_fn: Callable[
+        [
+            RecordMaker,
+        ],
+        RecordSet,
+    ],
+    error_fn: Callable[[int], Error],
+) -> None:
+    """Test Message to and from TaskRes."""
+    # Prepare
+    maker = RecordMaker(state=2)
+    metadata = maker.metadata()
+    metadata.dst_node_id = 0  # Assume driver node
+
+    original = Message(
+        metadata=metadata,
+        content=None if content_fn is None else content_fn(maker),
+        error=None if error_fn is None else error_fn(0),
+    )
+
+    # Execute
+    taskres = message_to_taskres(original)
+    taskres.task_id = metadata.message_id
+    deserialized = message_from_taskres(taskres)
+
+    # Assert
+    if original.has_content():
+        assert original.content == deserialized.content
+    if original.has_error():
+        assert original.error == deserialized.error
+    assert metadata == deserialized.metadata
diff --git a/src/py/flwr/common/telemetry.py b/src/py/flwr/common/telemetry.py
index fed8b5a978bc..8eb594085d31 100644
--- a/src/py/flwr/common/telemetry.py
+++ b/src/py/flwr/common/telemetry.py
@@ -32,7 +32,7 @@
 FLWR_TELEMETRY_ENABLED = os.getenv("FLWR_TELEMETRY_ENABLED", "1")
 FLWR_TELEMETRY_LOGGING = os.getenv("FLWR_TELEMETRY_LOGGING", "0")
 
-TELEMETRY_EVENTS_URL = "https://telemetry.flower.dev/api/v1/event"
+TELEMETRY_EVENTS_URL = "https://telemetry.flower.ai/api/v1/event"
 
 LOGGER_NAME = "flwr-telemetry"
 LOGGER_LEVEL = logging.DEBUG
@@ -137,8 +137,8 @@ def _generate_next_value_(name: str, start: int, count: int, last_values: List[A
     RUN_FLEET_API_LEAVE = auto()
 
     # Driver API and Fleet API
-    RUN_SERVER_ENTER = auto()
-    RUN_SERVER_LEAVE = auto()
+    RUN_SUPERLINK_ENTER = auto()
+    RUN_SUPERLINK_LEAVE = auto()
 
     # Simulation
     START_SIMULATION_ENTER = auto()
@@ -152,9 +152,13 @@ def _generate_next_value_(name: str, start: int, count: int, last_values: List[A
     START_DRIVER_ENTER = auto()
     START_DRIVER_LEAVE = auto()
 
-    # SuperNode: flower-client
-    RUN_CLIENT_ENTER = auto()
-    RUN_CLIENT_LEAVE = auto()
+    # flower-client-app
+    RUN_CLIENT_APP_ENTER = auto()
+    RUN_CLIENT_APP_LEAVE = auto()
+
+    # flower-server-app
+    RUN_SERVER_APP_ENTER = auto()
+    RUN_SERVER_APP_LEAVE = auto()
 
 
 # Use the ThreadPoolExecutor with max_workers=1 to have a queue
diff --git a/src/py/flwr/common/typing.py b/src/py/flwr/common/typing.py
index 6c0266f5eec8..d6b2ec9b158c 100644
--- a/src/py/flwr/common/typing.py
+++ b/src/py/flwr/common/typing.py
@@ -45,6 +45,15 @@
     List[str],
 ]
 
+# Value types for common.MetricsRecord
+MetricsScalar = Union[int, float]
+MetricsScalarList = Union[List[int], List[float]]
+MetricsRecordValues = Union[MetricsScalar, MetricsScalarList]
+# Value types for common.ConfigsRecord
+ConfigsScalar = Union[MetricsScalar, str, bytes, bool]
+ConfigsScalarList = Union[MetricsScalarList, List[str], List[bytes], List[bool]]
+ConfigsRecordValues = Union[ConfigsScalar, ConfigsScalarList]
+
 Metrics = Dict[str, Scalar]
 MetricsAggregationFn = Callable[[List[Tuple[int, Metrics]]], Metrics]
 
diff --git a/src/py/flwr/common/version.py b/src/py/flwr/common/version.py
index 9545b4b68073..6808c66606b1 100644
--- a/src/py/flwr/common/version.py
+++ b/src/py/flwr/common/version.py
@@ -1,6 +1,5 @@
 """Flower package version helper."""
 
-
 import importlib.metadata as importlib_metadata
 from typing import Tuple
 
diff --git a/src/py/flwr/driver/app_test.py b/src/py/flwr/driver/app_test.py
deleted file mode 100644
index 91b4fd30bc4b..000000000000
--- a/src/py/flwr/driver/app_test.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# 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.
-# 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 Driver app tests."""
-# pylint: disable=no-self-use
-
-
-import threading
-import time
-import unittest
-from unittest.mock import MagicMock
-
-from flwr.driver.app import update_client_manager
-from flwr.proto.driver_pb2 import CreateWorkloadResponse, GetNodesResponse
-from flwr.proto.node_pb2 import Node
-from flwr.server.client_manager import SimpleClientManager
-
-
-class TestClientManagerWithDriver(unittest.TestCase):
-    """Tests for ClientManager.
-
-    Considering multi-threading, all tests assume that the `update_client_manager()`
-    updates the ClientManager every 3 seconds.
-    """
-
-    def test_simple_client_manager_update(self) -> None:
-        """Tests if the node update works correctly."""
-        # Prepare
-        expected_nodes = [Node(node_id=i, anonymous=False) for i in range(100)]
-        expected_updated_nodes = [
-            Node(node_id=i, anonymous=False) for i in range(80, 120)
-        ]
-        driver = MagicMock()
-        driver.stub = "driver stub"
-        driver.create_workload.return_value = CreateWorkloadResponse(workload_id=1)
-        driver.get_nodes.return_value = GetNodesResponse(nodes=expected_nodes)
-        client_manager = SimpleClientManager()
-        lock = threading.Lock()
-
-        # Execute
-        thread = threading.Thread(
-            target=update_client_manager,
-            args=(
-                driver,
-                client_manager,
-                lock,
-            ),
-            daemon=True,
-        )
-        thread.start()
-        # Wait until all nodes are registered via `client_manager.sample()`
-        client_manager.sample(len(expected_nodes))
-        # Retrieve all nodes in `client_manager`
-        node_ids = {proxy.node_id for proxy in client_manager.all().values()}
-        # Update the GetNodesResponse and wait until the `client_manager` is updated
-        driver.get_nodes.return_value = GetNodesResponse(nodes=expected_updated_nodes)
-        while True:
-            with lock:
-                if len(client_manager.all()) == len(expected_updated_nodes):
-                    break
-            time.sleep(1.3)
-        # Retrieve all nodes in `client_manager`
-        updated_node_ids = {proxy.node_id for proxy in client_manager.all().values()}
-        # Simulate `driver.disconnect()`
-        driver.stub = None
-
-        # Assert
-        driver.create_workload.assert_called_once()
-        assert node_ids == {node.node_id for node in expected_nodes}
-        assert updated_node_ids == {node.node_id for node in expected_updated_nodes}
-
-        # Exit
-        thread.join()
diff --git a/src/py/flwr/driver/driver.py b/src/py/flwr/driver/driver.py
deleted file mode 100644
index f1a7c6663c11..000000000000
--- a/src/py/flwr/driver/driver.py
+++ /dev/null
@@ -1,110 +0,0 @@
-# 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.
-# 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 driver service client."""
-
-
-from typing import Iterable, List, Optional, Tuple
-
-from flwr.driver.grpc_driver import DEFAULT_SERVER_ADDRESS_DRIVER, GrpcDriver
-from flwr.proto.driver_pb2 import (
-    CreateWorkloadRequest,
-    GetNodesRequest,
-    PullTaskResRequest,
-    PushTaskInsRequest,
-)
-from flwr.proto.node_pb2 import Node
-from flwr.proto.task_pb2 import TaskIns, TaskRes
-
-
-class Driver:
-    """`Driver` class provides an interface to the Driver API.
-
-    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.
-    """
-
-    def __init__(
-        self,
-        driver_service_address: str = DEFAULT_SERVER_ADDRESS_DRIVER,
-        certificates: Optional[bytes] = None,
-    ) -> None:
-        self.addr = driver_service_address
-        self.certificates = certificates
-        self.grpc_driver: Optional[GrpcDriver] = None
-        self.workload_id: Optional[int] = None
-        self.node = Node(node_id=0, anonymous=True)
-
-    def _get_grpc_driver_and_workload_id(self) -> Tuple[GrpcDriver, int]:
-        # Check if the GrpcDriver is initialized
-        if self.grpc_driver is None or self.workload_id is None:
-            # Connect and create workload
-            self.grpc_driver = GrpcDriver(
-                driver_service_address=self.addr, certificates=self.certificates
-            )
-            self.grpc_driver.connect()
-            res = self.grpc_driver.create_workload(CreateWorkloadRequest())
-            self.workload_id = res.workload_id
-
-        return self.grpc_driver, self.workload_id
-
-    def get_nodes(self) -> List[Node]:
-        """Get node IDs."""
-        grpc_driver, workload_id = self._get_grpc_driver_and_workload_id()
-
-        # Call GrpcDriver method
-        res = grpc_driver.get_nodes(GetNodesRequest(workload_id=workload_id))
-        return list(res.nodes)
-
-    def push_task_ins(self, task_ins_list: List[TaskIns]) -> List[str]:
-        """Schedule tasks."""
-        grpc_driver, workload_id = self._get_grpc_driver_and_workload_id()
-
-        # Set workload_id
-        for task_ins in task_ins_list:
-            task_ins.workload_id = workload_id
-
-        # Call GrpcDriver method
-        res = grpc_driver.push_task_ins(PushTaskInsRequest(task_ins_list=task_ins_list))
-        return list(res.task_ids)
-
-    def pull_task_res(self, task_ids: Iterable[str]) -> List[TaskRes]:
-        """Get task results."""
-        grpc_driver, _ = self._get_grpc_driver_and_workload_id()
-
-        # Call GrpcDriver method
-        res = grpc_driver.pull_task_res(
-            PullTaskResRequest(node=self.node, task_ids=task_ids)
-        )
-        return list(res.task_res_list)
-
-    def __del__(self) -> None:
-        """Disconnect GrpcDriver if connected."""
-        # Check if GrpcDriver is initialized
-        if self.grpc_driver is None:
-            return
-
-        # Disconnect
-        self.grpc_driver.disconnect()
diff --git a/src/py/flwr/driver/driver_client_proxy.py b/src/py/flwr/driver/driver_client_proxy.py
deleted file mode 100644
index 6d60fc49159b..000000000000
--- a/src/py/flwr/driver/driver_client_proxy.py
+++ /dev/null
@@ -1,157 +0,0 @@
-# Copyright 2020 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 ClientProxy implementation for Driver API."""
-
-
-import time
-from typing import List, Optional, cast
-
-from flwr import common
-from flwr.common import serde
-from flwr.proto import driver_pb2, node_pb2, task_pb2, transport_pb2
-from flwr.server.client_proxy import ClientProxy
-
-from .grpc_driver import GrpcDriver
-
-SLEEP_TIME = 1
-
-
-class DriverClientProxy(ClientProxy):
-    """Flower client proxy which delegates work using the Driver API."""
-
-    def __init__(
-        self, node_id: int, driver: GrpcDriver, anonymous: bool, workload_id: int
-    ):
-        super().__init__(str(node_id))
-        self.node_id = node_id
-        self.driver = driver
-        self.workload_id = workload_id
-        self.anonymous = anonymous
-
-    def get_properties(
-        self, ins: common.GetPropertiesIns, timeout: Optional[float]
-    ) -> common.GetPropertiesRes:
-        """Return client's properties."""
-        server_message_proto: transport_pb2.ServerMessage = (
-            serde.server_message_to_proto(
-                server_message=common.ServerMessage(get_properties_ins=ins)
-            )
-        )
-        return cast(
-            common.GetPropertiesRes,
-            self._send_receive_msg(server_message_proto, timeout).get_properties_res,
-        )
-
-    def get_parameters(
-        self, ins: common.GetParametersIns, timeout: Optional[float]
-    ) -> common.GetParametersRes:
-        """Return the current local model parameters."""
-        server_message_proto: transport_pb2.ServerMessage = (
-            serde.server_message_to_proto(
-                server_message=common.ServerMessage(get_parameters_ins=ins)
-            )
-        )
-        return cast(
-            common.GetParametersRes,
-            self._send_receive_msg(server_message_proto, timeout).get_parameters_res,
-        )
-
-    def fit(self, ins: common.FitIns, timeout: Optional[float]) -> common.FitRes:
-        """Train model parameters on the locally held dataset."""
-        server_message_proto: transport_pb2.ServerMessage = (
-            serde.server_message_to_proto(
-                server_message=common.ServerMessage(fit_ins=ins)
-            )
-        )
-        return cast(
-            common.FitRes,
-            self._send_receive_msg(server_message_proto, timeout).fit_res,
-        )
-
-    def evaluate(
-        self, ins: common.EvaluateIns, timeout: Optional[float]
-    ) -> common.EvaluateRes:
-        """Evaluate model parameters on the locally held dataset."""
-        server_message_proto: transport_pb2.ServerMessage = (
-            serde.server_message_to_proto(
-                server_message=common.ServerMessage(evaluate_ins=ins)
-            )
-        )
-        return cast(
-            common.EvaluateRes,
-            self._send_receive_msg(server_message_proto, timeout).evaluate_res,
-        )
-
-    def reconnect(
-        self, ins: common.ReconnectIns, timeout: Optional[float]
-    ) -> common.DisconnectRes:
-        """Disconnect and (optionally) reconnect later."""
-        return common.DisconnectRes(reason="")  # Nothing to do here (yet)
-
-    def _send_receive_msg(
-        self, server_message: transport_pb2.ServerMessage, timeout: Optional[float]
-    ) -> transport_pb2.ClientMessage:
-        task_ins = task_pb2.TaskIns(
-            task_id="",
-            group_id="",
-            workload_id=self.workload_id,
-            task=task_pb2.Task(
-                producer=node_pb2.Node(
-                    node_id=0,
-                    anonymous=True,
-                ),
-                consumer=node_pb2.Node(
-                    node_id=self.node_id,
-                    anonymous=self.anonymous,
-                ),
-                legacy_server_message=server_message,
-            ),
-        )
-        push_task_ins_req = driver_pb2.PushTaskInsRequest(task_ins_list=[task_ins])
-
-        # Send TaskIns to Driver API
-        push_task_ins_res = self.driver.push_task_ins(req=push_task_ins_req)
-
-        if len(push_task_ins_res.task_ids) != 1:
-            raise ValueError("Unexpected number of task_ids")
-
-        task_id = push_task_ins_res.task_ids[0]
-        if task_id == "":
-            raise ValueError(f"Failed to schedule task for node {self.node_id}")
-
-        if timeout:
-            start_time = time.time()
-
-        while True:
-            pull_task_res_req = driver_pb2.PullTaskResRequest(
-                node=node_pb2.Node(node_id=0, anonymous=True),
-                task_ids=[task_id],
-            )
-
-            # Ask Driver API for TaskRes
-            pull_task_res_res = self.driver.pull_task_res(req=pull_task_res_req)
-
-            task_res_list: List[task_pb2.TaskRes] = list(
-                pull_task_res_res.task_res_list
-            )
-            if len(task_res_list) == 1:
-                task_res = task_res_list[0]
-                return serde.client_message_from_proto(  # type: ignore
-                    task_res.task.legacy_client_message
-                )
-
-            if timeout is not None and time.time() > start_time + timeout:
-                raise RuntimeError("Timeout reached")
-            time.sleep(SLEEP_TIME)
diff --git a/src/py/flwr/driver/driver_client_proxy_test.py b/src/py/flwr/driver/driver_client_proxy_test.py
deleted file mode 100644
index 82b5b46d7810..000000000000
--- a/src/py/flwr/driver/driver_client_proxy_test.py
+++ /dev/null
@@ -1,185 +0,0 @@
-# Copyright 2020 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.
-# ==============================================================================
-"""DriverClientProxy tests."""
-
-
-import unittest
-from unittest.mock import MagicMock
-
-import numpy as np
-
-import flwr
-from flwr.common.typing import Config, GetParametersIns
-from flwr.driver.driver_client_proxy import DriverClientProxy
-from flwr.proto import driver_pb2, node_pb2, task_pb2
-from flwr.proto.transport_pb2 import ClientMessage, Parameters, Scalar
-
-MESSAGE_PARAMETERS = Parameters(tensors=[b"abc"], tensor_type="np")
-
-CLIENT_PROPERTIES = {"tensor_type": Scalar(string="numpy.ndarray")}
-
-
-class DriverClientProxyTestCase(unittest.TestCase):
-    """Tests for DriverClientProxy."""
-
-    def setUp(self) -> None:
-        """Set up mocks for tests."""
-        self.driver = MagicMock()
-        self.driver.get_nodes.return_value = driver_pb2.GetNodesResponse(
-            nodes=[node_pb2.Node(node_id=1, anonymous=False)]
-        )
-
-    def test_get_properties(self) -> None:
-        """Test positive case."""
-        # Prepare
-        self.driver.push_task_ins.return_value = driver_pb2.PushTaskInsResponse(
-            task_ids=["19341fd7-62e1-4eb4-beb4-9876d3acda32"]
-        )
-        self.driver.pull_task_res.return_value = driver_pb2.PullTaskResResponse(
-            task_res_list=[
-                task_pb2.TaskRes(
-                    task_id="554bd3c8-8474-4b93-a7db-c7bec1bf0012",
-                    group_id="",
-                    workload_id=0,
-                    task=task_pb2.Task(
-                        legacy_client_message=ClientMessage(
-                            get_properties_res=ClientMessage.GetPropertiesRes(
-                                properties=CLIENT_PROPERTIES
-                            )
-                        )
-                    ),
-                )
-            ]
-        )
-        client = DriverClientProxy(
-            node_id=1, driver=self.driver, anonymous=True, workload_id=0
-        )
-        request_properties: Config = {"tensor_type": "str"}
-        ins: flwr.common.GetPropertiesIns = flwr.common.GetPropertiesIns(
-            config=request_properties
-        )
-
-        # Execute
-        value: flwr.common.GetPropertiesRes = client.get_properties(ins, timeout=None)
-
-        # Assert
-        assert value.properties["tensor_type"] == "numpy.ndarray"
-
-    def test_get_parameters(self) -> None:
-        """Test positive case."""
-        # Prepare
-        self.driver.push_task_ins.return_value = driver_pb2.PushTaskInsResponse(
-            task_ids=["19341fd7-62e1-4eb4-beb4-9876d3acda32"]
-        )
-        self.driver.pull_task_res.return_value = driver_pb2.PullTaskResResponse(
-            task_res_list=[
-                task_pb2.TaskRes(
-                    task_id="554bd3c8-8474-4b93-a7db-c7bec1bf0012",
-                    group_id="",
-                    workload_id=0,
-                    task=task_pb2.Task(
-                        legacy_client_message=ClientMessage(
-                            get_parameters_res=ClientMessage.GetParametersRes(
-                                parameters=MESSAGE_PARAMETERS,
-                            )
-                        )
-                    ),
-                )
-            ]
-        )
-        client = DriverClientProxy(
-            node_id=1, driver=self.driver, anonymous=True, workload_id=0
-        )
-        get_parameters_ins = GetParametersIns(config={})
-
-        # Execute
-        value: flwr.common.GetParametersRes = client.get_parameters(
-            ins=get_parameters_ins, timeout=None
-        )
-
-        # Assert
-        assert value.parameters.tensors[0] == b"abc"
-
-    def test_fit(self) -> None:
-        """Test positive case."""
-        # Prepare
-        self.driver.push_task_ins.return_value = driver_pb2.PushTaskInsResponse(
-            task_ids=["19341fd7-62e1-4eb4-beb4-9876d3acda32"]
-        )
-        self.driver.pull_task_res.return_value = driver_pb2.PullTaskResResponse(
-            task_res_list=[
-                task_pb2.TaskRes(
-                    task_id="554bd3c8-8474-4b93-a7db-c7bec1bf0012",
-                    group_id="",
-                    workload_id=0,
-                    task=task_pb2.Task(
-                        legacy_client_message=ClientMessage(
-                            fit_res=ClientMessage.FitRes(
-                                parameters=MESSAGE_PARAMETERS,
-                                num_examples=10,
-                            )
-                        )
-                    ),
-                )
-            ]
-        )
-        client = DriverClientProxy(
-            node_id=1, driver=self.driver, anonymous=True, workload_id=0
-        )
-        parameters = flwr.common.ndarrays_to_parameters([np.ones((2, 2))])
-        ins: flwr.common.FitIns = flwr.common.FitIns(parameters, {})
-
-        # Execute
-        fit_res = client.fit(ins=ins, timeout=None)
-
-        # Assert
-        assert fit_res.parameters.tensor_type == "np"
-        assert fit_res.parameters.tensors[0] == b"abc"
-        assert fit_res.num_examples == 10
-
-    def test_evaluate(self) -> None:
-        """Test positive case."""
-        # Prepare
-        self.driver.push_task_ins.return_value = driver_pb2.PushTaskInsResponse(
-            task_ids=["19341fd7-62e1-4eb4-beb4-9876d3acda32"]
-        )
-        self.driver.pull_task_res.return_value = driver_pb2.PullTaskResResponse(
-            task_res_list=[
-                task_pb2.TaskRes(
-                    task_id="554bd3c8-8474-4b93-a7db-c7bec1bf0012",
-                    group_id="",
-                    workload_id=0,
-                    task=task_pb2.Task(
-                        legacy_client_message=ClientMessage(
-                            evaluate_res=ClientMessage.EvaluateRes(
-                                loss=0.0, num_examples=0
-                            )
-                        )
-                    ),
-                )
-            ]
-        )
-        client = DriverClientProxy(
-            node_id=1, driver=self.driver, anonymous=True, workload_id=0
-        )
-        parameters = flwr.common.Parameters(tensors=[], tensor_type="np")
-        evaluate_ins: flwr.common.EvaluateIns = flwr.common.EvaluateIns(parameters, {})
-
-        # Execute
-        evaluate_res = client.evaluate(evaluate_ins, timeout=None)
-
-        # Assert
-        assert 0.0 == evaluate_res.loss
-        assert 0 == evaluate_res.num_examples
diff --git a/src/py/flwr/driver/driver_test.py b/src/py/flwr/driver/driver_test.py
deleted file mode 100644
index 820018788a8f..000000000000
--- a/src/py/flwr/driver/driver_test.py
+++ /dev/null
@@ -1,153 +0,0 @@
-# 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.
-# 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 driver SDK."""
-
-
-import unittest
-from unittest.mock import Mock, patch
-
-from flwr.driver.driver import Driver
-from flwr.proto.driver_pb2 import (
-    GetNodesRequest,
-    PullTaskResRequest,
-    PushTaskInsRequest,
-)
-from flwr.proto.task_pb2 import Task, TaskIns, TaskRes
-
-
-class TestDriver(unittest.TestCase):
-    """Tests for `Driver` class."""
-
-    def setUp(self) -> None:
-        """Initialize mock GrpcDriver and Driver instance before each test."""
-        mock_response = Mock()
-        mock_response.workload_id = 61016
-        self.mock_grpc_driver = Mock()
-        self.mock_grpc_driver.create_workload.return_value = mock_response
-        self.patcher = patch(
-            "flwr.driver.driver.GrpcDriver", return_value=self.mock_grpc_driver
-        )
-        self.patcher.start()
-        self.driver = Driver()
-
-    def tearDown(self) -> None:
-        """Cleanup after each test."""
-        self.patcher.stop()
-
-    def test_check_and_init_grpc_driver_already_initialized(self) -> None:
-        """Test that GrpcDriver doesn't initialize if workload is created."""
-        # Prepare
-        self.driver.grpc_driver = self.mock_grpc_driver
-        self.driver.workload_id = 61016
-
-        # Execute
-        # pylint: disable-next=protected-access
-        self.driver._get_grpc_driver_and_workload_id()
-
-        # Assert
-        self.mock_grpc_driver.connect.assert_not_called()
-
-    def test_check_and_init_grpc_driver_needs_initialization(self) -> None:
-        """Test GrpcDriver initialization when workload is not created."""
-        # Execute
-        # pylint: disable-next=protected-access
-        self.driver._get_grpc_driver_and_workload_id()
-
-        # Assert
-        self.mock_grpc_driver.connect.assert_called_once()
-        self.assertEqual(self.driver.workload_id, 61016)
-
-    def test_get_nodes(self) -> None:
-        """Test retrieval of nodes."""
-        # Prepare
-        mock_response = Mock()
-        mock_response.nodes = [Mock(), Mock()]
-        self.mock_grpc_driver.get_nodes.return_value = mock_response
-
-        # Execute
-        nodes = self.driver.get_nodes()
-        args, kwargs = self.mock_grpc_driver.get_nodes.call_args
-
-        # Assert
-        self.mock_grpc_driver.connect.assert_called_once()
-        self.assertEqual(len(args), 1)
-        self.assertEqual(len(kwargs), 0)
-        self.assertIsInstance(args[0], GetNodesRequest)
-        self.assertEqual(args[0].workload_id, 61016)
-        self.assertEqual(nodes, mock_response.nodes)
-
-    def test_push_task_ins(self) -> None:
-        """Test pushing task instructions."""
-        # Prepare
-        mock_response = Mock()
-        mock_response.task_ids = ["id1", "id2"]
-        self.mock_grpc_driver.push_task_ins.return_value = mock_response
-        task_ins_list = [TaskIns(), TaskIns()]
-
-        # Execute
-        task_ids = self.driver.push_task_ins(task_ins_list)
-        args, kwargs = self.mock_grpc_driver.push_task_ins.call_args
-
-        # Assert
-        self.mock_grpc_driver.connect.assert_called_once()
-        self.assertEqual(len(args), 1)
-        self.assertEqual(len(kwargs), 0)
-        self.assertIsInstance(args[0], PushTaskInsRequest)
-        self.assertEqual(task_ids, mock_response.task_ids)
-        for task_ins in args[0].task_ins_list:
-            self.assertEqual(task_ins.workload_id, 61016)
-
-    def test_pull_task_res_with_given_task_ids(self) -> None:
-        """Test pulling task results with specific task IDs."""
-        # Prepare
-        mock_response = Mock()
-        mock_response.task_res_list = [
-            TaskRes(task=Task(ancestry=["id2"])),
-            TaskRes(task=Task(ancestry=["id3"])),
-        ]
-        self.mock_grpc_driver.pull_task_res.return_value = mock_response
-        task_ids = ["id1", "id2", "id3"]
-
-        # Execute
-        task_res_list = self.driver.pull_task_res(task_ids)
-        args, kwargs = self.mock_grpc_driver.pull_task_res.call_args
-
-        # Assert
-        self.mock_grpc_driver.connect.assert_called_once()
-        self.assertEqual(len(args), 1)
-        self.assertEqual(len(kwargs), 0)
-        self.assertIsInstance(args[0], PullTaskResRequest)
-        self.assertEqual(args[0].task_ids, task_ids)
-        self.assertEqual(task_res_list, mock_response.task_res_list)
-
-    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_and_workload_id()
-
-        # Execute
-        self.driver.__del__()
-
-        # Assert
-        self.mock_grpc_driver.disconnect.assert_called_once()
-
-    def test_del_with_uninitialized_driver(self) -> None:
-        """Test cleanup behavior when Driver is not initialized."""
-        # Execute
-        self.driver.__del__()
-
-        # Assert
-        self.mock_grpc_driver.disconnect.assert_not_called()
diff --git a/src/py/flwr/proto/driver_pb2.py b/src/py/flwr/proto/driver_pb2.py
index c138507e03e9..fe9c33da0fa9 100644
--- a/src/py/flwr/proto/driver_pb2.py
+++ b/src/py/flwr/proto/driver_pb2.py
@@ -1,12 +1,12 @@
 # -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: flwr/proto/driver.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 message as _message
-from google.protobuf import reflection as _reflection
 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()
@@ -16,94 +16,29 @@
 from flwr.proto import task_pb2 as flwr_dot_proto_dot_task__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\"\x17\n\x15\x43reateWorkloadRequest\"-\n\x16\x43reateWorkloadResponse\x12\x13\n\x0bworkload_id\x18\x01 \x01(\x12\"&\n\x0fGetNodesRequest\x12\x13\n\x0bworkload_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\xd0\x02\n\x06\x44river\x12Y\n\x0e\x43reateWorkload\x12!.flwr.proto.CreateWorkloadRequest\x1a\".flwr.proto.CreateWorkloadResponse\"\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\"\x12\n\x10\x43reateRunRequest\"#\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')
 
-
-
-_CREATEWORKLOADREQUEST = DESCRIPTOR.message_types_by_name['CreateWorkloadRequest']
-_CREATEWORKLOADRESPONSE = DESCRIPTOR.message_types_by_name['CreateWorkloadResponse']
-_GETNODESREQUEST = DESCRIPTOR.message_types_by_name['GetNodesRequest']
-_GETNODESRESPONSE = DESCRIPTOR.message_types_by_name['GetNodesResponse']
-_PUSHTASKINSREQUEST = DESCRIPTOR.message_types_by_name['PushTaskInsRequest']
-_PUSHTASKINSRESPONSE = DESCRIPTOR.message_types_by_name['PushTaskInsResponse']
-_PULLTASKRESREQUEST = DESCRIPTOR.message_types_by_name['PullTaskResRequest']
-_PULLTASKRESRESPONSE = DESCRIPTOR.message_types_by_name['PullTaskResResponse']
-CreateWorkloadRequest = _reflection.GeneratedProtocolMessageType('CreateWorkloadRequest', (_message.Message,), {
-  'DESCRIPTOR' : _CREATEWORKLOADREQUEST,
-  '__module__' : 'flwr.proto.driver_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.CreateWorkloadRequest)
-  })
-_sym_db.RegisterMessage(CreateWorkloadRequest)
-
-CreateWorkloadResponse = _reflection.GeneratedProtocolMessageType('CreateWorkloadResponse', (_message.Message,), {
-  'DESCRIPTOR' : _CREATEWORKLOADRESPONSE,
-  '__module__' : 'flwr.proto.driver_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.CreateWorkloadResponse)
-  })
-_sym_db.RegisterMessage(CreateWorkloadResponse)
-
-GetNodesRequest = _reflection.GeneratedProtocolMessageType('GetNodesRequest', (_message.Message,), {
-  'DESCRIPTOR' : _GETNODESREQUEST,
-  '__module__' : 'flwr.proto.driver_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.GetNodesRequest)
-  })
-_sym_db.RegisterMessage(GetNodesRequest)
-
-GetNodesResponse = _reflection.GeneratedProtocolMessageType('GetNodesResponse', (_message.Message,), {
-  'DESCRIPTOR' : _GETNODESRESPONSE,
-  '__module__' : 'flwr.proto.driver_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.GetNodesResponse)
-  })
-_sym_db.RegisterMessage(GetNodesResponse)
-
-PushTaskInsRequest = _reflection.GeneratedProtocolMessageType('PushTaskInsRequest', (_message.Message,), {
-  'DESCRIPTOR' : _PUSHTASKINSREQUEST,
-  '__module__' : 'flwr.proto.driver_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.PushTaskInsRequest)
-  })
-_sym_db.RegisterMessage(PushTaskInsRequest)
-
-PushTaskInsResponse = _reflection.GeneratedProtocolMessageType('PushTaskInsResponse', (_message.Message,), {
-  'DESCRIPTOR' : _PUSHTASKINSRESPONSE,
-  '__module__' : 'flwr.proto.driver_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.PushTaskInsResponse)
-  })
-_sym_db.RegisterMessage(PushTaskInsResponse)
-
-PullTaskResRequest = _reflection.GeneratedProtocolMessageType('PullTaskResRequest', (_message.Message,), {
-  'DESCRIPTOR' : _PULLTASKRESREQUEST,
-  '__module__' : 'flwr.proto.driver_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.PullTaskResRequest)
-  })
-_sym_db.RegisterMessage(PullTaskResRequest)
-
-PullTaskResResponse = _reflection.GeneratedProtocolMessageType('PullTaskResResponse', (_message.Message,), {
-  'DESCRIPTOR' : _PULLTASKRESRESPONSE,
-  '__module__' : 'flwr.proto.driver_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.PullTaskResResponse)
-  })
-_sym_db.RegisterMessage(PullTaskResResponse)
-
-_DRIVER = DESCRIPTOR.services_by_name['Driver']
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flwr.proto.driver_pb2', _globals)
 if _descriptor._USE_C_DESCRIPTORS == False:
-
   DESCRIPTOR._options = None
-  _CREATEWORKLOADREQUEST._serialized_start=85
-  _CREATEWORKLOADREQUEST._serialized_end=108
-  _CREATEWORKLOADRESPONSE._serialized_start=110
-  _CREATEWORKLOADRESPONSE._serialized_end=155
-  _GETNODESREQUEST._serialized_start=157
-  _GETNODESREQUEST._serialized_end=195
-  _GETNODESRESPONSE._serialized_start=197
-  _GETNODESRESPONSE._serialized_end=248
-  _PUSHTASKINSREQUEST._serialized_start=250
-  _PUSHTASKINSREQUEST._serialized_end=314
-  _PUSHTASKINSRESPONSE._serialized_start=316
-  _PUSHTASKINSRESPONSE._serialized_end=355
-  _PULLTASKRESREQUEST._serialized_start=357
-  _PULLTASKRESREQUEST._serialized_end=427
-  _PULLTASKRESRESPONSE._serialized_start=429
-  _PULLTASKRESRESPONSE._serialized_end=494
-  _DRIVER._serialized_start=497
-  _DRIVER._serialized_end=833
+  _globals['_CREATERUNREQUEST']._serialized_start=85
+  _globals['_CREATERUNREQUEST']._serialized_end=103
+  _globals['_CREATERUNRESPONSE']._serialized_start=105
+  _globals['_CREATERUNRESPONSE']._serialized_end=140
+  _globals['_GETNODESREQUEST']._serialized_start=142
+  _globals['_GETNODESREQUEST']._serialized_end=175
+  _globals['_GETNODESRESPONSE']._serialized_start=177
+  _globals['_GETNODESRESPONSE']._serialized_end=228
+  _globals['_PUSHTASKINSREQUEST']._serialized_start=230
+  _globals['_PUSHTASKINSREQUEST']._serialized_end=294
+  _globals['_PUSHTASKINSRESPONSE']._serialized_start=296
+  _globals['_PUSHTASKINSRESPONSE']._serialized_end=335
+  _globals['_PULLTASKRESREQUEST']._serialized_start=337
+  _globals['_PULLTASKRESREQUEST']._serialized_end=407
+  _globals['_PULLTASKRESRESPONSE']._serialized_start=409
+  _globals['_PULLTASKRESRESPONSE']._serialized_end=474
+  _globals['_DRIVER']._serialized_start=477
+  _globals['_DRIVER']._serialized_end=798
 # @@protoc_insertion_point(module_scope)
diff --git a/src/py/flwr/proto/driver_pb2.pyi b/src/py/flwr/proto/driver_pb2.pyi
index 8b940972cb6d..8dc254a55e8c 100644
--- a/src/py/flwr/proto/driver_pb2.pyi
+++ b/src/py/flwr/proto/driver_pb2.pyi
@@ -13,34 +13,34 @@ import typing_extensions
 
 DESCRIPTOR: google.protobuf.descriptor.FileDescriptor
 
-class CreateWorkloadRequest(google.protobuf.message.Message):
-    """CreateWorkload"""
+class CreateRunRequest(google.protobuf.message.Message):
+    """CreateRun"""
     DESCRIPTOR: google.protobuf.descriptor.Descriptor
     def __init__(self,
         ) -> None: ...
-global___CreateWorkloadRequest = CreateWorkloadRequest
+global___CreateRunRequest = CreateRunRequest
 
-class CreateWorkloadResponse(google.protobuf.message.Message):
+class CreateRunResponse(google.protobuf.message.Message):
     DESCRIPTOR: google.protobuf.descriptor.Descriptor
-    WORKLOAD_ID_FIELD_NUMBER: builtins.int
-    workload_id: builtins.int
+    RUN_ID_FIELD_NUMBER: builtins.int
+    run_id: builtins.int
     def __init__(self,
         *,
-        workload_id: builtins.int = ...,
+        run_id: builtins.int = ...,
         ) -> None: ...
-    def ClearField(self, field_name: typing_extensions.Literal["workload_id",b"workload_id"]) -> None: ...
-global___CreateWorkloadResponse = CreateWorkloadResponse
+    def ClearField(self, field_name: typing_extensions.Literal["run_id",b"run_id"]) -> None: ...
+global___CreateRunResponse = CreateRunResponse
 
 class GetNodesRequest(google.protobuf.message.Message):
     """GetNodes messages"""
     DESCRIPTOR: google.protobuf.descriptor.Descriptor
-    WORKLOAD_ID_FIELD_NUMBER: builtins.int
-    workload_id: builtins.int
+    RUN_ID_FIELD_NUMBER: builtins.int
+    run_id: builtins.int
     def __init__(self,
         *,
-        workload_id: builtins.int = ...,
+        run_id: builtins.int = ...,
         ) -> None: ...
-    def ClearField(self, field_name: typing_extensions.Literal["workload_id",b"workload_id"]) -> None: ...
+    def ClearField(self, field_name: typing_extensions.Literal["run_id",b"run_id"]) -> None: ...
 global___GetNodesRequest = GetNodesRequest
 
 class GetNodesResponse(google.protobuf.message.Message):
diff --git a/src/py/flwr/proto/driver_pb2_grpc.py b/src/py/flwr/proto/driver_pb2_grpc.py
index ea33b843d945..ac6815023ebd 100644
--- a/src/py/flwr/proto/driver_pb2_grpc.py
+++ b/src/py/flwr/proto/driver_pb2_grpc.py
@@ -14,10 +14,10 @@ def __init__(self, channel):
         Args:
             channel: A grpc.Channel.
         """
-        self.CreateWorkload = channel.unary_unary(
-                '/flwr.proto.Driver/CreateWorkload',
-                request_serializer=flwr_dot_proto_dot_driver__pb2.CreateWorkloadRequest.SerializeToString,
-                response_deserializer=flwr_dot_proto_dot_driver__pb2.CreateWorkloadResponse.FromString,
+        self.CreateRun = channel.unary_unary(
+                '/flwr.proto.Driver/CreateRun',
+                request_serializer=flwr_dot_proto_dot_driver__pb2.CreateRunRequest.SerializeToString,
+                response_deserializer=flwr_dot_proto_dot_driver__pb2.CreateRunResponse.FromString,
                 )
         self.GetNodes = channel.unary_unary(
                 '/flwr.proto.Driver/GetNodes',
@@ -39,8 +39,8 @@ def __init__(self, channel):
 class DriverServicer(object):
     """Missing associated documentation comment in .proto file."""
 
-    def CreateWorkload(self, request, context):
-        """Request workload_id
+    def CreateRun(self, request, context):
+        """Request run_id
         """
         context.set_code(grpc.StatusCode.UNIMPLEMENTED)
         context.set_details('Method not implemented!')
@@ -70,10 +70,10 @@ def PullTaskRes(self, request, context):
 
 def add_DriverServicer_to_server(servicer, server):
     rpc_method_handlers = {
-            'CreateWorkload': grpc.unary_unary_rpc_method_handler(
-                    servicer.CreateWorkload,
-                    request_deserializer=flwr_dot_proto_dot_driver__pb2.CreateWorkloadRequest.FromString,
-                    response_serializer=flwr_dot_proto_dot_driver__pb2.CreateWorkloadResponse.SerializeToString,
+            'CreateRun': grpc.unary_unary_rpc_method_handler(
+                    servicer.CreateRun,
+                    request_deserializer=flwr_dot_proto_dot_driver__pb2.CreateRunRequest.FromString,
+                    response_serializer=flwr_dot_proto_dot_driver__pb2.CreateRunResponse.SerializeToString,
             ),
             'GetNodes': grpc.unary_unary_rpc_method_handler(
                     servicer.GetNodes,
@@ -101,7 +101,7 @@ class Driver(object):
     """Missing associated documentation comment in .proto file."""
 
     @staticmethod
-    def CreateWorkload(request,
+    def CreateRun(request,
             target,
             options=(),
             channel_credentials=None,
@@ -111,9 +111,9 @@ def CreateWorkload(request,
             wait_for_ready=None,
             timeout=None,
             metadata=None):
-        return grpc.experimental.unary_unary(request, target, '/flwr.proto.Driver/CreateWorkload',
-            flwr_dot_proto_dot_driver__pb2.CreateWorkloadRequest.SerializeToString,
-            flwr_dot_proto_dot_driver__pb2.CreateWorkloadResponse.FromString,
+        return grpc.experimental.unary_unary(request, target, '/flwr.proto.Driver/CreateRun',
+            flwr_dot_proto_dot_driver__pb2.CreateRunRequest.SerializeToString,
+            flwr_dot_proto_dot_driver__pb2.CreateRunResponse.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 1b10d71e943d..43cf45f39b25 100644
--- a/src/py/flwr/proto/driver_pb2_grpc.pyi
+++ b/src/py/flwr/proto/driver_pb2_grpc.pyi
@@ -8,10 +8,10 @@ import grpc
 
 class DriverStub:
     def __init__(self, channel: grpc.Channel) -> None: ...
-    CreateWorkload: grpc.UnaryUnaryMultiCallable[
-        flwr.proto.driver_pb2.CreateWorkloadRequest,
-        flwr.proto.driver_pb2.CreateWorkloadResponse]
-    """Request workload_id"""
+    CreateRun: grpc.UnaryUnaryMultiCallable[
+        flwr.proto.driver_pb2.CreateRunRequest,
+        flwr.proto.driver_pb2.CreateRunResponse]
+    """Request run_id"""
 
     GetNodes: grpc.UnaryUnaryMultiCallable[
         flwr.proto.driver_pb2.GetNodesRequest,
@@ -31,11 +31,11 @@ class DriverStub:
 
 class DriverServicer(metaclass=abc.ABCMeta):
     @abc.abstractmethod
-    def CreateWorkload(self,
-        request: flwr.proto.driver_pb2.CreateWorkloadRequest,
+    def CreateRun(self,
+        request: flwr.proto.driver_pb2.CreateRunRequest,
         context: grpc.ServicerContext,
-    ) -> flwr.proto.driver_pb2.CreateWorkloadResponse:
-        """Request workload_id"""
+    ) -> flwr.proto.driver_pb2.CreateRunResponse:
+        """Request run_id"""
         pass
 
     @abc.abstractmethod
diff --git a/src/py/flwr/proto/error_pb2.py b/src/py/flwr/proto/error_pb2.py
new file mode 100644
index 000000000000..41721ae08804
--- /dev/null
+++ b/src/py/flwr/proto/error_pb2.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: flwr/proto/error.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\x16\x66lwr/proto/error.proto\x12\nflwr.proto\"%\n\x05\x45rror\x12\x0c\n\x04\x63ode\x18\x01 \x01(\x12\x12\x0e\n\x06reason\x18\x02 \x01(\tb\x06proto3')
+
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flwr.proto.error_pb2', _globals)
+if _descriptor._USE_C_DESCRIPTORS == False:
+  DESCRIPTOR._options = None
+  _globals['_ERROR']._serialized_start=38
+  _globals['_ERROR']._serialized_end=75
+# @@protoc_insertion_point(module_scope)
diff --git a/src/py/flwr/proto/error_pb2.pyi b/src/py/flwr/proto/error_pb2.pyi
new file mode 100644
index 000000000000..1811e5aa0ca8
--- /dev/null
+++ b/src/py/flwr/proto/error_pb2.pyi
@@ -0,0 +1,25 @@
+"""
+@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 Error(google.protobuf.message.Message):
+    DESCRIPTOR: google.protobuf.descriptor.Descriptor
+    CODE_FIELD_NUMBER: builtins.int
+    REASON_FIELD_NUMBER: builtins.int
+    code: builtins.int
+    reason: typing.Text
+    def __init__(self,
+        *,
+        code: builtins.int = ...,
+        reason: typing.Text = ...,
+        ) -> None: ...
+    def ClearField(self, field_name: typing_extensions.Literal["code",b"code","reason",b"reason"]) -> None: ...
+global___Error = Error
diff --git a/src/py/flwr/proto/error_pb2_grpc.py b/src/py/flwr/proto/error_pb2_grpc.py
new file mode 100644
index 000000000000..2daafffebfc8
--- /dev/null
+++ b/src/py/flwr/proto/error_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/error_pb2_grpc.pyi b/src/py/flwr/proto/error_pb2_grpc.pyi
new file mode 100644
index 000000000000..f3a5a087ef5d
--- /dev/null
+++ b/src/py/flwr/proto/error_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 e86a53e2139e..e8443c296f0c 100644
--- a/src/py/flwr/proto/fleet_pb2.py
+++ b/src/py/flwr/proto/fleet_pb2.py
@@ -1,12 +1,12 @@
 # -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: flwr/proto/fleet.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 message as _message
-from google.protobuf import reflection as _reflection
 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()
@@ -18,115 +18,33 @@
 
 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\"\x13\n\x11\x43reateNodeRequest\"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\"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\x02\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\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\x62\x06proto3')
 
-
-
-_CREATENODEREQUEST = DESCRIPTOR.message_types_by_name['CreateNodeRequest']
-_CREATENODERESPONSE = DESCRIPTOR.message_types_by_name['CreateNodeResponse']
-_DELETENODEREQUEST = DESCRIPTOR.message_types_by_name['DeleteNodeRequest']
-_DELETENODERESPONSE = DESCRIPTOR.message_types_by_name['DeleteNodeResponse']
-_PULLTASKINSREQUEST = DESCRIPTOR.message_types_by_name['PullTaskInsRequest']
-_PULLTASKINSRESPONSE = DESCRIPTOR.message_types_by_name['PullTaskInsResponse']
-_PUSHTASKRESREQUEST = DESCRIPTOR.message_types_by_name['PushTaskResRequest']
-_PUSHTASKRESRESPONSE = DESCRIPTOR.message_types_by_name['PushTaskResResponse']
-_PUSHTASKRESRESPONSE_RESULTSENTRY = _PUSHTASKRESRESPONSE.nested_types_by_name['ResultsEntry']
-_RECONNECT = DESCRIPTOR.message_types_by_name['Reconnect']
-CreateNodeRequest = _reflection.GeneratedProtocolMessageType('CreateNodeRequest', (_message.Message,), {
-  'DESCRIPTOR' : _CREATENODEREQUEST,
-  '__module__' : 'flwr.proto.fleet_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.CreateNodeRequest)
-  })
-_sym_db.RegisterMessage(CreateNodeRequest)
-
-CreateNodeResponse = _reflection.GeneratedProtocolMessageType('CreateNodeResponse', (_message.Message,), {
-  'DESCRIPTOR' : _CREATENODERESPONSE,
-  '__module__' : 'flwr.proto.fleet_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.CreateNodeResponse)
-  })
-_sym_db.RegisterMessage(CreateNodeResponse)
-
-DeleteNodeRequest = _reflection.GeneratedProtocolMessageType('DeleteNodeRequest', (_message.Message,), {
-  'DESCRIPTOR' : _DELETENODEREQUEST,
-  '__module__' : 'flwr.proto.fleet_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.DeleteNodeRequest)
-  })
-_sym_db.RegisterMessage(DeleteNodeRequest)
-
-DeleteNodeResponse = _reflection.GeneratedProtocolMessageType('DeleteNodeResponse', (_message.Message,), {
-  'DESCRIPTOR' : _DELETENODERESPONSE,
-  '__module__' : 'flwr.proto.fleet_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.DeleteNodeResponse)
-  })
-_sym_db.RegisterMessage(DeleteNodeResponse)
-
-PullTaskInsRequest = _reflection.GeneratedProtocolMessageType('PullTaskInsRequest', (_message.Message,), {
-  'DESCRIPTOR' : _PULLTASKINSREQUEST,
-  '__module__' : 'flwr.proto.fleet_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.PullTaskInsRequest)
-  })
-_sym_db.RegisterMessage(PullTaskInsRequest)
-
-PullTaskInsResponse = _reflection.GeneratedProtocolMessageType('PullTaskInsResponse', (_message.Message,), {
-  'DESCRIPTOR' : _PULLTASKINSRESPONSE,
-  '__module__' : 'flwr.proto.fleet_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.PullTaskInsResponse)
-  })
-_sym_db.RegisterMessage(PullTaskInsResponse)
-
-PushTaskResRequest = _reflection.GeneratedProtocolMessageType('PushTaskResRequest', (_message.Message,), {
-  'DESCRIPTOR' : _PUSHTASKRESREQUEST,
-  '__module__' : 'flwr.proto.fleet_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.PushTaskResRequest)
-  })
-_sym_db.RegisterMessage(PushTaskResRequest)
-
-PushTaskResResponse = _reflection.GeneratedProtocolMessageType('PushTaskResResponse', (_message.Message,), {
-
-  'ResultsEntry' : _reflection.GeneratedProtocolMessageType('ResultsEntry', (_message.Message,), {
-    'DESCRIPTOR' : _PUSHTASKRESRESPONSE_RESULTSENTRY,
-    '__module__' : 'flwr.proto.fleet_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.PushTaskResResponse.ResultsEntry)
-    })
-  ,
-  'DESCRIPTOR' : _PUSHTASKRESRESPONSE,
-  '__module__' : 'flwr.proto.fleet_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.PushTaskResResponse)
-  })
-_sym_db.RegisterMessage(PushTaskResResponse)
-_sym_db.RegisterMessage(PushTaskResResponse.ResultsEntry)
-
-Reconnect = _reflection.GeneratedProtocolMessageType('Reconnect', (_message.Message,), {
-  'DESCRIPTOR' : _RECONNECT,
-  '__module__' : 'flwr.proto.fleet_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.Reconnect)
-  })
-_sym_db.RegisterMessage(Reconnect)
-
-_FLEET = DESCRIPTOR.services_by_name['Fleet']
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flwr.proto.fleet_pb2', _globals)
 if _descriptor._USE_C_DESCRIPTORS == False:
-
   DESCRIPTOR._options = None
-  _PUSHTASKRESRESPONSE_RESULTSENTRY._options = None
-  _PUSHTASKRESRESPONSE_RESULTSENTRY._serialized_options = b'8\001'
-  _CREATENODEREQUEST._serialized_start=84
-  _CREATENODEREQUEST._serialized_end=103
-  _CREATENODERESPONSE._serialized_start=105
-  _CREATENODERESPONSE._serialized_end=157
-  _DELETENODEREQUEST._serialized_start=159
-  _DELETENODEREQUEST._serialized_end=210
-  _DELETENODERESPONSE._serialized_start=212
-  _DELETENODERESPONSE._serialized_end=232
-  _PULLTASKINSREQUEST._serialized_start=234
-  _PULLTASKINSREQUEST._serialized_end=304
-  _PULLTASKINSRESPONSE._serialized_start=306
-  _PULLTASKINSRESPONSE._serialized_end=413
-  _PUSHTASKRESREQUEST._serialized_start=415
-  _PUSHTASKRESREQUEST._serialized_end=479
-  _PUSHTASKRESRESPONSE._serialized_start=482
-  _PUSHTASKRESRESPONSE._serialized_end=656
-  _PUSHTASKRESRESPONSE_RESULTSENTRY._serialized_start=610
-  _PUSHTASKRESRESPONSE_RESULTSENTRY._serialized_end=656
-  _RECONNECT._serialized_start=658
-  _RECONNECT._serialized_end=688
-  _FLEET._serialized_start=691
-  _FLEET._serialized_end=1020
+  _globals['_PUSHTASKRESRESPONSE_RESULTSENTRY']._options = None
+  _globals['_PUSHTASKRESRESPONSE_RESULTSENTRY']._serialized_options = b'8\001'
+  _globals['_CREATENODEREQUEST']._serialized_start=84
+  _globals['_CREATENODEREQUEST']._serialized_end=103
+  _globals['_CREATENODERESPONSE']._serialized_start=105
+  _globals['_CREATENODERESPONSE']._serialized_end=157
+  _globals['_DELETENODEREQUEST']._serialized_start=159
+  _globals['_DELETENODEREQUEST']._serialized_end=210
+  _globals['_DELETENODERESPONSE']._serialized_start=212
+  _globals['_DELETENODERESPONSE']._serialized_end=232
+  _globals['_PULLTASKINSREQUEST']._serialized_start=234
+  _globals['_PULLTASKINSREQUEST']._serialized_end=304
+  _globals['_PULLTASKINSRESPONSE']._serialized_start=306
+  _globals['_PULLTASKINSRESPONSE']._serialized_end=413
+  _globals['_PUSHTASKRESREQUEST']._serialized_start=415
+  _globals['_PUSHTASKRESREQUEST']._serialized_end=479
+  _globals['_PUSHTASKRESRESPONSE']._serialized_start=482
+  _globals['_PUSHTASKRESRESPONSE']._serialized_end=656
+  _globals['_PUSHTASKRESRESPONSE_RESULTSENTRY']._serialized_start=610
+  _globals['_PUSHTASKRESRESPONSE_RESULTSENTRY']._serialized_end=656
+  _globals['_RECONNECT']._serialized_start=658
+  _globals['_RECONNECT']._serialized_end=688
+  _globals['_FLEET']._serialized_start=691
+  _globals['_FLEET']._serialized_end=1020
 # @@protoc_insertion_point(module_scope)
diff --git a/src/py/flwr/proto/node_pb2.py b/src/py/flwr/proto/node_pb2.py
index 9d91900d8f53..b300f2c562c2 100644
--- a/src/py/flwr/proto/node_pb2.py
+++ b/src/py/flwr/proto/node_pb2.py
@@ -1,12 +1,12 @@
 # -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: flwr/proto/node.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 message as _message
-from google.protobuf import reflection as _reflection
 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()
@@ -16,19 +16,11 @@
 
 DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15\x66lwr/proto/node.proto\x12\nflwr.proto\"*\n\x04Node\x12\x0f\n\x07node_id\x18\x01 \x01(\x12\x12\x11\n\tanonymous\x18\x02 \x01(\x08\x62\x06proto3')
 
-
-
-_NODE = DESCRIPTOR.message_types_by_name['Node']
-Node = _reflection.GeneratedProtocolMessageType('Node', (_message.Message,), {
-  'DESCRIPTOR' : _NODE,
-  '__module__' : 'flwr.proto.node_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.Node)
-  })
-_sym_db.RegisterMessage(Node)
-
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flwr.proto.node_pb2', _globals)
 if _descriptor._USE_C_DESCRIPTORS == False:
-
   DESCRIPTOR._options = None
-  _NODE._serialized_start=37
-  _NODE._serialized_end=79
+  _globals['_NODE']._serialized_start=37
+  _globals['_NODE']._serialized_end=79
 # @@protoc_insertion_point(module_scope)
diff --git a/src/py/flwr/proto/recordset_pb2.py b/src/py/flwr/proto/recordset_pb2.py
new file mode 100644
index 000000000000..f7f74d72182b
--- /dev/null
+++ b/src/py/flwr/proto/recordset_pb2.py
@@ -0,0 +1,68 @@
+# -*- coding: utf-8 -*-
+# Generated by the protocol buffer compiler.  DO NOT EDIT!
+# source: flwr/proto/recordset.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\x1a\x66lwr/proto/recordset.proto\x12\nflwr.proto\"\x1a\n\nDoubleList\x12\x0c\n\x04vals\x18\x01 \x03(\x01\"\x1a\n\nSint64List\x12\x0c\n\x04vals\x18\x01 \x03(\x12\"\x18\n\x08\x42oolList\x12\x0c\n\x04vals\x18\x01 \x03(\x08\"\x1a\n\nStringList\x12\x0c\n\x04vals\x18\x01 \x03(\t\"\x19\n\tBytesList\x12\x0c\n\x04vals\x18\x01 \x03(\x0c\"B\n\x05\x41rray\x12\r\n\x05\x64type\x18\x01 \x01(\t\x12\r\n\x05shape\x18\x02 \x03(\x05\x12\r\n\x05stype\x18\x03 \x01(\t\x12\x0c\n\x04\x64\x61ta\x18\x04 \x01(\x0c\"\x9f\x01\n\x12MetricsRecordValue\x12\x10\n\x06\x64ouble\x18\x01 \x01(\x01H\x00\x12\x10\n\x06sint64\x18\x02 \x01(\x12H\x00\x12-\n\x0b\x64ouble_list\x18\x15 \x01(\x0b\x32\x16.flwr.proto.DoubleListH\x00\x12-\n\x0bsint64_list\x18\x16 \x01(\x0b\x32\x16.flwr.proto.Sint64ListH\x00\x42\x07\n\x05value\"\xd9\x02\n\x12\x43onfigsRecordValue\x12\x10\n\x06\x64ouble\x18\x01 \x01(\x01H\x00\x12\x10\n\x06sint64\x18\x02 \x01(\x12H\x00\x12\x0e\n\x04\x62ool\x18\x03 \x01(\x08H\x00\x12\x10\n\x06string\x18\x04 \x01(\tH\x00\x12\x0f\n\x05\x62ytes\x18\x05 \x01(\x0cH\x00\x12-\n\x0b\x64ouble_list\x18\x15 \x01(\x0b\x32\x16.flwr.proto.DoubleListH\x00\x12-\n\x0bsint64_list\x18\x16 \x01(\x0b\x32\x16.flwr.proto.Sint64ListH\x00\x12)\n\tbool_list\x18\x17 \x01(\x0b\x32\x14.flwr.proto.BoolListH\x00\x12-\n\x0bstring_list\x18\x18 \x01(\x0b\x32\x16.flwr.proto.StringListH\x00\x12+\n\nbytes_list\x18\x19 \x01(\x0b\x32\x15.flwr.proto.BytesListH\x00\x42\x07\n\x05value\"M\n\x10ParametersRecord\x12\x11\n\tdata_keys\x18\x01 \x03(\t\x12&\n\x0b\x64\x61ta_values\x18\x02 \x03(\x0b\x32\x11.flwr.proto.Array\"\x8f\x01\n\rMetricsRecord\x12\x31\n\x04\x64\x61ta\x18\x01 \x03(\x0b\x32#.flwr.proto.MetricsRecord.DataEntry\x1aK\n\tDataEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12-\n\x05value\x18\x02 \x01(\x0b\x32\x1e.flwr.proto.MetricsRecordValue:\x02\x38\x01\"\x8f\x01\n\rConfigsRecord\x12\x31\n\x04\x64\x61ta\x18\x01 \x03(\x0b\x32#.flwr.proto.ConfigsRecord.DataEntry\x1aK\n\tDataEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12-\n\x05value\x18\x02 \x01(\x0b\x32\x1e.flwr.proto.ConfigsRecordValue:\x02\x38\x01\"\x97\x03\n\tRecordSet\x12\x39\n\nparameters\x18\x01 \x03(\x0b\x32%.flwr.proto.RecordSet.ParametersEntry\x12\x33\n\x07metrics\x18\x02 \x03(\x0b\x32\".flwr.proto.RecordSet.MetricsEntry\x12\x33\n\x07\x63onfigs\x18\x03 \x03(\x0b\x32\".flwr.proto.RecordSet.ConfigsEntry\x1aO\n\x0fParametersEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12+\n\x05value\x18\x02 \x01(\x0b\x32\x1c.flwr.proto.ParametersRecord:\x02\x38\x01\x1aI\n\x0cMetricsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12(\n\x05value\x18\x02 \x01(\x0b\x32\x19.flwr.proto.MetricsRecord:\x02\x38\x01\x1aI\n\x0c\x43onfigsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12(\n\x05value\x18\x02 \x01(\x0b\x32\x19.flwr.proto.ConfigsRecord:\x02\x38\x01\x62\x06proto3')
+
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flwr.proto.recordset_pb2', _globals)
+if _descriptor._USE_C_DESCRIPTORS == False:
+  DESCRIPTOR._options = None
+  _globals['_METRICSRECORD_DATAENTRY']._options = None
+  _globals['_METRICSRECORD_DATAENTRY']._serialized_options = b'8\001'
+  _globals['_CONFIGSRECORD_DATAENTRY']._options = None
+  _globals['_CONFIGSRECORD_DATAENTRY']._serialized_options = b'8\001'
+  _globals['_RECORDSET_PARAMETERSENTRY']._options = None
+  _globals['_RECORDSET_PARAMETERSENTRY']._serialized_options = b'8\001'
+  _globals['_RECORDSET_METRICSENTRY']._options = None
+  _globals['_RECORDSET_METRICSENTRY']._serialized_options = b'8\001'
+  _globals['_RECORDSET_CONFIGSENTRY']._options = None
+  _globals['_RECORDSET_CONFIGSENTRY']._serialized_options = b'8\001'
+  _globals['_DOUBLELIST']._serialized_start=42
+  _globals['_DOUBLELIST']._serialized_end=68
+  _globals['_SINT64LIST']._serialized_start=70
+  _globals['_SINT64LIST']._serialized_end=96
+  _globals['_BOOLLIST']._serialized_start=98
+  _globals['_BOOLLIST']._serialized_end=122
+  _globals['_STRINGLIST']._serialized_start=124
+  _globals['_STRINGLIST']._serialized_end=150
+  _globals['_BYTESLIST']._serialized_start=152
+  _globals['_BYTESLIST']._serialized_end=177
+  _globals['_ARRAY']._serialized_start=179
+  _globals['_ARRAY']._serialized_end=245
+  _globals['_METRICSRECORDVALUE']._serialized_start=248
+  _globals['_METRICSRECORDVALUE']._serialized_end=407
+  _globals['_CONFIGSRECORDVALUE']._serialized_start=410
+  _globals['_CONFIGSRECORDVALUE']._serialized_end=755
+  _globals['_PARAMETERSRECORD']._serialized_start=757
+  _globals['_PARAMETERSRECORD']._serialized_end=834
+  _globals['_METRICSRECORD']._serialized_start=837
+  _globals['_METRICSRECORD']._serialized_end=980
+  _globals['_METRICSRECORD_DATAENTRY']._serialized_start=905
+  _globals['_METRICSRECORD_DATAENTRY']._serialized_end=980
+  _globals['_CONFIGSRECORD']._serialized_start=983
+  _globals['_CONFIGSRECORD']._serialized_end=1126
+  _globals['_CONFIGSRECORD_DATAENTRY']._serialized_start=1051
+  _globals['_CONFIGSRECORD_DATAENTRY']._serialized_end=1126
+  _globals['_RECORDSET']._serialized_start=1129
+  _globals['_RECORDSET']._serialized_end=1536
+  _globals['_RECORDSET_PARAMETERSENTRY']._serialized_start=1307
+  _globals['_RECORDSET_PARAMETERSENTRY']._serialized_end=1386
+  _globals['_RECORDSET_METRICSENTRY']._serialized_start=1388
+  _globals['_RECORDSET_METRICSENTRY']._serialized_end=1461
+  _globals['_RECORDSET_CONFIGSENTRY']._serialized_start=1463
+  _globals['_RECORDSET_CONFIGSENTRY']._serialized_end=1536
+# @@protoc_insertion_point(module_scope)
diff --git a/src/py/flwr/proto/recordset_pb2.pyi b/src/py/flwr/proto/recordset_pb2.pyi
new file mode 100644
index 000000000000..86244697129c
--- /dev/null
+++ b/src/py/flwr/proto/recordset_pb2.pyi
@@ -0,0 +1,305 @@
+"""
+@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 DoubleList(google.protobuf.message.Message):
+    DESCRIPTOR: google.protobuf.descriptor.Descriptor
+    VALS_FIELD_NUMBER: builtins.int
+    @property
+    def vals(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]: ...
+    def __init__(self,
+        *,
+        vals: typing.Optional[typing.Iterable[builtins.float]] = ...,
+        ) -> None: ...
+    def ClearField(self, field_name: typing_extensions.Literal["vals",b"vals"]) -> None: ...
+global___DoubleList = DoubleList
+
+class Sint64List(google.protobuf.message.Message):
+    DESCRIPTOR: google.protobuf.descriptor.Descriptor
+    VALS_FIELD_NUMBER: builtins.int
+    @property
+    def vals(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
+    def __init__(self,
+        *,
+        vals: typing.Optional[typing.Iterable[builtins.int]] = ...,
+        ) -> None: ...
+    def ClearField(self, field_name: typing_extensions.Literal["vals",b"vals"]) -> None: ...
+global___Sint64List = Sint64List
+
+class BoolList(google.protobuf.message.Message):
+    DESCRIPTOR: google.protobuf.descriptor.Descriptor
+    VALS_FIELD_NUMBER: builtins.int
+    @property
+    def vals(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bool]: ...
+    def __init__(self,
+        *,
+        vals: typing.Optional[typing.Iterable[builtins.bool]] = ...,
+        ) -> None: ...
+    def ClearField(self, field_name: typing_extensions.Literal["vals",b"vals"]) -> None: ...
+global___BoolList = BoolList
+
+class StringList(google.protobuf.message.Message):
+    DESCRIPTOR: google.protobuf.descriptor.Descriptor
+    VALS_FIELD_NUMBER: builtins.int
+    @property
+    def vals(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[typing.Text]: ...
+    def __init__(self,
+        *,
+        vals: typing.Optional[typing.Iterable[typing.Text]] = ...,
+        ) -> None: ...
+    def ClearField(self, field_name: typing_extensions.Literal["vals",b"vals"]) -> None: ...
+global___StringList = StringList
+
+class BytesList(google.protobuf.message.Message):
+    DESCRIPTOR: google.protobuf.descriptor.Descriptor
+    VALS_FIELD_NUMBER: builtins.int
+    @property
+    def vals(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bytes]: ...
+    def __init__(self,
+        *,
+        vals: typing.Optional[typing.Iterable[builtins.bytes]] = ...,
+        ) -> None: ...
+    def ClearField(self, field_name: typing_extensions.Literal["vals",b"vals"]) -> None: ...
+global___BytesList = BytesList
+
+class Array(google.protobuf.message.Message):
+    DESCRIPTOR: google.protobuf.descriptor.Descriptor
+    DTYPE_FIELD_NUMBER: builtins.int
+    SHAPE_FIELD_NUMBER: builtins.int
+    STYPE_FIELD_NUMBER: builtins.int
+    DATA_FIELD_NUMBER: builtins.int
+    dtype: typing.Text
+    @property
+    def shape(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
+    stype: typing.Text
+    data: builtins.bytes
+    def __init__(self,
+        *,
+        dtype: typing.Text = ...,
+        shape: typing.Optional[typing.Iterable[builtins.int]] = ...,
+        stype: typing.Text = ...,
+        data: builtins.bytes = ...,
+        ) -> None: ...
+    def ClearField(self, field_name: typing_extensions.Literal["data",b"data","dtype",b"dtype","shape",b"shape","stype",b"stype"]) -> None: ...
+global___Array = Array
+
+class MetricsRecordValue(google.protobuf.message.Message):
+    DESCRIPTOR: google.protobuf.descriptor.Descriptor
+    DOUBLE_FIELD_NUMBER: builtins.int
+    SINT64_FIELD_NUMBER: builtins.int
+    DOUBLE_LIST_FIELD_NUMBER: builtins.int
+    SINT64_LIST_FIELD_NUMBER: builtins.int
+    double: builtins.float
+    """Single element"""
+
+    sint64: builtins.int
+    @property
+    def double_list(self) -> global___DoubleList:
+        """List types"""
+        pass
+    @property
+    def sint64_list(self) -> global___Sint64List: ...
+    def __init__(self,
+        *,
+        double: builtins.float = ...,
+        sint64: builtins.int = ...,
+        double_list: typing.Optional[global___DoubleList] = ...,
+        sint64_list: typing.Optional[global___Sint64List] = ...,
+        ) -> None: ...
+    def HasField(self, field_name: typing_extensions.Literal["double",b"double","double_list",b"double_list","sint64",b"sint64","sint64_list",b"sint64_list","value",b"value"]) -> builtins.bool: ...
+    def ClearField(self, field_name: typing_extensions.Literal["double",b"double","double_list",b"double_list","sint64",b"sint64","sint64_list",b"sint64_list","value",b"value"]) -> None: ...
+    def WhichOneof(self, oneof_group: typing_extensions.Literal["value",b"value"]) -> typing.Optional[typing_extensions.Literal["double","sint64","double_list","sint64_list"]]: ...
+global___MetricsRecordValue = MetricsRecordValue
+
+class ConfigsRecordValue(google.protobuf.message.Message):
+    DESCRIPTOR: google.protobuf.descriptor.Descriptor
+    DOUBLE_FIELD_NUMBER: builtins.int
+    SINT64_FIELD_NUMBER: builtins.int
+    BOOL_FIELD_NUMBER: builtins.int
+    STRING_FIELD_NUMBER: builtins.int
+    BYTES_FIELD_NUMBER: builtins.int
+    DOUBLE_LIST_FIELD_NUMBER: builtins.int
+    SINT64_LIST_FIELD_NUMBER: builtins.int
+    BOOL_LIST_FIELD_NUMBER: builtins.int
+    STRING_LIST_FIELD_NUMBER: builtins.int
+    BYTES_LIST_FIELD_NUMBER: builtins.int
+    double: builtins.float
+    """Single element"""
+
+    sint64: builtins.int
+    bool: builtins.bool
+    string: typing.Text
+    bytes: builtins.bytes
+    @property
+    def double_list(self) -> global___DoubleList:
+        """List types"""
+        pass
+    @property
+    def sint64_list(self) -> global___Sint64List: ...
+    @property
+    def bool_list(self) -> global___BoolList: ...
+    @property
+    def string_list(self) -> global___StringList: ...
+    @property
+    def bytes_list(self) -> global___BytesList: ...
+    def __init__(self,
+        *,
+        double: builtins.float = ...,
+        sint64: builtins.int = ...,
+        bool: builtins.bool = ...,
+        string: typing.Text = ...,
+        bytes: builtins.bytes = ...,
+        double_list: typing.Optional[global___DoubleList] = ...,
+        sint64_list: typing.Optional[global___Sint64List] = ...,
+        bool_list: typing.Optional[global___BoolList] = ...,
+        string_list: typing.Optional[global___StringList] = ...,
+        bytes_list: typing.Optional[global___BytesList] = ...,
+        ) -> None: ...
+    def HasField(self, field_name: typing_extensions.Literal["bool",b"bool","bool_list",b"bool_list","bytes",b"bytes","bytes_list",b"bytes_list","double",b"double","double_list",b"double_list","sint64",b"sint64","sint64_list",b"sint64_list","string",b"string","string_list",b"string_list","value",b"value"]) -> builtins.bool: ...
+    def ClearField(self, field_name: typing_extensions.Literal["bool",b"bool","bool_list",b"bool_list","bytes",b"bytes","bytes_list",b"bytes_list","double",b"double","double_list",b"double_list","sint64",b"sint64","sint64_list",b"sint64_list","string",b"string","string_list",b"string_list","value",b"value"]) -> None: ...
+    def WhichOneof(self, oneof_group: typing_extensions.Literal["value",b"value"]) -> typing.Optional[typing_extensions.Literal["double","sint64","bool","string","bytes","double_list","sint64_list","bool_list","string_list","bytes_list"]]: ...
+global___ConfigsRecordValue = ConfigsRecordValue
+
+class ParametersRecord(google.protobuf.message.Message):
+    DESCRIPTOR: google.protobuf.descriptor.Descriptor
+    DATA_KEYS_FIELD_NUMBER: builtins.int
+    DATA_VALUES_FIELD_NUMBER: builtins.int
+    @property
+    def data_keys(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[typing.Text]: ...
+    @property
+    def data_values(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___Array]: ...
+    def __init__(self,
+        *,
+        data_keys: typing.Optional[typing.Iterable[typing.Text]] = ...,
+        data_values: typing.Optional[typing.Iterable[global___Array]] = ...,
+        ) -> None: ...
+    def ClearField(self, field_name: typing_extensions.Literal["data_keys",b"data_keys","data_values",b"data_values"]) -> None: ...
+global___ParametersRecord = ParametersRecord
+
+class MetricsRecord(google.protobuf.message.Message):
+    DESCRIPTOR: google.protobuf.descriptor.Descriptor
+    class DataEntry(google.protobuf.message.Message):
+        DESCRIPTOR: google.protobuf.descriptor.Descriptor
+        KEY_FIELD_NUMBER: builtins.int
+        VALUE_FIELD_NUMBER: builtins.int
+        key: typing.Text
+        @property
+        def value(self) -> global___MetricsRecordValue: ...
+        def __init__(self,
+            *,
+            key: typing.Text = ...,
+            value: typing.Optional[global___MetricsRecordValue] = ...,
+            ) -> None: ...
+        def HasField(self, field_name: typing_extensions.Literal["value",b"value"]) -> builtins.bool: ...
+        def ClearField(self, field_name: typing_extensions.Literal["key",b"key","value",b"value"]) -> None: ...
+
+    DATA_FIELD_NUMBER: builtins.int
+    @property
+    def data(self) -> google.protobuf.internal.containers.MessageMap[typing.Text, global___MetricsRecordValue]: ...
+    def __init__(self,
+        *,
+        data: typing.Optional[typing.Mapping[typing.Text, global___MetricsRecordValue]] = ...,
+        ) -> None: ...
+    def ClearField(self, field_name: typing_extensions.Literal["data",b"data"]) -> None: ...
+global___MetricsRecord = MetricsRecord
+
+class ConfigsRecord(google.protobuf.message.Message):
+    DESCRIPTOR: google.protobuf.descriptor.Descriptor
+    class DataEntry(google.protobuf.message.Message):
+        DESCRIPTOR: google.protobuf.descriptor.Descriptor
+        KEY_FIELD_NUMBER: builtins.int
+        VALUE_FIELD_NUMBER: builtins.int
+        key: typing.Text
+        @property
+        def value(self) -> global___ConfigsRecordValue: ...
+        def __init__(self,
+            *,
+            key: typing.Text = ...,
+            value: typing.Optional[global___ConfigsRecordValue] = ...,
+            ) -> None: ...
+        def HasField(self, field_name: typing_extensions.Literal["value",b"value"]) -> builtins.bool: ...
+        def ClearField(self, field_name: typing_extensions.Literal["key",b"key","value",b"value"]) -> None: ...
+
+    DATA_FIELD_NUMBER: builtins.int
+    @property
+    def data(self) -> google.protobuf.internal.containers.MessageMap[typing.Text, global___ConfigsRecordValue]: ...
+    def __init__(self,
+        *,
+        data: typing.Optional[typing.Mapping[typing.Text, global___ConfigsRecordValue]] = ...,
+        ) -> None: ...
+    def ClearField(self, field_name: typing_extensions.Literal["data",b"data"]) -> None: ...
+global___ConfigsRecord = ConfigsRecord
+
+class RecordSet(google.protobuf.message.Message):
+    DESCRIPTOR: google.protobuf.descriptor.Descriptor
+    class ParametersEntry(google.protobuf.message.Message):
+        DESCRIPTOR: google.protobuf.descriptor.Descriptor
+        KEY_FIELD_NUMBER: builtins.int
+        VALUE_FIELD_NUMBER: builtins.int
+        key: typing.Text
+        @property
+        def value(self) -> global___ParametersRecord: ...
+        def __init__(self,
+            *,
+            key: typing.Text = ...,
+            value: typing.Optional[global___ParametersRecord] = ...,
+            ) -> None: ...
+        def HasField(self, field_name: typing_extensions.Literal["value",b"value"]) -> builtins.bool: ...
+        def ClearField(self, field_name: typing_extensions.Literal["key",b"key","value",b"value"]) -> None: ...
+
+    class MetricsEntry(google.protobuf.message.Message):
+        DESCRIPTOR: google.protobuf.descriptor.Descriptor
+        KEY_FIELD_NUMBER: builtins.int
+        VALUE_FIELD_NUMBER: builtins.int
+        key: typing.Text
+        @property
+        def value(self) -> global___MetricsRecord: ...
+        def __init__(self,
+            *,
+            key: typing.Text = ...,
+            value: typing.Optional[global___MetricsRecord] = ...,
+            ) -> None: ...
+        def HasField(self, field_name: typing_extensions.Literal["value",b"value"]) -> builtins.bool: ...
+        def ClearField(self, field_name: typing_extensions.Literal["key",b"key","value",b"value"]) -> None: ...
+
+    class ConfigsEntry(google.protobuf.message.Message):
+        DESCRIPTOR: google.protobuf.descriptor.Descriptor
+        KEY_FIELD_NUMBER: builtins.int
+        VALUE_FIELD_NUMBER: builtins.int
+        key: typing.Text
+        @property
+        def value(self) -> global___ConfigsRecord: ...
+        def __init__(self,
+            *,
+            key: typing.Text = ...,
+            value: typing.Optional[global___ConfigsRecord] = ...,
+            ) -> None: ...
+        def HasField(self, field_name: typing_extensions.Literal["value",b"value"]) -> builtins.bool: ...
+        def ClearField(self, field_name: typing_extensions.Literal["key",b"key","value",b"value"]) -> None: ...
+
+    PARAMETERS_FIELD_NUMBER: builtins.int
+    METRICS_FIELD_NUMBER: builtins.int
+    CONFIGS_FIELD_NUMBER: builtins.int
+    @property
+    def parameters(self) -> google.protobuf.internal.containers.MessageMap[typing.Text, global___ParametersRecord]: ...
+    @property
+    def metrics(self) -> google.protobuf.internal.containers.MessageMap[typing.Text, global___MetricsRecord]: ...
+    @property
+    def configs(self) -> google.protobuf.internal.containers.MessageMap[typing.Text, global___ConfigsRecord]: ...
+    def __init__(self,
+        *,
+        parameters: typing.Optional[typing.Mapping[typing.Text, global___ParametersRecord]] = ...,
+        metrics: typing.Optional[typing.Mapping[typing.Text, global___MetricsRecord]] = ...,
+        configs: typing.Optional[typing.Mapping[typing.Text, global___ConfigsRecord]] = ...,
+        ) -> None: ...
+    def ClearField(self, field_name: typing_extensions.Literal["configs",b"configs","metrics",b"metrics","parameters",b"parameters"]) -> None: ...
+global___RecordSet = RecordSet
diff --git a/src/py/flwr/proto/recordset_pb2_grpc.py b/src/py/flwr/proto/recordset_pb2_grpc.py
new file mode 100644
index 000000000000..2daafffebfc8
--- /dev/null
+++ b/src/py/flwr/proto/recordset_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/recordset_pb2_grpc.pyi b/src/py/flwr/proto/recordset_pb2_grpc.pyi
new file mode 100644
index 000000000000..f3a5a087ef5d
--- /dev/null
+++ b/src/py/flwr/proto/recordset_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/task_pb2.py b/src/py/flwr/proto/task_pb2.py
index 6d8cf8fd3656..4d5f863e88dd 100644
--- a/src/py/flwr/proto/task_pb2.py
+++ b/src/py/flwr/proto/task_pb2.py
@@ -1,148 +1,34 @@
 # -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: flwr/proto/task.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 message as _message
-from google.protobuf import reflection as _reflection
 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()
 
 
 from flwr.proto import node_pb2 as flwr_dot_proto_dot_node__pb2
+from flwr.proto import recordset_pb2 as flwr_dot_proto_dot_recordset__pb2
 from flwr.proto import transport_pb2 as flwr_dot_proto_dot_transport__pb2
+from flwr.proto import error_pb2 as flwr_dot_proto_dot_error__pb2
 
 
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15\x66lwr/proto/task.proto\x12\nflwr.proto\x1a\x15\x66lwr/proto/node.proto\x1a\x1a\x66lwr/proto/transport.proto\"\xbe\x02\n\x04Task\x12\"\n\x08producer\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\"\n\x08\x63onsumer\x18\x02 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x12\n\ncreated_at\x18\x03 \x01(\t\x12\x14\n\x0c\x64\x65livered_at\x18\x04 \x01(\t\x12\x0b\n\x03ttl\x18\x05 \x01(\t\x12\x10\n\x08\x61ncestry\x18\x06 \x03(\t\x12)\n\x02sa\x18\x07 \x01(\x0b\x32\x1d.flwr.proto.SecureAggregation\x12<\n\x15legacy_server_message\x18\x65 \x01(\x0b\x32\x19.flwr.proto.ServerMessageB\x02\x18\x01\x12<\n\x15legacy_client_message\x18\x66 \x01(\x0b\x32\x19.flwr.proto.ClientMessageB\x02\x18\x01\"a\n\x07TaskIns\x12\x0f\n\x07task_id\x18\x01 \x01(\t\x12\x10\n\x08group_id\x18\x02 \x01(\t\x12\x13\n\x0bworkload_id\x18\x03 \x01(\x12\x12\x1e\n\x04task\x18\x04 \x01(\x0b\x32\x10.flwr.proto.Task\"a\n\x07TaskRes\x12\x0f\n\x07task_id\x18\x01 \x01(\t\x12\x10\n\x08group_id\x18\x02 \x01(\t\x12\x13\n\x0bworkload_id\x18\x03 \x01(\x12\x12\x1e\n\x04task\x18\x04 \x01(\x0b\x32\x10.flwr.proto.Task\"\xf3\x03\n\x05Value\x12\x10\n\x06\x64ouble\x18\x01 \x01(\x01H\x00\x12\x10\n\x06sint64\x18\x02 \x01(\x12H\x00\x12\x0e\n\x04\x62ool\x18\x03 \x01(\x08H\x00\x12\x10\n\x06string\x18\x04 \x01(\tH\x00\x12\x0f\n\x05\x62ytes\x18\x05 \x01(\x0cH\x00\x12\x33\n\x0b\x64ouble_list\x18\x15 \x01(\x0b\x32\x1c.flwr.proto.Value.DoubleListH\x00\x12\x33\n\x0bsint64_list\x18\x16 \x01(\x0b\x32\x1c.flwr.proto.Value.Sint64ListH\x00\x12/\n\tbool_list\x18\x17 \x01(\x0b\x32\x1a.flwr.proto.Value.BoolListH\x00\x12\x33\n\x0bstring_list\x18\x18 \x01(\x0b\x32\x1c.flwr.proto.Value.StringListH\x00\x12\x31\n\nbytes_list\x18\x19 \x01(\x0b\x32\x1b.flwr.proto.Value.BytesListH\x00\x1a\x1a\n\nDoubleList\x12\x0c\n\x04vals\x18\x01 \x03(\x01\x1a\x1a\n\nSint64List\x12\x0c\n\x04vals\x18\x01 \x03(\x12\x1a\x18\n\x08\x42oolList\x12\x0c\n\x04vals\x18\x01 \x03(\x08\x1a\x1a\n\nStringList\x12\x0c\n\x04vals\x18\x01 \x03(\t\x1a\x19\n\tBytesList\x12\x0c\n\x04vals\x18\x01 \x03(\x0c\x42\x07\n\x05value\"\xa0\x01\n\x11SecureAggregation\x12\x44\n\x0cnamed_values\x18\x01 \x03(\x0b\x32..flwr.proto.SecureAggregation.NamedValuesEntry\x1a\x45\n\x10NamedValuesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12 \n\x05value\x18\x02 \x01(\x0b\x32\x11.flwr.proto.Value:\x02\x38\x01\x62\x06proto3')
-
-
-
-_TASK = DESCRIPTOR.message_types_by_name['Task']
-_TASKINS = DESCRIPTOR.message_types_by_name['TaskIns']
-_TASKRES = DESCRIPTOR.message_types_by_name['TaskRes']
-_VALUE = DESCRIPTOR.message_types_by_name['Value']
-_VALUE_DOUBLELIST = _VALUE.nested_types_by_name['DoubleList']
-_VALUE_SINT64LIST = _VALUE.nested_types_by_name['Sint64List']
-_VALUE_BOOLLIST = _VALUE.nested_types_by_name['BoolList']
-_VALUE_STRINGLIST = _VALUE.nested_types_by_name['StringList']
-_VALUE_BYTESLIST = _VALUE.nested_types_by_name['BytesList']
-_SECUREAGGREGATION = DESCRIPTOR.message_types_by_name['SecureAggregation']
-_SECUREAGGREGATION_NAMEDVALUESENTRY = _SECUREAGGREGATION.nested_types_by_name['NamedValuesEntry']
-Task = _reflection.GeneratedProtocolMessageType('Task', (_message.Message,), {
-  'DESCRIPTOR' : _TASK,
-  '__module__' : 'flwr.proto.task_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.Task)
-  })
-_sym_db.RegisterMessage(Task)
-
-TaskIns = _reflection.GeneratedProtocolMessageType('TaskIns', (_message.Message,), {
-  'DESCRIPTOR' : _TASKINS,
-  '__module__' : 'flwr.proto.task_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.TaskIns)
-  })
-_sym_db.RegisterMessage(TaskIns)
-
-TaskRes = _reflection.GeneratedProtocolMessageType('TaskRes', (_message.Message,), {
-  'DESCRIPTOR' : _TASKRES,
-  '__module__' : 'flwr.proto.task_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.TaskRes)
-  })
-_sym_db.RegisterMessage(TaskRes)
-
-Value = _reflection.GeneratedProtocolMessageType('Value', (_message.Message,), {
-
-  'DoubleList' : _reflection.GeneratedProtocolMessageType('DoubleList', (_message.Message,), {
-    'DESCRIPTOR' : _VALUE_DOUBLELIST,
-    '__module__' : 'flwr.proto.task_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.Value.DoubleList)
-    })
-  ,
-
-  'Sint64List' : _reflection.GeneratedProtocolMessageType('Sint64List', (_message.Message,), {
-    'DESCRIPTOR' : _VALUE_SINT64LIST,
-    '__module__' : 'flwr.proto.task_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.Value.Sint64List)
-    })
-  ,
-
-  'BoolList' : _reflection.GeneratedProtocolMessageType('BoolList', (_message.Message,), {
-    'DESCRIPTOR' : _VALUE_BOOLLIST,
-    '__module__' : 'flwr.proto.task_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.Value.BoolList)
-    })
-  ,
-
-  'StringList' : _reflection.GeneratedProtocolMessageType('StringList', (_message.Message,), {
-    'DESCRIPTOR' : _VALUE_STRINGLIST,
-    '__module__' : 'flwr.proto.task_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.Value.StringList)
-    })
-  ,
-
-  'BytesList' : _reflection.GeneratedProtocolMessageType('BytesList', (_message.Message,), {
-    'DESCRIPTOR' : _VALUE_BYTESLIST,
-    '__module__' : 'flwr.proto.task_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.Value.BytesList)
-    })
-  ,
-  'DESCRIPTOR' : _VALUE,
-  '__module__' : 'flwr.proto.task_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.Value)
-  })
-_sym_db.RegisterMessage(Value)
-_sym_db.RegisterMessage(Value.DoubleList)
-_sym_db.RegisterMessage(Value.Sint64List)
-_sym_db.RegisterMessage(Value.BoolList)
-_sym_db.RegisterMessage(Value.StringList)
-_sym_db.RegisterMessage(Value.BytesList)
-
-SecureAggregation = _reflection.GeneratedProtocolMessageType('SecureAggregation', (_message.Message,), {
-
-  'NamedValuesEntry' : _reflection.GeneratedProtocolMessageType('NamedValuesEntry', (_message.Message,), {
-    'DESCRIPTOR' : _SECUREAGGREGATION_NAMEDVALUESENTRY,
-    '__module__' : 'flwr.proto.task_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.SecureAggregation.NamedValuesEntry)
-    })
-  ,
-  'DESCRIPTOR' : _SECUREAGGREGATION,
-  '__module__' : 'flwr.proto.task_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.SecureAggregation)
-  })
-_sym_db.RegisterMessage(SecureAggregation)
-_sym_db.RegisterMessage(SecureAggregation.NamedValuesEntry)
+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x15\x66lwr/proto/task.proto\x12\nflwr.proto\x1a\x15\x66lwr/proto/node.proto\x1a\x1a\x66lwr/proto/recordset.proto\x1a\x1a\x66lwr/proto/transport.proto\x1a\x16\x66lwr/proto/error.proto\"\xf6\x01\n\x04Task\x12\"\n\x08producer\x18\x01 \x01(\x0b\x32\x10.flwr.proto.Node\x12\"\n\x08\x63onsumer\x18\x02 \x01(\x0b\x32\x10.flwr.proto.Node\x12\x12\n\ncreated_at\x18\x03 \x01(\t\x12\x14\n\x0c\x64\x65livered_at\x18\x04 \x01(\t\x12\x0b\n\x03ttl\x18\x05 \x01(\t\x12\x10\n\x08\x61ncestry\x18\x06 \x03(\t\x12\x11\n\ttask_type\x18\x07 \x01(\t\x12(\n\trecordset\x18\x08 \x01(\x0b\x32\x15.flwr.proto.RecordSet\x12 \n\x05\x65rror\x18\t \x01(\x0b\x32\x11.flwr.proto.Error\"\\\n\x07TaskIns\x12\x0f\n\x07task_id\x18\x01 \x01(\t\x12\x10\n\x08group_id\x18\x02 \x01(\t\x12\x0e\n\x06run_id\x18\x03 \x01(\x12\x12\x1e\n\x04task\x18\x04 \x01(\x0b\x32\x10.flwr.proto.Task\"\\\n\x07TaskRes\x12\x0f\n\x07task_id\x18\x01 \x01(\t\x12\x10\n\x08group_id\x18\x02 \x01(\t\x12\x0e\n\x06run_id\x18\x03 \x01(\x12\x12\x1e\n\x04task\x18\x04 \x01(\x0b\x32\x10.flwr.proto.Taskb\x06proto3')
 
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flwr.proto.task_pb2', _globals)
 if _descriptor._USE_C_DESCRIPTORS == False:
-
   DESCRIPTOR._options = None
-  _TASK.fields_by_name['legacy_server_message']._options = None
-  _TASK.fields_by_name['legacy_server_message']._serialized_options = b'\030\001'
-  _TASK.fields_by_name['legacy_client_message']._options = None
-  _TASK.fields_by_name['legacy_client_message']._serialized_options = b'\030\001'
-  _SECUREAGGREGATION_NAMEDVALUESENTRY._options = None
-  _SECUREAGGREGATION_NAMEDVALUESENTRY._serialized_options = b'8\001'
-  _TASK._serialized_start=89
-  _TASK._serialized_end=407
-  _TASKINS._serialized_start=409
-  _TASKINS._serialized_end=506
-  _TASKRES._serialized_start=508
-  _TASKRES._serialized_end=605
-  _VALUE._serialized_start=608
-  _VALUE._serialized_end=1107
-  _VALUE_DOUBLELIST._serialized_start=963
-  _VALUE_DOUBLELIST._serialized_end=989
-  _VALUE_SINT64LIST._serialized_start=991
-  _VALUE_SINT64LIST._serialized_end=1017
-  _VALUE_BOOLLIST._serialized_start=1019
-  _VALUE_BOOLLIST._serialized_end=1043
-  _VALUE_STRINGLIST._serialized_start=1045
-  _VALUE_STRINGLIST._serialized_end=1071
-  _VALUE_BYTESLIST._serialized_start=1073
-  _VALUE_BYTESLIST._serialized_end=1098
-  _SECUREAGGREGATION._serialized_start=1110
-  _SECUREAGGREGATION._serialized_end=1270
-  _SECUREAGGREGATION_NAMEDVALUESENTRY._serialized_start=1201
-  _SECUREAGGREGATION_NAMEDVALUESENTRY._serialized_end=1270
+  _globals['_TASK']._serialized_start=141
+  _globals['_TASK']._serialized_end=387
+  _globals['_TASKINS']._serialized_start=389
+  _globals['_TASKINS']._serialized_end=481
+  _globals['_TASKRES']._serialized_start=483
+  _globals['_TASKRES']._serialized_end=575
 # @@protoc_insertion_point(module_scope)
diff --git a/src/py/flwr/proto/task_pb2.pyi b/src/py/flwr/proto/task_pb2.pyi
index 7cf96cb61edf..b9c10139cfb3 100644
--- a/src/py/flwr/proto/task_pb2.pyi
+++ b/src/py/flwr/proto/task_pb2.pyi
@@ -3,8 +3,9 @@
 isort:skip_file
 """
 import builtins
+import flwr.proto.error_pb2
 import flwr.proto.node_pb2
-import flwr.proto.transport_pb2
+import flwr.proto.recordset_pb2
 import google.protobuf.descriptor
 import google.protobuf.internal.containers
 import google.protobuf.message
@@ -21,9 +22,9 @@ class Task(google.protobuf.message.Message):
     DELIVERED_AT_FIELD_NUMBER: builtins.int
     TTL_FIELD_NUMBER: builtins.int
     ANCESTRY_FIELD_NUMBER: builtins.int
-    SA_FIELD_NUMBER: builtins.int
-    LEGACY_SERVER_MESSAGE_FIELD_NUMBER: builtins.int
-    LEGACY_CLIENT_MESSAGE_FIELD_NUMBER: builtins.int
+    TASK_TYPE_FIELD_NUMBER: builtins.int
+    RECORDSET_FIELD_NUMBER: builtins.int
+    ERROR_FIELD_NUMBER: builtins.int
     @property
     def producer(self) -> flwr.proto.node_pb2.Node: ...
     @property
@@ -33,12 +34,11 @@ class Task(google.protobuf.message.Message):
     ttl: typing.Text
     @property
     def ancestry(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[typing.Text]: ...
+    task_type: typing.Text
     @property
-    def sa(self) -> global___SecureAggregation: ...
+    def recordset(self) -> flwr.proto.recordset_pb2.RecordSet: ...
     @property
-    def legacy_server_message(self) -> flwr.proto.transport_pb2.ServerMessage: ...
-    @property
-    def legacy_client_message(self) -> flwr.proto.transport_pb2.ClientMessage: ...
+    def error(self) -> flwr.proto.error_pb2.Error: ...
     def __init__(self,
         *,
         producer: typing.Optional[flwr.proto.node_pb2.Node] = ...,
@@ -47,185 +47,54 @@ class Task(google.protobuf.message.Message):
         delivered_at: typing.Text = ...,
         ttl: typing.Text = ...,
         ancestry: typing.Optional[typing.Iterable[typing.Text]] = ...,
-        sa: typing.Optional[global___SecureAggregation] = ...,
-        legacy_server_message: typing.Optional[flwr.proto.transport_pb2.ServerMessage] = ...,
-        legacy_client_message: typing.Optional[flwr.proto.transport_pb2.ClientMessage] = ...,
+        task_type: typing.Text = ...,
+        recordset: typing.Optional[flwr.proto.recordset_pb2.RecordSet] = ...,
+        error: typing.Optional[flwr.proto.error_pb2.Error] = ...,
         ) -> None: ...
-    def HasField(self, field_name: typing_extensions.Literal["consumer",b"consumer","legacy_client_message",b"legacy_client_message","legacy_server_message",b"legacy_server_message","producer",b"producer","sa",b"sa"]) -> builtins.bool: ...
-    def ClearField(self, field_name: typing_extensions.Literal["ancestry",b"ancestry","consumer",b"consumer","created_at",b"created_at","delivered_at",b"delivered_at","legacy_client_message",b"legacy_client_message","legacy_server_message",b"legacy_server_message","producer",b"producer","sa",b"sa","ttl",b"ttl"]) -> None: ...
+    def HasField(self, field_name: typing_extensions.Literal["consumer",b"consumer","error",b"error","producer",b"producer","recordset",b"recordset"]) -> builtins.bool: ...
+    def ClearField(self, field_name: typing_extensions.Literal["ancestry",b"ancestry","consumer",b"consumer","created_at",b"created_at","delivered_at",b"delivered_at","error",b"error","producer",b"producer","recordset",b"recordset","task_type",b"task_type","ttl",b"ttl"]) -> None: ...
 global___Task = Task
 
 class TaskIns(google.protobuf.message.Message):
     DESCRIPTOR: google.protobuf.descriptor.Descriptor
     TASK_ID_FIELD_NUMBER: builtins.int
     GROUP_ID_FIELD_NUMBER: builtins.int
-    WORKLOAD_ID_FIELD_NUMBER: builtins.int
+    RUN_ID_FIELD_NUMBER: builtins.int
     TASK_FIELD_NUMBER: builtins.int
     task_id: typing.Text
     group_id: typing.Text
-    workload_id: builtins.int
+    run_id: builtins.int
     @property
     def task(self) -> global___Task: ...
     def __init__(self,
         *,
         task_id: typing.Text = ...,
         group_id: typing.Text = ...,
-        workload_id: builtins.int = ...,
+        run_id: builtins.int = ...,
         task: typing.Optional[global___Task] = ...,
         ) -> None: ...
     def HasField(self, field_name: typing_extensions.Literal["task",b"task"]) -> builtins.bool: ...
-    def ClearField(self, field_name: typing_extensions.Literal["group_id",b"group_id","task",b"task","task_id",b"task_id","workload_id",b"workload_id"]) -> None: ...
+    def ClearField(self, field_name: typing_extensions.Literal["group_id",b"group_id","run_id",b"run_id","task",b"task","task_id",b"task_id"]) -> None: ...
 global___TaskIns = TaskIns
 
 class TaskRes(google.protobuf.message.Message):
     DESCRIPTOR: google.protobuf.descriptor.Descriptor
     TASK_ID_FIELD_NUMBER: builtins.int
     GROUP_ID_FIELD_NUMBER: builtins.int
-    WORKLOAD_ID_FIELD_NUMBER: builtins.int
+    RUN_ID_FIELD_NUMBER: builtins.int
     TASK_FIELD_NUMBER: builtins.int
     task_id: typing.Text
     group_id: typing.Text
-    workload_id: builtins.int
+    run_id: builtins.int
     @property
     def task(self) -> global___Task: ...
     def __init__(self,
         *,
         task_id: typing.Text = ...,
         group_id: typing.Text = ...,
-        workload_id: builtins.int = ...,
+        run_id: builtins.int = ...,
         task: typing.Optional[global___Task] = ...,
         ) -> None: ...
     def HasField(self, field_name: typing_extensions.Literal["task",b"task"]) -> builtins.bool: ...
-    def ClearField(self, field_name: typing_extensions.Literal["group_id",b"group_id","task",b"task","task_id",b"task_id","workload_id",b"workload_id"]) -> None: ...
+    def ClearField(self, field_name: typing_extensions.Literal["group_id",b"group_id","run_id",b"run_id","task",b"task","task_id",b"task_id"]) -> None: ...
 global___TaskRes = TaskRes
-
-class Value(google.protobuf.message.Message):
-    DESCRIPTOR: google.protobuf.descriptor.Descriptor
-    class DoubleList(google.protobuf.message.Message):
-        DESCRIPTOR: google.protobuf.descriptor.Descriptor
-        VALS_FIELD_NUMBER: builtins.int
-        @property
-        def vals(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]: ...
-        def __init__(self,
-            *,
-            vals: typing.Optional[typing.Iterable[builtins.float]] = ...,
-            ) -> None: ...
-        def ClearField(self, field_name: typing_extensions.Literal["vals",b"vals"]) -> None: ...
-
-    class Sint64List(google.protobuf.message.Message):
-        DESCRIPTOR: google.protobuf.descriptor.Descriptor
-        VALS_FIELD_NUMBER: builtins.int
-        @property
-        def vals(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ...
-        def __init__(self,
-            *,
-            vals: typing.Optional[typing.Iterable[builtins.int]] = ...,
-            ) -> None: ...
-        def ClearField(self, field_name: typing_extensions.Literal["vals",b"vals"]) -> None: ...
-
-    class BoolList(google.protobuf.message.Message):
-        DESCRIPTOR: google.protobuf.descriptor.Descriptor
-        VALS_FIELD_NUMBER: builtins.int
-        @property
-        def vals(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bool]: ...
-        def __init__(self,
-            *,
-            vals: typing.Optional[typing.Iterable[builtins.bool]] = ...,
-            ) -> None: ...
-        def ClearField(self, field_name: typing_extensions.Literal["vals",b"vals"]) -> None: ...
-
-    class StringList(google.protobuf.message.Message):
-        DESCRIPTOR: google.protobuf.descriptor.Descriptor
-        VALS_FIELD_NUMBER: builtins.int
-        @property
-        def vals(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[typing.Text]: ...
-        def __init__(self,
-            *,
-            vals: typing.Optional[typing.Iterable[typing.Text]] = ...,
-            ) -> None: ...
-        def ClearField(self, field_name: typing_extensions.Literal["vals",b"vals"]) -> None: ...
-
-    class BytesList(google.protobuf.message.Message):
-        DESCRIPTOR: google.protobuf.descriptor.Descriptor
-        VALS_FIELD_NUMBER: builtins.int
-        @property
-        def vals(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bytes]: ...
-        def __init__(self,
-            *,
-            vals: typing.Optional[typing.Iterable[builtins.bytes]] = ...,
-            ) -> None: ...
-        def ClearField(self, field_name: typing_extensions.Literal["vals",b"vals"]) -> None: ...
-
-    DOUBLE_FIELD_NUMBER: builtins.int
-    SINT64_FIELD_NUMBER: builtins.int
-    BOOL_FIELD_NUMBER: builtins.int
-    STRING_FIELD_NUMBER: builtins.int
-    BYTES_FIELD_NUMBER: builtins.int
-    DOUBLE_LIST_FIELD_NUMBER: builtins.int
-    SINT64_LIST_FIELD_NUMBER: builtins.int
-    BOOL_LIST_FIELD_NUMBER: builtins.int
-    STRING_LIST_FIELD_NUMBER: builtins.int
-    BYTES_LIST_FIELD_NUMBER: builtins.int
-    double: builtins.float
-    """Single element"""
-
-    sint64: builtins.int
-    bool: builtins.bool
-    string: typing.Text
-    bytes: builtins.bytes
-    @property
-    def double_list(self) -> global___Value.DoubleList:
-        """List types"""
-        pass
-    @property
-    def sint64_list(self) -> global___Value.Sint64List: ...
-    @property
-    def bool_list(self) -> global___Value.BoolList: ...
-    @property
-    def string_list(self) -> global___Value.StringList: ...
-    @property
-    def bytes_list(self) -> global___Value.BytesList: ...
-    def __init__(self,
-        *,
-        double: builtins.float = ...,
-        sint64: builtins.int = ...,
-        bool: builtins.bool = ...,
-        string: typing.Text = ...,
-        bytes: builtins.bytes = ...,
-        double_list: typing.Optional[global___Value.DoubleList] = ...,
-        sint64_list: typing.Optional[global___Value.Sint64List] = ...,
-        bool_list: typing.Optional[global___Value.BoolList] = ...,
-        string_list: typing.Optional[global___Value.StringList] = ...,
-        bytes_list: typing.Optional[global___Value.BytesList] = ...,
-        ) -> None: ...
-    def HasField(self, field_name: typing_extensions.Literal["bool",b"bool","bool_list",b"bool_list","bytes",b"bytes","bytes_list",b"bytes_list","double",b"double","double_list",b"double_list","sint64",b"sint64","sint64_list",b"sint64_list","string",b"string","string_list",b"string_list","value",b"value"]) -> builtins.bool: ...
-    def ClearField(self, field_name: typing_extensions.Literal["bool",b"bool","bool_list",b"bool_list","bytes",b"bytes","bytes_list",b"bytes_list","double",b"double","double_list",b"double_list","sint64",b"sint64","sint64_list",b"sint64_list","string",b"string","string_list",b"string_list","value",b"value"]) -> None: ...
-    def WhichOneof(self, oneof_group: typing_extensions.Literal["value",b"value"]) -> typing.Optional[typing_extensions.Literal["double","sint64","bool","string","bytes","double_list","sint64_list","bool_list","string_list","bytes_list"]]: ...
-global___Value = Value
-
-class SecureAggregation(google.protobuf.message.Message):
-    DESCRIPTOR: google.protobuf.descriptor.Descriptor
-    class NamedValuesEntry(google.protobuf.message.Message):
-        DESCRIPTOR: google.protobuf.descriptor.Descriptor
-        KEY_FIELD_NUMBER: builtins.int
-        VALUE_FIELD_NUMBER: builtins.int
-        key: typing.Text
-        @property
-        def value(self) -> global___Value: ...
-        def __init__(self,
-            *,
-            key: typing.Text = ...,
-            value: typing.Optional[global___Value] = ...,
-            ) -> None: ...
-        def HasField(self, field_name: typing_extensions.Literal["value",b"value"]) -> builtins.bool: ...
-        def ClearField(self, field_name: typing_extensions.Literal["key",b"key","value",b"value"]) -> None: ...
-
-    NAMED_VALUES_FIELD_NUMBER: builtins.int
-    @property
-    def named_values(self) -> google.protobuf.internal.containers.MessageMap[typing.Text, global___Value]: ...
-    def __init__(self,
-        *,
-        named_values: typing.Optional[typing.Mapping[typing.Text, global___Value]] = ...,
-        ) -> None: ...
-    def ClearField(self, field_name: typing_extensions.Literal["named_values",b"named_values"]) -> None: ...
-global___SecureAggregation = SecureAggregation
diff --git a/src/py/flwr/proto/transport_pb2.py b/src/py/flwr/proto/transport_pb2.py
index 1e3785b0e312..d3aae72b63ab 100644
--- a/src/py/flwr/proto/transport_pb2.py
+++ b/src/py/flwr/proto/transport_pb2.py
@@ -1,13 +1,12 @@
 # -*- coding: utf-8 -*-
 # Generated by the protocol buffer compiler.  DO NOT EDIT!
 # source: flwr/proto/transport.proto
+# Protobuf Python Version: 4.25.0
 """Generated protocol buffer code."""
-from google.protobuf.internal import enum_type_wrapper
 from google.protobuf import descriptor as _descriptor
 from google.protobuf import descriptor_pool as _descriptor_pool
-from google.protobuf import message as _message
-from google.protobuf import reflection as _reflection
 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()
@@ -17,281 +16,73 @@
 
 DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x1a\x66lwr/proto/transport.proto\x12\nflwr.proto\"9\n\x06Status\x12\x1e\n\x04\x63ode\x18\x01 \x01(\x0e\x32\x10.flwr.proto.Code\x12\x0f\n\x07message\x18\x02 \x01(\t\"2\n\nParameters\x12\x0f\n\x07tensors\x18\x01 \x03(\x0c\x12\x13\n\x0btensor_type\x18\x02 \x01(\t\"\xba\x08\n\rServerMessage\x12?\n\rreconnect_ins\x18\x01 \x01(\x0b\x32&.flwr.proto.ServerMessage.ReconnectInsH\x00\x12H\n\x12get_properties_ins\x18\x02 \x01(\x0b\x32*.flwr.proto.ServerMessage.GetPropertiesInsH\x00\x12H\n\x12get_parameters_ins\x18\x03 \x01(\x0b\x32*.flwr.proto.ServerMessage.GetParametersInsH\x00\x12\x33\n\x07\x66it_ins\x18\x04 \x01(\x0b\x32 .flwr.proto.ServerMessage.FitInsH\x00\x12=\n\x0c\x65valuate_ins\x18\x05 \x01(\x0b\x32%.flwr.proto.ServerMessage.EvaluateInsH\x00\x1a\x1f\n\x0cReconnectIns\x12\x0f\n\x07seconds\x18\x01 \x01(\x03\x1a\x9d\x01\n\x10GetPropertiesIns\x12\x46\n\x06\x63onfig\x18\x01 \x03(\x0b\x32\x36.flwr.proto.ServerMessage.GetPropertiesIns.ConfigEntry\x1a\x41\n\x0b\x43onfigEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12!\n\x05value\x18\x02 \x01(\x0b\x32\x12.flwr.proto.Scalar:\x02\x38\x01\x1a\x9d\x01\n\x10GetParametersIns\x12\x46\n\x06\x63onfig\x18\x01 \x03(\x0b\x32\x36.flwr.proto.ServerMessage.GetParametersIns.ConfigEntry\x1a\x41\n\x0b\x43onfigEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12!\n\x05value\x18\x02 \x01(\x0b\x32\x12.flwr.proto.Scalar:\x02\x38\x01\x1a\xb5\x01\n\x06\x46itIns\x12*\n\nparameters\x18\x01 \x01(\x0b\x32\x16.flwr.proto.Parameters\x12<\n\x06\x63onfig\x18\x02 \x03(\x0b\x32,.flwr.proto.ServerMessage.FitIns.ConfigEntry\x1a\x41\n\x0b\x43onfigEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12!\n\x05value\x18\x02 \x01(\x0b\x32\x12.flwr.proto.Scalar:\x02\x38\x01\x1a\xbf\x01\n\x0b\x45valuateIns\x12*\n\nparameters\x18\x01 \x01(\x0b\x32\x16.flwr.proto.Parameters\x12\x41\n\x06\x63onfig\x18\x02 \x03(\x0b\x32\x31.flwr.proto.ServerMessage.EvaluateIns.ConfigEntry\x1a\x41\n\x0b\x43onfigEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12!\n\x05value\x18\x02 \x01(\x0b\x32\x12.flwr.proto.Scalar:\x02\x38\x01\x42\x05\n\x03msg\"\xa0\t\n\rClientMessage\x12\x41\n\x0e\x64isconnect_res\x18\x01 \x01(\x0b\x32\'.flwr.proto.ClientMessage.DisconnectResH\x00\x12H\n\x12get_properties_res\x18\x02 \x01(\x0b\x32*.flwr.proto.ClientMessage.GetPropertiesResH\x00\x12H\n\x12get_parameters_res\x18\x03 \x01(\x0b\x32*.flwr.proto.ClientMessage.GetParametersResH\x00\x12\x33\n\x07\x66it_res\x18\x04 \x01(\x0b\x32 .flwr.proto.ClientMessage.FitResH\x00\x12=\n\x0c\x65valuate_res\x18\x05 \x01(\x0b\x32%.flwr.proto.ClientMessage.EvaluateResH\x00\x1a\x33\n\rDisconnectRes\x12\"\n\x06reason\x18\x01 \x01(\x0e\x32\x12.flwr.proto.Reason\x1a\xcd\x01\n\x10GetPropertiesRes\x12\"\n\x06status\x18\x01 \x01(\x0b\x32\x12.flwr.proto.Status\x12N\n\nproperties\x18\x02 \x03(\x0b\x32:.flwr.proto.ClientMessage.GetPropertiesRes.PropertiesEntry\x1a\x45\n\x0fPropertiesEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12!\n\x05value\x18\x02 \x01(\x0b\x32\x12.flwr.proto.Scalar:\x02\x38\x01\x1a\x62\n\x10GetParametersRes\x12\"\n\x06status\x18\x01 \x01(\x0b\x32\x12.flwr.proto.Status\x12*\n\nparameters\x18\x02 \x01(\x0b\x32\x16.flwr.proto.Parameters\x1a\xf2\x01\n\x06\x46itRes\x12\"\n\x06status\x18\x01 \x01(\x0b\x32\x12.flwr.proto.Status\x12*\n\nparameters\x18\x02 \x01(\x0b\x32\x16.flwr.proto.Parameters\x12\x14\n\x0cnum_examples\x18\x03 \x01(\x03\x12>\n\x07metrics\x18\x04 \x03(\x0b\x32-.flwr.proto.ClientMessage.FitRes.MetricsEntry\x1a\x42\n\x0cMetricsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12!\n\x05value\x18\x02 \x01(\x0b\x32\x12.flwr.proto.Scalar:\x02\x38\x01\x1a\xde\x01\n\x0b\x45valuateRes\x12\"\n\x06status\x18\x01 \x01(\x0b\x32\x12.flwr.proto.Status\x12\x0c\n\x04loss\x18\x02 \x01(\x02\x12\x14\n\x0cnum_examples\x18\x03 \x01(\x03\x12\x43\n\x07metrics\x18\x04 \x03(\x0b\x32\x32.flwr.proto.ClientMessage.EvaluateRes.MetricsEntry\x1a\x42\n\x0cMetricsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12!\n\x05value\x18\x02 \x01(\x0b\x32\x12.flwr.proto.Scalar:\x02\x38\x01\x42\x05\n\x03msg\"i\n\x06Scalar\x12\x10\n\x06\x64ouble\x18\x01 \x01(\x01H\x00\x12\x10\n\x06sint64\x18\x08 \x01(\x12H\x00\x12\x0e\n\x04\x62ool\x18\r \x01(\x08H\x00\x12\x10\n\x06string\x18\x0e \x01(\tH\x00\x12\x0f\n\x05\x62ytes\x18\x0f \x01(\x0cH\x00\x42\x08\n\x06scalar*\x8d\x01\n\x04\x43ode\x12\x06\n\x02OK\x10\x00\x12\"\n\x1eGET_PROPERTIES_NOT_IMPLEMENTED\x10\x01\x12\"\n\x1eGET_PARAMETERS_NOT_IMPLEMENTED\x10\x02\x12\x17\n\x13\x46IT_NOT_IMPLEMENTED\x10\x03\x12\x1c\n\x18\x45VALUATE_NOT_IMPLEMENTED\x10\x04*[\n\x06Reason\x12\x0b\n\x07UNKNOWN\x10\x00\x12\r\n\tRECONNECT\x10\x01\x12\x16\n\x12POWER_DISCONNECTED\x10\x02\x12\x14\n\x10WIFI_UNAVAILABLE\x10\x03\x12\x07\n\x03\x41\x43K\x10\x04\x32S\n\rFlowerService\x12\x42\n\x04Join\x12\x19.flwr.proto.ClientMessage\x1a\x19.flwr.proto.ServerMessage\"\x00(\x01\x30\x01\x62\x06proto3')
 
-_CODE = DESCRIPTOR.enum_types_by_name['Code']
-Code = enum_type_wrapper.EnumTypeWrapper(_CODE)
-_REASON = DESCRIPTOR.enum_types_by_name['Reason']
-Reason = enum_type_wrapper.EnumTypeWrapper(_REASON)
-OK = 0
-GET_PROPERTIES_NOT_IMPLEMENTED = 1
-GET_PARAMETERS_NOT_IMPLEMENTED = 2
-FIT_NOT_IMPLEMENTED = 3
-EVALUATE_NOT_IMPLEMENTED = 4
-UNKNOWN = 0
-RECONNECT = 1
-POWER_DISCONNECTED = 2
-WIFI_UNAVAILABLE = 3
-ACK = 4
-
-
-_STATUS = DESCRIPTOR.message_types_by_name['Status']
-_PARAMETERS = DESCRIPTOR.message_types_by_name['Parameters']
-_SERVERMESSAGE = DESCRIPTOR.message_types_by_name['ServerMessage']
-_SERVERMESSAGE_RECONNECTINS = _SERVERMESSAGE.nested_types_by_name['ReconnectIns']
-_SERVERMESSAGE_GETPROPERTIESINS = _SERVERMESSAGE.nested_types_by_name['GetPropertiesIns']
-_SERVERMESSAGE_GETPROPERTIESINS_CONFIGENTRY = _SERVERMESSAGE_GETPROPERTIESINS.nested_types_by_name['ConfigEntry']
-_SERVERMESSAGE_GETPARAMETERSINS = _SERVERMESSAGE.nested_types_by_name['GetParametersIns']
-_SERVERMESSAGE_GETPARAMETERSINS_CONFIGENTRY = _SERVERMESSAGE_GETPARAMETERSINS.nested_types_by_name['ConfigEntry']
-_SERVERMESSAGE_FITINS = _SERVERMESSAGE.nested_types_by_name['FitIns']
-_SERVERMESSAGE_FITINS_CONFIGENTRY = _SERVERMESSAGE_FITINS.nested_types_by_name['ConfigEntry']
-_SERVERMESSAGE_EVALUATEINS = _SERVERMESSAGE.nested_types_by_name['EvaluateIns']
-_SERVERMESSAGE_EVALUATEINS_CONFIGENTRY = _SERVERMESSAGE_EVALUATEINS.nested_types_by_name['ConfigEntry']
-_CLIENTMESSAGE = DESCRIPTOR.message_types_by_name['ClientMessage']
-_CLIENTMESSAGE_DISCONNECTRES = _CLIENTMESSAGE.nested_types_by_name['DisconnectRes']
-_CLIENTMESSAGE_GETPROPERTIESRES = _CLIENTMESSAGE.nested_types_by_name['GetPropertiesRes']
-_CLIENTMESSAGE_GETPROPERTIESRES_PROPERTIESENTRY = _CLIENTMESSAGE_GETPROPERTIESRES.nested_types_by_name['PropertiesEntry']
-_CLIENTMESSAGE_GETPARAMETERSRES = _CLIENTMESSAGE.nested_types_by_name['GetParametersRes']
-_CLIENTMESSAGE_FITRES = _CLIENTMESSAGE.nested_types_by_name['FitRes']
-_CLIENTMESSAGE_FITRES_METRICSENTRY = _CLIENTMESSAGE_FITRES.nested_types_by_name['MetricsEntry']
-_CLIENTMESSAGE_EVALUATERES = _CLIENTMESSAGE.nested_types_by_name['EvaluateRes']
-_CLIENTMESSAGE_EVALUATERES_METRICSENTRY = _CLIENTMESSAGE_EVALUATERES.nested_types_by_name['MetricsEntry']
-_SCALAR = DESCRIPTOR.message_types_by_name['Scalar']
-Status = _reflection.GeneratedProtocolMessageType('Status', (_message.Message,), {
-  'DESCRIPTOR' : _STATUS,
-  '__module__' : 'flwr.proto.transport_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.Status)
-  })
-_sym_db.RegisterMessage(Status)
-
-Parameters = _reflection.GeneratedProtocolMessageType('Parameters', (_message.Message,), {
-  'DESCRIPTOR' : _PARAMETERS,
-  '__module__' : 'flwr.proto.transport_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.Parameters)
-  })
-_sym_db.RegisterMessage(Parameters)
-
-ServerMessage = _reflection.GeneratedProtocolMessageType('ServerMessage', (_message.Message,), {
-
-  'ReconnectIns' : _reflection.GeneratedProtocolMessageType('ReconnectIns', (_message.Message,), {
-    'DESCRIPTOR' : _SERVERMESSAGE_RECONNECTINS,
-    '__module__' : 'flwr.proto.transport_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.ServerMessage.ReconnectIns)
-    })
-  ,
-
-  'GetPropertiesIns' : _reflection.GeneratedProtocolMessageType('GetPropertiesIns', (_message.Message,), {
-
-    'ConfigEntry' : _reflection.GeneratedProtocolMessageType('ConfigEntry', (_message.Message,), {
-      'DESCRIPTOR' : _SERVERMESSAGE_GETPROPERTIESINS_CONFIGENTRY,
-      '__module__' : 'flwr.proto.transport_pb2'
-      # @@protoc_insertion_point(class_scope:flwr.proto.ServerMessage.GetPropertiesIns.ConfigEntry)
-      })
-    ,
-    'DESCRIPTOR' : _SERVERMESSAGE_GETPROPERTIESINS,
-    '__module__' : 'flwr.proto.transport_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.ServerMessage.GetPropertiesIns)
-    })
-  ,
-
-  'GetParametersIns' : _reflection.GeneratedProtocolMessageType('GetParametersIns', (_message.Message,), {
-
-    'ConfigEntry' : _reflection.GeneratedProtocolMessageType('ConfigEntry', (_message.Message,), {
-      'DESCRIPTOR' : _SERVERMESSAGE_GETPARAMETERSINS_CONFIGENTRY,
-      '__module__' : 'flwr.proto.transport_pb2'
-      # @@protoc_insertion_point(class_scope:flwr.proto.ServerMessage.GetParametersIns.ConfigEntry)
-      })
-    ,
-    'DESCRIPTOR' : _SERVERMESSAGE_GETPARAMETERSINS,
-    '__module__' : 'flwr.proto.transport_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.ServerMessage.GetParametersIns)
-    })
-  ,
-
-  'FitIns' : _reflection.GeneratedProtocolMessageType('FitIns', (_message.Message,), {
-
-    'ConfigEntry' : _reflection.GeneratedProtocolMessageType('ConfigEntry', (_message.Message,), {
-      'DESCRIPTOR' : _SERVERMESSAGE_FITINS_CONFIGENTRY,
-      '__module__' : 'flwr.proto.transport_pb2'
-      # @@protoc_insertion_point(class_scope:flwr.proto.ServerMessage.FitIns.ConfigEntry)
-      })
-    ,
-    'DESCRIPTOR' : _SERVERMESSAGE_FITINS,
-    '__module__' : 'flwr.proto.transport_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.ServerMessage.FitIns)
-    })
-  ,
-
-  'EvaluateIns' : _reflection.GeneratedProtocolMessageType('EvaluateIns', (_message.Message,), {
-
-    'ConfigEntry' : _reflection.GeneratedProtocolMessageType('ConfigEntry', (_message.Message,), {
-      'DESCRIPTOR' : _SERVERMESSAGE_EVALUATEINS_CONFIGENTRY,
-      '__module__' : 'flwr.proto.transport_pb2'
-      # @@protoc_insertion_point(class_scope:flwr.proto.ServerMessage.EvaluateIns.ConfigEntry)
-      })
-    ,
-    'DESCRIPTOR' : _SERVERMESSAGE_EVALUATEINS,
-    '__module__' : 'flwr.proto.transport_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.ServerMessage.EvaluateIns)
-    })
-  ,
-  'DESCRIPTOR' : _SERVERMESSAGE,
-  '__module__' : 'flwr.proto.transport_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.ServerMessage)
-  })
-_sym_db.RegisterMessage(ServerMessage)
-_sym_db.RegisterMessage(ServerMessage.ReconnectIns)
-_sym_db.RegisterMessage(ServerMessage.GetPropertiesIns)
-_sym_db.RegisterMessage(ServerMessage.GetPropertiesIns.ConfigEntry)
-_sym_db.RegisterMessage(ServerMessage.GetParametersIns)
-_sym_db.RegisterMessage(ServerMessage.GetParametersIns.ConfigEntry)
-_sym_db.RegisterMessage(ServerMessage.FitIns)
-_sym_db.RegisterMessage(ServerMessage.FitIns.ConfigEntry)
-_sym_db.RegisterMessage(ServerMessage.EvaluateIns)
-_sym_db.RegisterMessage(ServerMessage.EvaluateIns.ConfigEntry)
-
-ClientMessage = _reflection.GeneratedProtocolMessageType('ClientMessage', (_message.Message,), {
-
-  'DisconnectRes' : _reflection.GeneratedProtocolMessageType('DisconnectRes', (_message.Message,), {
-    'DESCRIPTOR' : _CLIENTMESSAGE_DISCONNECTRES,
-    '__module__' : 'flwr.proto.transport_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.ClientMessage.DisconnectRes)
-    })
-  ,
-
-  'GetPropertiesRes' : _reflection.GeneratedProtocolMessageType('GetPropertiesRes', (_message.Message,), {
-
-    'PropertiesEntry' : _reflection.GeneratedProtocolMessageType('PropertiesEntry', (_message.Message,), {
-      'DESCRIPTOR' : _CLIENTMESSAGE_GETPROPERTIESRES_PROPERTIESENTRY,
-      '__module__' : 'flwr.proto.transport_pb2'
-      # @@protoc_insertion_point(class_scope:flwr.proto.ClientMessage.GetPropertiesRes.PropertiesEntry)
-      })
-    ,
-    'DESCRIPTOR' : _CLIENTMESSAGE_GETPROPERTIESRES,
-    '__module__' : 'flwr.proto.transport_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.ClientMessage.GetPropertiesRes)
-    })
-  ,
-
-  'GetParametersRes' : _reflection.GeneratedProtocolMessageType('GetParametersRes', (_message.Message,), {
-    'DESCRIPTOR' : _CLIENTMESSAGE_GETPARAMETERSRES,
-    '__module__' : 'flwr.proto.transport_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.ClientMessage.GetParametersRes)
-    })
-  ,
-
-  'FitRes' : _reflection.GeneratedProtocolMessageType('FitRes', (_message.Message,), {
-
-    'MetricsEntry' : _reflection.GeneratedProtocolMessageType('MetricsEntry', (_message.Message,), {
-      'DESCRIPTOR' : _CLIENTMESSAGE_FITRES_METRICSENTRY,
-      '__module__' : 'flwr.proto.transport_pb2'
-      # @@protoc_insertion_point(class_scope:flwr.proto.ClientMessage.FitRes.MetricsEntry)
-      })
-    ,
-    'DESCRIPTOR' : _CLIENTMESSAGE_FITRES,
-    '__module__' : 'flwr.proto.transport_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.ClientMessage.FitRes)
-    })
-  ,
-
-  'EvaluateRes' : _reflection.GeneratedProtocolMessageType('EvaluateRes', (_message.Message,), {
-
-    'MetricsEntry' : _reflection.GeneratedProtocolMessageType('MetricsEntry', (_message.Message,), {
-      'DESCRIPTOR' : _CLIENTMESSAGE_EVALUATERES_METRICSENTRY,
-      '__module__' : 'flwr.proto.transport_pb2'
-      # @@protoc_insertion_point(class_scope:flwr.proto.ClientMessage.EvaluateRes.MetricsEntry)
-      })
-    ,
-    'DESCRIPTOR' : _CLIENTMESSAGE_EVALUATERES,
-    '__module__' : 'flwr.proto.transport_pb2'
-    # @@protoc_insertion_point(class_scope:flwr.proto.ClientMessage.EvaluateRes)
-    })
-  ,
-  'DESCRIPTOR' : _CLIENTMESSAGE,
-  '__module__' : 'flwr.proto.transport_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.ClientMessage)
-  })
-_sym_db.RegisterMessage(ClientMessage)
-_sym_db.RegisterMessage(ClientMessage.DisconnectRes)
-_sym_db.RegisterMessage(ClientMessage.GetPropertiesRes)
-_sym_db.RegisterMessage(ClientMessage.GetPropertiesRes.PropertiesEntry)
-_sym_db.RegisterMessage(ClientMessage.GetParametersRes)
-_sym_db.RegisterMessage(ClientMessage.FitRes)
-_sym_db.RegisterMessage(ClientMessage.FitRes.MetricsEntry)
-_sym_db.RegisterMessage(ClientMessage.EvaluateRes)
-_sym_db.RegisterMessage(ClientMessage.EvaluateRes.MetricsEntry)
-
-Scalar = _reflection.GeneratedProtocolMessageType('Scalar', (_message.Message,), {
-  'DESCRIPTOR' : _SCALAR,
-  '__module__' : 'flwr.proto.transport_pb2'
-  # @@protoc_insertion_point(class_scope:flwr.proto.Scalar)
-  })
-_sym_db.RegisterMessage(Scalar)
-
-_FLOWERSERVICE = DESCRIPTOR.services_by_name['FlowerService']
+_globals = globals()
+_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
+_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'flwr.proto.transport_pb2', _globals)
 if _descriptor._USE_C_DESCRIPTORS == False:
-
   DESCRIPTOR._options = None
-  _SERVERMESSAGE_GETPROPERTIESINS_CONFIGENTRY._options = None
-  _SERVERMESSAGE_GETPROPERTIESINS_CONFIGENTRY._serialized_options = b'8\001'
-  _SERVERMESSAGE_GETPARAMETERSINS_CONFIGENTRY._options = None
-  _SERVERMESSAGE_GETPARAMETERSINS_CONFIGENTRY._serialized_options = b'8\001'
-  _SERVERMESSAGE_FITINS_CONFIGENTRY._options = None
-  _SERVERMESSAGE_FITINS_CONFIGENTRY._serialized_options = b'8\001'
-  _SERVERMESSAGE_EVALUATEINS_CONFIGENTRY._options = None
-  _SERVERMESSAGE_EVALUATEINS_CONFIGENTRY._serialized_options = b'8\001'
-  _CLIENTMESSAGE_GETPROPERTIESRES_PROPERTIESENTRY._options = None
-  _CLIENTMESSAGE_GETPROPERTIESRES_PROPERTIESENTRY._serialized_options = b'8\001'
-  _CLIENTMESSAGE_FITRES_METRICSENTRY._options = None
-  _CLIENTMESSAGE_FITRES_METRICSENTRY._serialized_options = b'8\001'
-  _CLIENTMESSAGE_EVALUATERES_METRICSENTRY._options = None
-  _CLIENTMESSAGE_EVALUATERES_METRICSENTRY._serialized_options = b'8\001'
-  _CODE._serialized_start=2533
-  _CODE._serialized_end=2674
-  _REASON._serialized_start=2676
-  _REASON._serialized_end=2767
-  _STATUS._serialized_start=42
-  _STATUS._serialized_end=99
-  _PARAMETERS._serialized_start=101
-  _PARAMETERS._serialized_end=151
-  _SERVERMESSAGE._serialized_start=154
-  _SERVERMESSAGE._serialized_end=1236
-  _SERVERMESSAGE_RECONNECTINS._serialized_start=500
-  _SERVERMESSAGE_RECONNECTINS._serialized_end=531
-  _SERVERMESSAGE_GETPROPERTIESINS._serialized_start=534
-  _SERVERMESSAGE_GETPROPERTIESINS._serialized_end=691
-  _SERVERMESSAGE_GETPROPERTIESINS_CONFIGENTRY._serialized_start=626
-  _SERVERMESSAGE_GETPROPERTIESINS_CONFIGENTRY._serialized_end=691
-  _SERVERMESSAGE_GETPARAMETERSINS._serialized_start=694
-  _SERVERMESSAGE_GETPARAMETERSINS._serialized_end=851
-  _SERVERMESSAGE_GETPARAMETERSINS_CONFIGENTRY._serialized_start=626
-  _SERVERMESSAGE_GETPARAMETERSINS_CONFIGENTRY._serialized_end=691
-  _SERVERMESSAGE_FITINS._serialized_start=854
-  _SERVERMESSAGE_FITINS._serialized_end=1035
-  _SERVERMESSAGE_FITINS_CONFIGENTRY._serialized_start=626
-  _SERVERMESSAGE_FITINS_CONFIGENTRY._serialized_end=691
-  _SERVERMESSAGE_EVALUATEINS._serialized_start=1038
-  _SERVERMESSAGE_EVALUATEINS._serialized_end=1229
-  _SERVERMESSAGE_EVALUATEINS_CONFIGENTRY._serialized_start=626
-  _SERVERMESSAGE_EVALUATEINS_CONFIGENTRY._serialized_end=691
-  _CLIENTMESSAGE._serialized_start=1239
-  _CLIENTMESSAGE._serialized_end=2423
-  _CLIENTMESSAGE_DISCONNECTRES._serialized_start=1587
-  _CLIENTMESSAGE_DISCONNECTRES._serialized_end=1638
-  _CLIENTMESSAGE_GETPROPERTIESRES._serialized_start=1641
-  _CLIENTMESSAGE_GETPROPERTIESRES._serialized_end=1846
-  _CLIENTMESSAGE_GETPROPERTIESRES_PROPERTIESENTRY._serialized_start=1777
-  _CLIENTMESSAGE_GETPROPERTIESRES_PROPERTIESENTRY._serialized_end=1846
-  _CLIENTMESSAGE_GETPARAMETERSRES._serialized_start=1848
-  _CLIENTMESSAGE_GETPARAMETERSRES._serialized_end=1946
-  _CLIENTMESSAGE_FITRES._serialized_start=1949
-  _CLIENTMESSAGE_FITRES._serialized_end=2191
-  _CLIENTMESSAGE_FITRES_METRICSENTRY._serialized_start=2125
-  _CLIENTMESSAGE_FITRES_METRICSENTRY._serialized_end=2191
-  _CLIENTMESSAGE_EVALUATERES._serialized_start=2194
-  _CLIENTMESSAGE_EVALUATERES._serialized_end=2416
-  _CLIENTMESSAGE_EVALUATERES_METRICSENTRY._serialized_start=2125
-  _CLIENTMESSAGE_EVALUATERES_METRICSENTRY._serialized_end=2191
-  _SCALAR._serialized_start=2425
-  _SCALAR._serialized_end=2530
-  _FLOWERSERVICE._serialized_start=2769
-  _FLOWERSERVICE._serialized_end=2852
+  _globals['_SERVERMESSAGE_GETPROPERTIESINS_CONFIGENTRY']._options = None
+  _globals['_SERVERMESSAGE_GETPROPERTIESINS_CONFIGENTRY']._serialized_options = b'8\001'
+  _globals['_SERVERMESSAGE_GETPARAMETERSINS_CONFIGENTRY']._options = None
+  _globals['_SERVERMESSAGE_GETPARAMETERSINS_CONFIGENTRY']._serialized_options = b'8\001'
+  _globals['_SERVERMESSAGE_FITINS_CONFIGENTRY']._options = None
+  _globals['_SERVERMESSAGE_FITINS_CONFIGENTRY']._serialized_options = b'8\001'
+  _globals['_SERVERMESSAGE_EVALUATEINS_CONFIGENTRY']._options = None
+  _globals['_SERVERMESSAGE_EVALUATEINS_CONFIGENTRY']._serialized_options = b'8\001'
+  _globals['_CLIENTMESSAGE_GETPROPERTIESRES_PROPERTIESENTRY']._options = None
+  _globals['_CLIENTMESSAGE_GETPROPERTIESRES_PROPERTIESENTRY']._serialized_options = b'8\001'
+  _globals['_CLIENTMESSAGE_FITRES_METRICSENTRY']._options = None
+  _globals['_CLIENTMESSAGE_FITRES_METRICSENTRY']._serialized_options = b'8\001'
+  _globals['_CLIENTMESSAGE_EVALUATERES_METRICSENTRY']._options = None
+  _globals['_CLIENTMESSAGE_EVALUATERES_METRICSENTRY']._serialized_options = b'8\001'
+  _globals['_CODE']._serialized_start=2533
+  _globals['_CODE']._serialized_end=2674
+  _globals['_REASON']._serialized_start=2676
+  _globals['_REASON']._serialized_end=2767
+  _globals['_STATUS']._serialized_start=42
+  _globals['_STATUS']._serialized_end=99
+  _globals['_PARAMETERS']._serialized_start=101
+  _globals['_PARAMETERS']._serialized_end=151
+  _globals['_SERVERMESSAGE']._serialized_start=154
+  _globals['_SERVERMESSAGE']._serialized_end=1236
+  _globals['_SERVERMESSAGE_RECONNECTINS']._serialized_start=500
+  _globals['_SERVERMESSAGE_RECONNECTINS']._serialized_end=531
+  _globals['_SERVERMESSAGE_GETPROPERTIESINS']._serialized_start=534
+  _globals['_SERVERMESSAGE_GETPROPERTIESINS']._serialized_end=691
+  _globals['_SERVERMESSAGE_GETPROPERTIESINS_CONFIGENTRY']._serialized_start=626
+  _globals['_SERVERMESSAGE_GETPROPERTIESINS_CONFIGENTRY']._serialized_end=691
+  _globals['_SERVERMESSAGE_GETPARAMETERSINS']._serialized_start=694
+  _globals['_SERVERMESSAGE_GETPARAMETERSINS']._serialized_end=851
+  _globals['_SERVERMESSAGE_GETPARAMETERSINS_CONFIGENTRY']._serialized_start=626
+  _globals['_SERVERMESSAGE_GETPARAMETERSINS_CONFIGENTRY']._serialized_end=691
+  _globals['_SERVERMESSAGE_FITINS']._serialized_start=854
+  _globals['_SERVERMESSAGE_FITINS']._serialized_end=1035
+  _globals['_SERVERMESSAGE_FITINS_CONFIGENTRY']._serialized_start=626
+  _globals['_SERVERMESSAGE_FITINS_CONFIGENTRY']._serialized_end=691
+  _globals['_SERVERMESSAGE_EVALUATEINS']._serialized_start=1038
+  _globals['_SERVERMESSAGE_EVALUATEINS']._serialized_end=1229
+  _globals['_SERVERMESSAGE_EVALUATEINS_CONFIGENTRY']._serialized_start=626
+  _globals['_SERVERMESSAGE_EVALUATEINS_CONFIGENTRY']._serialized_end=691
+  _globals['_CLIENTMESSAGE']._serialized_start=1239
+  _globals['_CLIENTMESSAGE']._serialized_end=2423
+  _globals['_CLIENTMESSAGE_DISCONNECTRES']._serialized_start=1587
+  _globals['_CLIENTMESSAGE_DISCONNECTRES']._serialized_end=1638
+  _globals['_CLIENTMESSAGE_GETPROPERTIESRES']._serialized_start=1641
+  _globals['_CLIENTMESSAGE_GETPROPERTIESRES']._serialized_end=1846
+  _globals['_CLIENTMESSAGE_GETPROPERTIESRES_PROPERTIESENTRY']._serialized_start=1777
+  _globals['_CLIENTMESSAGE_GETPROPERTIESRES_PROPERTIESENTRY']._serialized_end=1846
+  _globals['_CLIENTMESSAGE_GETPARAMETERSRES']._serialized_start=1848
+  _globals['_CLIENTMESSAGE_GETPARAMETERSRES']._serialized_end=1946
+  _globals['_CLIENTMESSAGE_FITRES']._serialized_start=1949
+  _globals['_CLIENTMESSAGE_FITRES']._serialized_end=2191
+  _globals['_CLIENTMESSAGE_FITRES_METRICSENTRY']._serialized_start=2125
+  _globals['_CLIENTMESSAGE_FITRES_METRICSENTRY']._serialized_end=2191
+  _globals['_CLIENTMESSAGE_EVALUATERES']._serialized_start=2194
+  _globals['_CLIENTMESSAGE_EVALUATERES']._serialized_end=2416
+  _globals['_CLIENTMESSAGE_EVALUATERES_METRICSENTRY']._serialized_start=2125
+  _globals['_CLIENTMESSAGE_EVALUATERES_METRICSENTRY']._serialized_end=2191
+  _globals['_SCALAR']._serialized_start=2425
+  _globals['_SCALAR']._serialized_end=2530
+  _globals['_FLOWERSERVICE']._serialized_start=2769
+  _globals['_FLOWERSERVICE']._serialized_end=2852
 # @@protoc_insertion_point(module_scope)
diff --git a/src/py/flwr/server/__init__.py b/src/py/flwr/server/__init__.py
index 74abe8dd463c..633bd668b520 100644
--- a/src/py/flwr/server/__init__.py
+++ b/src/py/flwr/server/__init__.py
@@ -16,25 +16,37 @@
 
 
 from . import strategy
-from .app import ServerConfig as ServerConfig
+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_server as run_server
+from .app import run_superlink as run_superlink
 from .app import start_server as start_server
 from .client_manager import ClientManager as ClientManager
 from .client_manager import SimpleClientManager as SimpleClientManager
+from .compat import LegacyContext as LegacyContext
+from .compat import start_driver as start_driver
+from .driver import Driver as Driver
 from .history import History as History
+from .run_serverapp import run_server_app as run_server_app
 from .server import Server as Server
+from .server_app import ServerApp as ServerApp
+from .server_config import ServerConfig as ServerConfig
 
 __all__ = [
     "ClientManager",
+    "Driver",
     "History",
+    "LegacyContext",
     "run_driver_api",
     "run_fleet_api",
-    "run_server",
+    "run_server_app",
+    "run_superlink",
     "Server",
+    "ServerApp",
     "ServerConfig",
     "SimpleClientManager",
+    "start_driver",
     "start_server",
     "strategy",
+    "workflow",
 ]
diff --git a/src/py/flwr/server/app.py b/src/py/flwr/server/app.py
index 63c24c37a685..e04cfb37e118 100644
--- a/src/py/flwr/server/app.py
+++ b/src/py/flwr/server/app.py
@@ -14,17 +14,14 @@
 # ==============================================================================
 """Flower server app."""
 
-
 import argparse
+import asyncio
 import importlib.util
 import sys
 import threading
-from dataclasses import dataclass
 from logging import ERROR, INFO, WARN
 from os.path import isfile
 from pathlib import Path
-from signal import SIGINT, SIGTERM, signal
-from types import FrameType
 from typing import List, Optional, Tuple
 
 import grpc
@@ -33,27 +30,29 @@
 from flwr.common.address import parse_address
 from flwr.common.constant import (
     MISSING_EXTRA_REST,
-    TRANSPORT_TYPE_GRPC_BIDI,
     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
-from flwr.proto.driver_pb2_grpc import add_DriverServicer_to_server
-from flwr.proto.fleet_pb2_grpc import add_FleetServicer_to_server
-from flwr.proto.transport_pb2_grpc import add_FlowerServiceServicer_to_server
-from flwr.server.client_manager import ClientManager, SimpleClientManager
-from flwr.server.driver.driver_servicer import DriverServicer
-from flwr.server.fleet.grpc_bidi.driver_client_manager import DriverClientManager
-from flwr.server.fleet.grpc_bidi.flower_service_servicer import FlowerServiceServicer
-from flwr.server.fleet.grpc_bidi.grpc_server import (
+from flwr.proto.fleet_pb2_grpc import (  # pylint: disable=E0611
+    add_FleetServicer_to_server,
+)
+
+from .client_manager import ClientManager
+from .history import History
+from .server import Server, init_defaults, run_fl
+from .server_config import ServerConfig
+from .strategy import Strategy
+from .superlink.driver.driver_grpc import run_driver_api_grpc
+from .superlink.fleet.grpc_bidi.grpc_server import (
     generic_create_grpc_server,
     start_grpc_server,
 )
-from flwr.server.fleet.grpc_rere.fleet_servicer import FleetServicer
-from flwr.server.history import History
-from flwr.server.server import Server
-from flwr.server.state import StateFactory
-from flwr.server.strategy import FedAvg, Strategy
+from .superlink.fleet.grpc_rere.fleet_servicer import FleetServicer
+from .superlink.fleet.vce import start_vce
+from .superlink.state import StateFactory
 
 ADDRESS_DRIVER_API = "0.0.0.0:9091"
 ADDRESS_FLEET_API_GRPC_RERE = "0.0.0.0:9092"
@@ -63,18 +62,6 @@
 DATABASE = ":flwr-in-memory-state:"
 
 
-@dataclass
-class ServerConfig:
-    """Flower server config.
-
-    All attributes have default values which allows users to configure just the ones
-    they care about.
-    """
-
-    num_rounds: int = 1
-    round_timeout: Optional[float] = None
-
-
 def start_server(  # pylint: disable=too-many-arguments,too-many-locals
     *,
     server_address: str = ADDRESS_FLEET_API_GRPC_BIDI,
@@ -194,52 +181,11 @@ def start_server(  # pylint: disable=too-many-arguments,too-many-locals
     return hist
 
 
-def init_defaults(
-    server: Optional[Server],
-    config: Optional[ServerConfig],
-    strategy: Optional[Strategy],
-    client_manager: Optional[ClientManager],
-) -> Tuple[Server, ServerConfig]:
-    """Create server instance if none was given."""
-    if server is None:
-        if client_manager is None:
-            client_manager = SimpleClientManager()
-        if strategy is None:
-            strategy = FedAvg()
-        server = Server(client_manager=client_manager, strategy=strategy)
-    elif strategy is not None:
-        log(WARN, "Both server and strategy were provided, ignoring strategy")
-
-    # Set default config values
-    if config is None:
-        config = ServerConfig()
-
-    return server, config
-
-
-def run_fl(
-    server: Server,
-    config: ServerConfig,
-) -> History:
-    """Train a model on the given server and return the History object."""
-    hist = server.fit(num_rounds=config.num_rounds, timeout=config.round_timeout)
-    log(INFO, "app_fit: losses_distributed %s", str(hist.losses_distributed))
-    log(INFO, "app_fit: metrics_distributed_fit %s", str(hist.metrics_distributed_fit))
-    log(INFO, "app_fit: metrics_distributed %s", str(hist.metrics_distributed))
-    log(INFO, "app_fit: losses_centralized %s", str(hist.losses_centralized))
-    log(INFO, "app_fit: metrics_centralized %s", str(hist.metrics_centralized))
-
-    # Graceful shutdown
-    server.disconnect_all_clients(timeout=config.round_timeout)
-
-    return hist
-
-
 def run_driver_api() -> None:
     """Run Flower server (Driver API)."""
     log(INFO, "Starting Flower server (Driver API)")
     event(EventType.RUN_DRIVER_API_ENTER)
-    args = _parse_args_driver().parse_args()
+    args = _parse_args_run_driver_api().parse_args()
 
     # Parse IP address
     parsed_address = parse_address(args.driver_api_address)
@@ -255,17 +201,17 @@ def run_driver_api() -> None:
     state_factory = StateFactory(args.database)
 
     # Start server
-    grpc_server: grpc.Server = _run_driver_api_grpc(
+    grpc_server: grpc.Server = run_driver_api_grpc(
         address=address,
         state_factory=state_factory,
         certificates=certificates,
     )
 
     # Graceful shutdown
-    _register_exit_handlers(
+    register_exit_handlers(
+        event_type=EventType.RUN_DRIVER_API_LEAVE,
         grpc_servers=[grpc_server],
         bckg_threads=[],
-        event_type=EventType.RUN_DRIVER_API_LEAVE,
     )
 
     # Block
@@ -276,7 +222,7 @@ def run_fleet_api() -> None:
     """Run Flower server (Fleet API)."""
     log(INFO, "Starting Flower server (Fleet API)")
     event(EventType.RUN_FLEET_API_ENTER)
-    args = _parse_args_fleet().parse_args()
+    args = _parse_args_run_fleet_api().parse_args()
 
     # Obtain certificates
     certificates = _try_obtain_certificates(args)
@@ -313,19 +259,6 @@ def run_fleet_api() -> None:
         )
         fleet_thread.start()
         bckg_threads.append(fleet_thread)
-    elif args.fleet_api_type == TRANSPORT_TYPE_GRPC_BIDI:
-        address_arg = args.grpc_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_bidi(
-            address=address,
-            state_factory=state_factory,
-            certificates=certificates,
-        )
-        grpc_servers.append(fleet_server)
     elif args.fleet_api_type == TRANSPORT_TYPE_GRPC_RERE:
         address_arg = args.grpc_rere_fleet_api_address
         parsed_address = parse_address(address_arg)
@@ -343,10 +276,10 @@ def run_fleet_api() -> None:
         raise ValueError(f"Unknown fleet_api_type: {args.fleet_api_type}")
 
     # Graceful shutdown
-    _register_exit_handlers(
+    register_exit_handlers(
+        event_type=EventType.RUN_FLEET_API_LEAVE,
         grpc_servers=grpc_servers,
         bckg_threads=bckg_threads,
-        event_type=EventType.RUN_FLEET_API_LEAVE,
     )
 
     # Block
@@ -357,11 +290,11 @@ def run_fleet_api() -> None:
 
 
 # pylint: disable=too-many-branches, too-many-locals, too-many-statements
-def run_server() -> None:
+def run_superlink() -> None:
     """Run Flower server (Driver API and Fleet API)."""
     log(INFO, "Starting Flower server")
-    event(EventType.RUN_SERVER_ENTER)
-    args = _parse_args_server().parse_args()
+    event(EventType.RUN_SUPERLINK_ENTER)
+    args = _parse_args_run_superlink().parse_args()
 
     # Parse IP address
     parsed_address = parse_address(args.driver_api_address)
@@ -377,7 +310,7 @@ def run_server() -> None:
     state_factory = StateFactory(args.database)
 
     # Start Driver API
-    driver_server: grpc.Server = _run_driver_api_grpc(
+    driver_server: grpc.Server = run_driver_api_grpc(
         address=address,
         state_factory=state_factory,
         certificates=certificates,
@@ -412,19 +345,6 @@ def run_server() -> None:
         )
         fleet_thread.start()
         bckg_threads.append(fleet_thread)
-    elif args.fleet_api_type == TRANSPORT_TYPE_GRPC_BIDI:
-        address_arg = args.grpc_bidi_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_bidi(
-            address=address,
-            state_factory=state_factory,
-            certificates=certificates,
-        )
-        grpc_servers.append(fleet_server)
     elif args.fleet_api_type == TRANSPORT_TYPE_GRPC_RERE:
         address_arg = args.grpc_rere_fleet_api_address
         parsed_address = parse_address(address_arg)
@@ -438,14 +358,25 @@ def run_server() -> None:
             certificates=certificates,
         )
         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,
+            state_factory=state_factory,
+            f_stop=f_stop,
+        )
     else:
         raise ValueError(f"Unknown fleet_api_type: {args.fleet_api_type}")
 
     # Graceful shutdown
-    _register_exit_handlers(
+    register_exit_handlers(
+        event_type=EventType.RUN_SUPERLINK_LEAVE,
         grpc_servers=grpc_servers,
         bckg_threads=bckg_threads,
-        event_type=EventType.RUN_SERVER_LEAVE,
     )
 
     # Block
@@ -480,105 +411,6 @@ def _try_obtain_certificates(
     return certificates
 
 
-def _register_exit_handlers(
-    grpc_servers: List[grpc.Server],
-    bckg_threads: List[threading.Thread],
-    event_type: EventType,
-) -> None:
-    default_handlers = {
-        SIGINT: None,
-        SIGTERM: None,
-    }
-
-    def graceful_exit_handler(  # type: ignore
-        signalnum,
-        frame: FrameType,  # pylint: disable=unused-argument
-    ) -> None:
-        """Exit handler to be registered with signal.signal.
-
-        When called will reset signal handler to original signal handler from
-        default_handlers.
-        """
-        # Reset to default handler
-        signal(signalnum, default_handlers[signalnum])
-
-        event_res = event(event_type=event_type)
-
-        for grpc_server in grpc_servers:
-            grpc_server.stop(grace=1)
-
-        for bckg_thread in bckg_threads:
-            bckg_thread.join()
-
-        # Ensure event has happend
-        event_res.result()
-
-        # Setup things for graceful exit
-        sys.exit(0)
-
-    default_handlers[SIGINT] = signal(  # type: ignore
-        SIGINT,
-        graceful_exit_handler,  # type: ignore
-    )
-    default_handlers[SIGTERM] = signal(  # type: ignore
-        SIGTERM,
-        graceful_exit_handler,  # type: ignore
-    )
-
-
-def _run_driver_api_grpc(
-    address: str,
-    state_factory: StateFactory,
-    certificates: Optional[Tuple[bytes, bytes, bytes]],
-) -> grpc.Server:
-    """Run Driver API (gRPC, request-response)."""
-    # Create Driver API gRPC server
-    driver_servicer: grpc.Server = DriverServicer(
-        state_factory=state_factory,
-    )
-    driver_add_servicer_to_server_fn = add_DriverServicer_to_server
-    driver_grpc_server = generic_create_grpc_server(
-        servicer_and_add_fn=(driver_servicer, driver_add_servicer_to_server_fn),
-        server_address=address,
-        max_message_length=GRPC_MAX_MESSAGE_LENGTH,
-        certificates=certificates,
-    )
-
-    log(INFO, "Flower ECE: Starting Driver API (gRPC-rere) on %s", address)
-    driver_grpc_server.start()
-
-    return driver_grpc_server
-
-
-def _run_fleet_api_grpc_bidi(
-    address: str,
-    state_factory: StateFactory,
-    certificates: Optional[Tuple[bytes, bytes, bytes]],
-) -> grpc.Server:
-    """Run Fleet API (gRPC, bidirectional streaming)."""
-    # DriverClientManager
-    driver_client_manager = DriverClientManager(
-        state_factory=state_factory,
-    )
-
-    # Create (legacy) Fleet API gRPC server
-    fleet_servicer = FlowerServiceServicer(
-        client_manager=driver_client_manager,
-    )
-    fleet_add_servicer_to_server_fn = add_FlowerServiceServicer_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 (gRPC-bidi) on %s", address)
-    fleet_grpc_server.start()
-
-    return fleet_grpc_server
-
-
 def _run_fleet_api_grpc_rere(
     address: str,
     state_factory: StateFactory,
@@ -587,7 +419,7 @@ def _run_fleet_api_grpc_rere(
     """Run Fleet API (gRPC, request-response)."""
     # Create Fleet API gRPC server
     fleet_servicer = FleetServicer(
-        state=state_factory.state(),
+        state_factory=state_factory,
     )
     fleet_add_servicer_to_server_fn = add_FleetServicer_to_server
     fleet_grpc_server = generic_create_grpc_server(
@@ -603,6 +435,29 @@ 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,
+    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,
+        state_factory=state_factory,
+        app_dir=app_dir,
+        f_stop=f_stop,
+    )
+
+
 # pylint: disable=import-outside-toplevel,too-many-arguments
 def _run_fleet_api_rest(
     host: str,
@@ -616,7 +471,7 @@ def _run_fleet_api_rest(
     try:
         import uvicorn
 
-        from flwr.server.fleet.rest_rere.rest_api import app as fast_api_app
+        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:
@@ -639,7 +494,7 @@ def _run_fleet_api_rest(
         raise ValueError(validation_exceptions)
 
     uvicorn.run(
-        app="flwr.server.fleet.rest_rere.rest_api:app",
+        app="flwr.server.superlink.fleet.rest_rere.rest_api:app",
         port=port,
         host=host,
         reload=False,
@@ -676,7 +531,7 @@ def _validate_ssl_files(
     return validation_exceptions
 
 
-def _parse_args_driver() -> argparse.ArgumentParser:
+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. "
@@ -693,7 +548,7 @@ def _parse_args_driver() -> argparse.ArgumentParser:
     return parser
 
 
-def _parse_args_fleet() -> argparse.ArgumentParser:
+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."
@@ -710,7 +565,7 @@ def _parse_args_fleet() -> argparse.ArgumentParser:
     return parser
 
 
-def _parse_args_server() -> argparse.ArgumentParser:
+def _parse_args_run_superlink() -> argparse.ArgumentParser:
     """Parse command line arguments for both Driver API and Fleet API."""
     parser = argparse.ArgumentParser(
         description="This will start a Flower server "
@@ -779,12 +634,13 @@ def _add_args_fleet_api(parser: argparse.ArgumentParser) -> None:
         const=TRANSPORT_TYPE_REST,
         help="Start a Fleet API server (REST, experimental)",
     )
+
     ex_group.add_argument(
-        "--grpc-bidi",
+        "--vce",
         action="store_const",
         dest="fleet_api_type",
-        const=TRANSPORT_TYPE_GRPC_BIDI,
-        help="Start a Fleet API server (gRPC-bidi)",
+        const=TRANSPORT_TYPE_VCE,
+        help="Start a Fleet API server (VirtualClientEngine)",
     )
 
     # Fleet API gRPC-rere options
@@ -823,12 +679,35 @@ def _add_args_fleet_api(parser: argparse.ArgumentParser) -> None:
         default=1,
     )
 
-    # Fleet API gRPC-bidi options
-    grpc_bidi_group = parser.add_argument_group(
-        "Fleet API (gRPC-bidi) server options", ""
+    # 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`.",
     )
-    grpc_bidi_group.add_argument(
-        "--grpc-bidi-fleet-api-address",
-        help="Fleet API (gRPC-bidi) server address (IPv4, IPv6, or a domain name)",
-        default=ADDRESS_FLEET_API_GRPC_RERE,
+    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.",
+    )
+    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 \'{"<keyA>":<value>, "<keyB>":<value>}\' to '
+        "configure a backend. Values supported in <value> are those included by "
+        "`flwr.common.typing.ConfigsRecordValues`. ",
+    )
+    parser.add_argument(
+        "--app-dir",
+        default="",
+        help="Add specified directory to the PYTHONPATH and load"
+        "ClientApp from there."
+        " Default: current working directory.",
     )
diff --git a/src/py/flwr/server/client_manager_test.py b/src/py/flwr/server/client_manager_test.py
index 8145b9b2ab7f..5820881b6aad 100644
--- a/src/py/flwr/server/client_manager_test.py
+++ b/src/py/flwr/server/client_manager_test.py
@@ -18,7 +18,7 @@
 from unittest.mock import MagicMock
 
 from flwr.server.client_manager import SimpleClientManager
-from flwr.server.fleet.grpc_bidi.grpc_client_proxy import GrpcClientProxy
+from flwr.server.superlink.fleet.grpc_bidi.grpc_client_proxy import GrpcClientProxy
 
 
 def test_simple_client_manager_register() -> None:
diff --git a/src/py/flwr/server/client_proxy.py b/src/py/flwr/server/client_proxy.py
index 7d0547be304b..951e4ae992da 100644
--- a/src/py/flwr/server/client_proxy.py
+++ b/src/py/flwr/server/client_proxy.py
@@ -47,6 +47,7 @@ def get_properties(
         self,
         ins: GetPropertiesIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> GetPropertiesRes:
         """Return the client's properties."""
 
@@ -55,6 +56,7 @@ def get_parameters(
         self,
         ins: GetParametersIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> GetParametersRes:
         """Return the current local model parameters."""
 
@@ -63,6 +65,7 @@ def fit(
         self,
         ins: FitIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> FitRes:
         """Refine the provided parameters using the locally held dataset."""
 
@@ -71,6 +74,7 @@ def evaluate(
         self,
         ins: EvaluateIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> EvaluateRes:
         """Evaluate the provided parameters using the locally held dataset."""
 
@@ -79,5 +83,6 @@ def reconnect(
         self,
         ins: ReconnectIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> DisconnectRes:
         """Disconnect and (optionally) reconnect later."""
diff --git a/src/py/flwr/server/client_proxy_test.py b/src/py/flwr/server/client_proxy_test.py
index 266e4cbeb266..685698558e3a 100644
--- a/src/py/flwr/server/client_proxy_test.py
+++ b/src/py/flwr/server/client_proxy_test.py
@@ -42,6 +42,7 @@ def get_properties(
         self,
         ins: GetPropertiesIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> GetPropertiesRes:
         """Return the client's properties."""
         return GetPropertiesRes(status=Status(code=Code.OK, message=""), properties={})
@@ -50,6 +51,7 @@ def get_parameters(
         self,
         ins: GetParametersIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> GetParametersRes:
         """Return the current local model parameters."""
         return GetParametersRes(
@@ -61,6 +63,7 @@ def fit(
         self,
         ins: FitIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> FitRes:
         """Refine the provided weights using the locally held dataset."""
         return FitRes(
@@ -74,6 +77,7 @@ def evaluate(
         self,
         ins: EvaluateIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> EvaluateRes:
         """Evaluate the provided weights using the locally held dataset."""
         return EvaluateRes(
@@ -84,6 +88,7 @@ def reconnect(
         self,
         ins: ReconnectIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> DisconnectRes:
         """Disconnect and (optionally) reconnect later."""
         return DisconnectRes(reason="")
diff --git a/src/py/flwr/driver/__init__.py b/src/py/flwr/server/compat/__init__.py
similarity index 74%
rename from src/py/flwr/driver/__init__.py
rename to src/py/flwr/server/compat/__init__.py
index 1c3b09cc334b..7bae196ddb65 100644
--- a/src/py/flwr/driver/__init__.py
+++ b/src/py/flwr/server/compat/__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.
@@ -12,15 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Flower driver SDK."""
+"""Flower ServerApp compatibility package."""
 
 
-from .app import start_driver
-from .driver import Driver
-from .grpc_driver import GrpcDriver
+from .app import start_driver as start_driver
+from .legacy_context import LegacyContext as LegacyContext
 
 __all__ = [
-    "Driver",
-    "GrpcDriver",
+    "LegacyContext",
     "start_driver",
 ]
diff --git a/src/py/flwr/driver/app.py b/src/py/flwr/server/compat/app.py
similarity index 51%
rename from src/py/flwr/driver/app.py
rename to src/py/flwr/server/compat/app.py
index 3cb8652365d8..ff1d99b5366e 100644
--- a/src/py/flwr/driver/app.py
+++ b/src/py/flwr/server/compat/app.py
@@ -16,24 +16,21 @@
 
 
 import sys
-import threading
-import time
 from logging import INFO
 from pathlib import Path
-from typing import Dict, Optional, Union
+from typing import Optional, Union
 
 from flwr.common import EventType, event
 from flwr.common.address import parse_address
-from flwr.common.logger import log
-from flwr.proto import driver_pb2
-from flwr.server.app import ServerConfig, init_defaults, run_fl
+from flwr.common.logger import log, warn_deprecated_feature
 from flwr.server.client_manager import ClientManager
 from flwr.server.history import History
-from flwr.server.server import Server
+from flwr.server.server import Server, init_defaults, run_fl
+from flwr.server.server_config import ServerConfig
 from flwr.server.strategy import Strategy
 
-from .driver_client_proxy import DriverClientProxy
-from .grpc_driver import GrpcDriver
+from ..driver import Driver
+from .app_utils import start_update_client_manager_thread
 
 DEFAULT_SERVER_ADDRESS_DRIVER = "[::]:9091"
 
@@ -53,6 +50,7 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
     strategy: Optional[Strategy] = None,
     client_manager: Optional[ClientManager] = None,
     root_certificates: Optional[Union[bytes, str]] = None,
+    driver: Optional[Driver] = None,
 ) -> History:
     """Start a Flower Driver API server.
 
@@ -72,15 +70,16 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
         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`.
-    client_manager : Optional[flwr.driver.DriverClientManager] (default: None)
-        An implementation of the class
-        `flwr.driver.driver_client_manager.DriverClientManager`. If no
+    client_manager : Optional[flwr.server.ClientManager] (default: None)
+        An implementation of the class `flwr.server.ClientManager`. If no
         implementation is provided, then `start_driver` will use
-        `flwr.driver.driver_client_manager.DriverClientManager`.
+        `flwr.server.SimpleClientManager`.
     root_certificates : Optional[Union[bytes, str]] (default: None)
         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.
+    driver : Optional[Driver] (default: None)
+        The Driver object to use.
 
     Returns
     -------
@@ -101,19 +100,23 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
     """
     event(EventType.START_DRIVER_ENTER)
 
-    # Parse IP address
-    parsed_address = parse_address(server_address)
-    if not parsed_address:
-        sys.exit(f"Server IP address ({server_address}) cannot be parsed.")
-    host, port, is_v6 = parsed_address
-    address = f"[{host}]:{port}" if is_v6 else f"{host}:{port}"
+    if driver is None:
+        # Not passing a `Driver` object is deprecated
+        warn_deprecated_feature("start_driver")
 
-    # Create the Driver
-    if isinstance(root_certificates, str):
-        root_certificates = Path(root_certificates).read_bytes()
-    driver = GrpcDriver(driver_service_address=address, certificates=root_certificates)
-    driver.connect()
-    lock = threading.Lock()
+        # Parse IP address
+        parsed_address = parse_address(server_address)
+        if not parsed_address:
+            sys.exit(f"Server IP address ({server_address}) cannot be parsed.")
+        host, port, is_v6 = parsed_address
+        address = f"[{host}]:{port}" if is_v6 else f"{host}:{port}"
+
+        # Create the Driver
+        if isinstance(root_certificates, str):
+            root_certificates = Path(root_certificates).read_bytes()
+        driver = Driver(
+            driver_service_address=address, root_certificates=root_certificates
+        )
 
     # Initialize the Driver API server and config
     initialized_server, initialized_config = init_defaults(
@@ -124,20 +127,15 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
     )
     log(
         INFO,
-        "Starting Flower server, config: %s",
+        "Starting Flower ServerApp, config: %s",
         initialized_config,
     )
+    log(INFO, "")
 
     # Start the thread updating nodes
-    thread = threading.Thread(
-        target=update_client_manager,
-        args=(
-            driver,
-            initialized_server.client_manager(),
-            lock,
-        ),
+    thread, f_stop = start_update_client_manager_thread(
+        driver, initialized_server.client_manager()
     )
-    thread.start()
 
     # Start training
     hist = run_fl(
@@ -145,66 +143,10 @@ def start_driver(  # pylint: disable=too-many-arguments, too-many-locals
         config=initialized_config,
     )
 
-    # Stop the Driver API server and the thread
-    with lock:
-        driver.disconnect()
+    # Terminate the thread
+    f_stop.set()
     thread.join()
 
     event(EventType.START_SERVER_LEAVE)
 
     return hist
-
-
-def update_client_manager(
-    driver: GrpcDriver,
-    client_manager: ClientManager,
-    lock: threading.Lock,
-) -> None:
-    """Update the nodes list in the client manager.
-
-    This function periodically communicates with the associated driver to get all
-    node_ids. Each node_id is then converted into a `DriverClientProxy` instance
-    and stored in the `registered_nodes` dictionary with node_id as key.
-
-    New nodes will be added to the ClientManager via `client_manager.register()`,
-    and dead nodes will be removed from the ClientManager via
-    `client_manager.unregister()`.
-    """
-    # Request for workload_id
-    workload_id = driver.create_workload(driver_pb2.CreateWorkloadRequest()).workload_id
-
-    # Loop until the driver is disconnected
-    registered_nodes: Dict[int, DriverClientProxy] = {}
-    while True:
-        with lock:
-            # End the while loop if the driver is disconnected
-            if driver.stub is None:
-                break
-            get_nodes_res = driver.get_nodes(
-                req=driver_pb2.GetNodesRequest(workload_id=workload_id)
-            )
-        all_node_ids = {node.node_id for node in get_nodes_res.nodes}
-        dead_nodes = set(registered_nodes).difference(all_node_ids)
-        new_nodes = all_node_ids.difference(registered_nodes)
-
-        # Unregister dead nodes
-        for node_id in dead_nodes:
-            client_proxy = registered_nodes[node_id]
-            client_manager.unregister(client_proxy)
-            del registered_nodes[node_id]
-
-        # Register new nodes
-        for node_id in new_nodes:
-            client_proxy = DriverClientProxy(
-                node_id=node_id,
-                driver=driver,
-                anonymous=False,
-                workload_id=workload_id,
-            )
-            if client_manager.register(client_proxy):
-                registered_nodes[node_id] = client_proxy
-            else:
-                raise RuntimeError("Could not register node.")
-
-        # Sleep for 3 seconds
-        time.sleep(3)
diff --git a/src/py/flwr/server/compat/app_utils.py b/src/py/flwr/server/compat/app_utils.py
new file mode 100644
index 000000000000..696ec1132c4a
--- /dev/null
+++ b/src/py/flwr/server/compat/app_utils.py
@@ -0,0 +1,102 @@
+# 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.
+# ==============================================================================
+"""Utility functions for the `start_driver`."""
+
+
+import threading
+import time
+from typing import Dict, Tuple
+
+from ..client_manager import ClientManager
+from ..compat.driver_client_proxy import DriverClientProxy
+from ..driver import Driver
+
+
+def start_update_client_manager_thread(
+    driver: Driver,
+    client_manager: ClientManager,
+) -> Tuple[threading.Thread, threading.Event]:
+    """Periodically update the nodes list in the client manager in a thread.
+
+    This function starts a thread that periodically uses the associated driver to
+    get all node_ids. Each node_id is then converted into a `DriverClientProxy`
+    instance and stored in the `registered_nodes` dictionary with node_id as key.
+
+    New nodes will be added to the ClientManager via `client_manager.register()`,
+    and dead nodes will be removed from the ClientManager via
+    `client_manager.unregister()`.
+
+    Parameters
+    ----------
+    driver : Driver
+        The Driver object to use.
+    client_manager : ClientManager
+        The ClientManager object to be updated.
+
+    Returns
+    -------
+    threading.Thread
+        A thread that updates the ClientManager and handles the stop event.
+    threading.Event
+        An event that, when set, signals the thread to stop.
+    """
+    f_stop = threading.Event()
+    thread = threading.Thread(
+        target=_update_client_manager,
+        args=(
+            driver,
+            client_manager,
+            f_stop,
+        ),
+    )
+    thread.start()
+
+    return thread, f_stop
+
+
+def _update_client_manager(
+    driver: Driver,
+    client_manager: ClientManager,
+    f_stop: threading.Event,
+) -> None:
+    """Update the nodes list in the client manager."""
+    # Loop until the driver is disconnected
+    registered_nodes: Dict[int, DriverClientProxy] = {}
+    while not f_stop.is_set():
+        all_node_ids = set(driver.get_node_ids())
+        dead_nodes = set(registered_nodes).difference(all_node_ids)
+        new_nodes = all_node_ids.difference(registered_nodes)
+
+        # Unregister dead nodes
+        for node_id in dead_nodes:
+            client_proxy = registered_nodes[node_id]
+            client_manager.unregister(client_proxy)
+            del registered_nodes[node_id]
+
+        # Register new nodes
+        for node_id in new_nodes:
+            client_proxy = DriverClientProxy(
+                node_id=node_id,
+                driver=driver.grpc_driver,  # type: ignore
+                anonymous=False,
+                run_id=driver.run_id,  # type: ignore
+            )
+            if client_manager.register(client_proxy):
+                registered_nodes[node_id] = client_proxy
+            else:
+                raise RuntimeError("Could not register node.")
+
+        # Sleep for 3 seconds
+        time.sleep(3)
diff --git a/src/py/flwr/server/compat/app_utils_test.py b/src/py/flwr/server/compat/app_utils_test.py
new file mode 100644
index 000000000000..7e47e6eaaf32
--- /dev/null
+++ b/src/py/flwr/server/compat/app_utils_test.py
@@ -0,0 +1,62 @@
+# 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 utility functions for the `start_driver`."""
+
+
+import time
+import unittest
+from unittest.mock import Mock, patch
+
+from ..client_manager import SimpleClientManager
+from .app_utils import start_update_client_manager_thread
+
+
+class TestUtils(unittest.TestCase):
+    """Tests for utility functions."""
+
+    def test_start_update_client_manager_thread(self) -> None:
+        """Test start_update_client_manager_thread function."""
+        # Prepare
+        sleep = time.sleep
+        sleep_patch = patch("time.sleep", lambda x: sleep(x / 100))
+        sleep_patch.start()
+        expected_node_ids = list(range(100))
+        updated_expected_node_ids = list(range(80, 120))
+        driver = Mock()
+        driver.grpc_driver = Mock()
+        driver.run_id = 123
+        driver.get_node_ids.return_value = expected_node_ids
+        client_manager = SimpleClientManager()
+
+        # Execute
+        thread, f_stop = start_update_client_manager_thread(driver, client_manager)
+        # Wait until all nodes are registered via `client_manager.sample()`
+        client_manager.sample(len(expected_node_ids))
+        # Retrieve all nodes in `client_manager`
+        node_ids = {proxy.node_id for proxy in client_manager.all().values()}
+        # Update the GetNodesResponse and wait until the `client_manager` is updated
+        driver.get_node_ids.return_value = updated_expected_node_ids
+        sleep(0.1)
+        # Retrieve all nodes in `client_manager`
+        updated_node_ids = {proxy.node_id for proxy in client_manager.all().values()}
+        # Stop the thread
+        f_stop.set()
+
+        # Assert
+        assert node_ids == set(expected_node_ids)
+        assert updated_node_ids == set(updated_expected_node_ids)
+
+        # Exit
+        thread.join()
diff --git a/src/py/flwr/server/compat/driver_client_proxy.py b/src/py/flwr/server/compat/driver_client_proxy.py
new file mode 100644
index 000000000000..84c67149fad7
--- /dev/null
+++ b/src/py/flwr/server/compat/driver_client_proxy.py
@@ -0,0 +1,169 @@
+# Copyright 2020 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 ClientProxy implementation for Driver API."""
+
+
+import time
+from typing import List, Optional
+
+from flwr import common
+from flwr.common import MessageType, MessageTypeLegacy, RecordSet
+from flwr.common import recordset_compat as compat
+from flwr.common import serde
+from flwr.proto import driver_pb2, node_pb2, task_pb2  # pylint: disable=E0611
+from flwr.server.client_proxy import ClientProxy
+
+from ..driver.grpc_driver import GrpcDriver
+
+SLEEP_TIME = 1
+
+
+class DriverClientProxy(ClientProxy):
+    """Flower client proxy which delegates work using the Driver API."""
+
+    def __init__(self, node_id: int, driver: GrpcDriver, anonymous: bool, run_id: int):
+        super().__init__(str(node_id))
+        self.node_id = node_id
+        self.driver = driver
+        self.run_id = run_id
+        self.anonymous = anonymous
+
+    def get_properties(
+        self,
+        ins: common.GetPropertiesIns,
+        timeout: Optional[float],
+        group_id: Optional[int],
+    ) -> common.GetPropertiesRes:
+        """Return client's properties."""
+        # Ins to RecordSet
+        out_recordset = compat.getpropertiesins_to_recordset(ins)
+        # Fetch response
+        in_recordset = self._send_receive_recordset(
+            out_recordset, MessageTypeLegacy.GET_PROPERTIES, timeout, group_id
+        )
+        # RecordSet to Res
+        return compat.recordset_to_getpropertiesres(in_recordset)
+
+    def get_parameters(
+        self,
+        ins: common.GetParametersIns,
+        timeout: Optional[float],
+        group_id: Optional[int],
+    ) -> common.GetParametersRes:
+        """Return the current local model parameters."""
+        # Ins to RecordSet
+        out_recordset = compat.getparametersins_to_recordset(ins)
+        # Fetch response
+        in_recordset = self._send_receive_recordset(
+            out_recordset, MessageTypeLegacy.GET_PARAMETERS, timeout, group_id
+        )
+        # RecordSet to Res
+        return compat.recordset_to_getparametersres(in_recordset, False)
+
+    def fit(
+        self, ins: common.FitIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> common.FitRes:
+        """Train model parameters on the locally held dataset."""
+        # Ins to RecordSet
+        out_recordset = compat.fitins_to_recordset(ins, keep_input=True)
+        # Fetch response
+        in_recordset = self._send_receive_recordset(
+            out_recordset, MessageType.TRAIN, timeout, group_id
+        )
+        # RecordSet to Res
+        return compat.recordset_to_fitres(in_recordset, keep_input=False)
+
+    def evaluate(
+        self, ins: common.EvaluateIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> common.EvaluateRes:
+        """Evaluate model parameters on the locally held dataset."""
+        # Ins to RecordSet
+        out_recordset = compat.evaluateins_to_recordset(ins, keep_input=True)
+        # Fetch response
+        in_recordset = self._send_receive_recordset(
+            out_recordset, MessageType.EVALUATE, timeout, group_id
+        )
+        # RecordSet to Res
+        return compat.recordset_to_evaluateres(in_recordset)
+
+    def reconnect(
+        self,
+        ins: common.ReconnectIns,
+        timeout: Optional[float],
+        group_id: Optional[int],
+    ) -> common.DisconnectRes:
+        """Disconnect and (optionally) reconnect later."""
+        return common.DisconnectRes(reason="")  # Nothing to do here (yet)
+
+    def _send_receive_recordset(
+        self,
+        recordset: RecordSet,
+        task_type: str,
+        timeout: Optional[float],
+        group_id: Optional[int],
+    ) -> RecordSet:
+        task_ins = task_pb2.TaskIns(  # pylint: disable=E1101
+            task_id="",
+            group_id=str(group_id) if group_id is not None else "",
+            run_id=self.run_id,
+            task=task_pb2.Task(  # pylint: disable=E1101
+                producer=node_pb2.Node(  # pylint: disable=E1101
+                    node_id=0,
+                    anonymous=True,
+                ),
+                consumer=node_pb2.Node(  # pylint: disable=E1101
+                    node_id=self.node_id,
+                    anonymous=self.anonymous,
+                ),
+                task_type=task_type,
+                recordset=serde.recordset_to_proto(recordset),
+            ),
+        )
+        push_task_ins_req = driver_pb2.PushTaskInsRequest(  # pylint: disable=E1101
+            task_ins_list=[task_ins]
+        )
+
+        # Send TaskIns to Driver API
+        push_task_ins_res = self.driver.push_task_ins(req=push_task_ins_req)
+
+        if len(push_task_ins_res.task_ids) != 1:
+            raise ValueError("Unexpected number of task_ids")
+
+        task_id = push_task_ins_res.task_ids[0]
+        if task_id == "":
+            raise ValueError(f"Failed to schedule task for node {self.node_id}")
+
+        if timeout:
+            start_time = time.time()
+
+        while True:
+            pull_task_res_req = driver_pb2.PullTaskResRequest(  # pylint: disable=E1101
+                node=node_pb2.Node(node_id=0, anonymous=True),  # pylint: disable=E1101
+                task_ids=[task_id],
+            )
+
+            # Ask Driver API for TaskRes
+            pull_task_res_res = self.driver.pull_task_res(req=pull_task_res_req)
+
+            task_res_list: List[task_pb2.TaskRes] = list(  # pylint: disable=E1101
+                pull_task_res_res.task_res_list
+            )
+            if len(task_res_list) == 1:
+                task_res = task_res_list[0]
+                return serde.recordset_from_proto(task_res.task.recordset)
+
+            if timeout is not None and time.time() > start_time + timeout:
+                raise RuntimeError("Timeout reached")
+            time.sleep(SLEEP_TIME)
diff --git a/src/py/flwr/server/compat/driver_client_proxy_test.py b/src/py/flwr/server/compat/driver_client_proxy_test.py
new file mode 100644
index 000000000000..3494049c1064
--- /dev/null
+++ b/src/py/flwr/server/compat/driver_client_proxy_test.py
@@ -0,0 +1,245 @@
+# Copyright 2020 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.
+# ==============================================================================
+"""DriverClientProxy tests."""
+
+
+import unittest
+from typing import Union, cast
+from unittest.mock import MagicMock
+
+import numpy as np
+
+import flwr
+from flwr.common import recordset_compat as compat
+from flwr.common import serde
+from flwr.common.constant import MessageType, MessageTypeLegacy
+from flwr.common.typing import (
+    Code,
+    Config,
+    EvaluateIns,
+    EvaluateRes,
+    FitRes,
+    GetParametersIns,
+    GetParametersRes,
+    GetPropertiesRes,
+    Parameters,
+    Properties,
+    Status,
+)
+from flwr.proto import driver_pb2, node_pb2, task_pb2  # pylint: disable=E0611
+
+from .driver_client_proxy import DriverClientProxy
+
+MESSAGE_PARAMETERS = Parameters(tensors=[b"abc"], tensor_type="np")
+
+CLIENT_PROPERTIES = cast(Properties, {"tensor_type": "numpy.ndarray"})
+CLIENT_STATUS = Status(code=Code.OK, message="OK")
+
+
+def _make_task(
+    res: Union[GetParametersRes, GetPropertiesRes, FitRes, EvaluateRes]
+) -> task_pb2.Task:  # pylint: disable=E1101
+    if isinstance(res, GetParametersRes):
+        message_type = MessageTypeLegacy.GET_PARAMETERS
+        recordset = compat.getparametersres_to_recordset(res, True)
+    elif isinstance(res, GetPropertiesRes):
+        message_type = MessageTypeLegacy.GET_PROPERTIES
+        recordset = compat.getpropertiesres_to_recordset(res)
+    elif isinstance(res, FitRes):
+        message_type = MessageType.TRAIN
+        recordset = compat.fitres_to_recordset(res, True)
+    elif isinstance(res, EvaluateRes):
+        message_type = MessageType.EVALUATE
+        recordset = compat.evaluateres_to_recordset(res)
+    else:
+        raise ValueError(f"Unsupported type: {type(res)}")
+    return task_pb2.Task(  # pylint: disable=E1101
+        task_type=message_type,
+        recordset=serde.recordset_to_proto(recordset),
+    )
+
+
+class DriverClientProxyTestCase(unittest.TestCase):
+    """Tests for DriverClientProxy."""
+
+    def setUp(self) -> None:
+        """Set up mocks for tests."""
+        self.driver = MagicMock()
+        self.driver.get_nodes.return_value = (
+            driver_pb2.GetNodesResponse(  # pylint: disable=E1101
+                nodes=[
+                    node_pb2.Node(node_id=1, anonymous=False)  # pylint: disable=E1101
+                ]
+            )
+        )
+
+    def test_get_properties(self) -> None:
+        """Test positive case."""
+        # Prepare
+        self.driver.push_task_ins.return_value = (
+            driver_pb2.PushTaskInsResponse(  # pylint: disable=E1101
+                task_ids=["19341fd7-62e1-4eb4-beb4-9876d3acda32"]
+            )
+        )
+        self.driver.pull_task_res.return_value = (
+            driver_pb2.PullTaskResResponse(  # pylint: disable=E1101
+                task_res_list=[
+                    task_pb2.TaskRes(  # pylint: disable=E1101
+                        task_id="554bd3c8-8474-4b93-a7db-c7bec1bf0012",
+                        group_id=str(0),
+                        run_id=0,
+                        task=_make_task(
+                            GetPropertiesRes(
+                                status=CLIENT_STATUS, properties=CLIENT_PROPERTIES
+                            )
+                        ),
+                    )
+                ]
+            )
+        )
+        client = DriverClientProxy(
+            node_id=1, driver=self.driver, anonymous=True, run_id=0
+        )
+        request_properties: Config = {"tensor_type": "str"}
+        ins: flwr.common.GetPropertiesIns = flwr.common.GetPropertiesIns(
+            config=request_properties
+        )
+
+        # Execute
+        value: flwr.common.GetPropertiesRes = client.get_properties(
+            ins, timeout=None, group_id=0
+        )
+
+        # Assert
+        assert value.properties["tensor_type"] == "numpy.ndarray"
+
+    def test_get_parameters(self) -> None:
+        """Test positive case."""
+        # Prepare
+        self.driver.push_task_ins.return_value = (
+            driver_pb2.PushTaskInsResponse(  # pylint: disable=E1101
+                task_ids=["19341fd7-62e1-4eb4-beb4-9876d3acda32"]
+            )
+        )
+        self.driver.pull_task_res.return_value = (
+            driver_pb2.PullTaskResResponse(  # pylint: disable=E1101
+                task_res_list=[
+                    task_pb2.TaskRes(  # pylint: disable=E1101
+                        task_id="554bd3c8-8474-4b93-a7db-c7bec1bf0012",
+                        group_id=str(0),
+                        run_id=0,
+                        task=_make_task(
+                            GetParametersRes(
+                                status=CLIENT_STATUS,
+                                parameters=MESSAGE_PARAMETERS,
+                            )
+                        ),
+                    )
+                ]
+            )
+        )
+        client = DriverClientProxy(
+            node_id=1, driver=self.driver, anonymous=True, run_id=0
+        )
+        get_parameters_ins = GetParametersIns(config={})
+
+        # Execute
+        value: flwr.common.GetParametersRes = client.get_parameters(
+            ins=get_parameters_ins, timeout=None, group_id=0
+        )
+
+        # Assert
+        assert value.parameters.tensors[0] == b"abc"
+
+    def test_fit(self) -> None:
+        """Test positive case."""
+        # Prepare
+        self.driver.push_task_ins.return_value = (
+            driver_pb2.PushTaskInsResponse(  # pylint: disable=E1101
+                task_ids=["19341fd7-62e1-4eb4-beb4-9876d3acda32"]
+            )
+        )
+        self.driver.pull_task_res.return_value = (
+            driver_pb2.PullTaskResResponse(  # pylint: disable=E1101
+                task_res_list=[
+                    task_pb2.TaskRes(  # pylint: disable=E1101
+                        task_id="554bd3c8-8474-4b93-a7db-c7bec1bf0012",
+                        group_id=str(1),
+                        run_id=0,
+                        task=_make_task(
+                            FitRes(
+                                status=CLIENT_STATUS,
+                                parameters=MESSAGE_PARAMETERS,
+                                num_examples=10,
+                                metrics={},
+                            )
+                        ),
+                    )
+                ]
+            )
+        )
+        client = DriverClientProxy(
+            node_id=1, driver=self.driver, anonymous=True, run_id=0
+        )
+        parameters = flwr.common.ndarrays_to_parameters([np.ones((2, 2))])
+        ins: flwr.common.FitIns = flwr.common.FitIns(parameters, {})
+
+        # Execute
+        fit_res = client.fit(ins=ins, timeout=None, group_id=1)
+
+        # Assert
+        assert fit_res.parameters.tensor_type == "np"
+        assert fit_res.parameters.tensors[0] == b"abc"
+        assert fit_res.num_examples == 10
+
+    def test_evaluate(self) -> None:
+        """Test positive case."""
+        # Prepare
+        self.driver.push_task_ins.return_value = (
+            driver_pb2.PushTaskInsResponse(  # pylint: disable=E1101
+                task_ids=["19341fd7-62e1-4eb4-beb4-9876d3acda32"]
+            )
+        )
+        self.driver.pull_task_res.return_value = (
+            driver_pb2.PullTaskResResponse(  # pylint: disable=E1101
+                task_res_list=[
+                    task_pb2.TaskRes(  # pylint: disable=E1101
+                        task_id="554bd3c8-8474-4b93-a7db-c7bec1bf0012",
+                        group_id=str(1),
+                        run_id=0,
+                        task=_make_task(
+                            EvaluateRes(
+                                status=CLIENT_STATUS,
+                                loss=0.0,
+                                num_examples=0,
+                                metrics={},
+                            )
+                        ),
+                    )
+                ]
+            )
+        )
+        client = DriverClientProxy(
+            node_id=1, driver=self.driver, anonymous=True, run_id=0
+        )
+        parameters = Parameters(tensors=[], tensor_type="np")
+        evaluate_ins = EvaluateIns(parameters, {})
+
+        # Execute
+        evaluate_res = client.evaluate(evaluate_ins, timeout=None, group_id=1)
+
+        # Assert
+        assert 0.0 == evaluate_res.loss
+        assert 0 == evaluate_res.num_examples
diff --git a/src/py/flwr/server/compat/legacy_context.py b/src/py/flwr/server/compat/legacy_context.py
new file mode 100644
index 000000000000..0b00c98bb16d
--- /dev/null
+++ b/src/py/flwr/server/compat/legacy_context.py
@@ -0,0 +1,55 @@
+# 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.
+# ==============================================================================
+"""Legacy Context."""
+
+
+from dataclasses import dataclass
+from typing import Optional
+
+from flwr.common import Context, RecordSet
+
+from ..client_manager import ClientManager, SimpleClientManager
+from ..history import History
+from ..server_config import ServerConfig
+from ..strategy import FedAvg, Strategy
+
+
+@dataclass
+class LegacyContext(Context):
+    """Legacy Context."""
+
+    config: ServerConfig
+    strategy: Strategy
+    client_manager: ClientManager
+    history: History
+
+    def __init__(
+        self,
+        state: RecordSet,
+        config: Optional[ServerConfig] = None,
+        strategy: Optional[Strategy] = None,
+        client_manager: Optional[ClientManager] = None,
+    ) -> None:
+        if config is None:
+            config = ServerConfig()
+        if strategy is None:
+            strategy = FedAvg()
+        if client_manager is None:
+            client_manager = SimpleClientManager()
+        self.config = config
+        self.strategy = strategy
+        self.client_manager = client_manager
+        self.history = History()
+        super().__init__(state)
diff --git a/src/py/flwr/server/criterion_test.py b/src/py/flwr/server/criterion_test.py
index a7e5b62b5977..f678825f064e 100644
--- a/src/py/flwr/server/criterion_test.py
+++ b/src/py/flwr/server/criterion_test.py
@@ -20,7 +20,7 @@
 from flwr.server.client_manager import SimpleClientManager
 from flwr.server.client_proxy import ClientProxy
 from flwr.server.criterion import Criterion
-from flwr.server.fleet.grpc_bidi.grpc_client_proxy import GrpcClientProxy
+from flwr.server.superlink.fleet.grpc_bidi.grpc_client_proxy import GrpcClientProxy
 
 
 def test_criterion_applied() -> None:
diff --git a/src/py/flwr/server/driver/__init__.py b/src/py/flwr/server/driver/__init__.py
index 2bfe63e6065f..b61f6eebf6a8 100644
--- a/src/py/flwr/server/driver/__init__.py
+++ b/src/py/flwr/server/driver/__init__.py
@@ -12,4 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Flower driver service."""
+"""Flower driver SDK."""
+
+
+from .driver import Driver
+from .grpc_driver import GrpcDriver
+
+__all__ = [
+    "Driver",
+    "GrpcDriver",
+]
diff --git a/src/py/flwr/server/driver/driver.py b/src/py/flwr/server/driver/driver.py
new file mode 100644
index 000000000000..0098e0ce97c2
--- /dev/null
+++ b/src/py/flwr/server/driver/driver.py
@@ -0,0 +1,256 @@
+# 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.
+# 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 driver service client."""
+
+
+import time
+from typing import Iterable, List, Optional, Tuple
+
+from flwr.common import Message, Metadata, RecordSet
+from flwr.common.serde import message_from_taskres, message_to_taskins
+from flwr.proto.driver_pb2 import (  # pylint: disable=E0611
+    CreateRunRequest,
+    GetNodesRequest,
+    PullTaskResRequest,
+    PushTaskInsRequest,
+)
+from flwr.proto.node_pb2 import Node  # pylint: disable=E0611
+from flwr.proto.task_pb2 import TaskIns  # pylint: disable=E0611
+
+from .grpc_driver import DEFAULT_SERVER_ADDRESS_DRIVER, GrpcDriver
+
+
+class Driver:
+    """`Driver` class provides an interface to the Driver API.
+
+    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.
+    """
+
+    def __init__(
+        self,
+        driver_service_address: str = DEFAULT_SERVER_ADDRESS_DRIVER,
+        root_certificates: Optional[bytes] = None,
+    ) -> None:
+        self.addr = driver_service_address
+        self.root_certificates = root_certificates
+        self.grpc_driver: Optional[GrpcDriver] = None
+        self.run_id: Optional[int] = None
+        self.node = Node(node_id=0, anonymous=True)
+
+    def _get_grpc_driver_and_run_id(self) -> Tuple[GrpcDriver, int]:
+        # Check if the GrpcDriver is initialized
+        if self.grpc_driver is None or self.run_id is None:
+            # Connect and create run
+            self.grpc_driver = GrpcDriver(
+                driver_service_address=self.addr,
+                root_certificates=self.root_certificates,
+            )
+            self.grpc_driver.connect()
+            res = self.grpc_driver.create_run(CreateRunRequest())
+            self.run_id = res.run_id
+        return self.grpc_driver, self.run_id
+
+    def _check_message(self, message: Message) -> None:
+        # Check if the message is valid
+        if not (
+            message.metadata.run_id == self.run_id
+            and message.metadata.src_node_id == self.node.node_id
+            and message.metadata.message_id == ""
+            and message.metadata.reply_to_message == ""
+        ):
+            raise ValueError(f"Invalid message: {message}")
+
+    def create_message(  # pylint: disable=too-many-arguments
+        self,
+        content: RecordSet,
+        message_type: str,
+        dst_node_id: int,
+        group_id: str,
+        ttl: str,
+    ) -> Message:
+        """Create a new message with specified parameters.
+
+        This method constructs a new `Message` with given content and metadata.
+        The `run_id` and `src_node_id` will be set automatically.
+
+        Parameters
+        ----------
+        content : RecordSet
+            The content for the new message. This holds records that are to be sent
+            to the destination node.
+        message_type : str
+            The type of the message, defining the action to be executed on
+            the receiving end.
+        dst_node_id : int
+            The ID of the destination node to which the message is being sent.
+        group_id : str
+            The ID of the group to which this message is associated. In some settings,
+            this is used as the FL round.
+        ttl : str
+            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.
+
+        Returns
+        -------
+        message : Message
+            A new `Message` instance with the specified content and metadata.
+        """
+        _, run_id = self._get_grpc_driver_and_run_id()
+        metadata = Metadata(
+            run_id=run_id,
+            message_id="",  # Will be set by the server
+            src_node_id=self.node.node_id,
+            dst_node_id=dst_node_id,
+            reply_to_message="",
+            group_id=group_id,
+            ttl=ttl,
+            message_type=message_type,
+        )
+        return Message(metadata=metadata, content=content)
+
+    def get_node_ids(self) -> List[int]:
+        """Get node IDs."""
+        grpc_driver, run_id = self._get_grpc_driver_and_run_id()
+        # Call GrpcDriver method
+        res = grpc_driver.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]:
+        """Push messages to specified node IDs.
+
+        This method takes an iterable of messages and sends each message
+        to the node specified in `dst_node_id`.
+
+        Parameters
+        ----------
+        messages : Iterable[Message]
+            An iterable of messages to be sent.
+
+        Returns
+        -------
+        message_ids : Iterable[str]
+            An iterable of IDs for the messages that were sent, which can be used
+            to pull replies.
+        """
+        grpc_driver, _ = self._get_grpc_driver_and_run_id()
+        # Construct TaskIns
+        task_ins_list: List[TaskIns] = []
+        for msg in messages:
+            # Check message
+            self._check_message(msg)
+            # Convert Message to TaskIns
+            taskins = message_to_taskins(msg)
+            # Add to list
+            task_ins_list.append(taskins)
+        # Call GrpcDriver method
+        res = grpc_driver.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]:
+        """Pull messages based on message IDs.
+
+        This method is used to collect messages from the SuperLink
+        that correspond to a set of given message IDs.
+
+        Parameters
+        ----------
+        message_ids : Iterable[str]
+            An iterable of message IDs for which reply messages are to be retrieved.
+
+        Returns
+        -------
+        messages : Iterable[Message]
+            An iterable of messages received.
+        """
+        grpc_driver, _ = self._get_grpc_driver_and_run_id()
+        # Pull TaskRes
+        res = grpc_driver.pull_task_res(
+            PullTaskResRequest(node=self.node, task_ids=message_ids)
+        )
+        # Convert TaskRes to Message
+        msgs = [message_from_taskres(taskres) for taskres in res.task_res_list]
+        return msgs
+
+    def send_and_receive(
+        self,
+        messages: Iterable[Message],
+        *,
+        timeout: Optional[float] = None,
+    ) -> Iterable[Message]:
+        """Push messages to specified node IDs and pull the reply messages.
+
+        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.
+
+        Parameters
+        ----------
+        messages : Iterable[Message]
+            An iterable of messages to be sent.
+        timeout : Optional[float] (default: None)
+            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.
+
+        Returns
+        -------
+        replies : Iterable[Message]
+            An iterable of reply messages received from the SuperLink.
+
+        Notes
+        -----
+        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`.
+        """
+        # Push messages
+        msg_ids = set(self.push_messages(messages))
+
+        # Pull messages
+        end_time = time.time() + (timeout if timeout is not None else 0.0)
+        ret: List[Message] = []
+        while timeout is None or time.time() < end_time:
+            res_msgs = self.pull_messages(msg_ids)
+            ret.extend(res_msgs)
+            msg_ids.difference_update(
+                {msg.metadata.reply_to_message for msg in res_msgs}
+            )
+            if len(msg_ids) == 0:
+                break
+            # Sleep
+            time.sleep(3)
+        return ret
+
+    def close(self) -> None:
+        """Disconnect from the SuperLink if connected."""
+        # Check if GrpcDriver is initialized
+        if self.grpc_driver is None:
+            return
+        # Disconnect
+        self.grpc_driver.disconnect()
diff --git a/src/py/flwr/server/driver/driver_test.py b/src/py/flwr/server/driver/driver_test.py
new file mode 100644
index 000000000000..5136f4f90210
--- /dev/null
+++ b/src/py/flwr/server/driver/driver_test.py
@@ -0,0 +1,219 @@
+# 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.
+# 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 driver SDK."""
+
+
+import time
+import unittest
+from unittest.mock import Mock, patch
+
+from flwr.common import RecordSet
+from flwr.common.message import Error
+from flwr.common.serde import error_to_proto, recordset_to_proto
+from flwr.proto.driver_pb2 import (  # pylint: disable=E0611
+    GetNodesRequest,
+    PullTaskResRequest,
+    PushTaskInsRequest,
+)
+from flwr.proto.task_pb2 import Task, TaskRes  # pylint: disable=E0611
+
+from .driver import Driver
+
+
+class TestDriver(unittest.TestCase):
+    """Tests for `Driver` class."""
+
+    def setUp(self) -> None:
+        """Initialize mock GrpcDriver and Driver instance before each test."""
+        mock_response = Mock()
+        mock_response.run_id = 61016
+        self.mock_grpc_driver = Mock()
+        self.mock_grpc_driver.create_run.return_value = mock_response
+        self.patcher = patch(
+            "flwr.server.driver.driver.GrpcDriver", return_value=self.mock_grpc_driver
+        )
+        self.patcher.start()
+        self.driver = Driver()
+
+    def tearDown(self) -> None:
+        """Cleanup after each test."""
+        self.patcher.stop()
+
+    def test_check_and_init_grpc_driver_already_initialized(self) -> None:
+        """Test that GrpcDriver doesn't initialize if run is created."""
+        # Prepare
+        self.driver.grpc_driver = self.mock_grpc_driver
+        self.driver.run_id = 61016
+
+        # Execute
+        # pylint: disable-next=protected-access
+        self.driver._get_grpc_driver_and_run_id()
+
+        # Assert
+        self.mock_grpc_driver.connect.assert_not_called()
+
+    def test_check_and_init_grpc_driver_needs_initialization(self) -> None:
+        """Test GrpcDriver initialization when run is not created."""
+        # Execute
+        # pylint: disable-next=protected-access
+        self.driver._get_grpc_driver_and_run_id()
+
+        # Assert
+        self.mock_grpc_driver.connect.assert_called_once()
+        self.assertEqual(self.driver.run_id, 61016)
+
+    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.get_nodes.return_value = mock_response
+
+        # Execute
+        node_ids = self.driver.get_node_ids()
+        args, kwargs = self.mock_grpc_driver.get_nodes.call_args
+
+        # Assert
+        self.mock_grpc_driver.connect.assert_called_once()
+        self.assertEqual(len(args), 1)
+        self.assertEqual(len(kwargs), 0)
+        self.assertIsInstance(args[0], GetNodesRequest)
+        self.assertEqual(args[0].run_id, 61016)
+        self.assertEqual(node_ids, [404, 200])
+
+    def test_push_messages_valid(self) -> None:
+        """Test pushing valid messages."""
+        # Prepare
+        mock_response = Mock(task_ids=["id1", "id2"])
+        self.mock_grpc_driver.push_task_ins.return_value = mock_response
+        msgs = [
+            self.driver.create_message(RecordSet(), "", 0, "", "") for _ in range(2)
+        ]
+
+        # Execute
+        msg_ids = self.driver.push_messages(msgs)
+        args, kwargs = self.mock_grpc_driver.push_task_ins.call_args
+
+        # Assert
+        self.mock_grpc_driver.connect.assert_called_once()
+        self.assertEqual(len(args), 1)
+        self.assertEqual(len(kwargs), 0)
+        self.assertIsInstance(args[0], PushTaskInsRequest)
+        self.assertEqual(msg_ids, mock_response.task_ids)
+        for task_ins in args[0].task_ins_list:
+            self.assertEqual(task_ins.run_id, 61016)
+
+    def test_push_messages_invalid(self) -> None:
+        """Test pushing invalid messages."""
+        # Prepare
+        mock_response = Mock(task_ids=["id1", "id2"])
+        self.mock_grpc_driver.push_task_ins.return_value = mock_response
+        msgs = [
+            self.driver.create_message(RecordSet(), "", 0, "", "") for _ in range(2)
+        ]
+        # Use invalid run_id
+        msgs[1].metadata._run_id += 1  # pylint: disable=protected-access
+
+        # Execute and assert
+        with self.assertRaises(ValueError):
+            self.driver.push_messages(msgs)
+
+    def test_pull_messages_with_given_message_ids(self) -> None:
+        """Test pulling messages with specific message IDs."""
+        # Prepare
+        mock_response = Mock()
+        # A Message must have either content or error set so we prepare
+        # two tasks that contain these.
+        mock_response.task_res_list = [
+            TaskRes(
+                task=Task(ancestry=["id2"], recordset=recordset_to_proto(RecordSet()))
+            ),
+            TaskRes(task=Task(ancestry=["id3"], error=error_to_proto(Error(code=0)))),
+        ]
+        self.mock_grpc_driver.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.pull_task_res.call_args
+
+        # Assert
+        self.mock_grpc_driver.connect.assert_called_once()
+        self.assertEqual(len(args), 1)
+        self.assertEqual(len(kwargs), 0)
+        self.assertIsInstance(args[0], PullTaskResRequest)
+        self.assertEqual(args[0].task_ids, msg_ids)
+        self.assertEqual(reply_tos, {"id2", "id3"})
+
+    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.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.pull_task_res.return_value = mock_response
+        msgs = [self.driver.create_message(RecordSet(), "", 0, "", "")]
+
+        # Execute
+        ret_msgs = list(self.driver.send_and_receive(msgs))
+
+        # Assert
+        self.assertEqual(len(ret_msgs), 1)
+        self.assertEqual(ret_msgs[0].metadata.reply_to_message, "id1")
+
+    def test_send_and_receive_messages_timeout(self) -> None:
+        """Test send and receive messages but time out."""
+        # Prepare
+        sleep_fn = time.sleep
+        mock_response = Mock(task_ids=["id1"])
+        self.mock_grpc_driver.push_task_ins.return_value = mock_response
+        mock_response = Mock(task_res_list=[])
+        self.mock_grpc_driver.pull_task_res.return_value = mock_response
+        msgs = [self.driver.create_message(RecordSet(), "", 0, "", "")]
+
+        # Execute
+        with patch("time.sleep", side_effect=lambda t: sleep_fn(t * 0.01)):
+            start_time = time.time()
+            ret_msgs = list(self.driver.send_and_receive(msgs, timeout=0.15))
+
+        # Assert
+        self.assertLess(time.time() - start_time, 0.2)
+        self.assertEqual(len(ret_msgs), 0)
+
+    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_and_run_id()
+
+        # Execute
+        self.driver.close()
+
+        # Assert
+        self.mock_grpc_driver.disconnect.assert_called_once()
+
+    def test_del_with_uninitialized_driver(self) -> None:
+        """Test cleanup behavior when Driver is not initialized."""
+        # Execute
+        self.driver.close()
+
+        # Assert
+        self.mock_grpc_driver.disconnect.assert_not_called()
diff --git a/src/py/flwr/driver/grpc_driver.py b/src/py/flwr/server/driver/grpc_driver.py
similarity index 76%
rename from src/py/flwr/driver/grpc_driver.py
rename to src/py/flwr/server/driver/grpc_driver.py
index 7dd0a0f501c5..b6e2b2602cd5 100644
--- a/src/py/flwr/driver/grpc_driver.py
+++ b/src/py/flwr/server/driver/grpc_driver.py
@@ -15,7 +15,7 @@
 """Flower driver service client."""
 
 
-from logging import ERROR, INFO, WARNING
+from logging import DEBUG, ERROR, WARNING
 from typing import Optional
 
 import grpc
@@ -23,9 +23,9 @@
 from flwr.common import EventType, event
 from flwr.common.grpc import create_channel
 from flwr.common.logger import log
-from flwr.proto.driver_pb2 import (
-    CreateWorkloadRequest,
-    CreateWorkloadResponse,
+from flwr.proto.driver_pb2 import (  # pylint: disable=E0611
+    CreateRunRequest,
+    CreateRunResponse,
     GetNodesRequest,
     GetNodesResponse,
     PullTaskResRequest,
@@ -33,7 +33,7 @@
     PushTaskInsRequest,
     PushTaskInsResponse,
 )
-from flwr.proto.driver_pb2_grpc import DriverStub
+from flwr.proto.driver_pb2_grpc import DriverStub  # pylint: disable=E0611
 
 DEFAULT_SERVER_ADDRESS_DRIVER = "[::]:9091"
 
@@ -51,10 +51,10 @@ class GrpcDriver:
     def __init__(
         self,
         driver_service_address: str = DEFAULT_SERVER_ADDRESS_DRIVER,
-        certificates: Optional[bytes] = None,
+        root_certificates: Optional[bytes] = None,
     ) -> None:
         self.driver_service_address = driver_service_address
-        self.certificates = certificates
+        self.root_certificates = root_certificates
         self.channel: Optional[grpc.Channel] = None
         self.stub: Optional[DriverStub] = None
 
@@ -66,33 +66,33 @@ def connect(self) -> None:
             return
         self.channel = create_channel(
             server_address=self.driver_service_address,
-            insecure=(self.certificates is None),
-            root_certificates=self.certificates,
+            insecure=(self.root_certificates is None),
+            root_certificates=self.root_certificates,
         )
         self.stub = DriverStub(self.channel)
-        log(INFO, "[Driver] Connected to %s", self.driver_service_address)
+        log(DEBUG, "[Driver] Connected to %s", self.driver_service_address)
 
     def disconnect(self) -> None:
         """Disconnect from the Driver API."""
         event(EventType.DRIVER_DISCONNECT)
         if self.channel is None or self.stub is None:
-            log(WARNING, "Already disconnected")
+            log(DEBUG, "Already disconnected")
             return
         channel = self.channel
         self.channel = None
         self.stub = None
         channel.close()
-        log(INFO, "[Driver] Disconnected")
+        log(DEBUG, "[Driver] Disconnected")
 
-    def create_workload(self, req: CreateWorkloadRequest) -> CreateWorkloadResponse:
-        """Request for workload ID."""
+    def create_run(self, req: CreateRunRequest) -> CreateRunResponse:
+        """Request for run ID."""
         # Check if channel is open
         if self.stub is None:
             log(ERROR, ERROR_MESSAGE_DRIVER_NOT_CONNECTED)
-            raise Exception("`GrpcDriver` instance not connected")
+            raise ConnectionError("`GrpcDriver` instance not connected")
 
         # Call Driver API
-        res: CreateWorkloadResponse = self.stub.CreateWorkload(request=req)
+        res: CreateRunResponse = self.stub.CreateRun(request=req)
         return res
 
     def get_nodes(self, req: GetNodesRequest) -> GetNodesResponse:
@@ -100,7 +100,7 @@ def get_nodes(self, req: GetNodesRequest) -> GetNodesResponse:
         # Check if channel is open
         if self.stub is None:
             log(ERROR, ERROR_MESSAGE_DRIVER_NOT_CONNECTED)
-            raise Exception("`GrpcDriver` instance not connected")
+            raise ConnectionError("`GrpcDriver` instance not connected")
 
         # Call gRPC Driver API
         res: GetNodesResponse = self.stub.GetNodes(request=req)
@@ -111,7 +111,7 @@ 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 Exception("`GrpcDriver` instance not connected")
+            raise ConnectionError("`GrpcDriver` instance not connected")
 
         # Call gRPC Driver API
         res: PushTaskInsResponse = self.stub.PushTaskIns(request=req)
@@ -122,7 +122,7 @@ 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 Exception("`GrpcDriver` instance not connected")
+            raise ConnectionError("`GrpcDriver` instance not connected")
 
         # Call Driver API
         res: PullTaskResResponse = self.stub.PullTaskRes(request=req)
diff --git a/src/py/flwr/server/fleet/grpc_bidi/driver_client_manager.py b/src/py/flwr/server/fleet/grpc_bidi/driver_client_manager.py
deleted file mode 100644
index dc94bf3912d7..000000000000
--- a/src/py/flwr/server/fleet/grpc_bidi/driver_client_manager.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# Copyright 2020 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 DriverClientManager."""
-
-
-import threading
-from typing import Dict, List, Optional, Set, Tuple
-
-from flwr.server.client_manager import ClientManager
-from flwr.server.client_proxy import ClientProxy
-from flwr.server.criterion import Criterion
-from flwr.server.state import State, StateFactory
-
-from .ins_scheduler import InsScheduler
-
-
-class DriverClientManager(ClientManager):
-    """Provides a pool of available clients."""
-
-    def __init__(self, state_factory: StateFactory) -> None:
-        self._cv = threading.Condition()
-        self.nodes: Dict[str, Tuple[int, InsScheduler]] = {}
-        self.state_factory = state_factory
-
-    def __len__(self) -> int:
-        """Return the number of available clients.
-
-        Returns
-        -------
-        num_available : int
-            The number of currently available clients.
-        """
-        return len(self.nodes)
-
-    def num_available(self) -> int:
-        """Return the number of available clients.
-
-        Returns
-        -------
-        num_available : int
-            The number of currently available clients.
-        """
-        return len(self)
-
-    def register(self, client: ClientProxy) -> bool:
-        """Register Flower ClientProxy instance.
-
-        Parameters
-        ----------
-        client : flwr.server.client_proxy.ClientProxy
-
-        Returns
-        -------
-        success : bool
-            Indicating if registration was successful. False if ClientProxy is
-            already registered or can not be registered for any reason.
-        """
-        if client.cid in self.nodes:
-            return False
-
-        # Create node in State
-        state: State = self.state_factory.state()
-        client.node_id = state.create_node()
-
-        # Create and start the instruction scheduler
-        ins_scheduler = InsScheduler(
-            client_proxy=client,
-            state_factory=self.state_factory,
-        )
-        ins_scheduler.start()
-
-        # Store cid, node_id, and InsScheduler
-        self.nodes[client.cid] = (client.node_id, ins_scheduler)
-
-        with self._cv:
-            self._cv.notify_all()
-
-        return True
-
-    def unregister(self, client: ClientProxy) -> None:
-        """Unregister Flower ClientProxy instance.
-
-        This method is idempotent.
-
-        Parameters
-        ----------
-        client : flwr.server.client_proxy.ClientProxy
-        """
-        if client.cid in self.nodes:
-            node_id, ins_scheduler = self.nodes[client.cid]
-            del self.nodes[client.cid]
-            ins_scheduler.stop()
-
-            # Delete node_id in State
-            state: State = self.state_factory.state()
-            state.delete_node(node_id=node_id)
-
-            with self._cv:
-                self._cv.notify_all()
-
-    def all_ids(self) -> Set[int]:
-        """Return all available node ids.
-
-        Returns
-        -------
-        ids : Set[int]
-            The IDs of all currently available nodes.
-        """
-        return {node_id for _, (node_id, _) in self.nodes.items()}
-
-    # --- Unimplemented methods -----------------------------------------------
-
-    def all(self) -> Dict[str, ClientProxy]:
-        """Not implemented."""
-        raise NotImplementedError()
-
-    def wait_for(self, num_clients: int, timeout: int = 86400) -> bool:
-        """Not implemented."""
-        raise NotImplementedError()
-
-    def sample(
-        self,
-        num_clients: int,
-        min_num_clients: Optional[int] = None,
-        criterion: Optional[Criterion] = None,
-    ) -> List[ClientProxy]:
-        """Not implemented."""
-        raise NotImplementedError()
diff --git a/src/py/flwr/server/fleet/grpc_bidi/ins_scheduler.py b/src/py/flwr/server/fleet/grpc_bidi/ins_scheduler.py
deleted file mode 100644
index 1c737d31c7fc..000000000000
--- a/src/py/flwr/server/fleet/grpc_bidi/ins_scheduler.py
+++ /dev/null
@@ -1,171 +0,0 @@
-# Copyright 2020 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.
-# ==============================================================================
-"""Instruction scheduler for the legacy gRPC transport stack."""
-
-
-import threading
-import time
-from logging import DEBUG, ERROR
-from typing import Dict, List, Optional
-
-from flwr.client.message_handler.task_handler import configure_task_res
-from flwr.common import EvaluateRes, FitRes, GetParametersRes, GetPropertiesRes, serde
-from flwr.common.logger import log
-from flwr.proto.node_pb2 import Node
-from flwr.proto.task_pb2 import Task, TaskIns, TaskRes
-from flwr.proto.transport_pb2 import ClientMessage, ServerMessage
-from flwr.server.client_proxy import ClientProxy
-from flwr.server.state import State, StateFactory
-
-
-class InsScheduler:
-    """Schedule ClientProxy calls on a background thread."""
-
-    def __init__(self, client_proxy: ClientProxy, state_factory: StateFactory):
-        self.client_proxy = client_proxy
-        self.state_factory = state_factory
-        self.worker_thread: Optional[threading.Thread] = None
-        self.shared_memory_state = {"stop": False}
-
-    def start(self) -> None:
-        """Start the worker thread."""
-        self.worker_thread = threading.Thread(
-            target=_worker,
-            args=(
-                self.client_proxy,
-                self.shared_memory_state,
-                self.state_factory,
-            ),
-        )
-        self.worker_thread.start()
-
-    def stop(self) -> None:
-        """Stop the worker thread."""
-        if self.worker_thread is None:
-            log(ERROR, "InsScheduler.stop called, but worker_thread is None")
-            return
-        self.shared_memory_state["stop"] = True
-        self.worker_thread.join()
-        self.worker_thread = None
-        self.shared_memory_state["stop"] = False
-
-
-def _worker(
-    client_proxy: ClientProxy,
-    shared_memory_state: Dict[str, bool],
-    state_factory: StateFactory,
-) -> None:
-    """Sequentially call ClientProxy methods to process outstanding tasks."""
-    log(DEBUG, "Worker for node %i started", client_proxy.node_id)
-
-    state: State = state_factory.state()
-    while not shared_memory_state["stop"]:
-        log(DEBUG, "Worker for node %i checking state", client_proxy.node_id)
-
-        # Step 1: pull *Ins (next task) out of `state`
-        task_ins_list: List[TaskIns] = state.get_task_ins(
-            node_id=client_proxy.node_id,
-            limit=1,
-        )
-        if not task_ins_list:
-            log(DEBUG, "Worker for node %i: no task found", client_proxy.node_id)
-            time.sleep(3)
-            continue
-
-        task_ins = task_ins_list[0]
-        log(
-            DEBUG,
-            "Worker for node %i: FOUND task %s",
-            client_proxy.node_id,
-            task_ins.task_id,
-        )
-
-        # Step 2: call client_proxy.{fit,evaluate,...}
-        server_message = task_ins.task.legacy_server_message
-        client_message_proto = _call_client_proxy(
-            client_proxy=client_proxy,
-            server_message=server_message,
-            timeout=None,
-        )
-
-        # Step 3: wrap FitRes in a ClientMessage in a Task in a TaskRes
-        task_res = configure_task_res(
-            TaskRes(task=Task(legacy_client_message=client_message_proto)),
-            task_ins,
-            Node(node_id=client_proxy.node_id, anonymous=False),
-        )
-
-        # Step 4: write *Res (result) back to `state`
-        state.store_task_res(task_res=task_res)
-
-    # Exit worker thread
-    log(DEBUG, "Worker for node %i stopped", client_proxy.node_id)
-
-
-def _call_client_proxy(
-    client_proxy: ClientProxy, server_message: ServerMessage, timeout: Optional[float]
-) -> ClientMessage:
-    """."""
-    # pylint: disable=too-many-locals
-
-    field = server_message.WhichOneof("msg")
-
-    if field == "get_properties_ins":
-        get_properties_ins = serde.get_properties_ins_from_proto(
-            msg=server_message.get_properties_ins
-        )
-        get_properties_res: GetPropertiesRes = client_proxy.get_properties(
-            ins=get_properties_ins,
-            timeout=timeout,
-        )
-        get_properties_res_proto = serde.get_properties_res_to_proto(
-            res=get_properties_res
-        )
-        return ClientMessage(get_properties_res=get_properties_res_proto)
-
-    if field == "get_parameters_ins":
-        get_parameters_ins = serde.get_parameters_ins_from_proto(
-            msg=server_message.get_parameters_ins
-        )
-        get_parameters_res: GetParametersRes = client_proxy.get_parameters(
-            ins=get_parameters_ins,
-            timeout=timeout,
-        )
-        get_parameters_res_proto = serde.get_parameters_res_to_proto(
-            res=get_parameters_res
-        )
-        return ClientMessage(get_parameters_res=get_parameters_res_proto)
-
-    if field == "fit_ins":
-        fit_ins = serde.fit_ins_from_proto(msg=server_message.fit_ins)
-        fit_res: FitRes = client_proxy.fit(
-            ins=fit_ins,
-            timeout=timeout,
-        )
-        fit_res_proto = serde.fit_res_to_proto(res=fit_res)
-        return ClientMessage(fit_res=fit_res_proto)
-
-    if field == "evaluate_ins":
-        evaluate_ins = serde.evaluate_ins_from_proto(msg=server_message.evaluate_ins)
-        evaluate_res: EvaluateRes = client_proxy.evaluate(
-            ins=evaluate_ins,
-            timeout=timeout,
-        )
-        evaluate_res_proto = serde.evaluate_res_to_proto(res=evaluate_res)
-        return ClientMessage(evaluate_res=evaluate_res_proto)
-
-    raise Exception(
-        "Unsupported instruction in ServerMessage, cannot deserialize from ProtoBuf"
-    )
diff --git a/src/py/flwr/server/history.py b/src/py/flwr/server/history.py
index ad5f5d0fc870..c4298911d97b 100644
--- a/src/py/flwr/server/history.py
+++ b/src/py/flwr/server/history.py
@@ -15,6 +15,7 @@
 """Training history."""
 
 
+import pprint
 from functools import reduce
 from typing import Dict, List, Tuple
 
@@ -90,29 +91,35 @@ def __repr__(self) -> str:
         """
         rep = ""
         if self.losses_distributed:
-            rep += "History (loss, distributed):\n" + reduce(
-                lambda a, b: a + b,
-                [
-                    f"\tround {server_round}: {loss}\n"
-                    for server_round, loss in self.losses_distributed
-                ],
+            rep += "History (loss, distributed):\n" + pprint.pformat(
+                reduce(
+                    lambda a, b: a + b,
+                    [
+                        f"\tround {server_round}: {loss}\n"
+                        for server_round, loss in self.losses_distributed
+                    ],
+                )
             )
         if self.losses_centralized:
-            rep += "History (loss, centralized):\n" + reduce(
-                lambda a, b: a + b,
-                [
-                    f"\tround {server_round}: {loss}\n"
-                    for server_round, loss in self.losses_centralized
-                ],
+            rep += "History (loss, centralized):\n" + pprint.pformat(
+                reduce(
+                    lambda a, b: a + b,
+                    [
+                        f"\tround {server_round}: {loss}\n"
+                        for server_round, loss in self.losses_centralized
+                    ],
+                )
             )
         if self.metrics_distributed_fit:
-            rep += "History (metrics, distributed, fit):\n" + str(
+            rep += "History (metrics, distributed, fit):\n" + pprint.pformat(
                 self.metrics_distributed_fit
             )
         if self.metrics_distributed:
-            rep += "History (metrics, distributed, evaluate):\n" + str(
+            rep += "History (metrics, distributed, evaluate):\n" + pprint.pformat(
                 self.metrics_distributed
             )
         if self.metrics_centralized:
-            rep += "History (metrics, centralized):\n" + str(self.metrics_centralized)
+            rep += "History (metrics, centralized):\n" + pprint.pformat(
+                self.metrics_centralized
+            )
         return rep
diff --git a/src/py/flwr/server/run_serverapp.py b/src/py/flwr/server/run_serverapp.py
new file mode 100644
index 000000000000..2f0f1185847e
--- /dev/null
+++ b/src/py/flwr/server/run_serverapp.py
@@ -0,0 +1,187 @@
+# 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.
+# ==============================================================================
+"""Run ServerApp."""
+
+
+import argparse
+import sys
+from logging import DEBUG, INFO, WARN
+from pathlib import Path
+from typing import Optional
+
+from flwr.common import Context, EventType, RecordSet, event
+from flwr.common.logger import log, update_console_handler
+from flwr.common.object_ref import load_app
+
+from .driver.driver import Driver
+from .server_app import LoadServerAppError, ServerApp
+
+
+def run(
+    driver: Driver,
+    server_app_dir: str,
+    server_app_attr: Optional[str] = None,
+    loaded_server_app: Optional[ServerApp] = None,
+) -> None:
+    """Run ServerApp with a given Driver."""
+    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. "
+        )
+
+    if server_app_dir is not None:
+        sys.path.insert(0, server_app_dir)
+
+    # Load ServerApp if needed
+    def _load() -> ServerApp:
+        if server_app_attr:
+            server_app: ServerApp = load_app(server_app_attr, LoadServerAppError)
+
+            if not isinstance(server_app, ServerApp):
+                raise LoadServerAppError(
+                    f"Attribute {server_app_attr} is not of type {ServerApp}",
+                ) from None
+
+        if loaded_server_app:
+            server_app = loaded_server_app
+        return server_app
+
+    server_app = _load()
+
+    # Initialize Context
+    context = Context(state=RecordSet())
+
+    # Call ServerApp
+    server_app(driver=driver, context=context)
+
+    log(DEBUG, "ServerApp finished running.")
+
+
+def run_server_app() -> None:
+    """Run Flower server app."""
+    event(EventType.RUN_SERVER_APP_ENTER)
+
+    args = _parse_args_run_server_app().parse_args()
+
+    update_console_handler(
+        level=DEBUG if args.verbose else INFO,
+        timestamps=args.verbose,
+        colored=True,
+    )
+
+    # Obtain certificates
+    if args.insecure:
+        if args.root_certificates is not None:
+            sys.exit(
+                "Conflicting options: The '--insecure' flag disables HTTPS, "
+                "but '--root-certificates' was also specified. Please remove "
+                "the '--root-certificates' option when running in insecure mode, "
+                "or omit '--insecure' to use HTTPS."
+            )
+        log(
+            WARN,
+            "Option `--insecure` was set. "
+            "Starting insecure HTTP client connected to %s.",
+            args.server,
+        )
+        root_certificates = None
+    else:
+        # Load the certificates if provided, or load the system certificates
+        cert_path = args.root_certificates
+        if cert_path is None:
+            root_certificates = None
+        else:
+            root_certificates = Path(cert_path).read_bytes()
+        log(
+            DEBUG,
+            "Starting secure HTTPS client connected to %s "
+            "with the following certificates: %s.",
+            args.server,
+            cert_path,
+        )
+
+    log(
+        DEBUG,
+        "Flower will load ServerApp `%s`",
+        getattr(args, "server-app"),
+    )
+
+    log(
+        DEBUG,
+        "root_certificates: `%s`",
+        root_certificates,
+    )
+
+    server_app_dir = args.dir
+    server_app_attr = getattr(args, "server-app")
+
+    # Initialize Driver
+    driver = Driver(
+        driver_service_address=args.server,
+        root_certificates=root_certificates,
+    )
+
+    # Run the Server App with the Driver
+    run(driver=driver, server_app_dir=server_app_dir, server_app_attr=server_app_attr)
+
+    # Clean up
+    driver.close()
+
+    event(EventType.RUN_SERVER_APP_LEAVE)
+
+
+def _parse_args_run_server_app() -> argparse.ArgumentParser:
+    """Parse flower-server-app command line arguments."""
+    parser = argparse.ArgumentParser(
+        description="Start a Flower server app",
+    )
+
+    parser.add_argument(
+        "server-app",
+        help="For example: `server:app` or `project.package.module:wrapper.app`",
+    )
+    parser.add_argument(
+        "--insecure",
+        action="store_true",
+        help="Run the server app without HTTPS. By default, the app runs with "
+        "HTTPS enabled. Use this flag only if you understand the risks.",
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="Set the logging to `DEBUG`.",
+    )
+    parser.add_argument(
+        "--root-certificates",
+        metavar="ROOT_CERT",
+        type=str,
+        help="Specifies the path to the PEM-encoded root certificate file for "
+        "establishing secure HTTPS connections.",
+    )
+    parser.add_argument(
+        "--server",
+        default="0.0.0.0:9091",
+        help="Server address",
+    )
+    parser.add_argument(
+        "--dir",
+        default="",
+        help="Add specified directory to the PYTHONPATH and load Flower "
+        "app from there."
+        " Default: current working directory.",
+    )
+
+    return parser
diff --git a/src/py/flwr/server/server.py b/src/py/flwr/server/server.py
index cf3a4d9aa07c..981325a6df08 100644
--- a/src/py/flwr/server/server.py
+++ b/src/py/flwr/server/server.py
@@ -16,8 +16,9 @@
 
 
 import concurrent.futures
+import io
 import timeit
-from logging import DEBUG, INFO
+from logging import INFO, WARN
 from typing import Dict, List, Optional, Tuple, Union
 
 from flwr.common import (
@@ -33,11 +34,13 @@
 )
 from flwr.common.logger import log
 from flwr.common.typing import GetParametersIns
-from flwr.server.client_manager import ClientManager
+from flwr.server.client_manager import ClientManager, SimpleClientManager
 from flwr.server.client_proxy import ClientProxy
 from flwr.server.history import History
 from flwr.server.strategy import FedAvg, Strategy
 
+from .server_config import ServerConfig
+
 FitResultsAndFailures = Tuple[
     List[Tuple[ClientProxy, FitRes]],
     List[Union[Tuple[ClientProxy, FitRes], BaseException]],
@@ -81,14 +84,14 @@ def client_manager(self) -> ClientManager:
         return self._client_manager
 
     # pylint: disable=too-many-locals
-    def fit(self, num_rounds: int, timeout: Optional[float]) -> History:
+    def fit(self, num_rounds: int, timeout: Optional[float]) -> Tuple[History, float]:
         """Run federated averaging for a number of rounds."""
         history = History()
 
         # Initialize parameters
-        log(INFO, "Initializing global parameters")
-        self.parameters = self._get_initial_parameters(timeout=timeout)
-        log(INFO, "Evaluating initial parameters")
+        log(INFO, "[INIT]")
+        self.parameters = self._get_initial_parameters(server_round=0, timeout=timeout)
+        log(INFO, "Evaluating initial global parameters")
         res = self.strategy.evaluate(0, parameters=self.parameters)
         if res is not None:
             log(
@@ -101,10 +104,11 @@ def fit(self, num_rounds: int, timeout: Optional[float]) -> History:
             history.add_metrics_centralized(server_round=0, metrics=res[1])
 
         # Run federated learning for num_rounds
-        log(INFO, "FL starting")
         start_time = timeit.default_timer()
 
         for current_round in range(1, num_rounds + 1):
+            log(INFO, "")
+            log(INFO, "[ROUND %s]", current_round)
             # Train model and replace previous global model
             res_fit = self.fit_round(
                 server_round=current_round,
@@ -150,8 +154,7 @@ def fit(self, num_rounds: int, timeout: Optional[float]) -> History:
         # Bookkeeping
         end_time = timeit.default_timer()
         elapsed = end_time - start_time
-        log(INFO, "FL finished in %s", elapsed)
-        return history
+        return history, elapsed
 
     def evaluate_round(
         self,
@@ -168,12 +171,11 @@ def evaluate_round(
             client_manager=self._client_manager,
         )
         if not client_instructions:
-            log(INFO, "evaluate_round %s: no clients selected, cancel", server_round)
+            log(INFO, "configure_evaluate: no clients selected, skipping evaluation")
             return None
         log(
-            DEBUG,
-            "evaluate_round %s: strategy sampled %s clients (out of %s)",
-            server_round,
+            INFO,
+            "configure_evaluate: strategy sampled %s clients (out of %s)",
             len(client_instructions),
             self._client_manager.num_available(),
         )
@@ -183,11 +185,11 @@ def evaluate_round(
             client_instructions,
             max_workers=self.max_workers,
             timeout=timeout,
+            group_id=server_round,
         )
         log(
-            DEBUG,
-            "evaluate_round %s received %s results and %s failures",
-            server_round,
+            INFO,
+            "aggregate_evaluate: received %s results and %s failures",
             len(results),
             len(failures),
         )
@@ -217,12 +219,11 @@ def fit_round(
         )
 
         if not client_instructions:
-            log(INFO, "fit_round %s: no clients selected, cancel", server_round)
+            log(INFO, "configure_fit: no clients selected, cancel")
             return None
         log(
-            DEBUG,
-            "fit_round %s: strategy sampled %s clients (out of %s)",
-            server_round,
+            INFO,
+            "configure_fit: strategy sampled %s clients (out of %s)",
             len(client_instructions),
             self._client_manager.num_available(),
         )
@@ -232,11 +233,11 @@ def fit_round(
             client_instructions=client_instructions,
             max_workers=self.max_workers,
             timeout=timeout,
+            group_id=server_round,
         )
         log(
-            DEBUG,
-            "fit_round %s received %s results and %s failures",
-            server_round,
+            INFO,
+            "aggregate_fit: received %s results and %s failures",
             len(results),
             len(failures),
         )
@@ -262,21 +263,25 @@ def disconnect_all_clients(self, timeout: Optional[float]) -> None:
             timeout=timeout,
         )
 
-    def _get_initial_parameters(self, timeout: Optional[float]) -> Parameters:
+    def _get_initial_parameters(
+        self, server_round: int, timeout: Optional[float]
+    ) -> Parameters:
         """Get initial parameters from one of the available clients."""
         # Server-side parameter initialization
         parameters: Optional[Parameters] = self.strategy.initialize_parameters(
             client_manager=self._client_manager
         )
         if parameters is not None:
-            log(INFO, "Using initial parameters provided by strategy")
+            log(INFO, "Using initial global parameters provided by strategy")
             return parameters
 
         # Get initial parameters from one of the clients
         log(INFO, "Requesting initial parameters from one random client")
         random_client = self._client_manager.sample(1)[0]
         ins = GetParametersIns(config={})
-        get_parameters_res = random_client.get_parameters(ins=ins, timeout=timeout)
+        get_parameters_res = random_client.get_parameters(
+            ins=ins, timeout=timeout, group_id=server_round
+        )
         log(INFO, "Received initial parameters from one random client")
         return get_parameters_res.parameters
 
@@ -319,6 +324,7 @@ def reconnect_client(
     disconnect = client.reconnect(
         reconnect,
         timeout=timeout,
+        group_id=None,
     )
     return client, disconnect
 
@@ -327,11 +333,12 @@ def fit_clients(
     client_instructions: List[Tuple[ClientProxy, FitIns]],
     max_workers: Optional[int],
     timeout: Optional[float],
+    group_id: int,
 ) -> FitResultsAndFailures:
     """Refine parameters concurrently on all selected clients."""
     with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
         submitted_fs = {
-            executor.submit(fit_client, client_proxy, ins, timeout)
+            executor.submit(fit_client, client_proxy, ins, timeout, group_id)
             for client_proxy, ins in client_instructions
         }
         finished_fs, _ = concurrent.futures.wait(
@@ -350,10 +357,10 @@ def fit_clients(
 
 
 def fit_client(
-    client: ClientProxy, ins: FitIns, timeout: Optional[float]
+    client: ClientProxy, ins: FitIns, timeout: Optional[float], group_id: int
 ) -> Tuple[ClientProxy, FitRes]:
     """Refine parameters on a single client."""
-    fit_res = client.fit(ins, timeout=timeout)
+    fit_res = client.fit(ins, timeout=timeout, group_id=group_id)
     return client, fit_res
 
 
@@ -386,11 +393,12 @@ def evaluate_clients(
     client_instructions: List[Tuple[ClientProxy, EvaluateIns]],
     max_workers: Optional[int],
     timeout: Optional[float],
+    group_id: int,
 ) -> EvaluateResultsAndFailures:
     """Evaluate parameters concurrently on all selected clients."""
     with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
         submitted_fs = {
-            executor.submit(evaluate_client, client_proxy, ins, timeout)
+            executor.submit(evaluate_client, client_proxy, ins, timeout, group_id)
             for client_proxy, ins in client_instructions
         }
         finished_fs, _ = concurrent.futures.wait(
@@ -412,9 +420,10 @@ def evaluate_client(
     client: ClientProxy,
     ins: EvaluateIns,
     timeout: Optional[float],
+    group_id: int,
 ) -> Tuple[ClientProxy, EvaluateRes]:
     """Evaluate parameters on a single client."""
-    evaluate_res = client.evaluate(ins, timeout=timeout)
+    evaluate_res = client.evaluate(ins, timeout=timeout, group_id=group_id)
     return client, evaluate_res
 
 
@@ -441,3 +450,51 @@ def _handle_finished_future_after_evaluate(
 
     # Not successful, client returned a result where the status code is not OK
     failures.append(result)
+
+
+def init_defaults(
+    server: Optional[Server],
+    config: Optional[ServerConfig],
+    strategy: Optional[Strategy],
+    client_manager: Optional[ClientManager],
+) -> Tuple[Server, ServerConfig]:
+    """Create server instance if none was given."""
+    if server is None:
+        if client_manager is None:
+            client_manager = SimpleClientManager()
+        if strategy is None:
+            strategy = FedAvg()
+        server = Server(client_manager=client_manager, strategy=strategy)
+    elif strategy is not None:
+        log(WARN, "Both server and strategy were provided, ignoring strategy")
+
+    # Set default config values
+    if config is None:
+        config = ServerConfig()
+
+    return server, config
+
+
+def run_fl(
+    server: Server,
+    config: ServerConfig,
+) -> History:
+    """Train a model on the given server and return the History object."""
+    hist, elapsed_time = server.fit(
+        num_rounds=config.num_rounds, timeout=config.round_timeout
+    )
+
+    log(INFO, "")
+    log(INFO, "[SUMMARY]")
+    log(INFO, "Run finished %s rounds in %.2fs", config.num_rounds, elapsed_time)
+    for idx, line in enumerate(io.StringIO(str(hist))):
+        if idx == 0:
+            log(INFO, "%s", line.strip("\n"))
+        else:
+            log(INFO, "\t%s", line.strip("\n"))
+    log(INFO, "")
+
+    # Graceful shutdown
+    server.disconnect_all_clients(timeout=config.round_timeout)
+
+    return hist
diff --git a/src/py/flwr/server/server_app.py b/src/py/flwr/server/server_app.py
new file mode 100644
index 000000000000..1b2eab87fdaa
--- /dev/null
+++ b/src/py/flwr/server/server_app.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.
+# ==============================================================================
+"""Flower ServerApp."""
+
+
+from typing import Callable, Optional
+
+from flwr.common import Context, RecordSet
+from flwr.server.strategy import Strategy
+
+from .client_manager import ClientManager
+from .compat import start_driver
+from .driver import Driver
+from .server import Server
+from .server_config import ServerConfig
+from .typing import ServerAppCallable
+
+
+class ServerApp:
+    """Flower ServerApp.
+
+    Examples
+    --------
+    Use the `ServerApp` with an existing `Strategy`:
+
+    >>> server_config = ServerConfig(num_rounds=3)
+    >>> strategy = FedAvg()
+    >>>
+    >>> app = ServerApp()
+    >>>     server_config=server_config,
+    >>>     strategy=strategy,
+    >>> )
+
+    Use the `ServerApp` with a custom main function:
+
+    >>> app = ServerApp()
+    >>>
+    >>> @app.main()
+    >>> def main(driver: Driver, context: Context) -> None:
+    >>>    print("ServerApp running")
+    """
+
+    def __init__(
+        self,
+        server: Optional[Server] = None,
+        config: Optional[ServerConfig] = None,
+        strategy: Optional[Strategy] = None,
+        client_manager: Optional[ClientManager] = None,
+    ) -> None:
+        self._server = server
+        self._config = config
+        self._strategy = strategy
+        self._client_manager = client_manager
+        self._main: Optional[ServerAppCallable] = None
+
+    def __call__(self, driver: Driver, context: Context) -> None:
+        """Execute `ServerApp`."""
+        # Compatibility mode
+        if not self._main:
+            start_driver(
+                server=self._server,
+                config=self._config,
+                strategy=self._strategy,
+                client_manager=self._client_manager,
+                driver=driver,
+            )
+            return
+
+        # New execution mode
+        context = Context(state=RecordSet())
+        self._main(driver, context)
+
+    def main(self) -> Callable[[ServerAppCallable], ServerAppCallable]:
+        """Return a decorator that registers the main fn with the server app.
+
+        Examples
+        --------
+        >>> app = ServerApp()
+        >>>
+        >>> @app.main()
+        >>> def main(driver: Driver, context: Context) -> None:
+        >>>    print("ServerApp running")
+        """
+
+        def main_decorator(main_fn: ServerAppCallable) -> ServerAppCallable:
+            """Register the main fn with the ServerApp object."""
+            if self._server or self._config or self._strategy or self._client_manager:
+                raise ValueError(
+                    """Use either a custom main function or a `Strategy`, but not both.
+
+                    Use the `ServerApp` with an existing `Strategy`:
+
+                    >>> server_config = ServerConfig(num_rounds=3)
+                    >>> strategy = FedAvg()
+                    >>>
+                    >>> app = ServerApp()
+                    >>>     server_config=server_config,
+                    >>>     strategy=strategy,
+                    >>> )
+
+                    Use the `ServerApp` with a custom main function:
+
+                    >>> app = ServerApp()
+                    >>>
+                    >>> @app.main()
+                    >>> def main(driver: Driver, context: Context) -> None:
+                    >>>    print("ServerApp running")
+                    """,
+                )
+
+            # Register provided function with the ServerApp object
+            self._main = main_fn
+
+            # Return provided function unmodified
+            return main_fn
+
+        return main_decorator
+
+
+class LoadServerAppError(Exception):
+    """Error when trying to load `ServerApp`."""
diff --git a/src/py/flwr/server/server_app_test.py b/src/py/flwr/server/server_app_test.py
new file mode 100644
index 000000000000..38c0d6240d90
--- /dev/null
+++ b/src/py/flwr/server/server_app_test.py
@@ -0,0 +1,62 @@
+# Copyright 2020 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 ServerApp."""
+
+
+from unittest.mock import MagicMock
+
+import pytest
+
+from flwr.common import Context, RecordSet
+from flwr.server import ServerApp, ServerConfig
+from flwr.server.driver import Driver
+
+
+def test_server_app_custom_mode() -> None:
+    """Test sampling w/o criterion."""
+    # Prepare
+    app = ServerApp()
+    driver = MagicMock()
+    context = Context(state=RecordSet())
+
+    called = {"called": False}
+
+    # pylint: disable=unused-argument
+    @app.main()
+    def custom_main(driver: Driver, context: Context) -> None:
+        called["called"] = True
+
+    # pylint: enable=unused-argument
+
+    # Execute
+    app(driver, context)
+
+    # Assert
+    assert called["called"]
+
+
+def test_server_app_exception_when_both_modes() -> None:
+    """Test ServerApp error when both compat mode and custom fns are used."""
+    # Prepare
+    app = ServerApp(config=ServerConfig(num_rounds=3))
+
+    # Execute and assert
+    with pytest.raises(ValueError):
+        # pylint: disable=unused-argument
+        @app.main()
+        def custom_main(driver: Driver, context: Context) -> None:
+            pass
+
+        # pylint: enable=unused-argument
diff --git a/src/py/flwr/server/server_config.py b/src/py/flwr/server/server_config.py
new file mode 100644
index 000000000000..c47367eab4c0
--- /dev/null
+++ b/src/py/flwr/server/server_config.py
@@ -0,0 +1,40 @@
+# 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 ServerConfig."""
+
+
+from dataclasses import dataclass
+from typing import Optional
+
+
+@dataclass
+class ServerConfig:
+    """Flower server config.
+
+    All attributes have default values which allows users to configure just the ones
+    they care about.
+    """
+
+    num_rounds: int = 1
+    round_timeout: Optional[float] = None
+
+    def __repr__(self) -> str:
+        """Return the string representation of the ServerConfig."""
+        timeout_string = (
+            "no round_timeout"
+            if self.round_timeout is None
+            else f"round_timeout={self.round_timeout}s"
+        )
+        return f"num_rounds={self.num_rounds}, {timeout_string}"
diff --git a/src/py/flwr/server/server_test.py b/src/py/flwr/server/server_test.py
index 63ec1021ff5c..274e5289fee1 100644
--- a/src/py/flwr/server/server_test.py
+++ b/src/py/flwr/server/server_test.py
@@ -45,18 +45,20 @@ class SuccessClient(ClientProxy):
     """Test class."""
 
     def get_properties(
-        self, ins: GetPropertiesIns, timeout: Optional[float]
+        self, ins: GetPropertiesIns, timeout: Optional[float], group_id: Optional[int]
     ) -> GetPropertiesRes:
-        """Raise an Exception because this method is not expected to be called."""
-        raise Exception()
+        """Raise an error because this method is not expected to be called."""
+        raise NotImplementedError()
 
     def get_parameters(
-        self, ins: GetParametersIns, timeout: Optional[float]
+        self, ins: GetParametersIns, timeout: Optional[float], group_id: Optional[int]
     ) -> GetParametersRes:
-        """Raise an Exception because this method is not expected to be called."""
-        raise Exception()
+        """Raise a error because this method is not expected to be called."""
+        raise NotImplementedError()
 
-    def fit(self, ins: FitIns, timeout: Optional[float]) -> FitRes:
+    def fit(
+        self, ins: FitIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> FitRes:
         """Simulate fit by returning a success FitRes with simple set of weights."""
         arr = np.array([[1, 2], [3, 4], [5, 6]])
         arr_serialized = ndarray_to_bytes(arr)
@@ -67,7 +69,9 @@ def fit(self, ins: FitIns, timeout: Optional[float]) -> FitRes:
             metrics={},
         )
 
-    def evaluate(self, ins: EvaluateIns, timeout: Optional[float]) -> EvaluateRes:
+    def evaluate(
+        self, ins: EvaluateIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> EvaluateRes:
         """Simulate evaluate by returning a success EvaluateRes with loss 1.0."""
         return EvaluateRes(
             status=Status(code=Code.OK, message="Success"),
@@ -76,7 +80,9 @@ def evaluate(self, ins: EvaluateIns, timeout: Optional[float]) -> EvaluateRes:
             metrics={},
         )
 
-    def reconnect(self, ins: ReconnectIns, timeout: Optional[float]) -> DisconnectRes:
+    def reconnect(
+        self, ins: ReconnectIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> DisconnectRes:
         """Simulate reconnect by returning a DisconnectRes with UNKNOWN reason."""
         return DisconnectRes(reason="UNKNOWN")
 
@@ -85,28 +91,34 @@ class FailingClient(ClientProxy):
     """Test class."""
 
     def get_properties(
-        self, ins: GetPropertiesIns, timeout: Optional[float]
+        self, ins: GetPropertiesIns, timeout: Optional[float], group_id: Optional[int]
     ) -> GetPropertiesRes:
-        """Raise an Exception to simulate failure in the client."""
-        raise Exception()
+        """Raise a NotImplementedError to simulate failure in the client."""
+        raise NotImplementedError()
 
     def get_parameters(
-        self, ins: GetParametersIns, timeout: Optional[float]
+        self, ins: GetParametersIns, timeout: Optional[float], group_id: Optional[int]
     ) -> GetParametersRes:
-        """Raise an Exception to simulate failure in the client."""
-        raise Exception()
+        """Raise a NotImplementedError to simulate failure in the client."""
+        raise NotImplementedError()
 
-    def fit(self, ins: FitIns, timeout: Optional[float]) -> FitRes:
-        """Raise an Exception to simulate failure in the client."""
-        raise Exception()
+    def fit(
+        self, ins: FitIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> FitRes:
+        """Raise a NotImplementedError to simulate failure in the client."""
+        raise NotImplementedError()
 
-    def evaluate(self, ins: EvaluateIns, timeout: Optional[float]) -> EvaluateRes:
-        """Raise an Exception to simulate failure in the client."""
-        raise Exception()
+    def evaluate(
+        self, ins: EvaluateIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> EvaluateRes:
+        """Raise a NotImplementedError to simulate failure in the client."""
+        raise NotImplementedError()
 
-    def reconnect(self, ins: ReconnectIns, timeout: Optional[float]) -> DisconnectRes:
-        """Raise an Exception to simulate failure in the client."""
-        raise Exception()
+    def reconnect(
+        self, ins: ReconnectIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> DisconnectRes:
+        """Raise a NotImplementedError to simulate failure in the client."""
+        raise NotImplementedError()
 
 
 def test_fit_clients() -> None:
@@ -122,7 +134,7 @@ def test_fit_clients() -> None:
     client_instructions = [(c, ins) for c in clients]
 
     # Execute
-    results, failures = fit_clients(client_instructions, None, None)
+    results, failures = fit_clients(client_instructions, None, None, 0)
 
     # Assert
     assert len(results) == 1
@@ -150,6 +162,7 @@ def test_eval_clients() -> None:
         client_instructions=client_instructions,
         max_workers=None,
         timeout=None,
+        group_id=0,
     )
 
     # Assert
diff --git a/src/py/flwr/server/strategy/__init__.py b/src/py/flwr/server/strategy/__init__.py
index 1750a7522379..b7de9a946fff 100644
--- a/src/py/flwr/server/strategy/__init__.py
+++ b/src/py/flwr/server/strategy/__init__.py
@@ -16,6 +16,18 @@
 
 
 from .bulyan import Bulyan as Bulyan
+from .dp_adaptive_clipping import (
+    DifferentialPrivacyClientSideAdaptiveClipping as DifferentialPrivacyClientSideAdaptiveClipping,
+)
+from .dp_adaptive_clipping import (
+    DifferentialPrivacyServerSideAdaptiveClipping as DifferentialPrivacyServerSideAdaptiveClipping,
+)
+from .dp_fixed_clipping import (
+    DifferentialPrivacyClientSideFixedClipping as DifferentialPrivacyClientSideFixedClipping,
+)
+from .dp_fixed_clipping import (
+    DifferentialPrivacyServerSideFixedClipping as DifferentialPrivacyServerSideFixedClipping,
+)
 from .dpfedavg_adaptive import DPFedAvgAdaptive as DPFedAvgAdaptive
 from .dpfedavg_fixed import DPFedAvgFixed as DPFedAvgFixed
 from .fault_tolerant_fedavg import FaultTolerantFedAvg as FaultTolerantFedAvg
@@ -37,24 +49,28 @@
 from .strategy import Strategy as Strategy
 
 __all__ = [
-    "FaultTolerantFedAvg",
+    "Bulyan",
+    "DPFedAvgAdaptive",
+    "DPFedAvgFixed",
+    "DifferentialPrivacyClientSideAdaptiveClipping",
+    "DifferentialPrivacyServerSideAdaptiveClipping",
+    "DifferentialPrivacyClientSideFixedClipping",
+    "DifferentialPrivacyServerSideFixedClipping",
     "FedAdagrad",
     "FedAdam",
     "FedAvg",
-    "FedXgbNnAvg",
-    "FedXgbBagging",
-    "FedXgbCyclic",
     "FedAvgAndroid",
     "FedAvgM",
+    "FedMedian",
     "FedOpt",
     "FedProx",
-    "FedYogi",
-    "QFedAvg",
-    "FedMedian",
     "FedTrimmedAvg",
+    "FedXgbBagging",
+    "FedXgbCyclic",
+    "FedXgbNnAvg",
+    "FedYogi",
+    "FaultTolerantFedAvg",
     "Krum",
-    "Bulyan",
-    "DPFedAvgAdaptive",
-    "DPFedAvgFixed",
+    "QFedAvg",
     "Strategy",
 ]
diff --git a/src/py/flwr/server/strategy/aggregate.py b/src/py/flwr/server/strategy/aggregate.py
index 63926f2eaa51..c668b55eebe6 100644
--- a/src/py/flwr/server/strategy/aggregate.py
+++ b/src/py/flwr/server/strategy/aggregate.py
@@ -20,13 +20,14 @@
 
 import numpy as np
 
-from flwr.common import NDArray, NDArrays
+from flwr.common import FitRes, NDArray, NDArrays, parameters_to_ndarrays
+from flwr.server.client_proxy import ClientProxy
 
 
 def aggregate(results: List[Tuple[NDArrays, int]]) -> NDArrays:
     """Compute weighted average."""
     # Calculate the total number of examples used during training
-    num_examples_total = sum([num_examples for _, num_examples in results])
+    num_examples_total = sum(num_examples for (_, num_examples) in results)
 
     # Create a list of weights, each multiplied by the related number of examples
     weighted_weights = [
@@ -41,6 +42,31 @@ def aggregate(results: List[Tuple[NDArrays, int]]) -> NDArrays:
     return weights_prime
 
 
+def aggregate_inplace(results: List[Tuple[ClientProxy, FitRes]]) -> NDArrays:
+    """Compute in-place weighted average."""
+    # Count total examples
+    num_examples_total = sum(fit_res.num_examples for (_, fit_res) in results)
+
+    # Compute scaling factors for each result
+    scaling_factors = [
+        fit_res.num_examples / num_examples_total for _, fit_res in results
+    ]
+
+    # Let's do in-place aggregation
+    # Get first result, then add up each other
+    params = [
+        scaling_factors[0] * x for x in parameters_to_ndarrays(results[0][1].parameters)
+    ]
+    for i, (_, fit_res) in enumerate(results[1:]):
+        res = (
+            scaling_factors[i + 1] * x
+            for x in parameters_to_ndarrays(fit_res.parameters)
+        )
+        params = [reduce(np.add, layer_updates) for layer_updates in zip(params, res)]
+
+    return params
+
+
 def aggregate_median(results: List[Tuple[NDArrays, int]]) -> NDArrays:
     """Compute median."""
     # Create a list of weights and ignore the number of examples
@@ -69,9 +95,9 @@ def aggregate_krum(
     # For each client, take the n-f-2 closest parameters vectors
     num_closest = max(1, len(weights) - num_malicious - 2)
     closest_indices = []
-    for i, _ in enumerate(distance_matrix):
+    for distance in distance_matrix:
         closest_indices.append(
-            np.argsort(distance_matrix[i])[1 : num_closest + 1].tolist()  # noqa: E203
+            np.argsort(distance)[1 : num_closest + 1].tolist()  # noqa: E203
         )
 
     # Compute the score for each client, that is the sum of the distances
@@ -176,7 +202,7 @@ def aggregate_bulyan(
 
 def weighted_loss_avg(results: List[Tuple[int, float]]) -> float:
     """Aggregate evaluation results obtained from multiple clients."""
-    num_total_evaluation_examples = sum([num_examples for num_examples, _ in results])
+    num_total_evaluation_examples = sum(num_examples for (num_examples, _) in results)
     weighted_losses = [num_examples * loss for num_examples, loss in results]
     return sum(weighted_losses) / num_total_evaluation_examples
 
@@ -207,9 +233,9 @@ def _compute_distances(weights: List[NDArrays]) -> NDArray:
     """
     flat_w = np.array([np.concatenate(p, axis=None).ravel() for p in weights])
     distance_matrix = np.zeros((len(weights), len(weights)))
-    for i, _ in enumerate(flat_w):
-        for j, _ in enumerate(flat_w):
-            delta = flat_w[i] - flat_w[j]
+    for i, flat_w_i in enumerate(flat_w):
+        for j, flat_w_j in enumerate(flat_w):
+            delta = flat_w_i - flat_w_j
             norm = np.linalg.norm(delta)
             distance_matrix[i, j] = norm**2
     return distance_matrix
diff --git a/src/py/flwr/server/strategy/dp_adaptive_clipping.py b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
new file mode 100644
index 000000000000..1acfd4613a0a
--- /dev/null
+++ b/src/py/flwr/server/strategy/dp_adaptive_clipping.py
@@ -0,0 +1,470 @@
+# 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.
+# ==============================================================================
+"""Central differential privacy with adaptive clipping.
+
+Paper (Andrew et al.): https://arxiv.org/abs/1905.03871
+"""
+
+
+import math
+from logging import INFO, WARNING
+from typing import Dict, List, Optional, Tuple, Union
+
+import numpy as np
+
+from flwr.common import (
+    EvaluateIns,
+    EvaluateRes,
+    FitIns,
+    FitRes,
+    NDArrays,
+    Parameters,
+    Scalar,
+    ndarrays_to_parameters,
+    parameters_to_ndarrays,
+)
+from flwr.common.differential_privacy import (
+    adaptive_clip_inputs_inplace,
+    add_gaussian_noise_to_params,
+    compute_adaptive_noise_params,
+    compute_stdv,
+)
+from flwr.common.differential_privacy_constants import (
+    CLIENTS_DISCREPANCY_WARNING,
+    KEY_CLIPPING_NORM,
+    KEY_NORM_BIT,
+)
+from flwr.common.logger import log
+from flwr.server.client_manager import ClientManager
+from flwr.server.client_proxy import ClientProxy
+from flwr.server.strategy.strategy import Strategy
+
+
+class DifferentialPrivacyServerSideAdaptiveClipping(Strategy):
+    """Strategy wrapper for central DP with server-side adaptive clipping.
+
+    Parameters
+    ----------
+    strategy: Strategy
+        The strategy to which DP functionalities will be added by this wrapper.
+    noise_multiplier : float
+        The noise multiplier for the Gaussian mechanism for model updates.
+    num_sampled_clients : int
+        The number of clients that are sampled on each round.
+    initial_clipping_norm : float
+        The initial value of clipping norm. Defaults to 0.1.
+        Andrew et al. recommends to set to 0.1.
+    target_clipped_quantile : float
+        The desired quantile of updates which should be clipped. Defaults to 0.5.
+    clip_norm_lr : float
+        The learning rate for the clipping norm adaptation. Defaults to 0.2.
+        Andrew et al. recommends to set to 0.2.
+    clipped_count_stddev : float
+        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`
+
+    Examples
+    --------
+    Create a strategy:
+
+    >>> strategy = fl.server.strategy.FedAvg( ... )
+
+    Wrap the strategy with the DifferentialPrivacyServerSideAdaptiveClipping wrapper
+
+    >>> dp_strategy = DifferentialPrivacyServerSideAdaptiveClipping(
+    >>>     strategy, cfg.noise_multiplier, cfg.num_sampled_clients, ...
+    >>> )
+    """
+
+    # pylint: disable=too-many-arguments,too-many-instance-attributes
+    def __init__(
+        self,
+        strategy: Strategy,
+        noise_multiplier: float,
+        num_sampled_clients: int,
+        initial_clipping_norm: float = 0.1,
+        target_clipped_quantile: float = 0.5,
+        clip_norm_lr: float = 0.2,
+        clipped_count_stddev: Optional[float] = None,
+    ) -> None:
+        super().__init__()
+
+        if strategy is None:
+            raise ValueError("The passed strategy is None.")
+
+        if noise_multiplier < 0:
+            raise ValueError("The noise multiplier should be a non-negative value.")
+
+        if num_sampled_clients <= 0:
+            raise ValueError(
+                "The number of sampled clients should be a positive value."
+            )
+
+        if initial_clipping_norm <= 0:
+            raise ValueError("The initial clipping norm should be a positive value.")
+
+        if not 0 <= target_clipped_quantile <= 1:
+            raise ValueError(
+                "The target clipped quantile must be between 0 and 1 (inclusive)."
+            )
+
+        if clip_norm_lr <= 0:
+            raise ValueError("The learning rate must be positive.")
+
+        if clipped_count_stddev is not None:
+            if clipped_count_stddev < 0:
+                raise ValueError("The `clipped_count_stddev` must be non-negative.")
+
+        self.strategy = strategy
+        self.num_sampled_clients = num_sampled_clients
+        self.clipping_norm = initial_clipping_norm
+        self.target_clipped_quantile = target_clipped_quantile
+        self.clip_norm_lr = clip_norm_lr
+        (
+            self.clipped_count_stddev,
+            self.noise_multiplier,
+        ) = compute_adaptive_noise_params(
+            noise_multiplier,
+            num_sampled_clients,
+            clipped_count_stddev,
+        )
+
+        self.current_round_params: NDArrays = []
+
+    def __repr__(self) -> str:
+        """Compute a string representation of the strategy."""
+        rep = "Differential Privacy Strategy Wrapper (Server-Side Adaptive Clipping)"
+        return rep
+
+    def initialize_parameters(
+        self, client_manager: ClientManager
+    ) -> Optional[Parameters]:
+        """Initialize global model parameters using given strategy."""
+        return self.strategy.initialize_parameters(client_manager)
+
+    def configure_fit(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, FitIns]]:
+        """Configure the next round of training."""
+        self.current_round_params = parameters_to_ndarrays(parameters)
+        return self.strategy.configure_fit(server_round, parameters, client_manager)
+
+    def configure_evaluate(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, EvaluateIns]]:
+        """Configure the next round of evaluation."""
+        return self.strategy.configure_evaluate(
+            server_round, parameters, client_manager
+        )
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        """Aggregate training results and update clip norms."""
+        if failures:
+            return None, {}
+
+        if len(results) != self.num_sampled_clients:
+            log(
+                WARNING,
+                CLIENTS_DISCREPANCY_WARNING,
+                len(results),
+                self.num_sampled_clients,
+            )
+
+        norm_bit_set_count = 0
+        for _, res in results:
+            param = parameters_to_ndarrays(res.parameters)
+            # Compute and clip update
+            model_update = [
+                np.subtract(x, y) for (x, y) in zip(param, self.current_round_params)
+            ]
+
+            norm_bit = adaptive_clip_inputs_inplace(model_update, self.clipping_norm)
+            norm_bit_set_count += norm_bit
+
+            log(
+                INFO,
+                "aggregate_fit: parameters are clipped by value: %s.",
+                self.clipping_norm,
+            )
+
+            for i, _ in enumerate(self.current_round_params):
+                param[i] = self.current_round_params[i] + model_update[i]
+            # Convert back to parameters
+            res.parameters = ndarrays_to_parameters(param)
+
+        # Noising the count
+        noised_norm_bit_set_count = float(
+            np.random.normal(norm_bit_set_count, self.clipped_count_stddev)
+        )
+        noised_norm_bit_set_fraction = noised_norm_bit_set_count / len(results)
+        # Geometric update
+        self.clipping_norm *= math.exp(
+            -self.clip_norm_lr
+            * (noised_norm_bit_set_fraction - self.target_clipped_quantile)
+        )
+
+        aggregated_params, metrics = self.strategy.aggregate_fit(
+            server_round, results, failures
+        )
+
+        # Add Gaussian noise to the aggregated parameters
+        if aggregated_params:
+            aggregated_params = add_gaussian_noise_to_params(
+                aggregated_params,
+                self.noise_multiplier,
+                self.clipping_norm,
+                self.num_sampled_clients,
+            )
+            log(
+                INFO,
+                "aggregate_fit: central DP noise with standard deviation: %s added to parameters.",
+                compute_stdv(
+                    self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
+                ),
+            )
+
+        return aggregated_params, metrics
+
+    def aggregate_evaluate(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, EvaluateRes]],
+        failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
+    ) -> Tuple[Optional[float], Dict[str, Scalar]]:
+        """Aggregate evaluation losses using the given strategy."""
+        return self.strategy.aggregate_evaluate(server_round, results, failures)
+
+    def evaluate(
+        self, server_round: int, parameters: Parameters
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """Evaluate model parameters using an evaluation function from the strategy."""
+        return self.strategy.evaluate(server_round, parameters)
+
+
+class DifferentialPrivacyClientSideAdaptiveClipping(Strategy):
+    """Strategy wrapper for central DP with client-side adaptive clipping.
+
+    Use `adaptiveclipping_mod` modifier at the client side.
+
+    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`.
+
+    Parameters
+    ----------
+    strategy : Strategy
+        The strategy to which DP functionalities will be added by this wrapper.
+    noise_multiplier : float
+        The noise multiplier for the Gaussian mechanism for model updates.
+    num_sampled_clients : int
+        The number of clients that are sampled on each round.
+    initial_clipping_norm : float
+        The initial value of clipping norm. Defaults to 0.1.
+        Andrew et al. recommends to set to 0.1.
+    target_clipped_quantile : float
+        The desired quantile of updates which should be clipped. Defaults to 0.5.
+    clip_norm_lr : float
+        The learning rate for the clipping norm adaptation. Defaults to 0.2.
+        Andrew et al. recommends to set to 0.2.
+    clipped_count_stddev : float
+        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`
+
+    Examples
+    --------
+    Create a strategy:
+
+    >>> strategy = fl.server.strategy.FedAvg(...)
+
+    Wrap the strategy with the `DifferentialPrivacyClientSideAdaptiveClipping` wrapper:
+
+    >>> dp_strategy = DifferentialPrivacyClientSideAdaptiveClipping(
+    >>>     strategy, cfg.noise_multiplier, cfg.num_sampled_clients
+    >>> )
+
+    On the client, add the `adaptiveclipping_mod` to the client-side mods:
+
+    >>> app = fl.client.ClientApp(
+    >>>     client_fn=client_fn, mods=[adaptiveclipping_mod]
+    >>> )
+    """
+
+    # pylint: disable=too-many-arguments,too-many-instance-attributes
+    def __init__(
+        self,
+        strategy: Strategy,
+        noise_multiplier: float,
+        num_sampled_clients: int,
+        initial_clipping_norm: float = 0.1,
+        target_clipped_quantile: float = 0.5,
+        clip_norm_lr: float = 0.2,
+        clipped_count_stddev: Optional[float] = None,
+    ) -> None:
+        super().__init__()
+
+        if strategy is None:
+            raise ValueError("The passed strategy is None.")
+
+        if noise_multiplier < 0:
+            raise ValueError("The noise multiplier should be a non-negative value.")
+
+        if num_sampled_clients <= 0:
+            raise ValueError(
+                "The number of sampled clients should be a positive value."
+            )
+
+        if initial_clipping_norm <= 0:
+            raise ValueError("The initial clipping norm should be a positive value.")
+
+        if not 0 <= target_clipped_quantile <= 1:
+            raise ValueError(
+                "The target clipped quantile must be between 0 and 1 (inclusive)."
+            )
+
+        if clip_norm_lr <= 0:
+            raise ValueError("The learning rate must be positive.")
+
+        if clipped_count_stddev is not None and clipped_count_stddev < 0:
+            raise ValueError("The `clipped_count_stddev` must be non-negative.")
+
+        self.strategy = strategy
+        self.num_sampled_clients = num_sampled_clients
+        self.clipping_norm = initial_clipping_norm
+        self.target_clipped_quantile = target_clipped_quantile
+        self.clip_norm_lr = clip_norm_lr
+        (
+            self.clipped_count_stddev,
+            self.noise_multiplier,
+        ) = compute_adaptive_noise_params(
+            noise_multiplier,
+            num_sampled_clients,
+            clipped_count_stddev,
+        )
+
+    def __repr__(self) -> str:
+        """Compute a string representation of the strategy."""
+        rep = "Differential Privacy Strategy Wrapper (Client-Side Adaptive Clipping)"
+        return rep
+
+    def initialize_parameters(
+        self, client_manager: ClientManager
+    ) -> Optional[Parameters]:
+        """Initialize global model parameters using given strategy."""
+        return self.strategy.initialize_parameters(client_manager)
+
+    def configure_fit(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, FitIns]]:
+        """Configure the next round of training."""
+        additional_config = {KEY_CLIPPING_NORM: self.clipping_norm}
+        inner_strategy_config_result = self.strategy.configure_fit(
+            server_round, parameters, client_manager
+        )
+        for _, fit_ins in inner_strategy_config_result:
+            fit_ins.config.update(additional_config)
+
+        return inner_strategy_config_result
+
+    def configure_evaluate(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, EvaluateIns]]:
+        """Configure the next round of evaluation."""
+        return self.strategy.configure_evaluate(
+            server_round, parameters, client_manager
+        )
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        """Aggregate training results and update clip norms."""
+        if failures:
+            return None, {}
+
+        if len(results) != self.num_sampled_clients:
+            log(
+                WARNING,
+                CLIENTS_DISCREPANCY_WARNING,
+                len(results),
+                self.num_sampled_clients,
+            )
+
+        aggregated_params, metrics = self.strategy.aggregate_fit(
+            server_round, results, failures
+        )
+        self._update_clip_norm(results)
+
+        # Add Gaussian noise to the aggregated parameters
+        if aggregated_params:
+            aggregated_params = add_gaussian_noise_to_params(
+                aggregated_params,
+                self.noise_multiplier,
+                self.clipping_norm,
+                self.num_sampled_clients,
+            )
+            log(
+                INFO,
+                "aggregate_fit: central DP noise with standard deviation: %s added to parameters.",
+                compute_stdv(
+                    self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
+                ),
+            )
+
+        return aggregated_params, metrics
+
+    def _update_clip_norm(self, results: List[Tuple[ClientProxy, FitRes]]) -> None:
+        # Calculate the number of clients which set the norm indicator bit
+        norm_bit_set_count = 0
+        for client_proxy, fit_res in results:
+            if KEY_NORM_BIT not in fit_res.metrics:
+                raise KeyError(
+                    f"{KEY_NORM_BIT} not returned by client with id {client_proxy.cid}."
+                )
+            if fit_res.metrics[KEY_NORM_BIT]:
+                norm_bit_set_count += 1
+        # Add noise to the count
+        noised_norm_bit_set_count = float(
+            np.random.normal(norm_bit_set_count, self.clipped_count_stddev)
+        )
+
+        noised_norm_bit_set_fraction = noised_norm_bit_set_count / len(results)
+        # Geometric update
+        self.clipping_norm *= math.exp(
+            -self.clip_norm_lr
+            * (noised_norm_bit_set_fraction - self.target_clipped_quantile)
+        )
+
+    def aggregate_evaluate(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, EvaluateRes]],
+        failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
+    ) -> Tuple[Optional[float], Dict[str, Scalar]]:
+        """Aggregate evaluation losses using the given strategy."""
+        return self.strategy.aggregate_evaluate(server_round, results, failures)
+
+    def evaluate(
+        self, server_round: int, parameters: Parameters
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """Evaluate model parameters using an evaluation function from the strategy."""
+        return self.strategy.evaluate(server_round, parameters)
diff --git a/src/py/flwr/server/strategy/dp_fixed_clipping.py b/src/py/flwr/server/strategy/dp_fixed_clipping.py
new file mode 100644
index 000000000000..61e8123e28d7
--- /dev/null
+++ b/src/py/flwr/server/strategy/dp_fixed_clipping.py
@@ -0,0 +1,360 @@
+# 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.
+# ==============================================================================
+"""Central differential privacy with fixed clipping.
+
+Papers: https://arxiv.org/abs/1712.07557, https://arxiv.org/abs/1710.06963
+"""
+
+
+from logging import INFO, WARNING
+from typing import Dict, List, Optional, Tuple, Union
+
+from flwr.common import (
+    EvaluateIns,
+    EvaluateRes,
+    FitIns,
+    FitRes,
+    NDArrays,
+    Parameters,
+    Scalar,
+    ndarrays_to_parameters,
+    parameters_to_ndarrays,
+)
+from flwr.common.differential_privacy import (
+    add_gaussian_noise_to_params,
+    compute_clip_model_update,
+    compute_stdv,
+)
+from flwr.common.differential_privacy_constants import (
+    CLIENTS_DISCREPANCY_WARNING,
+    KEY_CLIPPING_NORM,
+)
+from flwr.common.logger import log
+from flwr.server.client_manager import ClientManager
+from flwr.server.client_proxy import ClientProxy
+from flwr.server.strategy.strategy import Strategy
+
+
+class DifferentialPrivacyServerSideFixedClipping(Strategy):
+    """Strategy wrapper for central DP with server-side fixed clipping.
+
+    Parameters
+    ----------
+    strategy : Strategy
+        The strategy to which DP functionalities will be added by this wrapper.
+    noise_multiplier : float
+        The noise multiplier for the Gaussian mechanism for model updates.
+        A value of 1.0 or higher is recommended for strong privacy.
+    clipping_norm : float
+        The value of the clipping norm.
+    num_sampled_clients : int
+        The number of clients that are sampled on each round.
+
+    Examples
+    --------
+    Create a strategy:
+
+    >>> strategy = fl.server.strategy.FedAvg( ... )
+
+    Wrap the strategy with the DifferentialPrivacyServerSideFixedClipping wrapper
+
+    >>> dp_strategy = DifferentialPrivacyServerSideFixedClipping(
+    >>>     strategy, cfg.noise_multiplier, cfg.clipping_norm, cfg.num_sampled_clients
+    >>> )
+    """
+
+    # pylint: disable=too-many-arguments,too-many-instance-attributes
+    def __init__(
+        self,
+        strategy: Strategy,
+        noise_multiplier: float,
+        clipping_norm: float,
+        num_sampled_clients: int,
+    ) -> None:
+        super().__init__()
+
+        self.strategy = strategy
+
+        if noise_multiplier < 0:
+            raise ValueError("The noise multiplier should be a non-negative value.")
+
+        if clipping_norm <= 0:
+            raise ValueError("The clipping norm should be a positive value.")
+
+        if num_sampled_clients <= 0:
+            raise ValueError(
+                "The number of sampled clients should be a positive value."
+            )
+
+        self.noise_multiplier = noise_multiplier
+        self.clipping_norm = clipping_norm
+        self.num_sampled_clients = num_sampled_clients
+
+        self.current_round_params: NDArrays = []
+
+    def __repr__(self) -> str:
+        """Compute a string representation of the strategy."""
+        rep = "Differential Privacy Strategy Wrapper (Server-Side Fixed Clipping)"
+        return rep
+
+    def initialize_parameters(
+        self, client_manager: ClientManager
+    ) -> Optional[Parameters]:
+        """Initialize global model parameters using given strategy."""
+        return self.strategy.initialize_parameters(client_manager)
+
+    def configure_fit(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, FitIns]]:
+        """Configure the next round of training."""
+        self.current_round_params = parameters_to_ndarrays(parameters)
+        return self.strategy.configure_fit(server_round, parameters, client_manager)
+
+    def configure_evaluate(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, EvaluateIns]]:
+        """Configure the next round of evaluation."""
+        return self.strategy.configure_evaluate(
+            server_round, parameters, client_manager
+        )
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        """Compute the updates, clip, and pass them for aggregation.
+
+        Afterward, add noise to the aggregated parameters.
+        """
+        if failures:
+            return None, {}
+
+        if len(results) != self.num_sampled_clients:
+            log(
+                WARNING,
+                CLIENTS_DISCREPANCY_WARNING,
+                len(results),
+                self.num_sampled_clients,
+            )
+        for _, res in results:
+            param = parameters_to_ndarrays(res.parameters)
+            # Compute and clip update
+            compute_clip_model_update(
+                param, self.current_round_params, self.clipping_norm
+            )
+            log(
+                INFO,
+                "aggregate_fit: parameters are clipped by value: %s.",
+                self.clipping_norm,
+            )
+            # Convert back to parameters
+            res.parameters = ndarrays_to_parameters(param)
+
+        # Pass the new parameters for aggregation
+        aggregated_params, metrics = self.strategy.aggregate_fit(
+            server_round, results, failures
+        )
+
+        # Add Gaussian noise to the aggregated parameters
+        if aggregated_params:
+            aggregated_params = add_gaussian_noise_to_params(
+                aggregated_params,
+                self.noise_multiplier,
+                self.clipping_norm,
+                self.num_sampled_clients,
+            )
+
+            log(
+                INFO,
+                "aggregate_fit: central DP noise with standard deviation: %s added to parameters.",
+                compute_stdv(
+                    self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
+                ),
+            )
+
+        return aggregated_params, metrics
+
+    def aggregate_evaluate(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, EvaluateRes]],
+        failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
+    ) -> Tuple[Optional[float], Dict[str, Scalar]]:
+        """Aggregate evaluation losses using the given strategy."""
+        return self.strategy.aggregate_evaluate(server_round, results, failures)
+
+    def evaluate(
+        self, server_round: int, parameters: Parameters
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """Evaluate model parameters using an evaluation function from the strategy."""
+        return self.strategy.evaluate(server_round, parameters)
+
+
+class DifferentialPrivacyClientSideFixedClipping(Strategy):
+    """Strategy wrapper for central DP with client-side fixed clipping.
+
+    Use `fixedclipping_mod` modifier at the client side.
+
+    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`.
+
+    Parameters
+    ----------
+    strategy : Strategy
+        The strategy to which DP functionalities will be added by this wrapper.
+    noise_multiplier : float
+        The noise multiplier for the Gaussian mechanism for model updates.
+        A value of 1.0 or higher is recommended for strong privacy.
+    clipping_norm : float
+        The value of the clipping norm.
+    num_sampled_clients : int
+        The number of clients that are sampled on each round.
+
+    Examples
+    --------
+    Create a strategy:
+
+    >>> strategy = fl.server.strategy.FedAvg(...)
+
+    Wrap the strategy with the `DifferentialPrivacyClientSideFixedClipping` wrapper:
+
+    >>> dp_strategy = DifferentialPrivacyClientSideFixedClipping(
+    >>>     strategy, cfg.noise_multiplier, cfg.clipping_norm, cfg.num_sampled_clients
+    >>> )
+
+    On the client, add the `fixedclipping_mod` to the client-side mods:
+
+    >>> app = fl.client.ClientApp(
+    >>>     client_fn=client_fn, mods=[fixedclipping_mod]
+    >>> )
+    """
+
+    # pylint: disable=too-many-arguments,too-many-instance-attributes
+    def __init__(
+        self,
+        strategy: Strategy,
+        noise_multiplier: float,
+        clipping_norm: float,
+        num_sampled_clients: int,
+    ) -> None:
+        super().__init__()
+
+        self.strategy = strategy
+
+        if noise_multiplier < 0:
+            raise ValueError("The noise multiplier should be a non-negative value.")
+
+        if clipping_norm <= 0:
+            raise ValueError("The clipping threshold should be a positive value.")
+
+        if num_sampled_clients <= 0:
+            raise ValueError(
+                "The number of sampled clients should be a positive value."
+            )
+
+        self.noise_multiplier = noise_multiplier
+        self.clipping_norm = clipping_norm
+        self.num_sampled_clients = num_sampled_clients
+
+    def __repr__(self) -> str:
+        """Compute a string representation of the strategy."""
+        rep = "Differential Privacy Strategy Wrapper (Client-Side Fixed Clipping)"
+        return rep
+
+    def initialize_parameters(
+        self, client_manager: ClientManager
+    ) -> Optional[Parameters]:
+        """Initialize global model parameters using given strategy."""
+        return self.strategy.initialize_parameters(client_manager)
+
+    def configure_fit(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, FitIns]]:
+        """Configure the next round of training."""
+        additional_config = {KEY_CLIPPING_NORM: self.clipping_norm}
+        inner_strategy_config_result = self.strategy.configure_fit(
+            server_round, parameters, client_manager
+        )
+        for _, fit_ins in inner_strategy_config_result:
+            fit_ins.config.update(additional_config)
+
+        return inner_strategy_config_result
+
+    def configure_evaluate(
+        self, server_round: int, parameters: Parameters, client_manager: ClientManager
+    ) -> List[Tuple[ClientProxy, EvaluateIns]]:
+        """Configure the next round of evaluation."""
+        return self.strategy.configure_evaluate(
+            server_round, parameters, client_manager
+        )
+
+    def aggregate_fit(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, FitRes]],
+        failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]],
+    ) -> Tuple[Optional[Parameters], Dict[str, Scalar]]:
+        """Add noise to the aggregated parameters."""
+        if failures:
+            return None, {}
+
+        if len(results) != self.num_sampled_clients:
+            log(
+                WARNING,
+                CLIENTS_DISCREPANCY_WARNING,
+                len(results),
+                self.num_sampled_clients,
+            )
+
+        # Pass the new parameters for aggregation
+        aggregated_params, metrics = self.strategy.aggregate_fit(
+            server_round, results, failures
+        )
+
+        # Add Gaussian noise to the aggregated parameters
+        if aggregated_params:
+            aggregated_params = add_gaussian_noise_to_params(
+                aggregated_params,
+                self.noise_multiplier,
+                self.clipping_norm,
+                self.num_sampled_clients,
+            )
+            log(
+                INFO,
+                "aggregate_fit: central DP noise with standard deviation: %s added to parameters.",
+                compute_stdv(
+                    self.noise_multiplier, self.clipping_norm, self.num_sampled_clients
+                ),
+            )
+        return aggregated_params, metrics
+
+    def aggregate_evaluate(
+        self,
+        server_round: int,
+        results: List[Tuple[ClientProxy, EvaluateRes]],
+        failures: List[Union[Tuple[ClientProxy, EvaluateRes], BaseException]],
+    ) -> Tuple[Optional[float], Dict[str, Scalar]]:
+        """Aggregate evaluation losses using the given strategy."""
+        return self.strategy.aggregate_evaluate(server_round, results, failures)
+
+    def evaluate(
+        self, server_round: int, parameters: Parameters
+    ) -> Optional[Tuple[float, Dict[str, Scalar]]]:
+        """Evaluate model parameters using an evaluation function from the strategy."""
+        return self.strategy.evaluate(server_round, parameters)
diff --git a/src/py/flwr/server/strategy/dpfedavg_adaptive.py b/src/py/flwr/server/strategy/dpfedavg_adaptive.py
index 3269735e9d73..a908679ed668 100644
--- a/src/py/flwr/server/strategy/dpfedavg_adaptive.py
+++ b/src/py/flwr/server/strategy/dpfedavg_adaptive.py
@@ -24,6 +24,7 @@
 import numpy as np
 
 from flwr.common import FitIns, FitRes, Parameters, Scalar
+from flwr.common.logger import warn_deprecated_feature
 from flwr.server.client_manager import ClientManager
 from flwr.server.client_proxy import ClientProxy
 from flwr.server.strategy.dpfedavg_fixed import DPFedAvgFixed
@@ -31,7 +32,12 @@
 
 
 class DPFedAvgAdaptive(DPFedAvgFixed):
-    """Wrapper for configuring a Strategy for DP with Adaptive Clipping."""
+    """Wrapper for configuring a Strategy for DP with Adaptive Clipping.
+
+    Warning
+    -------
+    This class is deprecated and will be removed in a future release.
+    """
 
     # pylint: disable=too-many-arguments,too-many-instance-attributes
     def __init__(
@@ -45,6 +51,7 @@ def __init__(
         clip_norm_target_quantile: float = 0.5,
         clip_count_stddev: Optional[float] = None,
     ) -> None:
+        warn_deprecated_feature("`DPFedAvgAdaptive` wrapper")
         super().__init__(
             strategy=strategy,
             num_sampled_clients=num_sampled_clients,
@@ -91,7 +98,7 @@ def _update_clip_norm(self, results: List[Tuple[ClientProxy, FitRes]]) -> None:
         norm_bit_set_count = 0
         for client_proxy, fit_res in results:
             if "dpfedavg_norm_bit" not in fit_res.metrics:
-                raise Exception(
+                raise KeyError(
                     f"Indicator bit not returned by client with id {client_proxy.cid}."
                 )
             if fit_res.metrics["dpfedavg_norm_bit"]:
diff --git a/src/py/flwr/server/strategy/dpfedavg_fixed.py b/src/py/flwr/server/strategy/dpfedavg_fixed.py
index 0154cfd79fc5..c54379fc7087 100644
--- a/src/py/flwr/server/strategy/dpfedavg_fixed.py
+++ b/src/py/flwr/server/strategy/dpfedavg_fixed.py
@@ -17,11 +17,11 @@
 Paper: arxiv.org/pdf/1710.06963.pdf
 """
 
-
 from typing import Dict, List, Optional, Tuple, Union
 
 from flwr.common import EvaluateIns, EvaluateRes, FitIns, FitRes, Parameters, Scalar
 from flwr.common.dp import add_gaussian_noise
+from flwr.common.logger import warn_deprecated_feature
 from flwr.common.parameter import ndarrays_to_parameters, parameters_to_ndarrays
 from flwr.server.client_manager import ClientManager
 from flwr.server.client_proxy import ClientProxy
@@ -29,7 +29,12 @@
 
 
 class DPFedAvgFixed(Strategy):
-    """Wrapper for configuring a Strategy for DP with Fixed Clipping."""
+    """Wrapper for configuring a Strategy for DP with Fixed Clipping.
+
+    Warning
+    -------
+    This class is deprecated and will be removed in a future release.
+    """
 
     # pylint: disable=too-many-arguments,too-many-instance-attributes
     def __init__(
@@ -40,17 +45,18 @@ def __init__(
         noise_multiplier: float = 1,
         server_side_noising: bool = True,
     ) -> None:
+        warn_deprecated_feature("`DPFedAvgFixed` wrapper")
         super().__init__()
         self.strategy = strategy
         # Doing fixed-size subsampling as in https://arxiv.org/abs/1905.03871.
         self.num_sampled_clients = num_sampled_clients
 
         if clip_norm <= 0:
-            raise Exception("The clipping threshold should be a positive value.")
+            raise ValueError("The clipping threshold should be a positive value.")
         self.clip_norm = clip_norm
 
         if noise_multiplier < 0:
-            raise Exception("The noise multiplier should be a non-negative value.")
+            raise ValueError("The noise multiplier should be a non-negative value.")
         self.noise_multiplier = noise_multiplier
 
         self.server_side_noising = server_side_noising
@@ -98,9 +104,9 @@ def configure_fit(
         """
         additional_config = {"dpfedavg_clip_norm": self.clip_norm}
         if not self.server_side_noising:
-            additional_config[
-                "dpfedavg_noise_stddev"
-            ] = self._calc_client_noise_stddev()
+            additional_config["dpfedavg_noise_stddev"] = (
+                self._calc_client_noise_stddev()
+            )
 
         client_instructions = self.strategy.configure_fit(
             server_round, parameters, client_manager
diff --git a/src/py/flwr/server/strategy/fedadagrad_test.py b/src/py/flwr/server/strategy/fedadagrad_test.py
index b3380a5be2f9..0c966442ecaf 100644
--- a/src/py/flwr/server/strategy/fedadagrad_test.py
+++ b/src/py/flwr/server/strategy/fedadagrad_test.py
@@ -30,7 +30,7 @@
     parameters_to_ndarrays,
 )
 from flwr.server.client_proxy import ClientProxy
-from flwr.server.fleet.grpc_bidi.grpc_client_proxy import GrpcClientProxy
+from flwr.server.superlink.fleet.grpc_bidi.grpc_client_proxy import GrpcClientProxy
 
 from .fedadagrad import FedAdagrad
 
diff --git a/src/py/flwr/server/strategy/fedavg.py b/src/py/flwr/server/strategy/fedavg.py
index c93c8cb8b83e..3b9b2640c2b5 100644
--- a/src/py/flwr/server/strategy/fedavg.py
+++ b/src/py/flwr/server/strategy/fedavg.py
@@ -37,7 +37,7 @@
 from flwr.server.client_manager import ClientManager
 from flwr.server.client_proxy import ClientProxy
 
-from .aggregate import aggregate, weighted_loss_avg
+from .aggregate import aggregate, aggregate_inplace, weighted_loss_avg
 from .strategy import Strategy
 
 WARNING_MIN_AVAILABLE_CLIENTS_TOO_LOW = """
@@ -84,6 +84,8 @@ class FedAvg(Strategy):
         Metrics aggregation function, optional.
     evaluate_metrics_aggregation_fn : Optional[MetricsAggregationFn]
         Metrics aggregation function, optional.
+    inplace : bool (default: True)
+        Enable (True) or disable (False) in-place aggregation of model updates.
     """
 
     # pylint: disable=too-many-arguments,too-many-instance-attributes, line-too-long
@@ -107,6 +109,7 @@ def __init__(
         initial_parameters: Optional[Parameters] = None,
         fit_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
         evaluate_metrics_aggregation_fn: Optional[MetricsAggregationFn] = None,
+        inplace: bool = True,
     ) -> None:
         super().__init__()
 
@@ -128,6 +131,7 @@ def __init__(
         self.initial_parameters = initial_parameters
         self.fit_metrics_aggregation_fn = fit_metrics_aggregation_fn
         self.evaluate_metrics_aggregation_fn = evaluate_metrics_aggregation_fn
+        self.inplace = inplace
 
     def __repr__(self) -> str:
         """Compute a string representation of the strategy."""
@@ -226,12 +230,18 @@ def aggregate_fit(
         if not self.accept_failures and failures:
             return None, {}
 
-        # Convert results
-        weights_results = [
-            (parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples)
-            for _, fit_res in results
-        ]
-        parameters_aggregated = ndarrays_to_parameters(aggregate(weights_results))
+        if self.inplace:
+            # Does in-place weighted average of results
+            aggregated_ndarrays = aggregate_inplace(results)
+        else:
+            # Convert results
+            weights_results = [
+                (parameters_to_ndarrays(fit_res.parameters), fit_res.num_examples)
+                for _, fit_res in results
+            ]
+            aggregated_ndarrays = aggregate(weights_results)
+
+        parameters_aggregated = ndarrays_to_parameters(aggregated_ndarrays)
 
         # Aggregate custom metrics if aggregation fn was provided
         metrics_aggregated = {}
diff --git a/src/py/flwr/server/strategy/fedavg_android.py b/src/py/flwr/server/strategy/fedavg_android.py
index e890f7216020..6678b7ced114 100644
--- a/src/py/flwr/server/strategy/fedavg_android.py
+++ b/src/py/flwr/server/strategy/fedavg_android.py
@@ -234,12 +234,10 @@ def parameters_to_ndarrays(self, parameters: Parameters) -> NDArrays:
         """Convert parameters object to NumPy weights."""
         return [self.bytes_to_ndarray(tensor) for tensor in parameters.tensors]
 
-    # pylint: disable=R0201
     def ndarray_to_bytes(self, ndarray: NDArray) -> bytes:
         """Serialize NumPy array to bytes."""
         return ndarray.tobytes()
 
-    # pylint: disable=R0201
     def bytes_to_ndarray(self, tensor: bytes) -> NDArray:
         """Deserialize NumPy array from bytes."""
         ndarray_deserialized = np.frombuffer(tensor, dtype=np.float32)
diff --git a/src/py/flwr/server/strategy/fedavg_test.py b/src/py/flwr/server/strategy/fedavg_test.py
index 947736f4a571..e62eaa5c5832 100644
--- a/src/py/flwr/server/strategy/fedavg_test.py
+++ b/src/py/flwr/server/strategy/fedavg_test.py
@@ -15,6 +15,16 @@
 """FedAvg tests."""
 
 
+from typing import List, Tuple, Union
+from unittest.mock import MagicMock
+
+import numpy as np
+from numpy.testing import assert_allclose
+
+from flwr.common import Code, FitRes, Status, parameters_to_ndarrays
+from flwr.common.parameter import ndarrays_to_parameters
+from flwr.server.client_proxy import ClientProxy
+
 from .fedavg import FedAvg
 
 
@@ -120,3 +130,51 @@ def test_fedavg_num_evaluation_clients_minimum() -> None:
 
     # Assert
     assert expected == actual
+
+
+def test_inplace_aggregate_fit_equivalence() -> None:
+    """Test aggregate_fit equivalence between FedAvg and its inplace version."""
+    # Prepare
+    weights0_0 = np.random.randn(100, 64)
+    weights0_1 = np.random.randn(314, 628, 3)
+    weights1_0 = np.random.randn(100, 64)
+    weights1_1 = np.random.randn(314, 628, 3)
+
+    results: List[Tuple[ClientProxy, FitRes]] = [
+        (
+            MagicMock(),
+            FitRes(
+                status=Status(code=Code.OK, message="Success"),
+                parameters=ndarrays_to_parameters([weights0_0, weights0_1]),
+                num_examples=1,
+                metrics={},
+            ),
+        ),
+        (
+            MagicMock(),
+            FitRes(
+                status=Status(code=Code.OK, message="Success"),
+                parameters=ndarrays_to_parameters([weights1_0, weights1_1]),
+                num_examples=5,
+                metrics={},
+            ),
+        ),
+    ]
+    failures: List[Union[Tuple[ClientProxy, FitRes], BaseException]] = []
+
+    fedavg_reference = FedAvg(inplace=False)
+    fedavg_inplace = FedAvg()
+
+    # Execute
+    reference, _ = fedavg_reference.aggregate_fit(1, results, failures)
+    assert reference
+    inplace, _ = fedavg_inplace.aggregate_fit(1, results, failures)
+    assert inplace
+
+    # Convert to NumPy to check similarity
+    reference_np = parameters_to_ndarrays(reference)
+    inplace_np = parameters_to_ndarrays(inplace)
+
+    # Assert
+    for ref, inp in zip(reference_np, inplace_np):
+        assert_allclose(ref, inp)
diff --git a/src/py/flwr/server/strategy/fedmedian.py b/src/py/flwr/server/strategy/fedmedian.py
index 7a5bf1425b44..17e979d92beb 100644
--- a/src/py/flwr/server/strategy/fedmedian.py
+++ b/src/py/flwr/server/strategy/fedmedian.py
@@ -36,7 +36,7 @@
 
 
 class FedMedian(FedAvg):
-    """Configurable FedAvg with Momentum strategy implementation."""
+    """Configurable FedMedian strategy implementation."""
 
     def __repr__(self) -> str:
         """Compute a string representation of the strategy."""
diff --git a/src/py/flwr/server/strategy/fedmedian_test.py b/src/py/flwr/server/strategy/fedmedian_test.py
index 180503df6c80..57cf08d8c01d 100644
--- a/src/py/flwr/server/strategy/fedmedian_test.py
+++ b/src/py/flwr/server/strategy/fedmedian_test.py
@@ -30,7 +30,7 @@
     parameters_to_ndarrays,
 )
 from flwr.server.client_proxy import ClientProxy
-from flwr.server.fleet.grpc_bidi.grpc_client_proxy import GrpcClientProxy
+from flwr.server.superlink.fleet.grpc_bidi.grpc_client_proxy import GrpcClientProxy
 
 from .fedmedian import FedMedian
 
diff --git a/src/py/flwr/server/strategy/fedxgb_bagging.py b/src/py/flwr/server/strategy/fedxgb_bagging.py
index cafb466c2e8b..a8e8adddafbb 100644
--- a/src/py/flwr/server/strategy/fedxgb_bagging.py
+++ b/src/py/flwr/server/strategy/fedxgb_bagging.py
@@ -44,6 +44,11 @@ def __init__(
         self.global_model: Optional[bytes] = None
         super().__init__(**kwargs)
 
+    def __repr__(self) -> str:
+        """Compute a string representation of the strategy."""
+        rep = f"FedXgbBagging(accept_failures={self.accept_failures})"
+        return rep
+
     def aggregate_fit(
         self,
         server_round: int,
diff --git a/src/py/flwr/server/strategy/fedxgb_cyclic.py b/src/py/flwr/server/strategy/fedxgb_cyclic.py
index e2707b02d19d..2605daab29f4 100644
--- a/src/py/flwr/server/strategy/fedxgb_cyclic.py
+++ b/src/py/flwr/server/strategy/fedxgb_cyclic.py
@@ -37,6 +37,11 @@ def __init__(
         self.global_model: Optional[bytes] = None
         super().__init__(**kwargs)
 
+    def __repr__(self) -> str:
+        """Compute a string representation of the strategy."""
+        rep = f"FedXgbCyclic(accept_failures={self.accept_failures})"
+        return rep
+
     def aggregate_fit(
         self,
         server_round: int,
diff --git a/src/py/flwr/server/strategy/fedxgb_nn_avg.py b/src/py/flwr/server/strategy/fedxgb_nn_avg.py
index f300633d0d9f..8dedc925f350 100644
--- a/src/py/flwr/server/strategy/fedxgb_nn_avg.py
+++ b/src/py/flwr/server/strategy/fedxgb_nn_avg.py
@@ -25,7 +25,7 @@
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 from flwr.common import FitRes, Scalar, ndarrays_to_parameters, parameters_to_ndarrays
-from flwr.common.logger import log
+from flwr.common.logger import log, warn_deprecated_feature
 from flwr.server.client_proxy import ClientProxy
 
 from .aggregate import aggregate
@@ -33,7 +33,13 @@
 
 
 class FedXgbNnAvg(FedAvg):
-    """Configurable FedXgbNnAvg strategy implementation."""
+    """Configurable FedXgbNnAvg strategy implementation.
+
+    Warning
+    -------
+    This strategy is deprecated, but a copy of it is available in Flower Baselines:
+    https://github.com/adap/flower/tree/main/baselines/hfedxgboost.
+    """
 
     def __init__(self, *args: Any, **kwargs: Any) -> None:
         """Federated XGBoost [Ma et al., 2023] strategy.
@@ -41,6 +47,7 @@ def __init__(self, *args: Any, **kwargs: Any) -> None:
         Implementation based on https://arxiv.org/abs/2304.07537.
         """
         super().__init__(*args, **kwargs)
+        warn_deprecated_feature("`FedXgbNnAvg` strategy")
 
     def __repr__(self) -> str:
         """Compute a string representation of the strategy."""
diff --git a/src/py/flwr/server/strategy/krum_test.py b/src/py/flwr/server/strategy/krum_test.py
index 81e59230739a..653dc9a8475d 100644
--- a/src/py/flwr/server/strategy/krum_test.py
+++ b/src/py/flwr/server/strategy/krum_test.py
@@ -30,7 +30,7 @@
     parameters_to_ndarrays,
 )
 from flwr.server.client_proxy import ClientProxy
-from flwr.server.fleet.grpc_bidi.grpc_client_proxy import GrpcClientProxy
+from flwr.server.superlink.fleet.grpc_bidi.grpc_client_proxy import GrpcClientProxy
 
 from .krum import Krum
 
diff --git a/src/py/flwr/server/strategy/multikrum_test.py b/src/py/flwr/server/strategy/multikrum_test.py
index 1469db104252..f874dc2f9800 100644
--- a/src/py/flwr/server/strategy/multikrum_test.py
+++ b/src/py/flwr/server/strategy/multikrum_test.py
@@ -30,7 +30,7 @@
     parameters_to_ndarrays,
 )
 from flwr.server.client_proxy import ClientProxy
-from flwr.server.fleet.grpc_bidi.grpc_client_proxy import GrpcClientProxy
+from flwr.server.superlink.fleet.grpc_bidi.grpc_client_proxy import GrpcClientProxy
 
 from .krum import Krum
 
diff --git a/src/py/flwr/server/strategy/qfedavg.py b/src/py/flwr/server/strategy/qfedavg.py
index 94a67fbcbfae..758e8e608e9f 100644
--- a/src/py/flwr/server/strategy/qfedavg.py
+++ b/src/py/flwr/server/strategy/qfedavg.py
@@ -185,7 +185,7 @@ def norm_grad(grad_list: NDArrays) -> float:
         hs_ffl = []
 
         if self.pre_weights is None:
-            raise Exception("QffedAvg pre_weights are None in aggregate_fit")
+            raise AttributeError("QffedAvg pre_weights are None in aggregate_fit")
 
         weights_before = self.pre_weights
         eval_result = self.evaluate(
diff --git a/src/py/flwr/server/superlink/__init__.py b/src/py/flwr/server/superlink/__init__.py
new file mode 100644
index 000000000000..94102100de26
--- /dev/null
+++ b/src/py/flwr/server/superlink/__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 SuperLink."""
diff --git a/dev/publish.sh b/src/py/flwr/server/superlink/driver/__init__.py
old mode 100755
new mode 100644
similarity index 84%
rename from dev/publish.sh
rename to src/py/flwr/server/superlink/driver/__init__.py
index fb4df1694530..2bfe63e6065f
--- a/dev/publish.sh
+++ b/src/py/flwr/server/superlink/driver/__init__.py
@@ -1,5 +1,3 @@
-#!/bin/bash
-
 # Copyright 2022 Flower Labs GmbH. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
@@ -14,8 +12,4 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-
-set -e
-cd "$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"/../
-
-python -m poetry publish
+"""Flower driver service."""
diff --git a/src/py/flwr/server/superlink/driver/driver_grpc.py b/src/py/flwr/server/superlink/driver/driver_grpc.py
new file mode 100644
index 000000000000..f74000bc59c4
--- /dev/null
+++ b/src/py/flwr/server/superlink/driver/driver_grpc.py
@@ -0,0 +1,54 @@
+# Copyright 2020 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.
+# ==============================================================================
+"""Driver 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.driver_pb2_grpc import (  # pylint: disable=E0611
+    add_DriverServicer_to_server,
+)
+from flwr.server.superlink.state import StateFactory
+
+from ..fleet.grpc_bidi.grpc_server import generic_create_grpc_server
+from .driver_servicer import DriverServicer
+
+
+def run_driver_api_grpc(
+    address: str,
+    state_factory: StateFactory,
+    certificates: Optional[Tuple[bytes, bytes, bytes]],
+) -> grpc.Server:
+    """Run Driver API (gRPC, request-response)."""
+    # Create Driver API gRPC server
+    driver_servicer: grpc.Server = DriverServicer(
+        state_factory=state_factory,
+    )
+    driver_add_servicer_to_server_fn = add_DriverServicer_to_server
+    driver_grpc_server = generic_create_grpc_server(
+        servicer_and_add_fn=(driver_servicer, driver_add_servicer_to_server_fn),
+        server_address=address,
+        max_message_length=GRPC_MAX_MESSAGE_LENGTH,
+        certificates=certificates,
+    )
+
+    log(INFO, "Flower ECE: Starting Driver API (gRPC-rere) on %s", address)
+    driver_grpc_server.start()
+
+    return driver_grpc_server
diff --git a/src/py/flwr/server/driver/driver_servicer.py b/src/py/flwr/server/superlink/driver/driver_servicer.py
similarity index 79%
rename from src/py/flwr/server/driver/driver_servicer.py
rename to src/py/flwr/server/superlink/driver/driver_servicer.py
index f96b3b1262ac..59e51ef52d8e 100644
--- a/src/py/flwr/server/driver/driver_servicer.py
+++ b/src/py/flwr/server/superlink/driver/driver_servicer.py
@@ -15,17 +15,17 @@
 """Driver API servicer."""
 
 
-from logging import INFO
+from logging import DEBUG, INFO
 from typing import List, Optional, Set
 from uuid import UUID
 
 import grpc
 
 from flwr.common.logger import log
-from flwr.proto import driver_pb2_grpc
-from flwr.proto.driver_pb2 import (
-    CreateWorkloadRequest,
-    CreateWorkloadResponse,
+from flwr.proto import driver_pb2_grpc  # pylint: disable=E0611
+from flwr.proto.driver_pb2 import (  # pylint: disable=E0611
+    CreateRunRequest,
+    CreateRunResponse,
     GetNodesRequest,
     GetNodesResponse,
     PullTaskResRequest,
@@ -33,9 +33,9 @@
     PushTaskInsRequest,
     PushTaskInsResponse,
 )
-from flwr.proto.node_pb2 import Node
-from flwr.proto.task_pb2 import TaskRes
-from flwr.server.state import State, StateFactory
+from flwr.proto.node_pb2 import Node  # pylint: disable=E0611
+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
 
 
@@ -49,28 +49,28 @@ def GetNodes(
         self, request: GetNodesRequest, context: grpc.ServicerContext
     ) -> GetNodesResponse:
         """Get available nodes."""
-        log(INFO, "DriverServicer.GetNodes")
+        log(DEBUG, "DriverServicer.GetNodes")
         state: State = self.state_factory.state()
-        all_ids: Set[int] = state.get_nodes(request.workload_id)
+        all_ids: Set[int] = state.get_nodes(request.run_id)
         nodes: List[Node] = [
             Node(node_id=node_id, anonymous=False) for node_id in all_ids
         ]
         return GetNodesResponse(nodes=nodes)
 
-    def CreateWorkload(
-        self, request: CreateWorkloadRequest, context: grpc.ServicerContext
-    ) -> CreateWorkloadResponse:
-        """Create workload ID."""
-        log(INFO, "DriverServicer.CreateWorkload")
+    def CreateRun(
+        self, request: CreateRunRequest, context: grpc.ServicerContext
+    ) -> CreateRunResponse:
+        """Create run ID."""
+        log(INFO, "DriverServicer.CreateRun")
         state: State = self.state_factory.state()
-        workload_id = state.create_workload()
-        return CreateWorkloadResponse(workload_id=workload_id)
+        run_id = state.create_run()
+        return CreateRunResponse(run_id=run_id)
 
     def PushTaskIns(
         self, request: PushTaskInsRequest, context: grpc.ServicerContext
     ) -> PushTaskInsResponse:
         """Push a set of TaskIns."""
-        log(INFO, "DriverServicer.PushTaskIns")
+        log(DEBUG, "DriverServicer.PushTaskIns")
 
         # Validate request
         _raise_if(len(request.task_ins_list) == 0, "`task_ins_list` must not be empty")
@@ -95,7 +95,7 @@ def PullTaskRes(
         self, request: PullTaskResRequest, context: grpc.ServicerContext
     ) -> PullTaskResResponse:
         """Pull a set of TaskRes."""
-        log(INFO, "DriverServicer.PullTaskRes")
+        log(DEBUG, "DriverServicer.PullTaskRes")
 
         # Convert each task_id str to UUID
         task_ids: Set[UUID] = {UUID(task_id) for task_id in request.task_ids}
@@ -105,7 +105,7 @@ def PullTaskRes(
 
         # Register callback
         def on_rpc_done() -> None:
-            log(INFO, "DriverServicer.PullTaskRes callback: delete TaskIns/TaskRes")
+            log(DEBUG, "DriverServicer.PullTaskRes callback: delete TaskIns/TaskRes")
 
             if context.is_active():
                 return
diff --git a/src/py/flwr/server/driver/driver_servicer_test.py b/src/py/flwr/server/superlink/driver/driver_servicer_test.py
similarity index 95%
rename from src/py/flwr/server/driver/driver_servicer_test.py
rename to src/py/flwr/server/superlink/driver/driver_servicer_test.py
index c432c026a632..99f7cc007a89 100644
--- a/src/py/flwr/server/driver/driver_servicer_test.py
+++ b/src/py/flwr/server/superlink/driver/driver_servicer_test.py
@@ -15,7 +15,7 @@
 """DriverServicer tests."""
 
 
-from flwr.server.driver.driver_servicer import _raise_if
+from flwr.server.superlink.driver.driver_servicer import _raise_if
 
 # pylint: disable=broad-except
 
diff --git a/src/py/flwr/server/fleet/__init__.py b/src/py/flwr/server/superlink/fleet/__init__.py
similarity index 100%
rename from src/py/flwr/server/fleet/__init__.py
rename to src/py/flwr/server/superlink/fleet/__init__.py
diff --git a/src/py/flwr/server/fleet/grpc_bidi/__init__.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/__init__.py
similarity index 100%
rename from src/py/flwr/server/fleet/grpc_bidi/__init__.py
rename to src/py/flwr/server/superlink/fleet/grpc_bidi/__init__.py
diff --git a/src/py/flwr/server/fleet/grpc_bidi/flower_service_servicer.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/flower_service_servicer.py
similarity index 88%
rename from src/py/flwr/server/fleet/grpc_bidi/flower_service_servicer.py
rename to src/py/flwr/server/superlink/fleet/grpc_bidi/flower_service_servicer.py
index 1f7a8e9259fc..6f94ea844e38 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/flower_service_servicer.py
+++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/flower_service_servicer.py
@@ -18,17 +18,24 @@
 - https://github.com/grpc/grpc/blob/master/doc/statuscodes.md
 """
 
-
+import uuid
 from typing import Callable, Iterator
 
 import grpc
 from iterators import TimeoutIterator
 
-from flwr.proto import transport_pb2_grpc
-from flwr.proto.transport_pb2 import ClientMessage, ServerMessage
+from flwr.proto import transport_pb2_grpc  # pylint: disable=E0611
+from flwr.proto.transport_pb2 import (  # pylint: disable=E0611
+    ClientMessage,
+    ServerMessage,
+)
 from flwr.server.client_manager import ClientManager
-from flwr.server.fleet.grpc_bidi.grpc_bridge import GrpcBridge, InsWrapper, ResWrapper
-from flwr.server.fleet.grpc_bidi.grpc_client_proxy import GrpcClientProxy
+from flwr.server.superlink.fleet.grpc_bidi.grpc_bridge import (
+    GrpcBridge,
+    InsWrapper,
+    ResWrapper,
+)
+from flwr.server.superlink.fleet.grpc_bidi.grpc_client_proxy import GrpcClientProxy
 
 
 def default_bridge_factory() -> GrpcBridge:
@@ -88,9 +95,12 @@ def Join(  # pylint: disable=invalid-name
           wrapping the actual message
         - The `Join` method is (pretty much) unaware of the protocol
         """
-        peer: str = context.peer()
+        # When running Flower behind a proxy, the peer can be the same for
+        # different clients, so instead of `cid: str = context.peer()` we
+        # use a `UUID4` that is unique.
+        cid: str = uuid.uuid4().hex
         bridge = self.grpc_bridge_factory()
-        client_proxy = self.client_proxy_factory(peer, bridge)
+        client_proxy = self.client_proxy_factory(cid, bridge)
         is_success = register_client_proxy(self.client_manager, client_proxy, context)
 
         if is_success:
diff --git a/src/py/flwr/server/fleet/grpc_bidi/flower_service_servicer_test.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/flower_service_servicer_test.py
similarity index 89%
rename from src/py/flwr/server/fleet/grpc_bidi/flower_service_servicer_test.py
rename to src/py/flwr/server/superlink/fleet/grpc_bidi/flower_service_servicer_test.py
index 64140ed274c9..bd93554a6a32 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/flower_service_servicer_test.py
+++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/flower_service_servicer_test.py
@@ -16,18 +16,23 @@
 
 
 import unittest
+import uuid
 from unittest.mock import MagicMock, call
 
-from flwr.proto.transport_pb2 import ClientMessage, ServerMessage
-from flwr.server.fleet.grpc_bidi.flower_service_servicer import (
+from flwr.proto.transport_pb2 import (  # pylint: disable=E0611
+    ClientMessage,
+    ServerMessage,
+)
+from flwr.server.superlink.fleet.grpc_bidi.flower_service_servicer import (
     FlowerServiceServicer,
     register_client_proxy,
 )
-from flwr.server.fleet.grpc_bidi.grpc_bridge import InsWrapper, ResWrapper
+from flwr.server.superlink.fleet.grpc_bidi.grpc_bridge import InsWrapper, ResWrapper
 
 CLIENT_MESSAGE = ClientMessage()
 SERVER_MESSAGE = ServerMessage()
-CLIENT_CID = "some_client_cid"
+
+CID: str = uuid.uuid4().hex
 
 
 class FlowerServiceServicerTestCase(unittest.TestCase):
@@ -39,7 +44,6 @@ def setUp(self) -> None:
         """Create mocks for tests."""
         # Mock for the gRPC context argument
         self.context_mock = MagicMock()
-        self.context_mock.peer.return_value = CLIENT_CID
 
         # Define client_messages to be processed by FlowerServiceServicer instance
         self.client_messages = [CLIENT_MESSAGE for _ in range(5)]
@@ -67,7 +71,7 @@ def setUp(self) -> None:
         # Create a GrpcClientProxy mock which we will use to test if correct
         # methods where called and client_messages are getting passed to it
         self.grpc_client_proxy_mock = MagicMock()
-        self.grpc_client_proxy_mock.cid = CLIENT_CID
+        self.grpc_client_proxy_mock.cid = CID
 
         self.client_proxy_factory_mock = MagicMock()
         self.client_proxy_factory_mock.return_value = self.grpc_client_proxy_mock
@@ -124,11 +128,7 @@ def test_join(self) -> None:
             num_server_messages += 1
 
         assert len(self.client_messages) == num_server_messages
-        assert self.grpc_client_proxy_mock.cid == CLIENT_CID
-
-        self.client_proxy_factory_mock.assert_called_once_with(
-            CLIENT_CID, self.grpc_bridge_mock
-        )
+        assert self.grpc_client_proxy_mock.cid == CID
 
         # Check if the client was registered with the client_manager
         self.client_manager_mock.register.assert_called_once_with(
diff --git a/src/py/flwr/server/fleet/grpc_bidi/grpc_bridge.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_bridge.py
similarity index 93%
rename from src/py/flwr/server/fleet/grpc_bidi/grpc_bridge.py
rename to src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_bridge.py
index 6ae38ea3d805..d5b4a915c609 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/grpc_bridge.py
+++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_bridge.py
@@ -20,7 +20,10 @@
 from threading import Condition
 from typing import Iterator, Optional
 
-from flwr.proto.transport_pb2 import ClientMessage, ServerMessage
+from flwr.proto.transport_pb2 import (  # pylint: disable=E0611
+    ClientMessage,
+    ServerMessage,
+)
 
 
 @dataclass
@@ -113,7 +116,7 @@ def _transition(self, next_status: Status) -> None:
         ):
             self._status = next_status
         else:
-            raise Exception(f"Invalid transition: {self._status} to {next_status}")
+            raise ValueError(f"Invalid transition: {self._status} to {next_status}")
 
         self._cv.notify_all()
 
@@ -129,7 +132,7 @@ def request(self, ins_wrapper: InsWrapper) -> ResWrapper:
             self._raise_if_closed()
 
             if self._status != Status.AWAITING_INS_WRAPPER:
-                raise Exception("This should not happen")
+                raise ValueError("This should not happen")
 
             self._ins_wrapper = ins_wrapper  # Write
             self._transition(Status.INS_WRAPPER_AVAILABLE)
@@ -146,7 +149,7 @@ def request(self, ins_wrapper: InsWrapper) -> ResWrapper:
             self._transition(Status.AWAITING_INS_WRAPPER)
 
         if res_wrapper is None:
-            raise Exception("ResWrapper can not be None")
+            raise ValueError("ResWrapper can not be None")
 
         return res_wrapper
 
@@ -170,7 +173,7 @@ def ins_wrapper_iterator(self) -> Iterator[InsWrapper]:
                 self._transition(Status.AWAITING_RES_WRAPPER)
 
             if ins_wrapper is None:
-                raise Exception("InsWrapper can not be None")
+                raise ValueError("InsWrapper can not be None")
 
             yield ins_wrapper
 
@@ -180,7 +183,7 @@ def set_res_wrapper(self, res_wrapper: ResWrapper) -> None:
             self._raise_if_closed()
 
             if self._status != Status.AWAITING_RES_WRAPPER:
-                raise Exception("This should not happen")
+                raise ValueError("This should not happen")
 
             self._res_wrapper = res_wrapper  # Write
             self._transition(Status.RES_WRAPPER_AVAILABLE)
diff --git a/src/py/flwr/server/fleet/grpc_bidi/grpc_bridge_test.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_bridge_test.py
similarity index 95%
rename from src/py/flwr/server/fleet/grpc_bidi/grpc_bridge_test.py
rename to src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_bridge_test.py
index 18a2144072ed..f7c236acd7a1 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/grpc_bridge_test.py
+++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_bridge_test.py
@@ -19,8 +19,11 @@
 from threading import Thread
 from typing import List, Union
 
-from flwr.proto.transport_pb2 import ClientMessage, ServerMessage
-from flwr.server.fleet.grpc_bidi.grpc_bridge import (
+from flwr.proto.transport_pb2 import (  # pylint: disable=E0611
+    ClientMessage,
+    ServerMessage,
+)
+from flwr.server.superlink.fleet.grpc_bidi.grpc_bridge import (
     GrpcBridge,
     GrpcBridgeClosed,
     InsWrapper,
@@ -70,6 +73,7 @@ def test_workflow_successful() -> None:
             _ = next(ins_wrapper_iterator)
             bridge.set_res_wrapper(ResWrapper(client_message=ClientMessage()))
         except Exception as exception:
+            # pylint: disable-next=broad-exception-raised
             raise Exception from exception
 
     # Wait until worker_thread is finished
diff --git a/src/py/flwr/server/fleet/grpc_bidi/grpc_client_proxy.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy.py
similarity index 92%
rename from src/py/flwr/server/fleet/grpc_bidi/grpc_client_proxy.py
rename to src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy.py
index b9bc7330db31..ac62ad014950 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/grpc_client_proxy.py
+++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy.py
@@ -19,9 +19,16 @@
 
 from flwr import common
 from flwr.common import serde
-from flwr.proto.transport_pb2 import ClientMessage, ServerMessage
+from flwr.proto.transport_pb2 import (  # pylint: disable=E0611
+    ClientMessage,
+    ServerMessage,
+)
 from flwr.server.client_proxy import ClientProxy
-from flwr.server.fleet.grpc_bidi.grpc_bridge import GrpcBridge, InsWrapper, ResWrapper
+from flwr.server.superlink.fleet.grpc_bidi.grpc_bridge import (
+    GrpcBridge,
+    InsWrapper,
+    ResWrapper,
+)
 
 
 class GrpcClientProxy(ClientProxy):
@@ -39,6 +46,7 @@ def get_properties(
         self,
         ins: common.GetPropertiesIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.GetPropertiesRes:
         """Request client's set of internal properties."""
         get_properties_msg = serde.get_properties_ins_to_proto(ins)
@@ -58,6 +66,7 @@ def get_parameters(
         self,
         ins: common.GetParametersIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.GetParametersRes:
         """Return the current local model parameters."""
         get_parameters_msg = serde.get_parameters_ins_to_proto(ins)
@@ -77,6 +86,7 @@ def fit(
         self,
         ins: common.FitIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.FitRes:
         """Refine the provided parameters using the locally held dataset."""
         fit_ins_msg = serde.fit_ins_to_proto(ins)
@@ -95,6 +105,7 @@ def evaluate(
         self,
         ins: common.EvaluateIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.EvaluateRes:
         """Evaluate the provided parameters using the locally held dataset."""
         evaluate_msg = serde.evaluate_ins_to_proto(ins)
@@ -112,6 +123,7 @@ def reconnect(
         self,
         ins: common.ReconnectIns,
         timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.DisconnectRes:
         """Disconnect and (optionally) reconnect later."""
         reconnect_ins_msg = serde.reconnect_ins_to_proto(ins)
diff --git a/src/py/flwr/server/fleet/grpc_bidi/grpc_client_proxy_test.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy_test.py
similarity index 90%
rename from src/py/flwr/server/fleet/grpc_bidi/grpc_client_proxy_test.py
rename to src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy_test.py
index 329f29b3f616..e7077dfd39ae 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/grpc_client_proxy_test.py
+++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_client_proxy_test.py
@@ -22,9 +22,13 @@
 
 import flwr
 from flwr.common.typing import Config, GetParametersIns
-from flwr.proto.transport_pb2 import ClientMessage, Parameters, Scalar
-from flwr.server.fleet.grpc_bidi.grpc_bridge import ResWrapper
-from flwr.server.fleet.grpc_bidi.grpc_client_proxy import GrpcClientProxy
+from flwr.proto.transport_pb2 import (  # pylint: disable=E0611
+    ClientMessage,
+    Parameters,
+    Scalar,
+)
+from flwr.server.superlink.fleet.grpc_bidi.grpc_bridge import ResWrapper
+from flwr.server.superlink.fleet.grpc_bidi.grpc_client_proxy import GrpcClientProxy
 
 MESSAGE_PARAMETERS = Parameters(tensors=[], tensor_type="np")
 MESSAGE_FIT_RES = ClientMessage(
@@ -67,7 +71,7 @@ def test_get_parameters(self) -> None:
 
         # Execute
         value: flwr.common.GetParametersRes = client.get_parameters(
-            ins=get_parameters_ins, timeout=None
+            ins=get_parameters_ins, timeout=None, group_id=0
         )
 
         # Assert
@@ -84,7 +88,7 @@ def test_fit(self) -> None:
         ins: flwr.common.FitIns = flwr.common.FitIns(parameters, {})
 
         # Execute
-        fit_res = client.fit(ins=ins, timeout=None)
+        fit_res = client.fit(ins=ins, timeout=None, group_id=0)
 
         # Assert
         assert fit_res.parameters.tensor_type == "np"
@@ -102,7 +106,7 @@ def test_evaluate(self) -> None:
         evaluate_ins: flwr.common.EvaluateIns = flwr.common.EvaluateIns(parameters, {})
 
         # Execute
-        evaluate_res = client.evaluate(evaluate_ins, timeout=None)
+        evaluate_res = client.evaluate(evaluate_ins, timeout=None, group_id=1)
 
         # Assert
         assert (0, 0.0) == (
@@ -123,7 +127,9 @@ def test_get_properties(self) -> None:
         )
 
         # Execute
-        value: flwr.common.GetPropertiesRes = client.get_properties(ins, timeout=None)
+        value: flwr.common.GetPropertiesRes = client.get_properties(
+            ins, timeout=None, group_id=0
+        )
 
         # Assert
         assert value.properties["tensor_type"] == "numpy.ndarray"
diff --git a/src/py/flwr/server/fleet/grpc_bidi/grpc_server.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_server.py
similarity index 96%
rename from src/py/flwr/server/fleet/grpc_bidi/grpc_server.py
rename to src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_server.py
index fc81e8eb8f4c..82f049844bd6 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/grpc_server.py
+++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_server.py
@@ -24,11 +24,15 @@
 
 from flwr.common import GRPC_MAX_MESSAGE_LENGTH
 from flwr.common.logger import log
-from flwr.proto.transport_pb2_grpc import add_FlowerServiceServicer_to_server
+from flwr.proto.transport_pb2_grpc import (  # pylint: disable=E0611
+    add_FlowerServiceServicer_to_server,
+)
 from flwr.server.client_manager import ClientManager
-from flwr.server.driver.driver_servicer import DriverServicer
-from flwr.server.fleet.grpc_bidi.flower_service_servicer import FlowerServiceServicer
-from flwr.server.fleet.grpc_rere.fleet_servicer import FleetServicer
+from flwr.server.superlink.driver.driver_servicer import DriverServicer
+from flwr.server.superlink.fleet.grpc_bidi.flower_service_servicer import (
+    FlowerServiceServicer,
+)
+from flwr.server.superlink.fleet.grpc_rere.fleet_servicer import FleetServicer
 
 INVALID_CERTIFICATES_ERR_MSG = """
     When setting any of root_certificate, certificate, or private_key,
diff --git a/src/py/flwr/server/fleet/grpc_bidi/grpc_server_test.py b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_server_test.py
similarity index 96%
rename from src/py/flwr/server/fleet/grpc_bidi/grpc_server_test.py
rename to src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_server_test.py
index 4cd093d6ab0f..8afa37515950 100644
--- a/src/py/flwr/server/fleet/grpc_bidi/grpc_server_test.py
+++ b/src/py/flwr/server/superlink/fleet/grpc_bidi/grpc_server_test.py
@@ -23,12 +23,12 @@
 from typing import Tuple, cast
 
 from flwr.server.client_manager import SimpleClientManager
-from flwr.server.fleet.grpc_bidi.grpc_server import (
+from flwr.server.superlink.fleet.grpc_bidi.grpc_server import (
     start_grpc_server,
     valid_certificates,
 )
 
-root_dir = dirname(abspath(join(__file__, "../../../../../..")))
+root_dir = dirname(abspath(join(__file__, "../../../../../../..")))
 
 
 def load_certificates() -> Tuple[str, str, str]:
diff --git a/src/py/flwr/server/fleet/grpc_rere/__init__.py b/src/py/flwr/server/superlink/fleet/grpc_rere/__init__.py
similarity index 100%
rename from src/py/flwr/server/fleet/grpc_rere/__init__.py
rename to src/py/flwr/server/superlink/fleet/grpc_rere/__init__.py
diff --git a/src/py/flwr/server/fleet/grpc_rere/fleet_servicer.py b/src/py/flwr/server/superlink/fleet/grpc_rere/fleet_servicer.py
similarity index 80%
rename from src/py/flwr/server/fleet/grpc_rere/fleet_servicer.py
rename to src/py/flwr/server/superlink/fleet/grpc_rere/fleet_servicer.py
index 022470cffe8a..278474477379 100644
--- a/src/py/flwr/server/fleet/grpc_rere/fleet_servicer.py
+++ b/src/py/flwr/server/superlink/fleet/grpc_rere/fleet_servicer.py
@@ -20,8 +20,8 @@
 import grpc
 
 from flwr.common.logger import log
-from flwr.proto import fleet_pb2_grpc
-from flwr.proto.fleet_pb2 import (
+from flwr.proto import fleet_pb2_grpc  # pylint: disable=E0611
+from flwr.proto.fleet_pb2 import (  # pylint: disable=E0611
     CreateNodeRequest,
     CreateNodeResponse,
     DeleteNodeRequest,
@@ -31,15 +31,15 @@
     PushTaskResRequest,
     PushTaskResResponse,
 )
-from flwr.server.fleet.message_handler import message_handler
-from flwr.server.state import State
+from flwr.server.superlink.fleet.message_handler import message_handler
+from flwr.server.superlink.state import StateFactory
 
 
 class FleetServicer(fleet_pb2_grpc.FleetServicer):
     """Fleet API servicer."""
 
-    def __init__(self, state: State) -> None:
-        self.state = state
+    def __init__(self, state_factory: StateFactory) -> None:
+        self.state_factory = state_factory
 
     def CreateNode(
         self, request: CreateNodeRequest, context: grpc.ServicerContext
@@ -48,7 +48,7 @@ def CreateNode(
         log(INFO, "FleetServicer.CreateNode")
         return message_handler.create_node(
             request=request,
-            state=self.state,
+            state=self.state_factory.state(),
         )
 
     def DeleteNode(
@@ -58,7 +58,7 @@ def DeleteNode(
         log(INFO, "FleetServicer.DeleteNode")
         return message_handler.delete_node(
             request=request,
-            state=self.state,
+            state=self.state_factory.state(),
         )
 
     def PullTaskIns(
@@ -68,7 +68,7 @@ def PullTaskIns(
         log(INFO, "FleetServicer.PullTaskIns")
         return message_handler.pull_task_ins(
             request=request,
-            state=self.state,
+            state=self.state_factory.state(),
         )
 
     def PushTaskRes(
@@ -78,5 +78,5 @@ def PushTaskRes(
         log(INFO, "FleetServicer.PushTaskRes")
         return message_handler.push_task_res(
             request=request,
-            state=self.state,
+            state=self.state_factory.state(),
         )
diff --git a/src/py/flwr/server/fleet/message_handler/__init__.py b/src/py/flwr/server/superlink/fleet/message_handler/__init__.py
similarity index 100%
rename from src/py/flwr/server/fleet/message_handler/__init__.py
rename to src/py/flwr/server/superlink/fleet/message_handler/__init__.py
diff --git a/src/py/flwr/server/fleet/message_handler/message_handler.py b/src/py/flwr/server/superlink/fleet/message_handler/message_handler.py
similarity index 89%
rename from src/py/flwr/server/fleet/message_handler/message_handler.py
rename to src/py/flwr/server/superlink/fleet/message_handler/message_handler.py
index 71876386f059..c99a7854d53a 100644
--- a/src/py/flwr/server/fleet/message_handler/message_handler.py
+++ b/src/py/flwr/server/superlink/fleet/message_handler/message_handler.py
@@ -18,7 +18,7 @@
 from typing import List, Optional
 from uuid import UUID
 
-from flwr.proto.fleet_pb2 import (
+from flwr.proto.fleet_pb2 import (  # pylint: disable=E0611
     CreateNodeRequest,
     CreateNodeResponse,
     DeleteNodeRequest,
@@ -29,9 +29,9 @@
     PushTaskResResponse,
     Reconnect,
 )
-from flwr.proto.node_pb2 import Node
-from flwr.proto.task_pb2 import TaskIns, TaskRes
-from flwr.server.state import State
+from flwr.proto.node_pb2 import Node  # pylint: disable=E0611
+from flwr.proto.task_pb2 import TaskIns, TaskRes  # pylint: disable=E0611
+from flwr.server.superlink.state import State
 
 
 def create_node(
@@ -47,7 +47,7 @@ def create_node(
 def delete_node(request: DeleteNodeRequest, state: State) -> DeleteNodeResponse:
     """."""
     # Validate node_id
-    if request.node.anonymous or request.node.node_id <= 0:
+    if request.node.anonymous or request.node.node_id == 0:
         return DeleteNodeResponse()
 
     # Update state
diff --git a/src/py/flwr/server/fleet/message_handler/message_handler_test.py b/src/py/flwr/server/superlink/fleet/message_handler/message_handler_test.py
similarity index 94%
rename from src/py/flwr/server/fleet/message_handler/message_handler_test.py
rename to src/py/flwr/server/superlink/fleet/message_handler/message_handler_test.py
index 25fd822492f2..c135f6fb7b61 100644
--- a/src/py/flwr/server/fleet/message_handler/message_handler_test.py
+++ b/src/py/flwr/server/superlink/fleet/message_handler/message_handler_test.py
@@ -17,14 +17,14 @@
 
 from unittest.mock import MagicMock
 
-from flwr.proto.fleet_pb2 import (
+from flwr.proto.fleet_pb2 import (  # pylint: disable=E0611
     CreateNodeRequest,
     DeleteNodeRequest,
     PullTaskInsRequest,
     PushTaskResRequest,
 )
-from flwr.proto.node_pb2 import Node
-from flwr.proto.task_pb2 import Task, TaskRes
+from flwr.proto.node_pb2 import Node  # pylint: disable=E0611
+from flwr.proto.task_pb2 import Task, TaskRes  # pylint: disable=E0611
 
 from .message_handler import create_node, delete_node, pull_task_ins, push_task_res
 
@@ -109,7 +109,7 @@ def test_push_task_res() -> None:
             TaskRes(
                 task_id="",
                 group_id="",
-                workload_id=0,
+                run_id=0,
                 task=Task(),
             ),
         ],
diff --git a/src/py/flwr/server/fleet/rest_rere/__init__.py b/src/py/flwr/server/superlink/fleet/rest_rere/__init__.py
similarity index 100%
rename from src/py/flwr/server/fleet/rest_rere/__init__.py
rename to src/py/flwr/server/superlink/fleet/rest_rere/__init__.py
diff --git a/src/py/flwr/server/fleet/rest_rere/rest_api.py b/src/py/flwr/server/superlink/fleet/rest_rere/rest_api.py
similarity index 96%
rename from src/py/flwr/server/fleet/rest_rere/rest_api.py
rename to src/py/flwr/server/superlink/fleet/rest_rere/rest_api.py
index cd1e47f24f00..b022b34c68c8 100644
--- a/src/py/flwr/server/fleet/rest_rere/rest_api.py
+++ b/src/py/flwr/server/superlink/fleet/rest_rere/rest_api.py
@@ -18,14 +18,14 @@
 import sys
 
 from flwr.common.constant import MISSING_EXTRA_REST
-from flwr.proto.fleet_pb2 import (
+from flwr.proto.fleet_pb2 import (  # pylint: disable=E0611
     CreateNodeRequest,
     DeleteNodeRequest,
     PullTaskInsRequest,
     PushTaskResRequest,
 )
-from flwr.server.fleet.message_handler import message_handler
-from flwr.server.state import State
+from flwr.server.superlink.fleet.message_handler import message_handler
+from flwr.server.superlink.state import State
 
 try:
     from starlette.applications import Starlette
diff --git a/src/py/flwr/server/superlink/fleet/vce/__init__.py b/src/py/flwr/server/superlink/fleet/vce/__init__.py
new file mode 100644
index 000000000000..57d39688b527
--- /dev/null
+++ b/src/py/flwr/server/superlink/fleet/vce/__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.
+# ==============================================================================
+"""Fleet Simulation Engine side."""
+
+from .vce_api import start_vce
+
+__all__ = [
+    "start_vce",
+]
diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/__init__.py b/src/py/flwr/server/superlink/fleet/vce/backend/__init__.py
new file mode 100644
index 000000000000..d751cf4bcae1
--- /dev/null
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/__init__.py
@@ -0,0 +1,48 @@
+# 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.
+# ==============================================================================
+"""Simulation Engine Backends."""
+
+import importlib
+from typing import Dict, Type
+
+from .backend import Backend, BackendConfig
+
+is_ray_installed = importlib.util.find_spec("ray") is not None
+
+# Mapping of supported backends
+supported_backends: Dict[str, Type[Backend]] = {}
+
+# To log backend-specific error message when chosen backend isn't available
+error_messages_backends: Dict[str, str] = {}
+
+if is_ray_installed:
+    from .raybackend import RayBackend
+
+    supported_backends["ray"] = RayBackend
+else:
+    error_messages_backends[
+        "ray"
+    ] = """Unable to import module `ray`.
+
+    To install the necessary dependencies, install `flwr` with the `simulation` extra:
+
+        pip install -U flwr["simulation"]
+    """
+
+
+__all__ = [
+    "Backend",
+    "BackendConfig",
+]
diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/backend.py b/src/py/flwr/server/superlink/fleet/vce/backend/backend.py
new file mode 100644
index 000000000000..1d5e3a6a51ad
--- /dev/null
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/backend.py
@@ -0,0 +1,67 @@
+# 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.
+# ==============================================================================
+"""Generic Backend class for Fleet API using the Simulation Engine."""
+
+
+from abc import ABC, abstractmethod
+from typing import Callable, Dict, Tuple
+
+from flwr.client.client_app import ClientApp
+from flwr.common.context import Context
+from flwr.common.message import Message
+from flwr.common.typing import ConfigsRecordValues
+
+BackendConfig = Dict[str, Dict[str, ConfigsRecordValues]]
+
+
+class Backend(ABC):
+    """Abstract base class for a Simulation Engine Backend."""
+
+    def __init__(self, backend_config: BackendConfig, work_dir: str) -> None:
+        """Construct a backend."""
+
+    @abstractmethod
+    async def build(self) -> None:
+        """Build backend asynchronously.
+
+        Different components need to be in place before workers in a backend are ready
+        to accept jobs. When this method finishes executing, the backend should be fully
+        ready to run jobs.
+        """
+
+    @property
+    def num_workers(self) -> int:
+        """Return number of workers in the backend.
+
+        This is the number of TaskIns that can be processed concurrently.
+        """
+        return 0
+
+    @abstractmethod
+    def is_worker_idle(self) -> bool:
+        """Report whether a backend worker is idle and can therefore run a ClientApp."""
+
+    @abstractmethod
+    async def terminate(self) -> None:
+        """Terminate backend."""
+
+    @abstractmethod
+    async def process_message(
+        self,
+        app: Callable[[], ClientApp],
+        message: Message,
+        context: Context,
+    ) -> Tuple[Message, Context]:
+        """Submit a job to the backend."""
diff --git a/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
new file mode 100644
index 000000000000..8ef0d54622ae
--- /dev/null
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend.py
@@ -0,0 +1,175 @@
+# 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.
+# ==============================================================================
+"""Ray backend for the Fleet API using the Simulation Engine."""
+
+import pathlib
+from logging import ERROR, INFO
+from typing import Callable, Dict, List, Tuple, Union
+
+import ray
+
+from flwr.client.client_app import ClientApp, LoadClientAppError
+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.simulation.ray_transport.utils import enable_tf_gpu_growth
+
+from .backend import Backend, BackendConfig
+
+ClientResourcesDict = Dict[str, Union[int, float]]
+
+
+class RayBackend(Backend):
+    """A backend that submits jobs to a `BasicActorPool`."""
+
+    def __init__(
+        self,
+        backend_config: BackendConfig,
+        work_dir: str,
+    ) -> None:
+        """Prepare RayBackend by initialising Ray and creating the ActorPool."""
+        log(INFO, "Initialising: %s", self.__class__.__name__)
+        log(INFO, "Backend config: %s", backend_config)
+
+        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
+        )
+        init_ray(runtime_env=runtime_env)
+
+        # Validate client resources
+        self.client_resources_key = "client_resources"
+
+        # Create actor pool
+        use_tf = backend_config.get("tensorflow", False)
+        actor_kwargs = {"on_actor_init_fn": enable_tf_gpu_growth} if use_tf else {}
+
+        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]]]:
+        """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}
+
+        excludes = []
+        path = pathlib.Path(work_dir)
+        for p in path.rglob("*"):
+            # Exclude files need to be relative to the working_dir
+            if p.is_file() and not str(p).endswith(".py"):
+                excludes.append(str(p.relative_to(path)))
+        runtime_env["excludes"] = excludes
+
+        return runtime_env
+
+    def _validate_client_resources(self, config: BackendConfig) -> ClientResourcesDict:
+        client_resources_config = config.get(self.client_resources_key)
+        client_resources: ClientResourcesDict = {}
+        valid_types = (int, float)
+        if client_resources_config:
+            for k, v in client_resources_config.items():
+                if not isinstance(k, str):
+                    raise ValueError(
+                        f"client resources keys are expected to be `str` but you used "
+                        f"{type(k)} for `{k}`"
+                    )
+                if not isinstance(v, valid_types):
+                    raise ValueError(
+                        f"client resources are expected to be of type {valid_types} "
+                        f"but found `{type(v)}` for key `{k}`",
+                    )
+                client_resources[k] = v
+
+        else:
+            client_resources = {"num_cpus": 2, "num_gpus": 0.0}
+            log(
+                INFO,
+                "`%s` not specified in backend config. Applying default setting: %s",
+                self.client_resources_key,
+                client_resources,
+            )
+
+        return client_resources
+
+    @property
+    def num_workers(self) -> int:
+        """Return number of actors in pool."""
+        return self.pool.num_actors
+
+    def is_worker_idle(self) -> bool:
+        """Report whether the pool has idle actors."""
+        return self.pool.is_actor_available()
+
+    async def build(self) -> None:
+        """Build pool of Ray actors that this backend will submit jobs to."""
+        await self.pool.add_actors_to_pool(self.pool.actors_capacity)
+        log(INFO, "Constructed ActorPool with: %i actors", self.pool.num_actors)
+
+    async def process_message(
+        self,
+        app: Callable[[], ClientApp],
+        message: Message,
+        context: Context,
+    ) -> Tuple[Message, Context]:
+        """Run ClientApp that process a given message.
+
+        Return output message and updated context.
+        """
+        partition_id = message.metadata.partition_id
+
+        try:
+            # Submite 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),
+            )
+
+            await future
+
+            # Fetch result
+            (
+                out_mssg,
+                updated_context,
+            ) = await self.pool.fetch_result_and_return_actor_to_pool(future)
+
+            return out_mssg, updated_context
+
+        except LoadClientAppError as load_ex:
+            log(
+                ERROR,
+                "An exception was raised when processing a message by %s",
+                self.__class__.__name__,
+            )
+            raise load_ex
+
+    async def terminate(self) -> None:
+        """Terminate all actors in actor pool."""
+        await self.pool.terminate_all_actors()
+        ray.shutdown()
+        log(INFO, "Terminated %s", self.__class__.__name__)
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
new file mode 100644
index 000000000000..2610307bb749
--- /dev/null
+++ b/src/py/flwr/server/superlink/fleet/vce/backend/raybackend_test.py
@@ -0,0 +1,216 @@
+# 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 for Ray backend for the Fleet API using the Simulation Engine."""
+
+import asyncio
+from math import pi
+from pathlib import Path
+from typing import Callable, Dict, Optional, Tuple, Union
+from unittest import IsolatedAsyncioTestCase
+
+import ray
+
+from flwr.client import Client, NumPyClient
+from flwr.client.client_app import ClientApp, LoadClientAppError
+from flwr.common import (
+    Config,
+    ConfigsRecord,
+    Context,
+    GetPropertiesIns,
+    Message,
+    MessageTypeLegacy,
+    Metadata,
+    RecordSet,
+    Scalar,
+)
+from flwr.common.object_ref import load_app
+from flwr.common.recordset_compat import getpropertiesins_to_recordset
+from flwr.server.superlink.fleet.vce.backend.raybackend import RayBackend
+
+
+class DummyClient(NumPyClient):
+    """A dummy NumPyClient for tests."""
+
+    def get_properties(self, config: Config) -> Dict[str, Scalar]:
+        """Return properties by doing a simple calculation."""
+        result = float(config["factor"]) * pi
+
+        # store something in context
+        self.context.state.configs_records["result"] = ConfigsRecord({"result": result})
+        return {"result": result}
+
+
+def get_dummy_client(cid: str) -> Client:  # pylint: disable=unused-argument
+    """Return a DummyClient converted to Client type."""
+    return DummyClient().to_client()
+
+
+def _load_app() -> ClientApp:
+    return ClientApp(client_fn=get_dummy_client)
+
+
+client_app = ClientApp(
+    client_fn=get_dummy_client,
+)
+
+
+def _load_from_module(client_app_module_name: str) -> Callable[[], ClientApp]:
+    def _load_app() -> ClientApp:
+        app = load_app(client_app_module_name, LoadClientAppError)
+
+        if not isinstance(app, ClientApp):
+            raise LoadClientAppError(
+                f"Attribute {client_app_module_name} is not of type {ClientApp}",
+            ) from None
+
+        return app
+
+    return _load_app
+
+
+async def backend_build_process_and_termination(
+    backend: RayBackend,
+    process_args: Optional[Tuple[Callable[[], ClientApp], Message, Context]] = None,
+) -> Union[Tuple[Message, Context], None]:
+    """Build, process job and terminate RayBackend."""
+    await backend.build()
+    to_return = None
+
+    if process_args:
+        to_return = await backend.process_message(*process_args)
+
+    await backend.terminate()
+
+    return to_return
+
+
+def _create_message_and_context() -> Tuple[Message, Context, float]:
+
+    # Construct a Message
+    mult_factor = 2024
+    getproperties_ins = GetPropertiesIns(config={"factor": mult_factor})
+    recordset = getpropertiesins_to_recordset(getproperties_ins)
+    message = Message(
+        content=recordset,
+        metadata=Metadata(
+            run_id=0,
+            message_id="",
+            group_id="",
+            src_node_id=0,
+            dst_node_id=0,
+            reply_to_message="",
+            ttl="",
+            message_type=MessageTypeLegacy.GET_PROPERTIES,
+        ),
+    )
+
+    # Construct emtpy Context
+    context = Context(state=RecordSet())
+
+    # Expected output
+    expected_output = pi * mult_factor
+
+    return message, context, expected_output
+
+
+class AsyncTestRayBackend(IsolatedAsyncioTestCase):
+    """A basic class that allows runnig multliple asyncio tests."""
+
+    async def on_cleanup(self) -> None:
+        """Ensure Ray has shutdown."""
+        if ray.is_initialized():
+            ray.shutdown()
+
+    def test_backend_creation_and_termination(self) -> None:
+        """Test creation of RayBackend and its termination."""
+        backend = RayBackend(backend_config={}, work_dir="")
+        asyncio.run(
+            backend_build_process_and_termination(backend=backend, process_args=None)
+        )
+
+    def test_backend_creation_submit_and_termination(
+        self,
+        client_app_loader: Callable[[], ClientApp] = _load_app,
+        workdir: str = "",
+    ) -> None:
+        """Test submitting a message to a given ClientApp."""
+        backend = RayBackend(backend_config={}, work_dir=workdir)
+
+        # Define ClientApp
+        client_app_callable = client_app_loader
+
+        message, context, expected_output = _create_message_and_context()
+
+        res = asyncio.run(
+            backend_build_process_and_termination(
+                backend=backend, process_args=(client_app_callable, message, context)
+            )
+        )
+
+        if res is None:
+            raise AssertionError("This shouldn't happen")
+
+        out_mssg, updated_context = res
+
+        # Verify message content is as expected
+        content = out_mssg.content
+        assert (
+            content.configs_records["getpropertiesres.properties"]["result"]
+            == expected_output
+        )
+
+        # Verify context is correct
+        obtained_result_in_context = updated_context.state.configs_records["result"][
+            "result"
+        ]
+        assert obtained_result_in_context == expected_output
+
+    def test_backend_creation_submit_and_termination_non_existing_client_app(
+        self,
+    ) -> None:
+        """Testing with ClientApp module that does not exist."""
+        with self.assertRaises(LoadClientAppError):
+            self.test_backend_creation_submit_and_termination(
+                client_app_loader=_load_from_module("a_non_existing_module:app")
+            )
+        self.addAsyncCleanup(self.on_cleanup)
+
+    def test_backend_creation_submit_and_termination_existing_client_app(
+        self,
+    ) -> None:
+        """Testing with ClientApp module that exist."""
+        # Resolve what should be the workdir to pass upon Backend initialisation
+        file_path = Path(__file__)
+        working_dir = Path.cwd()
+        rel_workdir = file_path.relative_to(working_dir)
+
+        # Susbtract last element
+        rel_workdir_str = str(rel_workdir.parent)
+
+        self.test_backend_creation_submit_and_termination(
+            client_app_loader=_load_from_module("raybackend_test:client_app"),
+            workdir=rel_workdir_str,
+        )
+
+    def test_backend_creation_submit_and_termination_existing_client_app_unsetworkdir(
+        self,
+    ) -> None:
+        """Testing with ClientApp module that exist but the passed workdir does not."""
+        with self.assertRaises(ValueError):
+            self.test_backend_creation_submit_and_termination(
+                client_app_loader=_load_from_module("raybackend_test:client_app"),
+                workdir="/?&%$^#%@$!",
+            )
+        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
new file mode 100644
index 000000000000..a693c968d0e8
--- /dev/null
+++ b/src/py/flwr/server/superlink/fleet/vce/vce_api.py
@@ -0,0 +1,331 @@
+# 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 Simulation Engine API."""
+
+
+import asyncio
+import json
+import traceback
+from logging import DEBUG, ERROR, INFO, WARN
+from typing import Callable, Dict, List, Optional
+
+from flwr.client.client_app import ClientApp, LoadClientAppError
+from flwr.client.node_state import NodeState
+from flwr.common.logger import log
+from flwr.common.object_ref import load_app
+from flwr.common.serde import message_from_taskins, message_to_taskres
+from flwr.proto.task_pb2 import TaskIns  # pylint: disable=E0611
+from flwr.server.superlink.state import StateFactory
+
+from .backend import Backend, error_messages_backends, supported_backends
+
+NodeToPartitionMapping = Dict[int, int]
+
+
+def _register_nodes(
+    num_nodes: int, state_factory: StateFactory
+) -> NodeToPartitionMapping:
+    """Register nodes with the StateFactory and create node-id:partition-id mapping."""
+    nodes_mapping: NodeToPartitionMapping = {}
+    state = state_factory.state()
+    for i in range(num_nodes):
+        node_id = state.create_node()
+        nodes_mapping[node_id] = i
+    log(INFO, "Registered %i nodes", len(nodes_mapping))
+    return nodes_mapping
+
+
+# pylint: disable=too-many-arguments,too-many-locals
+async def worker(
+    app_fn: Callable[[], ClientApp],
+    queue: "asyncio.Queue[TaskIns]",
+    node_states: Dict[int, NodeState],
+    state_factory: StateFactory,
+    nodes_mapping: NodeToPartitionMapping,
+    backend: Backend,
+) -> None:
+    """Get TaskIns from queue and pass it to an actor in the pool to execute it."""
+    state = state_factory.state()
+    while True:
+        try:
+            task_ins: TaskIns = await queue.get()
+            node_id = task_ins.task.consumer.node_id
+
+            # Register and retrieve runstate
+            node_states[node_id].register_context(run_id=task_ins.run_id)
+            context = node_states[node_id].retrieve_context(run_id=task_ins.run_id)
+
+            # Convert TaskIns to Message
+            message = message_from_taskins(task_ins)
+            # Set partition_id
+            message.metadata.partition_id = nodes_mapping[node_id]
+
+            # Let backend process message
+            out_mssg, updated_context = await backend.process_message(
+                app_fn, message, context
+            )
+
+            # Update Context
+            node_states[node_id].update_context(
+                task_ins.run_id, context=updated_context
+            )
+
+            # Convert to TaskRes
+            task_res = message_to_taskres(out_mssg)
+            # Store TaskRes in state
+            state.store_task_res(task_res)
+
+        except asyncio.CancelledError as e:
+            log(DEBUG, "Async worker: %s", e)
+            break
+
+        except LoadClientAppError as app_ex:
+            log(ERROR, "Async worker: %s", app_ex)
+            log(ERROR, traceback.format_exc())
+            raise
+
+        except Exception as ex:  # pylint: disable=broad-exception-caught
+            log(ERROR, ex)
+            log(ERROR, traceback.format_exc())
+            break
+
+
+async def add_taskins_to_queue(
+    queue: "asyncio.Queue[TaskIns]",
+    state_factory: StateFactory,
+    nodes_mapping: NodeToPartitionMapping,
+    backend: Backend,
+    consumers: List["asyncio.Task[None]"],
+    f_stop: asyncio.Event,
+) -> None:
+    """Retrieve TaskIns and add it to the queue."""
+    state = state_factory.state()
+    num_initial_consumers = len(consumers)
+    while not f_stop.is_set():
+        for node_id in nodes_mapping.keys():
+            task_ins = state.get_task_ins(node_id=node_id, limit=1)
+            if task_ins:
+                await queue.put(task_ins[0])
+
+        # Count consumers that are running
+        num_active = sum(not (cc.done()) for cc in consumers)
+
+        # Alert if number of consumers decreased by half
+        if num_active < num_initial_consumers // 2:
+            log(
+                WARN,
+                "Number of active workers has more than halved: (%i/%i active)",
+                num_active,
+                num_initial_consumers,
+            )
+
+        # Break if consumers died
+        if num_active == 0:
+            raise RuntimeError("All workers have died. Ending Simulation.")
+
+        # Log some stats
+        log(
+            DEBUG,
+            "Simulation Engine stats: "
+            "Active workers: (%i/%i) | %s (%i workers) | Tasks in queue: %i)",
+            num_active,
+            num_initial_consumers,
+            backend.__class__.__name__,
+            backend.num_workers,
+            queue.qsize(),
+        )
+        await asyncio.sleep(1.0)
+    log(DEBUG, "Async producer: Stopped pulling from StateFactory.")
+
+
+async def run(
+    app_fn: Callable[[], ClientApp],
+    backend_fn: Callable[[], Backend],
+    nodes_mapping: NodeToPartitionMapping,
+    state_factory: StateFactory,
+    node_states: Dict[int, NodeState],
+    f_stop: asyncio.Event,
+) -> None:
+    """Run the VCE async."""
+    queue: "asyncio.Queue[TaskIns]" = asyncio.Queue(128)
+
+    try:
+
+        # Instantiate backend
+        backend = backend_fn()
+
+        # Build backend
+        await backend.build()
+
+        # Add workers (they submit Messages to Backend)
+        worker_tasks = [
+            asyncio.create_task(
+                worker(
+                    app_fn, queue, node_states, state_factory, nodes_mapping, backend
+                )
+            )
+            for _ in range(backend.num_workers)
+        ]
+        # Create producer (adds TaskIns into Queue)
+        producer = asyncio.create_task(
+            add_taskins_to_queue(
+                queue, state_factory, nodes_mapping, backend, worker_tasks, f_stop
+            )
+        )
+
+        # Wait for producer to finish
+        # The producer runs forever until f_stop is set or until
+        # all worker (consumer) coroutines are completed. Workers
+        # also run forever and only end if an exception is raised.
+        await asyncio.gather(producer)
+
+    except Exception as ex:
+
+        log(ERROR, "An exception occured!! %s", ex)
+        log(ERROR, traceback.format_exc())
+        log(WARN, "Stopping Simulation Engine.")
+
+        # Manually trigger stopping event
+        f_stop.set()
+
+        # Raise exception
+        raise RuntimeError("Simulation Engine crashed.") from ex
+
+    finally:
+        # Produced task terminated, now cancel worker tasks
+        for w_t in worker_tasks:
+            _ = w_t.cancel()
+
+        while not all(w_t.done() for w_t in worker_tasks):
+            log(DEBUG, "Terminating async workers...")
+            await asyncio.sleep(0.5)
+
+        await asyncio.gather(*[w_t for w_t in worker_tasks if not w_t.done()])
+
+        # Terminate backend
+        await backend.terminate()
+
+
+# pylint: disable=too-many-arguments,unused-argument,too-many-locals
+def start_vce(
+    backend_name: str,
+    backend_config_json_stream: str,
+    app_dir: str,
+    f_stop: asyncio.Event,
+    client_app: Optional[ClientApp] = None,
+    client_app_attr: Optional[str] = None,
+    num_supernodes: Optional[int] = None,
+    state_factory: Optional[StateFactory] = None,
+    existing_nodes_mapping: Optional[NodeToPartitionMapping] = None,
+) -> None:
+    """Start Fleet API with the Simulation Engine."""
+    if client_app_attr is not None and client_app is not None:
+        raise ValueError(
+            "Both `client_app_attr` and `client_app` are provided, "
+            "but only one is allowed."
+        )
+
+    if num_supernodes is not None and existing_nodes_mapping is not None:
+        raise ValueError(
+            "Both `num_supernodes` and `existing_nodes_mapping` are provided, "
+            "but only one is allowed."
+        )
+    if num_supernodes is None:
+        if state_factory is None or existing_nodes_mapping is None:
+            raise ValueError(
+                "If not passing an existing `state_factory` and associated "
+                "`existing_nodes_mapping` you must supply `num_supernodes` to indicate "
+                "how many nodes to insert into a new StateFactory that will be created."
+            )
+    if existing_nodes_mapping:
+        if state_factory is None:
+            raise ValueError(
+                "`existing_nodes_mapping` was passed, but no `state_factory` was "
+                "passed."
+            )
+        log(INFO, "Using exiting NodeToPartitionMapping and StateFactory.")
+        # Use mapping constructed externally. This also means nodes
+        # have previously being registered.
+        nodes_mapping = existing_nodes_mapping
+
+    if not state_factory:
+        log(INFO, "A StateFactory was not supplied to the SimulationEngine.")
+        # Create an empty in-memory state factory
+        state_factory = StateFactory(":flwr-in-memory-state:")
+        log(INFO, "Created new %s.", state_factory.__class__.__name__)
+
+    if num_supernodes:
+        # Register SuperNodes
+        nodes_mapping = _register_nodes(
+            num_nodes=num_supernodes, state_factory=state_factory
+        )
+
+    # Construct mapping of NodeStates
+    node_states: Dict[int, NodeState] = {}
+    for node_id in nodes_mapping:
+        node_states[node_id] = NodeState()
+
+    # Load backend config
+    log(INFO, "Supported backends: %s", list(supported_backends.keys()))
+    backend_config = json.loads(backend_config_json_stream)
+
+    try:
+        backend_type = supported_backends[backend_name]
+    except KeyError as ex:
+        log(
+            ERROR,
+            "Backend `%s`, is not supported. Use any of %s or add support "
+            "for a new backend.",
+            backend_name,
+            list(supported_backends.keys()),
+        )
+        if backend_name in error_messages_backends:
+            log(ERROR, error_messages_backends[backend_name])
+
+        raise ex
+
+    def backend_fn() -> Backend:
+        """Instantiate a Backend."""
+        return backend_type(backend_config, work_dir=app_dir)
+
+    log(INFO, "client_app_attr = %s", client_app_attr)
+
+    # Load ClientApp if needed
+    def _load() -> ClientApp:
+
+        if client_app_attr:
+            app: ClientApp = load_app(client_app_attr, LoadClientAppError)
+
+            if not isinstance(app, ClientApp):
+                raise LoadClientAppError(
+                    f"Attribute {client_app_attr} is not of type {ClientApp}",
+                ) from None
+
+        if client_app:
+            app = client_app
+        return app
+
+    app_fn = _load
+
+    asyncio.run(
+        run(
+            app_fn,
+            backend_fn,
+            nodes_mapping,
+            state_factory,
+            node_states,
+            f_stop,
+        )
+    )
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
new file mode 100644
index 000000000000..8c37399ae295
--- /dev/null
+++ b/src/py/flwr/server/superlink/fleet/vce/vce_api_test.py
@@ -0,0 +1,291 @@
+# 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 Fleet Simulation Engine API."""
+
+
+import asyncio
+import threading
+from itertools import cycle
+from json import JSONDecodeError
+from math import pi
+from pathlib import Path
+from time import sleep
+from typing import Dict, Optional, Set, Tuple
+from unittest import IsolatedAsyncioTestCase
+from uuid import UUID
+
+from flwr.common import GetPropertiesIns, Message, MessageTypeLegacy, Metadata
+from flwr.common.recordset_compat import getpropertiesins_to_recordset
+from flwr.common.serde import message_from_taskres, message_to_taskins
+from flwr.server.superlink.fleet.vce.vce_api import (
+    NodeToPartitionMapping,
+    _register_nodes,
+    start_vce,
+)
+from flwr.server.superlink.state import InMemoryState, StateFactory
+
+
+def terminate_simulation(f_stop: asyncio.Event, sleep_duration: int) -> None:
+    """Set event to terminate Simulation Engine after `sleep_duration` seconds."""
+    sleep(sleep_duration)
+    f_stop.set()
+
+
+def init_state_factory_nodes_mapping(
+    num_nodes: int,
+    num_messages: int,
+    erroneous_message: Optional[bool] = False,
+) -> Tuple[StateFactory, NodeToPartitionMapping, Dict[UUID, float]]:
+    """Instatiate StateFactory, register nodes and pre-insert messages in the state."""
+    # Register a state and a run_id in it
+    run_id = 1234
+    state_factory = StateFactory(":flwr-in-memory-state:")
+
+    # Register a few nodes
+    nodes_mapping = _register_nodes(num_nodes=num_nodes, state_factory=state_factory)
+
+    expected_results = register_messages_into_state(
+        state_factory=state_factory,
+        nodes_mapping=nodes_mapping,
+        run_id=run_id,
+        num_messages=num_messages,
+        erroneous_message=erroneous_message,
+    )
+    return state_factory, nodes_mapping, expected_results
+
+
+# pylint: disable=too-many-locals
+def register_messages_into_state(
+    state_factory: StateFactory,
+    nodes_mapping: NodeToPartitionMapping,
+    run_id: int,
+    num_messages: int,
+    erroneous_message: Optional[bool] = False,
+) -> Dict[UUID, float]:
+    """Register `num_messages` into the state factory."""
+    state: InMemoryState = state_factory.state()  # type: ignore
+    state.run_ids.add(run_id)
+    # 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
+    task_ids: Set[UUID] = set()  # so we can retrieve them later
+    expected_results = {}
+    for i in range(num_messages):
+        dst_node_id = next(nodes_cycle)
+        # Construct a Message
+        mult_factor = 2024 + i
+        getproperties_ins = GetPropertiesIns(config={"factor": mult_factor})
+        recordset = getpropertiesins_to_recordset(getproperties_ins)
+        message = Message(
+            content=recordset,
+            metadata=Metadata(
+                run_id=run_id,
+                message_id="",
+                group_id="",
+                src_node_id=0,
+                dst_node_id=dst_node_id,  # indicate destination node
+                reply_to_message="",
+                ttl="",
+                message_type=(
+                    "a bad message"
+                    if erroneous_message
+                    else MessageTypeLegacy.GET_PROPERTIES
+                ),
+            ),
+        )
+        # Convert Message to TaskIns
+        taskins = message_to_taskins(message)
+        # Instert in state
+        task_id = state.store_task_ins(taskins)
+        if task_id:
+            # Add to UUID set
+            task_ids.add(task_id)
+            # Store expected output for check later on
+            expected_results[task_id] = mult_factor * pi
+
+    return expected_results
+
+
+def _autoresolve_app_dir(rel_client_app_dir: str = "backend") -> str:
+    """Correctly resolve working directory for the app."""
+    file_path = Path(__file__)
+    app_dir = Path.cwd()
+    rel_app_dir = file_path.relative_to(app_dir)
+
+    # Susbtract lats element and append "backend/test" (wher the client module is.)
+    return str(rel_app_dir.parent / rel_client_app_dir)
+
+
+# pylint: disable=too-many-arguments
+def start_and_shutdown(
+    backend: str = "ray",
+    client_app_attr: str = "raybackend_test:client_app",
+    app_dir: str = "",
+    num_supernodes: Optional[int] = None,
+    state_factory: Optional[StateFactory] = None,
+    nodes_mapping: Optional[NodeToPartitionMapping] = None,
+    duration: int = 0,
+    backend_config: str = "{}",
+) -> None:
+    """Start Simulation Engine and terminate after specified number of seconds.
+
+    Some tests need to be terminated by triggering externally an asyncio.Event. This
+    is enabled whtn passing `duration`>0.
+    """
+    f_stop = asyncio.Event()
+
+    if duration:
+
+        # Setup thread that will set the f_stop event, triggering the termination of all
+        # asyncio logic in the Simulation Engine. It will also terminate the Backend.
+        termination_th = threading.Thread(
+            target=terminate_simulation, args=(f_stop, duration)
+        )
+        termination_th.start()
+
+    # Resolve working directory if not passed
+    if not app_dir:
+        app_dir = _autoresolve_app_dir()
+
+    start_vce(
+        num_supernodes=num_supernodes,
+        client_app_attr=client_app_attr,
+        backend_name=backend,
+        backend_config_json_stream=backend_config,
+        state_factory=state_factory,
+        app_dir=app_dir,
+        f_stop=f_stop,
+        existing_nodes_mapping=nodes_mapping,
+    )
+
+    if duration:
+        termination_th.join()
+
+
+class AsyncTestFleetSimulationEngineRayBackend(IsolatedAsyncioTestCase):
+    """A basic class that enables testing asyncio functionalities."""
+
+    def test_erroneous_no_supernodes_client_mapping(self) -> None:
+        """Test with unset arguments."""
+        with self.assertRaises(ValueError):
+            start_and_shutdown(duration=2)
+
+    def test_erroneous_client_app_attr(self) -> None:
+        """Tests attempt to load a ClientApp that can't be found."""
+        num_messages = 7
+        num_nodes = 59
+
+        state_factory, nodes_mapping, _ = init_state_factory_nodes_mapping(
+            num_nodes=num_nodes, num_messages=num_messages
+        )
+        with self.assertRaises(RuntimeError):
+            start_and_shutdown(
+                client_app_attr="totally_fictitious_app:client",
+                state_factory=state_factory,
+                nodes_mapping=nodes_mapping,
+            )
+
+    def test_erroneous_messages(self) -> None:
+        """Test handling of error in async worker (consumer).
+
+        We register messages which will trigger an error when handling, triggering an
+        error.
+        """
+        num_messages = 100
+        num_nodes = 59
+
+        state_factory, nodes_mapping, _ = init_state_factory_nodes_mapping(
+            num_nodes=num_nodes, num_messages=num_messages, erroneous_message=True
+        )
+
+        with self.assertRaises(RuntimeError):
+            start_and_shutdown(
+                state_factory=state_factory,
+                nodes_mapping=nodes_mapping,
+            )
+
+    def test_erroneous_backend_config(self) -> None:
+        """Backend Config should be a JSON stream."""
+        with self.assertRaises(JSONDecodeError):
+            start_and_shutdown(num_supernodes=50, backend_config="not a proper config")
+
+    def test_with_nonexistent_backend(self) -> None:
+        """Test specifying a backend that does not exist."""
+        with self.assertRaises(KeyError):
+            start_and_shutdown(num_supernodes=50, backend="this-backend-does-not-exist")
+
+    def test_erroneous_arguments_num_supernodes_and_existing_mapping(self) -> None:
+        """Test ValueError if a node mapping is passed but also num_supernodes.
+
+        Passing `num_supernodes` does nothing since we assume that if a node mapping
+        is supplied, nodes have been registered externally already. Therefore passing
+        `num_supernodes` might give the impression that that many nodes will be
+        registered. We don't do that since a mapping already exists.
+        """
+        with self.assertRaises(ValueError):
+            start_and_shutdown(num_supernodes=50, nodes_mapping={0: 1})
+
+    def test_erroneous_arguments_existing_mapping_but_no_state_factory(self) -> None:
+        """Test ValueError if a node mapping is passed but no state.
+
+        Passing a node mapping indicates that (externally) nodes have registered with a
+        state factory. Therefore, that state factory should be passed too.
+        """
+        with self.assertRaises(ValueError):
+            start_and_shutdown(nodes_mapping={0: 1})
+
+    def test_start_and_shutdown(self) -> None:
+        """Start Simulation Engine Fleet and terminate it."""
+        start_and_shutdown(num_supernodes=50, duration=10)
+
+    # pylint: disable=too-many-locals
+    def test_start_and_shutdown_with_tasks_in_state(self) -> None:
+        """Run Simulation Engine with some TasksIns in State.
+
+        This test creates a few nodes and submits a few messages that need to be
+        executed by the Backend. In order for that to happen the asyncio
+        producer/consumer logic must function. This also severs to evaluate a valid
+        ClientApp.
+        """
+        num_messages = 229
+        num_nodes = 59
+
+        state_factory, nodes_mapping, expected_results = (
+            init_state_factory_nodes_mapping(
+                num_nodes=num_nodes, num_messages=num_messages
+            )
+        )
+
+        # Run
+        start_and_shutdown(
+            state_factory=state_factory, nodes_mapping=nodes_mapping, duration=10
+        )
+
+        # Get all TaskRes
+        state = state_factory.state()
+        task_ids = set(expected_results.keys())
+        task_res_list = state.get_task_res(task_ids=task_ids, limit=len(task_ids))
+
+        # Check results by first converting to Message
+        for task_res in task_res_list:
+
+            message = message_from_taskres(task_res)
+
+            # Verify message content is as expected
+            content = message.content
+            assert (
+                content.configs_records["getpropertiesres.properties"]["result"]
+                == expected_results[UUID(task_res.task.ancestry[0])]
+            )
diff --git a/src/py/flwr/server/state/__init__.py b/src/py/flwr/server/superlink/state/__init__.py
similarity index 100%
rename from src/py/flwr/server/state/__init__.py
rename to src/py/flwr/server/superlink/state/__init__.py
diff --git a/src/py/flwr/server/state/in_memory_state.py b/src/py/flwr/server/superlink/state/in_memory_state.py
similarity index 59%
rename from src/py/flwr/server/state/in_memory_state.py
rename to src/py/flwr/server/superlink/state/in_memory_state.py
index 384839b7461f..ac1ab158e254 100644
--- a/src/py/flwr/server/state/in_memory_state.py
+++ b/src/py/flwr/server/superlink/state/in_memory_state.py
@@ -16,14 +16,15 @@
 
 
 import os
+import threading
 from datetime import datetime, timedelta
 from logging import ERROR
 from typing import Dict, List, Optional, Set
 from uuid import UUID, uuid4
 
 from flwr.common import log, now
-from flwr.proto.task_pb2 import TaskIns, TaskRes
-from flwr.server.state.state import State
+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
 
 
@@ -32,9 +33,10 @@ class InMemoryState(State):
 
     def __init__(self) -> None:
         self.node_ids: Set[int] = set()
-        self.workload_ids: Set[int] = set()
+        self.run_ids: Set[int] = set()
         self.task_ins_store: Dict[UUID, TaskIns] = {}
         self.task_res_store: Dict[UUID, TaskRes] = {}
+        self.lock = threading.Lock()
 
     def store_task_ins(self, task_ins: TaskIns) -> Optional[UUID]:
         """Store one TaskIns."""
@@ -43,9 +45,9 @@ def store_task_ins(self, task_ins: TaskIns) -> Optional[UUID]:
         if any(errors):
             log(ERROR, errors)
             return None
-        # Validate workload_id
-        if task_ins.workload_id not in self.workload_ids:
-            log(ERROR, "`workload_id` is invalid")
+        # Validate run_id
+        if task_ins.run_id not in self.run_ids:
+            log(ERROR, "`run_id` is invalid")
             return None
 
         # Create task_id, created_at and ttl
@@ -57,7 +59,8 @@ def store_task_ins(self, task_ins: TaskIns) -> Optional[UUID]:
         task_ins.task_id = str(task_id)
         task_ins.task.created_at = created_at.isoformat()
         task_ins.task.ttl = ttl.isoformat()
-        self.task_ins_store[task_id] = task_ins
+        with self.lock:
+            self.task_ins_store[task_id] = task_ins
 
         # Return the new task_id
         return task_id
@@ -71,22 +74,23 @@ def get_task_ins(
 
         # Find TaskIns for node_id that were not delivered yet
         task_ins_list: List[TaskIns] = []
-        for _, task_ins in self.task_ins_store.items():
-            # pylint: disable=too-many-boolean-expressions
-            if (
-                node_id is not None  # Not anonymous
-                and task_ins.task.consumer.anonymous is False
-                and task_ins.task.consumer.node_id == node_id
-                and task_ins.task.delivered_at == ""
-            ) or (
-                node_id is None  # Anonymous
-                and task_ins.task.consumer.anonymous is True
-                and task_ins.task.consumer.node_id == 0
-                and task_ins.task.delivered_at == ""
-            ):
-                task_ins_list.append(task_ins)
-            if limit and len(task_ins_list) == limit:
-                break
+        with self.lock:
+            for _, task_ins in self.task_ins_store.items():
+                # pylint: disable=too-many-boolean-expressions
+                if (
+                    node_id is not None  # Not anonymous
+                    and task_ins.task.consumer.anonymous is False
+                    and task_ins.task.consumer.node_id == node_id
+                    and task_ins.task.delivered_at == ""
+                ) or (
+                    node_id is None  # Anonymous
+                    and task_ins.task.consumer.anonymous is True
+                    and task_ins.task.consumer.node_id == 0
+                    and task_ins.task.delivered_at == ""
+                ):
+                    task_ins_list.append(task_ins)
+                if limit and len(task_ins_list) == limit:
+                    break
 
         # Mark all of them as delivered
         delivered_at = now().isoformat()
@@ -104,9 +108,9 @@ def store_task_res(self, task_res: TaskRes) -> Optional[UUID]:
             log(ERROR, errors)
             return None
 
-        # Validate workload_id
-        if task_res.workload_id not in self.workload_ids:
-            log(ERROR, "`workload_id` is invalid")
+        # Validate run_id
+        if task_res.run_id not in self.run_ids:
+            log(ERROR, "`run_id` is invalid")
             return None
 
         # Create task_id, created_at and ttl
@@ -118,7 +122,8 @@ def store_task_res(self, task_res: TaskRes) -> Optional[UUID]:
         task_res.task_id = str(task_id)
         task_res.task.created_at = created_at.isoformat()
         task_res.task.ttl = ttl.isoformat()
-        self.task_res_store[task_id] = task_res
+        with self.lock:
+            self.task_res_store[task_id] = task_res
 
         # Return the new task_id
         return task_id
@@ -128,45 +133,47 @@ def get_task_res(self, task_ids: Set[UUID], limit: Optional[int]) -> List[TaskRe
         if limit is not None and limit < 1:
             raise AssertionError("`limit` must be >= 1")
 
-        # Find TaskRes that were not delivered yet
-        task_res_list: List[TaskRes] = []
-        for _, task_res in self.task_res_store.items():
-            if (
-                UUID(task_res.task.ancestry[0]) in task_ids
-                and task_res.task.delivered_at == ""
-            ):
-                task_res_list.append(task_res)
-            if limit and len(task_res_list) == limit:
-                break
-
-        # Mark all of them as delivered
-        delivered_at = now().isoformat()
-        for task_res in task_res_list:
-            task_res.task.delivered_at = delivered_at
-
-        # Return TaskRes
-        return task_res_list
+        with self.lock:
+            # Find TaskRes that were not delivered yet
+            task_res_list: List[TaskRes] = []
+            for _, task_res in self.task_res_store.items():
+                if (
+                    UUID(task_res.task.ancestry[0]) in task_ids
+                    and task_res.task.delivered_at == ""
+                ):
+                    task_res_list.append(task_res)
+                if limit and len(task_res_list) == limit:
+                    break
+
+            # Mark all of them as delivered
+            delivered_at = now().isoformat()
+            for task_res in task_res_list:
+                task_res.task.delivered_at = delivered_at
+
+            # Return TaskRes
+            return task_res_list
 
     def delete_tasks(self, task_ids: Set[UUID]) -> None:
         """Delete all delivered TaskIns/TaskRes pairs."""
         task_ins_to_be_deleted: Set[UUID] = set()
         task_res_to_be_deleted: Set[UUID] = set()
 
-        for task_ins_id in task_ids:
-            # Find the task_id of the matching task_res
-            for task_res_id, task_res in self.task_res_store.items():
-                if UUID(task_res.task.ancestry[0]) != task_ins_id:
-                    continue
-                if task_res.task.delivered_at == "":
-                    continue
+        with self.lock:
+            for task_ins_id in task_ids:
+                # Find the task_id of the matching task_res
+                for task_res_id, task_res in self.task_res_store.items():
+                    if UUID(task_res.task.ancestry[0]) != task_ins_id:
+                        continue
+                    if task_res.task.delivered_at == "":
+                        continue
 
-                task_ins_to_be_deleted.add(task_ins_id)
-                task_res_to_be_deleted.add(task_res_id)
+                    task_ins_to_be_deleted.add(task_ins_id)
+                    task_res_to_be_deleted.add(task_res_id)
 
-        for task_id in task_ins_to_be_deleted:
-            del self.task_ins_store[task_id]
-        for task_id in task_res_to_be_deleted:
-            del self.task_res_store[task_id]
+            for task_id in task_ins_to_be_deleted:
+                del self.task_ins_store[task_id]
+            for task_id in task_res_to_be_deleted:
+                del self.task_res_store[task_id]
 
     def num_task_ins(self) -> int:
         """Calculate the number of task_ins in store.
@@ -199,25 +206,25 @@ def delete_node(self, node_id: int) -> None:
             raise ValueError(f"Node {node_id} not found")
         self.node_ids.remove(node_id)
 
-    def get_nodes(self, workload_id: int) -> Set[int]:
+    def get_nodes(self, run_id: int) -> Set[int]:
         """Return all available client nodes.
 
         Constraints
         -----------
-        If the provided `workload_id` does not exist or has no matching nodes,
+        If the provided `run_id` does not exist or has no matching nodes,
         an empty `Set` MUST be returned.
         """
-        if workload_id not in self.workload_ids:
+        if run_id not in self.run_ids:
             return set()
         return self.node_ids
 
-    def create_workload(self) -> int:
-        """Create one workload."""
-        # Sample a random int64 as workload_id
-        workload_id: int = int.from_bytes(os.urandom(8), "little", signed=True)
+    def create_run(self) -> int:
+        """Create one run."""
+        # Sample a random int64 as run_id
+        run_id: int = int.from_bytes(os.urandom(8), "little", signed=True)
 
-        if workload_id not in self.workload_ids:
-            self.workload_ids.add(workload_id)
-            return workload_id
-        log(ERROR, "Unexpected workload creation failure.")
+        if run_id not in self.run_ids:
+            self.run_ids.add(run_id)
+            return run_id
+        log(ERROR, "Unexpected run creation failure.")
         return 0
diff --git a/src/py/flwr/server/state/sqlite_state.py b/src/py/flwr/server/superlink/state/sqlite_state.py
similarity index 87%
rename from src/py/flwr/server/state/sqlite_state.py
rename to src/py/flwr/server/superlink/state/sqlite_state.py
index f3ff60f370e9..224c16cdf013 100644
--- a/src/py/flwr/server/state/sqlite_state.py
+++ b/src/py/flwr/server/superlink/state/sqlite_state.py
@@ -24,9 +24,9 @@
 from uuid import UUID, uuid4
 
 from flwr.common import log, now
-from flwr.proto.node_pb2 import Node
-from flwr.proto.task_pb2 import Task, TaskIns, TaskRes
-from flwr.proto.transport_pb2 import ClientMessage, ServerMessage
+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
 from flwr.server.utils.validator import validate_task_ins_or_res
 
 from .state import State
@@ -37,9 +37,9 @@
 );
 """
 
-SQL_CREATE_TABLE_WORKLOAD = """
-CREATE TABLE IF NOT EXISTS workload(
-    workload_id INTEGER UNIQUE
+SQL_CREATE_TABLE_RUN = """
+CREATE TABLE IF NOT EXISTS run(
+    run_id INTEGER UNIQUE
 );
 """
 
@@ -47,7 +47,7 @@
 CREATE TABLE IF NOT EXISTS task_ins(
     task_id                 TEXT UNIQUE,
     group_id                TEXT,
-    workload_id             INTEGER,
+    run_id                  INTEGER,
     producer_anonymous      BOOLEAN,
     producer_node_id        INTEGER,
     consumer_anonymous      BOOLEAN,
@@ -56,9 +56,9 @@
     delivered_at            TEXT,
     ttl                     TEXT,
     ancestry                TEXT,
-    legacy_server_message   BLOB,
-    legacy_client_message   BLOB,
-    FOREIGN KEY(workload_id) REFERENCES workload(workload_id)
+    task_type               TEXT,
+    recordset               BLOB,
+    FOREIGN KEY(run_id) REFERENCES run(run_id)
 );
 """
 
@@ -67,7 +67,7 @@
 CREATE TABLE IF NOT EXISTS task_res(
     task_id                 TEXT UNIQUE,
     group_id                TEXT,
-    workload_id             INTEGER,
+    run_id                  INTEGER,
     producer_anonymous      BOOLEAN,
     producer_node_id        INTEGER,
     consumer_anonymous      BOOLEAN,
@@ -76,9 +76,9 @@
     delivered_at            TEXT,
     ttl                     TEXT,
     ancestry                TEXT,
-    legacy_server_message   BLOB,
-    legacy_client_message   BLOB,
-    FOREIGN KEY(workload_id) REFERENCES workload(workload_id)
+    task_type               TEXT,
+    recordset               BLOB,
+    FOREIGN KEY(run_id) REFERENCES run(run_id)
 );
 """
 
@@ -119,7 +119,7 @@ def initialize(self, log_queries: bool = False) -> List[Tuple[str]]:
         cur = self.conn.cursor()
 
         # Create each table if not exists queries
-        cur.execute(SQL_CREATE_TABLE_WORKLOAD)
+        cur.execute(SQL_CREATE_TABLE_RUN)
         cur.execute(SQL_CREATE_TABLE_TASK_INS)
         cur.execute(SQL_CREATE_TABLE_TASK_RES)
         cur.execute(SQL_CREATE_TABLE_NODE)
@@ -134,7 +134,7 @@ def query(
     ) -> List[Dict[str, Any]]:
         """Execute a SQL query."""
         if self.conn is None:
-            raise Exception("State is not initialized.")
+            raise AttributeError("State is not initialized.")
 
         if data is None:
             data = []
@@ -198,12 +198,12 @@ def store_task_ins(self, task_ins: TaskIns) -> Optional[UUID]:
         columns = ", ".join([f":{key}" for key in data[0]])
         query = f"INSERT INTO task_ins VALUES({columns});"
 
-        # Only invalid workload_id can trigger IntegrityError.
+        # Only invalid run_id can trigger IntegrityError.
         # This may need to be changed in the future version with more integrity checks.
         try:
             self.query(query, data)
         except sqlite3.IntegrityError:
-            log(ERROR, "`workload` is invalid")
+            log(ERROR, "`run` is invalid")
             return None
 
         return task_id
@@ -333,12 +333,12 @@ def store_task_res(self, task_res: TaskRes) -> Optional[UUID]:
         columns = ", ".join([f":{key}" for key in data[0]])
         query = f"INSERT INTO task_res VALUES({columns});"
 
-        # Only invalid workload_id can trigger IntegrityError.
+        # Only invalid run_id can trigger IntegrityError.
         # This may need to be changed in the future version with more integrity checks.
         try:
             self.query(query, data)
         except sqlite3.IntegrityError:
-            log(ERROR, "`workload` is invalid")
+            log(ERROR, "`run` is invalid")
             return None
 
         return task_id
@@ -459,7 +459,7 @@ def delete_tasks(self, task_ids: Set[UUID]) -> None:
         """
 
         if self.conn is None:
-            raise Exception("State not intitialized")
+            raise AttributeError("State not intitialized")
 
         with self.conn:
             self.conn.execute(query_1, data)
@@ -485,17 +485,17 @@ def delete_node(self, node_id: int) -> None:
         query = "DELETE FROM node WHERE node_id = :node_id;"
         self.query(query, {"node_id": node_id})
 
-    def get_nodes(self, workload_id: int) -> Set[int]:
+    def get_nodes(self, run_id: int) -> Set[int]:
         """Retrieve all currently stored node IDs as a set.
 
         Constraints
         -----------
-        If the provided `workload_id` does not exist or has no matching nodes,
+        If the provided `run_id` does not exist or has no matching nodes,
         an empty `Set` MUST be returned.
         """
-        # Validate workload ID
-        query = "SELECT COUNT(*) FROM workload WHERE workload_id = ?;"
-        if self.query(query, (workload_id,))[0]["COUNT(*)"] == 0:
+        # Validate run ID
+        query = "SELECT COUNT(*) FROM run WHERE run_id = ?;"
+        if self.query(query, (run_id,))[0]["COUNT(*)"] == 0:
             return set()
 
         # Get nodes
@@ -504,19 +504,19 @@ def get_nodes(self, workload_id: int) -> Set[int]:
         result: Set[int] = {row["node_id"] for row in rows}
         return result
 
-    def create_workload(self) -> int:
-        """Create one workload and store it in state."""
-        # Sample a random int64 as workload_id
-        workload_id: int = int.from_bytes(os.urandom(8), "little", signed=True)
+    def create_run(self) -> int:
+        """Create one run and store it in state."""
+        # Sample a random int64 as run_id
+        run_id: int = int.from_bytes(os.urandom(8), "little", signed=True)
 
         # Check conflicts
-        query = "SELECT COUNT(*) FROM workload WHERE workload_id = ?;"
-        # If workload_id does not exist
-        if self.query(query, (workload_id,))[0]["COUNT(*)"] == 0:
-            query = "INSERT INTO workload VALUES(:workload_id);"
-            self.query(query, {"workload_id": workload_id})
-            return workload_id
-        log(ERROR, "Unexpected workload creation failure.")
+        query = "SELECT COUNT(*) FROM run WHERE run_id = ?;"
+        # If run_id does not exist
+        if self.query(query, (run_id,))[0]["COUNT(*)"] == 0:
+            query = "INSERT INTO run VALUES(:run_id);"
+            self.query(query, {"run_id": run_id})
+            return run_id
+        log(ERROR, "Unexpected run creation failure.")
         return 0
 
 
@@ -537,7 +537,7 @@ def task_ins_to_dict(task_msg: TaskIns) -> Dict[str, Any]:
     result = {
         "task_id": task_msg.task_id,
         "group_id": task_msg.group_id,
-        "workload_id": task_msg.workload_id,
+        "run_id": task_msg.run_id,
         "producer_anonymous": task_msg.task.producer.anonymous,
         "producer_node_id": task_msg.task.producer.node_id,
         "consumer_anonymous": task_msg.task.consumer.anonymous,
@@ -546,10 +546,8 @@ def task_ins_to_dict(task_msg: TaskIns) -> Dict[str, Any]:
         "delivered_at": task_msg.task.delivered_at,
         "ttl": task_msg.task.ttl,
         "ancestry": ",".join(task_msg.task.ancestry),
-        "legacy_server_message": (
-            task_msg.task.legacy_server_message.SerializeToString()
-        ),
-        "legacy_client_message": None,
+        "task_type": task_msg.task.task_type,
+        "recordset": task_msg.task.recordset.SerializeToString(),
     }
     return result
 
@@ -559,7 +557,7 @@ def task_res_to_dict(task_msg: TaskRes) -> Dict[str, Any]:
     result = {
         "task_id": task_msg.task_id,
         "group_id": task_msg.group_id,
-        "workload_id": task_msg.workload_id,
+        "run_id": task_msg.run_id,
         "producer_anonymous": task_msg.task.producer.anonymous,
         "producer_node_id": task_msg.task.producer.node_id,
         "consumer_anonymous": task_msg.task.consumer.anonymous,
@@ -568,23 +566,21 @@ def task_res_to_dict(task_msg: TaskRes) -> Dict[str, Any]:
         "delivered_at": task_msg.task.delivered_at,
         "ttl": task_msg.task.ttl,
         "ancestry": ",".join(task_msg.task.ancestry),
-        "legacy_server_message": None,
-        "legacy_client_message": (
-            task_msg.task.legacy_client_message.SerializeToString()
-        ),
+        "task_type": task_msg.task.task_type,
+        "recordset": task_msg.task.recordset.SerializeToString(),
     }
     return result
 
 
 def dict_to_task_ins(task_dict: Dict[str, Any]) -> TaskIns:
     """Turn task_dict into protobuf message."""
-    server_message = ServerMessage()
-    server_message.ParseFromString(task_dict["legacy_server_message"])
+    recordset = RecordSet()
+    recordset.ParseFromString(task_dict["recordset"])
 
     result = TaskIns(
         task_id=task_dict["task_id"],
         group_id=task_dict["group_id"],
-        workload_id=task_dict["workload_id"],
+        run_id=task_dict["run_id"],
         task=Task(
             producer=Node(
                 node_id=task_dict["producer_node_id"],
@@ -598,7 +594,8 @@ def dict_to_task_ins(task_dict: Dict[str, Any]) -> TaskIns:
             delivered_at=task_dict["delivered_at"],
             ttl=task_dict["ttl"],
             ancestry=task_dict["ancestry"].split(","),
-            legacy_server_message=server_message,
+            task_type=task_dict["task_type"],
+            recordset=recordset,
         ),
     )
     return result
@@ -606,13 +603,13 @@ def dict_to_task_ins(task_dict: Dict[str, Any]) -> TaskIns:
 
 def dict_to_task_res(task_dict: Dict[str, Any]) -> TaskRes:
     """Turn task_dict into protobuf message."""
-    client_message = ClientMessage()
-    client_message.ParseFromString(task_dict["legacy_client_message"])
+    recordset = RecordSet()
+    recordset.ParseFromString(task_dict["recordset"])
 
     result = TaskRes(
         task_id=task_dict["task_id"],
         group_id=task_dict["group_id"],
-        workload_id=task_dict["workload_id"],
+        run_id=task_dict["run_id"],
         task=Task(
             producer=Node(
                 node_id=task_dict["producer_node_id"],
@@ -626,7 +623,8 @@ def dict_to_task_res(task_dict: Dict[str, Any]) -> TaskRes:
             delivered_at=task_dict["delivered_at"],
             ttl=task_dict["ttl"],
             ancestry=task_dict["ancestry"].split(","),
-            legacy_client_message=client_message,
+            task_type=task_dict["task_type"],
+            recordset=recordset,
         ),
     )
     return result
diff --git a/src/py/flwr/server/state/sqlite_state_test.py b/src/py/flwr/server/superlink/state/sqlite_state_test.py
similarity index 83%
rename from src/py/flwr/server/state/sqlite_state_test.py
rename to src/py/flwr/server/superlink/state/sqlite_state_test.py
index da8fead1438e..9eef71e396e3 100644
--- a/src/py/flwr/server/state/sqlite_state_test.py
+++ b/src/py/flwr/server/superlink/state/sqlite_state_test.py
@@ -13,12 +13,12 @@
 # limitations under the License.
 # ==============================================================================
 """Test for utility functions."""
-# pylint: disable=no-self-use, invalid-name, disable=R0904
+# pylint: disable=invalid-name, disable=R0904
 
 import unittest
 
-from flwr.server.state.sqlite_state import task_ins_to_dict
-from flwr.server.state.state_test import create_task_ins
+from flwr.server.superlink.state.sqlite_state import task_ins_to_dict
+from flwr.server.superlink.state.state_test import create_task_ins
 
 
 class SqliteStateTest(unittest.TestCase):
@@ -27,11 +27,11 @@ class SqliteStateTest(unittest.TestCase):
     def test_ins_res_to_dict(self) -> None:
         """Check if all required keys are included in return value."""
         # Prepare
-        ins_res = create_task_ins(consumer_node_id=1, anonymous=True, workload_id=0)
+        ins_res = create_task_ins(consumer_node_id=1, anonymous=True, run_id=0)
         expected_keys = [
             "task_id",
             "group_id",
-            "workload_id",
+            "run_id",
             "producer_anonymous",
             "producer_node_id",
             "consumer_anonymous",
@@ -40,8 +40,8 @@ def test_ins_res_to_dict(self) -> None:
             "delivered_at",
             "ttl",
             "ancestry",
-            "legacy_server_message",
-            "legacy_client_message",
+            "task_type",
+            "recordset",
         ]
 
         # Execute
diff --git a/src/py/flwr/server/state/state.py b/src/py/flwr/server/superlink/state/state.py
similarity index 93%
rename from src/py/flwr/server/state/state.py
rename to src/py/flwr/server/superlink/state/state.py
index fd8bbc8e8e25..9337ae6d8624 100644
--- a/src/py/flwr/server/state/state.py
+++ b/src/py/flwr/server/superlink/state/state.py
@@ -19,7 +19,7 @@
 from typing import List, Optional, Set
 from uuid import UUID
 
-from flwr.proto.task_pb2 import TaskIns, TaskRes
+from flwr.proto.task_pb2 import TaskIns, TaskRes  # pylint: disable=E0611
 
 
 class State(abc.ABC):
@@ -43,7 +43,7 @@ def store_task_ins(self, task_ins: TaskIns) -> Optional[UUID]:
         If `task_ins.task.consumer.anonymous` is `False`, then
         `task_ins.task.consumer.node_id` MUST be set (not 0)
 
-        If `task_ins.workload_id` is invalid, then
+        If `task_ins.run_id` is invalid, then
         storing the `task_ins` MUST fail.
         """
 
@@ -92,7 +92,7 @@ def store_task_res(self, task_res: TaskRes) -> Optional[UUID]:
         If `task_res.task.consumer.anonymous` is `False`, then
         `task_res.task.consumer.node_id` MUST be set (not 0)
 
-        If `task_res.workload_id` is invalid, then
+        If `task_res.run_id` is invalid, then
         storing the `task_res` MUST fail.
         """
 
@@ -140,15 +140,15 @@ def delete_node(self, node_id: int) -> None:
         """Remove `node_id` from state."""
 
     @abc.abstractmethod
-    def get_nodes(self, workload_id: int) -> Set[int]:
+    def get_nodes(self, run_id: int) -> Set[int]:
         """Retrieve all currently stored node IDs as a set.
 
         Constraints
         -----------
-        If the provided `workload_id` does not exist or has no matching nodes,
+        If the provided `run_id` does not exist or has no matching nodes,
         an empty `Set` MUST be returned.
         """
 
     @abc.abstractmethod
-    def create_workload(self) -> int:
-        """Create one workload."""
+    def create_run(self) -> int:
+        """Create one run."""
diff --git a/src/py/flwr/server/state/state_factory.py b/src/py/flwr/server/superlink/state/state_factory.py
similarity index 100%
rename from src/py/flwr/server/state/state_factory.py
rename to src/py/flwr/server/superlink/state/state_factory.py
diff --git a/src/py/flwr/server/state/state_test.py b/src/py/flwr/server/superlink/state/state_test.py
similarity index 81%
rename from src/py/flwr/server/state/state_test.py
rename to src/py/flwr/server/superlink/state/state_test.py
index 59299451c3d8..d0470a7ce7f7 100644
--- a/src/py/flwr/server/state/state_test.py
+++ b/src/py/flwr/server/superlink/state/state_test.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 # ==============================================================================
 """Tests all state implemenations have to conform to."""
-# pylint: disable=no-self-use, invalid-name, disable=R0904
+# pylint: disable=invalid-name, disable=R0904
 
 import tempfile
 import unittest
@@ -22,10 +22,10 @@
 from typing import List
 from uuid import uuid4
 
-from flwr.proto.node_pb2 import Node
-from flwr.proto.task_pb2 import Task, TaskIns, TaskRes
-from flwr.proto.transport_pb2 import ClientMessage, ServerMessage
-from flwr.server.state import InMemoryState, SqliteState, State
+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
+from flwr.server.superlink.state import InMemoryState, SqliteState, State
 
 
 class StateTest(unittest.TestCase):
@@ -66,9 +66,9 @@ def test_store_task_ins_one(self) -> None:
         # Prepare
         consumer_node_id = 1
         state = self.state_factory()
-        workload_id = state.create_workload()
+        run_id = state.create_run()
         task_ins = create_task_ins(
-            consumer_node_id=consumer_node_id, anonymous=False, workload_id=workload_id
+            consumer_node_id=consumer_node_id, anonymous=False, run_id=run_id
         )
 
         assert task_ins.task.created_at == ""  # pylint: disable=no-member
@@ -108,15 +108,15 @@ def test_store_and_delete_tasks(self) -> None:
         # Prepare
         consumer_node_id = 1
         state = self.state_factory()
-        workload_id = state.create_workload()
+        run_id = state.create_run()
         task_ins_0 = create_task_ins(
-            consumer_node_id=consumer_node_id, anonymous=False, workload_id=workload_id
+            consumer_node_id=consumer_node_id, anonymous=False, run_id=run_id
         )
         task_ins_1 = create_task_ins(
-            consumer_node_id=consumer_node_id, anonymous=False, workload_id=workload_id
+            consumer_node_id=consumer_node_id, anonymous=False, run_id=run_id
         )
         task_ins_2 = create_task_ins(
-            consumer_node_id=consumer_node_id, anonymous=False, workload_id=workload_id
+            consumer_node_id=consumer_node_id, anonymous=False, run_id=run_id
         )
 
         # Insert three TaskIns
@@ -136,7 +136,7 @@ def test_store_and_delete_tasks(self) -> None:
             producer_node_id=100,
             anonymous=False,
             ancestry=[str(task_id_0)],
-            workload_id=workload_id,
+            run_id=run_id,
         )
 
         _ = state.store_task_res(task_res=task_res_0)
@@ -147,7 +147,7 @@ def test_store_and_delete_tasks(self) -> None:
             producer_node_id=100,
             anonymous=False,
             ancestry=[str(task_id_1)],
-            workload_id=workload_id,
+            run_id=run_id,
         )
         _ = state.store_task_res(task_res=task_res_1)
 
@@ -182,10 +182,8 @@ def test_task_ins_store_anonymous_and_retrieve_anonymous(self) -> None:
         """
         # Prepare
         state: State = self.state_factory()
-        workload_id = state.create_workload()
-        task_ins = create_task_ins(
-            consumer_node_id=0, anonymous=True, workload_id=workload_id
-        )
+        run_id = state.create_run()
+        task_ins = create_task_ins(consumer_node_id=0, anonymous=True, run_id=run_id)
 
         # Execute
         task_ins_uuid = state.store_task_ins(task_ins)
@@ -199,10 +197,8 @@ def test_task_ins_store_anonymous_and_fail_retrieving_identitiy(self) -> None:
         """Store anonymous TaskIns and fail to retrieve it."""
         # Prepare
         state: State = self.state_factory()
-        workload_id = state.create_workload()
-        task_ins = create_task_ins(
-            consumer_node_id=0, anonymous=True, workload_id=workload_id
-        )
+        run_id = state.create_run()
+        task_ins = create_task_ins(consumer_node_id=0, anonymous=True, run_id=run_id)
 
         # Execute
         _ = state.store_task_ins(task_ins)
@@ -215,10 +211,8 @@ def test_task_ins_store_identity_and_fail_retrieving_anonymous(self) -> None:
         """Store identity TaskIns and fail retrieving it as anonymous."""
         # Prepare
         state: State = self.state_factory()
-        workload_id = state.create_workload()
-        task_ins = create_task_ins(
-            consumer_node_id=1, anonymous=False, workload_id=workload_id
-        )
+        run_id = state.create_run()
+        task_ins = create_task_ins(consumer_node_id=1, anonymous=False, run_id=run_id)
 
         # Execute
         _ = state.store_task_ins(task_ins)
@@ -231,10 +225,8 @@ def test_task_ins_store_identity_and_retrieve_identity(self) -> None:
         """Store identity TaskIns and retrieve it."""
         # Prepare
         state: State = self.state_factory()
-        workload_id = state.create_workload()
-        task_ins = create_task_ins(
-            consumer_node_id=1, anonymous=False, workload_id=workload_id
-        )
+        run_id = state.create_run()
+        task_ins = create_task_ins(consumer_node_id=1, anonymous=False, run_id=run_id)
 
         # Execute
         task_ins_uuid = state.store_task_ins(task_ins)
@@ -250,10 +242,8 @@ def test_task_ins_store_delivered_and_fail_retrieving(self) -> None:
         """Fail retrieving delivered task."""
         # Prepare
         state: State = self.state_factory()
-        workload_id = state.create_workload()
-        task_ins = create_task_ins(
-            consumer_node_id=1, anonymous=False, workload_id=workload_id
-        )
+        run_id = state.create_run()
+        task_ins = create_task_ins(consumer_node_id=1, anonymous=False, run_id=run_id)
 
         # Execute
         _ = state.store_task_ins(task_ins)
@@ -278,13 +268,11 @@ def test_get_task_ins_limit_throws_for_limit_zero(self) -> None:
         with self.assertRaises(AssertionError):
             state.get_task_ins(node_id=1, limit=0)
 
-    def test_task_ins_store_invalid_workload_id_and_fail(self) -> None:
-        """Store TaskIns with invalid workload_id and fail."""
+    def test_task_ins_store_invalid_run_id_and_fail(self) -> None:
+        """Store TaskIns with invalid run_id and fail."""
         # Prepare
         state: State = self.state_factory()
-        task_ins = create_task_ins(
-            consumer_node_id=0, anonymous=True, workload_id=61016
-        )
+        task_ins = create_task_ins(consumer_node_id=0, anonymous=True, run_id=61016)
 
         # Execute
         task_id = state.store_task_ins(task_ins)
@@ -297,13 +285,13 @@ def test_task_res_store_and_retrieve_by_task_ins_id(self) -> None:
         """Store TaskRes retrieve it by task_ins_id."""
         # Prepare
         state: State = self.state_factory()
-        workload_id = state.create_workload()
+        run_id = state.create_run()
         task_ins_id = uuid4()
         task_res = create_task_res(
             producer_node_id=0,
             anonymous=True,
             ancestry=[str(task_ins_id)],
-            workload_id=workload_id,
+            run_id=run_id,
         )
 
         # Execute
@@ -318,10 +306,10 @@ def test_node_ids_initial_state(self) -> None:
         """Test retrieving all node_ids and empty initial state."""
         # Prepare
         state: State = self.state_factory()
-        workload_id = state.create_workload()
+        run_id = state.create_run()
 
         # Execute
-        retrieved_node_ids = state.get_nodes(workload_id)
+        retrieved_node_ids = state.get_nodes(run_id)
 
         # Assert
         assert len(retrieved_node_ids) == 0
@@ -330,13 +318,13 @@ def test_create_node_and_get_nodes(self) -> None:
         """Test creating a client node."""
         # Prepare
         state: State = self.state_factory()
-        workload_id = state.create_workload()
+        run_id = state.create_run()
         node_ids = []
 
         # Execute
         for _ in range(10):
             node_ids.append(state.create_node())
-        retrieved_node_ids = state.get_nodes(workload_id)
+        retrieved_node_ids = state.get_nodes(run_id)
 
         # Assert
         for i in retrieved_node_ids:
@@ -346,26 +334,26 @@ def test_delete_node(self) -> None:
         """Test deleting a client node."""
         # Prepare
         state: State = self.state_factory()
-        workload_id = state.create_workload()
+        run_id = state.create_run()
         node_id = state.create_node()
 
         # Execute
         state.delete_node(node_id)
-        retrieved_node_ids = state.get_nodes(workload_id)
+        retrieved_node_ids = state.get_nodes(run_id)
 
         # Assert
         assert len(retrieved_node_ids) == 0
 
-    def test_get_nodes_invalid_workload_id(self) -> None:
-        """Test retrieving all node_ids with invalid workload_id."""
+    def test_get_nodes_invalid_run_id(self) -> None:
+        """Test retrieving all node_ids with invalid run_id."""
         # Prepare
         state: State = self.state_factory()
-        state.create_workload()
-        invalid_workload_id = 61016
+        state.create_run()
+        invalid_run_id = 61016
         state.create_node()
 
         # Execute
-        retrieved_node_ids = state.get_nodes(invalid_workload_id)
+        retrieved_node_ids = state.get_nodes(invalid_run_id)
 
         # Assert
         assert len(retrieved_node_ids) == 0
@@ -374,13 +362,9 @@ def test_num_task_ins(self) -> None:
         """Test if num_tasks returns correct number of not delivered task_ins."""
         # Prepare
         state: State = self.state_factory()
-        workload_id = state.create_workload()
-        task_0 = create_task_ins(
-            consumer_node_id=0, anonymous=True, workload_id=workload_id
-        )
-        task_1 = create_task_ins(
-            consumer_node_id=0, anonymous=True, workload_id=workload_id
-        )
+        run_id = state.create_run()
+        task_0 = create_task_ins(consumer_node_id=0, anonymous=True, run_id=run_id)
+        task_1 = create_task_ins(consumer_node_id=0, anonymous=True, run_id=run_id)
 
         # Store two tasks
         state.store_task_ins(task_0)
@@ -396,12 +380,12 @@ def test_num_task_res(self) -> None:
         """Test if num_tasks returns correct number of not delivered task_res."""
         # Prepare
         state: State = self.state_factory()
-        workload_id = state.create_workload()
+        run_id = state.create_run()
         task_0 = create_task_res(
-            producer_node_id=0, anonymous=True, ancestry=["1"], workload_id=workload_id
+            producer_node_id=0, anonymous=True, ancestry=["1"], run_id=run_id
         )
         task_1 = create_task_res(
-            producer_node_id=0, anonymous=True, ancestry=["1"], workload_id=workload_id
+            producer_node_id=0, anonymous=True, ancestry=["1"], run_id=run_id
         )
 
         # Store two tasks
@@ -418,7 +402,7 @@ def test_num_task_res(self) -> None:
 def create_task_ins(
     consumer_node_id: int,
     anonymous: bool,
-    workload_id: int,
+    run_id: int,
     delivered_at: str = "",
 ) -> TaskIns:
     """Create a TaskIns for testing."""
@@ -429,14 +413,13 @@ def create_task_ins(
     task = TaskIns(
         task_id="",
         group_id="",
-        workload_id=workload_id,
+        run_id=run_id,
         task=Task(
             delivered_at=delivered_at,
             producer=Node(node_id=0, anonymous=True),
             consumer=consumer,
-            legacy_server_message=ServerMessage(
-                reconnect_ins=ServerMessage.ReconnectIns()
-            ),
+            task_type="mock",
+            recordset=RecordSet(parameters={}, metrics={}, configs={}),
         ),
     )
     return task
@@ -446,20 +429,19 @@ def create_task_res(
     producer_node_id: int,
     anonymous: bool,
     ancestry: List[str],
-    workload_id: int,
+    run_id: int,
 ) -> TaskRes:
     """Create a TaskRes for testing."""
     task_res = TaskRes(
         task_id="",
         group_id="",
-        workload_id=workload_id,
+        run_id=run_id,
         task=Task(
             producer=Node(node_id=producer_node_id, anonymous=anonymous),
             consumer=Node(node_id=0, anonymous=True),
             ancestry=ancestry,
-            legacy_client_message=ClientMessage(
-                disconnect_res=ClientMessage.DisconnectRes()
-            ),
+            task_type="mock",
+            recordset=RecordSet(parameters={}, metrics={}, configs={}),
         ),
     )
     return task_res
diff --git a/src/py/flwr/server/typing.py b/src/py/flwr/server/typing.py
new file mode 100644
index 000000000000..01143af74392
--- /dev/null
+++ b/src/py/flwr/server/typing.py
@@ -0,0 +1,25 @@
+# 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.
+# ==============================================================================
+"""Custom types for Flower servers."""
+
+
+from typing import Callable
+
+from flwr.common import Context
+
+from .driver import Driver
+
+ServerAppCallable = Callable[[Driver, Context], None]
+Workflow = Callable[[Driver, Context], None]
diff --git a/src/py/flwr/server/utils/validator.py b/src/py/flwr/server/utils/validator.py
index fd89a01e4a4e..f9b271beafdc 100644
--- a/src/py/flwr/server/utils/validator.py
+++ b/src/py/flwr/server/utils/validator.py
@@ -17,7 +17,7 @@
 
 from typing import List, Union
 
-from flwr.proto.task_pb2 import TaskIns, TaskRes
+from flwr.proto.task_pb2 import TaskIns, TaskRes  # pylint: disable=E0611
 
 
 # pylint: disable-next=too-many-branches,too-many-statements
@@ -64,21 +64,10 @@ def validate_task_ins_or_res(tasks_ins_res: Union[TaskIns, TaskRes]) -> List[str
             validation_errors.append("non-anonymous consumer MUST provide a `node_id`")
 
         # Content check
-        has_fields = {
-            "sa": tasks_ins_res.task.HasField("sa"),
-            "legacy_server_message": tasks_ins_res.task.HasField(
-                "legacy_server_message"
-            ),
-        }
-        if not (has_fields["sa"] or has_fields["legacy_server_message"]):
-            err_msg = ", ".join([f"`{field}`" for field in has_fields])
-            validation_errors.append(
-                f"`task` in `TaskIns` must set at least one of fields {{{err_msg}}}"
-            )
-        if has_fields[
-            "legacy_server_message"
-        ] and not tasks_ins_res.task.legacy_server_message.HasField("msg"):
-            validation_errors.append("`legacy_server_message` does not set field `msg`")
+        if tasks_ins_res.task.task_type == "":
+            validation_errors.append("`task_type` MUST be set")
+        if not tasks_ins_res.task.HasField("recordset"):
+            validation_errors.append("`recordset` MUST be set")
 
         # Ancestors
         if len(tasks_ins_res.task.ancestry) != 0:
@@ -115,21 +104,10 @@ def validate_task_ins_or_res(tasks_ins_res: Union[TaskIns, TaskRes]) -> List[str
             validation_errors.append("non-anonymous consumer MUST provide a `node_id`")
 
         # Content check
-        has_fields = {
-            "sa": tasks_ins_res.task.HasField("sa"),
-            "legacy_client_message": tasks_ins_res.task.HasField(
-                "legacy_client_message"
-            ),
-        }
-        if not (has_fields["sa"] or has_fields["legacy_client_message"]):
-            err_msg = ", ".join([f"`{field}`" for field in has_fields])
-            validation_errors.append(
-                f"`task` in `TaskRes` must set at least one of fields {{{err_msg}}}"
-            )
-        if has_fields[
-            "legacy_client_message"
-        ] and not tasks_ins_res.task.legacy_client_message.HasField("msg"):
-            validation_errors.append("`legacy_client_message` does not set field `msg`")
+        if tasks_ins_res.task.task_type == "":
+            validation_errors.append("`task_type` MUST be set")
+        if not tasks_ins_res.task.HasField("recordset"):
+            validation_errors.append("`recordset` MUST be set")
 
         # Ancestors
         if len(tasks_ins_res.task.ancestry) == 0:
diff --git a/src/py/flwr/server/utils/validator_test.py b/src/py/flwr/server/utils/validator_test.py
index cab51fbf46de..8e0849508020 100644
--- a/src/py/flwr/server/utils/validator_test.py
+++ b/src/py/flwr/server/utils/validator_test.py
@@ -18,9 +18,9 @@
 import unittest
 from typing import List, Tuple
 
-from flwr.proto.node_pb2 import Node
-from flwr.proto.task_pb2 import SecureAggregation, Task, TaskIns, TaskRes
-from flwr.proto.transport_pb2 import ClientMessage, ServerMessage
+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
 
 from .validator import validate_task_ins_or_res
 
@@ -37,16 +37,12 @@ def test_task_ins(self) -> None:
 
         # Execute & Assert
         for consumer_node_id, anonymous in valid_ins:
-            msg = create_task_ins(
-                consumer_node_id, anonymous, has_legacy_server_message=True
-            )
+            msg = create_task_ins(consumer_node_id, anonymous)
             val_errors = validate_task_ins_or_res(msg)
             self.assertFalse(val_errors)
 
         for consumer_node_id, anonymous in invalid_ins:
-            msg = create_task_ins(
-                consumer_node_id, anonymous, has_legacy_server_message=True
-            )
+            msg = create_task_ins(consumer_node_id, anonymous)
             val_errors = validate_task_ins_or_res(msg)
             self.assertTrue(val_errors)
 
@@ -70,61 +66,19 @@ def test_is_valid_task_res(self) -> None:
 
         # Execute & Assert
         for producer_node_id, anonymous, ancestry in valid_res:
-            msg = create_task_res(
-                producer_node_id, anonymous, ancestry, has_legacy_client_message=True
-            )
+            msg = create_task_res(producer_node_id, anonymous, ancestry)
             val_errors = validate_task_ins_or_res(msg)
             self.assertFalse(val_errors)
 
         for producer_node_id, anonymous, ancestry in invalid_res:
-            msg = create_task_res(
-                producer_node_id, anonymous, ancestry, has_legacy_client_message=True
-            )
+            msg = create_task_res(producer_node_id, anonymous, ancestry)
             val_errors = validate_task_ins_or_res(msg)
             self.assertTrue(val_errors, (producer_node_id, anonymous, ancestry))
 
-    def test_task_ins_secure_aggregation(self) -> None:
-        """Test is_valid task_ins for Secure Aggregation."""
-        # Prepare
-        # (has_legacy_server_message, has_sa)
-        valid_ins = [(True, True), (False, True)]
-        invalid_ins = [(False, False)]
-
-        # Execute & Assert
-        for has_legacy_server_message, has_sa in valid_ins:
-            msg = create_task_ins(1, False, has_legacy_server_message, has_sa)
-            val_errors = validate_task_ins_or_res(msg)
-            self.assertFalse(val_errors)
-
-        for has_legacy_server_message, has_sa in invalid_ins:
-            msg = create_task_ins(1, False, has_legacy_server_message, has_sa)
-            val_errors = validate_task_ins_or_res(msg)
-            self.assertTrue(val_errors)
-
-    def test_task_res_secure_aggregation(self) -> None:
-        """Test is_valid task_res for Secure Aggregation."""
-        # Prepare
-        # (has_legacy_server_message, has_sa)
-        valid_res = [(True, True), (False, True)]
-        invalid_res = [(False, False)]
-
-        # Execute & Assert
-        for has_legacy_client_message, has_sa in valid_res:
-            msg = create_task_res(0, True, ["1"], has_legacy_client_message, has_sa)
-            val_errors = validate_task_ins_or_res(msg)
-            self.assertFalse(val_errors)
-
-        for has_legacy_client_message, has_sa in invalid_res:
-            msg = create_task_res(0, True, ["1"], has_legacy_client_message, has_sa)
-            val_errors = validate_task_ins_or_res(msg)
-            self.assertTrue(val_errors)
-
 
 def create_task_ins(
     consumer_node_id: int,
     anonymous: bool,
-    has_legacy_server_message: bool = False,
-    has_sa: bool = False,
     delivered_at: str = "",
 ) -> TaskIns:
     """Create a TaskIns for testing."""
@@ -135,17 +89,13 @@ def create_task_ins(
     task = TaskIns(
         task_id="",
         group_id="",
-        workload_id=0,
+        run_id=0,
         task=Task(
             delivered_at=delivered_at,
             producer=Node(node_id=0, anonymous=True),
             consumer=consumer,
-            legacy_server_message=ServerMessage(
-                reconnect_ins=ServerMessage.ReconnectIns()
-            )
-            if has_legacy_server_message
-            else None,
-            sa=SecureAggregation(named_values={}) if has_sa else None,
+            task_type="mock",
+            recordset=RecordSet(parameters={}, metrics={}, configs={}),
         ),
     )
     return task
@@ -155,24 +105,18 @@ def create_task_res(
     producer_node_id: int,
     anonymous: bool,
     ancestry: List[str],
-    has_legacy_client_message: bool = False,
-    has_sa: bool = False,
 ) -> TaskRes:
     """Create a TaskRes for testing."""
     task_res = TaskRes(
         task_id="",
         group_id="",
-        workload_id=0,
+        run_id=0,
         task=Task(
             producer=Node(node_id=producer_node_id, anonymous=anonymous),
             consumer=Node(node_id=0, anonymous=True),
             ancestry=ancestry,
-            legacy_client_message=ClientMessage(
-                disconnect_res=ClientMessage.DisconnectRes()
-            )
-            if has_legacy_client_message
-            else None,
-            sa=SecureAggregation(named_values={}) if has_sa else None,
+            task_type="mock",
+            recordset=RecordSet(parameters={}, metrics={}, configs={}),
         ),
     )
     return task_res
diff --git a/src/py/flwr/server/workflow/__init__.py b/src/py/flwr/server/workflow/__init__.py
new file mode 100644
index 000000000000..31dee89a185d
--- /dev/null
+++ b/src/py/flwr/server/workflow/__init__.py
@@ -0,0 +1,25 @@
+# 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.
+# ==============================================================================
+"""Workflows."""
+
+
+from .default_workflows import DefaultWorkflow
+from .secure_aggregation import SecAggPlusWorkflow, SecAggWorkflow
+
+__all__ = [
+    "DefaultWorkflow",
+    "SecAggPlusWorkflow",
+    "SecAggWorkflow",
+]
diff --git a/src/py/flwr/server/workflow/constant.py b/src/py/flwr/server/workflow/constant.py
new file mode 100644
index 000000000000..068e05b27e12
--- /dev/null
+++ b/src/py/flwr/server/workflow/constant.py
@@ -0,0 +1,32 @@
+# 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 default workflows."""
+
+
+from __future__ import annotations
+
+MAIN_CONFIGS_RECORD = "config"
+MAIN_PARAMS_RECORD = "parameters"
+
+
+class Key:
+    """Constants for default workflows."""
+
+    CURRENT_ROUND = "current_round"
+    START_TIME = "start_time"
+
+    def __new__(cls) -> Key:
+        """Prevent instantiation."""
+        raise TypeError(f"{cls.__name__} cannot be instantiated.")
diff --git a/src/py/flwr/server/workflow/default_workflows.py b/src/py/flwr/server/workflow/default_workflows.py
new file mode 100644
index 000000000000..876ae56dcadc
--- /dev/null
+++ b/src/py/flwr/server/workflow/default_workflows.py
@@ -0,0 +1,347 @@
+# 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.
+# ==============================================================================
+"""Legacy default workflows."""
+
+
+import io
+import timeit
+from logging import INFO
+from typing import Optional, cast
+
+import flwr.common.recordset_compat as compat
+from flwr.common import ConfigsRecord, Context, GetParametersIns, log
+from flwr.common.constant import MessageType, MessageTypeLegacy
+
+from ..compat.app_utils import start_update_client_manager_thread
+from ..compat.legacy_context import LegacyContext
+from ..driver import Driver
+from ..typing import Workflow
+from .constant import MAIN_CONFIGS_RECORD, MAIN_PARAMS_RECORD, Key
+
+
+class DefaultWorkflow:
+    """Default workflow in Flower."""
+
+    def __init__(
+        self,
+        fit_workflow: Optional[Workflow] = None,
+        evaluate_workflow: Optional[Workflow] = None,
+    ) -> None:
+        if fit_workflow is None:
+            fit_workflow = default_fit_workflow
+        if evaluate_workflow is None:
+            evaluate_workflow = default_evaluate_workflow
+        self.fit_workflow: Workflow = fit_workflow
+        self.evaluate_workflow: Workflow = evaluate_workflow
+
+    def __call__(self, driver: Driver, context: Context) -> None:
+        """Execute the workflow."""
+        if not isinstance(context, LegacyContext):
+            raise TypeError(
+                f"Expect a LegacyContext, but get {type(context).__name__}."
+            )
+
+        # Start the thread updating nodes
+        thread, f_stop = start_update_client_manager_thread(
+            driver, context.client_manager
+        )
+
+        # Initialize parameters
+        log(INFO, "[INIT]")
+        default_init_params_workflow(driver, context)
+
+        # Run federated learning for num_rounds
+        start_time = timeit.default_timer()
+        cfg = ConfigsRecord()
+        cfg[Key.START_TIME] = start_time
+        context.state.configs_records[MAIN_CONFIGS_RECORD] = cfg
+
+        for current_round in range(1, context.config.num_rounds + 1):
+            log(INFO, "")
+            log(INFO, "[ROUND %s]", current_round)
+            cfg[Key.CURRENT_ROUND] = current_round
+
+            # Fit round
+            self.fit_workflow(driver, context)
+
+            # Centralized evaluation
+            default_centralized_evaluation_workflow(driver, context)
+
+            # Evaluate round
+            self.evaluate_workflow(driver, context)
+
+        # Bookkeeping and log results
+        end_time = timeit.default_timer()
+        elapsed = end_time - start_time
+        hist = context.history
+        log(INFO, "")
+        log(INFO, "[SUMMARY]")
+        log(INFO, "Run finished %s rounds in %.2fs", context.config.num_rounds, elapsed)
+        for idx, line in enumerate(io.StringIO(str(hist))):
+            if idx == 0:
+                log(INFO, "%s", line.strip("\n"))
+            else:
+                log(INFO, "\t%s", line.strip("\n"))
+        log(INFO, "")
+
+        # Terminate the thread
+        f_stop.set()
+        thread.join()
+
+
+def default_init_params_workflow(driver: Driver, context: Context) -> None:
+    """Execute the default workflow for parameters initialization."""
+    if not isinstance(context, LegacyContext):
+        raise TypeError(f"Expect a LegacyContext, but get {type(context).__name__}.")
+
+    parameters = context.strategy.initialize_parameters(
+        client_manager=context.client_manager
+    )
+    if parameters is not None:
+        log(INFO, "Using initial global parameters provided by strategy")
+        paramsrecord = compat.parameters_to_parametersrecord(
+            parameters, keep_input=True
+        )
+    else:
+        # Get initial parameters from one of the clients
+        log(INFO, "Requesting initial parameters from one random client")
+        random_client = context.client_manager.sample(1)[0]
+        # Send GetParametersIns and get the response
+        content = compat.getparametersins_to_recordset(GetParametersIns({}))
+        messages = driver.send_and_receive(
+            [
+                driver.create_message(
+                    content=content,
+                    message_type=MessageTypeLegacy.GET_PARAMETERS,
+                    dst_node_id=random_client.node_id,
+                    group_id="0",
+                    ttl="",
+                )
+            ]
+        )
+        log(INFO, "Received initial parameters from one random client")
+        msg = list(messages)[0]
+        paramsrecord = next(iter(msg.content.parameters_records.values()))
+
+    context.state.parameters_records[MAIN_PARAMS_RECORD] = paramsrecord
+
+    # Evaluate initial parameters
+    log(INFO, "Evaluating initial global parameters")
+    parameters = compat.parametersrecord_to_parameters(paramsrecord, keep_input=True)
+    res = context.strategy.evaluate(0, parameters=parameters)
+    if res is not None:
+        log(
+            INFO,
+            "initial parameters (loss, other metrics): %s, %s",
+            res[0],
+            res[1],
+        )
+        context.history.add_loss_centralized(server_round=0, loss=res[0])
+        context.history.add_metrics_centralized(server_round=0, metrics=res[1])
+
+
+def default_centralized_evaluation_workflow(_: Driver, context: Context) -> None:
+    """Execute the default workflow for centralized evaluation."""
+    if not isinstance(context, LegacyContext):
+        raise TypeError(f"Expect a LegacyContext, but get {type(context).__name__}.")
+
+    # Retrieve current_round and start_time from the context
+    cfg = context.state.configs_records[MAIN_CONFIGS_RECORD]
+    current_round = cast(int, cfg[Key.CURRENT_ROUND])
+    start_time = cast(float, cfg[Key.START_TIME])
+
+    # Centralized evaluation
+    parameters = compat.parametersrecord_to_parameters(
+        record=context.state.parameters_records[MAIN_PARAMS_RECORD],
+        keep_input=True,
+    )
+    res_cen = context.strategy.evaluate(current_round, parameters=parameters)
+    if res_cen is not None:
+        loss_cen, metrics_cen = res_cen
+        log(
+            INFO,
+            "fit progress: (%s, %s, %s, %s)",
+            current_round,
+            loss_cen,
+            metrics_cen,
+            timeit.default_timer() - start_time,
+        )
+        context.history.add_loss_centralized(server_round=current_round, loss=loss_cen)
+        context.history.add_metrics_centralized(
+            server_round=current_round, metrics=metrics_cen
+        )
+
+
+def default_fit_workflow(  # pylint: disable=R0914
+    driver: Driver, context: Context
+) -> None:
+    """Execute the default workflow for a single fit round."""
+    if not isinstance(context, LegacyContext):
+        raise TypeError(f"Expect a LegacyContext, but get {type(context).__name__}.")
+
+    # Get current_round and parameters
+    cfg = context.state.configs_records[MAIN_CONFIGS_RECORD]
+    current_round = cast(int, cfg[Key.CURRENT_ROUND])
+    parametersrecord = context.state.parameters_records[MAIN_PARAMS_RECORD]
+    parameters = compat.parametersrecord_to_parameters(
+        parametersrecord, keep_input=True
+    )
+
+    # Get clients and their respective instructions from strategy
+    client_instructions = context.strategy.configure_fit(
+        server_round=current_round,
+        parameters=parameters,
+        client_manager=context.client_manager,
+    )
+
+    if not client_instructions:
+        log(INFO, "configure_fit: no clients selected, cancel")
+        return
+    log(
+        INFO,
+        "configure_fit: strategy sampled %s clients (out of %s)",
+        len(client_instructions),
+        context.client_manager.num_available(),
+    )
+
+    # Build dictionary mapping node_id to ClientProxy
+    node_id_to_proxy = {proxy.node_id: proxy for proxy, _ in client_instructions}
+
+    # Build out messages
+    out_messages = [
+        driver.create_message(
+            content=compat.fitins_to_recordset(fitins, True),
+            message_type=MessageType.TRAIN,
+            dst_node_id=proxy.node_id,
+            group_id=str(current_round),
+            ttl="",
+        )
+        for proxy, fitins in client_instructions
+    ]
+
+    # Send instructions to clients and
+    # collect `fit` results from all clients participating in this round
+    messages = list(driver.send_and_receive(out_messages))
+    del out_messages
+    num_failures = len([msg for msg in messages if msg.has_error()])
+
+    # No exception/failure handling currently
+    log(
+        INFO,
+        "aggregate_fit: received %s results and %s failures",
+        len(messages) - num_failures,
+        num_failures,
+    )
+
+    # Aggregate training results
+    results = [
+        (
+            node_id_to_proxy[msg.metadata.src_node_id],
+            compat.recordset_to_fitres(msg.content, False),
+        )
+        for msg in messages
+    ]
+    aggregated_result = context.strategy.aggregate_fit(current_round, results, [])
+    parameters_aggregated, metrics_aggregated = aggregated_result
+
+    # Update the parameters and write history
+    if parameters_aggregated:
+        paramsrecord = compat.parameters_to_parametersrecord(
+            parameters_aggregated, True
+        )
+        context.state.parameters_records[MAIN_PARAMS_RECORD] = paramsrecord
+        context.history.add_metrics_distributed_fit(
+            server_round=current_round, metrics=metrics_aggregated
+        )
+
+
+def default_evaluate_workflow(driver: Driver, context: Context) -> None:
+    """Execute the default workflow for a single evaluate round."""
+    if not isinstance(context, LegacyContext):
+        raise TypeError(f"Expect a LegacyContext, but get {type(context).__name__}.")
+
+    # Get current_round and parameters
+    cfg = context.state.configs_records[MAIN_CONFIGS_RECORD]
+    current_round = cast(int, cfg[Key.CURRENT_ROUND])
+    parametersrecord = context.state.parameters_records[MAIN_PARAMS_RECORD]
+    parameters = compat.parametersrecord_to_parameters(
+        parametersrecord, keep_input=True
+    )
+
+    # Get clients and their respective instructions from strategy
+    client_instructions = context.strategy.configure_evaluate(
+        server_round=current_round,
+        parameters=parameters,
+        client_manager=context.client_manager,
+    )
+    if not client_instructions:
+        log(INFO, "configure_evaluate: no clients selected, skipping evaluation")
+        return
+    log(
+        INFO,
+        "configure_evaluate: strategy sampled %s clients (out of %s)",
+        len(client_instructions),
+        context.client_manager.num_available(),
+    )
+
+    # Build dictionary mapping node_id to ClientProxy
+    node_id_to_proxy = {proxy.node_id: proxy for proxy, _ in client_instructions}
+
+    # Build out messages
+    out_messages = [
+        driver.create_message(
+            content=compat.evaluateins_to_recordset(evalins, True),
+            message_type=MessageType.EVALUATE,
+            dst_node_id=proxy.node_id,
+            group_id=str(current_round),
+            ttl="",
+        )
+        for proxy, evalins in client_instructions
+    ]
+
+    # Send instructions to clients and
+    # collect `evaluate` results from all clients participating in this round
+    messages = list(driver.send_and_receive(out_messages))
+    del out_messages
+    num_failures = len([msg for msg in messages if msg.has_error()])
+
+    # No exception/failure handling currently
+    log(
+        INFO,
+        "aggregate_evaluate: received %s results and %s failures",
+        len(messages) - num_failures,
+        num_failures,
+    )
+
+    # Aggregate the evaluation results
+    results = [
+        (
+            node_id_to_proxy[msg.metadata.src_node_id],
+            compat.recordset_to_evaluateres(msg.content),
+        )
+        for msg in messages
+    ]
+    aggregated_result = context.strategy.aggregate_evaluate(current_round, results, [])
+
+    loss_aggregated, metrics_aggregated = aggregated_result
+
+    # Write history
+    if loss_aggregated is not None:
+        context.history.add_loss_distributed(
+            server_round=current_round, loss=loss_aggregated
+        )
+        context.history.add_metrics_distributed(
+            server_round=current_round, metrics=metrics_aggregated
+        )
diff --git a/src/py/flwr/client/secure_aggregation/__init__.py b/src/py/flwr/server/workflow/secure_aggregation/__init__.py
similarity index 72%
rename from src/py/flwr/client/secure_aggregation/__init__.py
rename to src/py/flwr/server/workflow/secure_aggregation/__init__.py
index 37c816a390de..25e2a32da334 100644
--- a/src/py/flwr/client/secure_aggregation/__init__.py
+++ b/src/py/flwr/server/workflow/secure_aggregation/__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.
@@ -12,13 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Secure Aggregation handlers."""
+"""Secure Aggregation workflows."""
 
 
-from .handler import SecureAggregationHandler
-from .secaggplus_handler import SecAggPlusHandler
+from .secagg_workflow import SecAggWorkflow
+from .secaggplus_workflow import SecAggPlusWorkflow
 
 __all__ = [
-    "SecAggPlusHandler",
-    "SecureAggregationHandler",
+    "SecAggPlusWorkflow",
+    "SecAggWorkflow",
 ]
diff --git a/src/py/flwr/server/workflow/secure_aggregation/secagg_workflow.py b/src/py/flwr/server/workflow/secure_aggregation/secagg_workflow.py
new file mode 100644
index 000000000000..f56423e4a0d0
--- /dev/null
+++ b/src/py/flwr/server/workflow/secure_aggregation/secagg_workflow.py
@@ -0,0 +1,112 @@
+# 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.
+# ==============================================================================
+"""Workflow for the SecAgg protocol."""
+
+
+from typing import Optional, Union
+
+from .secaggplus_workflow import SecAggPlusWorkflow
+
+
+class SecAggWorkflow(SecAggPlusWorkflow):
+    """The workflow for the SecAgg protocol.
+
+    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 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
+      key shares.
+    - 'collect masked vectors': Forward encrypted secret key shares to target clients
+      and collect masked model parameters.
+    - 'unmask': Collect secret key shares to decrypt and aggregate the model parameters.
+
+    Only the aggregated model parameters are exposed and passed to
+    `Strategy.aggregate_fit`, ensuring individual data privacy.
+
+    Parameters
+    ----------
+    reconstruction_threshold : Union[int, float]
+        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.
+    max_weight : Optional[float] (default: 1000.0)
+        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.
+    clipping_range : float, optional (default: 8.0)
+        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.
+    quantization_range : int, optional (default: 4194304, this equals 2**22)
+        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.
+    modulus_range : int, optional (default: 4294967296, this equals 2**32)
+        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.
+    timeout : Optional[float] (default: None)
+        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.
+
+    Notes
+    -----
+    - Each client's private key is split into N shares under the SecAgg protocol, where
+      N is the number of selected clients.
+    - Generally, higher `reconstruction_threshold` means better privacy guarantees but
+      less tolerance to dropouts.
+    - Too large `max_weight` may compromise the precision of the quantization.
+    - `modulus_range` must be 2**n and larger than `quantization_range`.
+    - 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_threshold`, and the quantization parameters
+      (`clipping_range`, `quantization_range`, `modulus_range`) play critical roles in
+      balancing privacy, robustness, and efficiency within the SecAgg protocol.
+    """
+
+    def __init__(  # pylint: disable=R0913
+        self,
+        reconstruction_threshold: Union[int, float],
+        *,
+        max_weight: float = 1000.0,
+        clipping_range: float = 8.0,
+        quantization_range: int = 4194304,
+        modulus_range: int = 4294967296,
+        timeout: Optional[float] = None,
+    ) -> None:
+        super().__init__(
+            num_shares=1.0,
+            reconstruction_threshold=reconstruction_threshold,
+            max_weight=max_weight,
+            clipping_range=clipping_range,
+            quantization_range=quantization_range,
+            modulus_range=modulus_range,
+            timeout=timeout,
+        )
diff --git a/src/py/flwr/server/workflow/secure_aggregation/secaggplus_workflow.py b/src/py/flwr/server/workflow/secure_aggregation/secaggplus_workflow.py
new file mode 100644
index 000000000000..42ee9c15f1cd
--- /dev/null
+++ b/src/py/flwr/server/workflow/secure_aggregation/secaggplus_workflow.py
@@ -0,0 +1,673 @@
+# 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.
+# ==============================================================================
+"""Workflow for the SecAgg+ protocol."""
+
+
+import random
+from dataclasses import dataclass, field
+from logging import DEBUG, ERROR, INFO, WARN
+from typing import Dict, List, Optional, Set, Tuple, Union, cast
+
+import flwr.common.recordset_compat as compat
+from flwr.common import (
+    ConfigsRecord,
+    Context,
+    FitRes,
+    Message,
+    MessageType,
+    NDArrays,
+    RecordSet,
+    bytes_to_ndarray,
+    log,
+    ndarrays_to_parameters,
+)
+from flwr.common.secure_aggregation.crypto.shamir import combine_shares
+from flwr.common.secure_aggregation.crypto.symmetric_encryption import (
+    bytes_to_private_key,
+    bytes_to_public_key,
+    generate_shared_key,
+)
+from flwr.common.secure_aggregation.ndarrays_arithmetic import (
+    factor_extract,
+    get_parameters_shape,
+    parameters_addition,
+    parameters_mod,
+    parameters_subtraction,
+)
+from flwr.common.secure_aggregation.quantization import dequantize
+from flwr.common.secure_aggregation.secaggplus_constants import (
+    RECORD_KEY_CONFIGS,
+    Key,
+    Stage,
+)
+from flwr.common.secure_aggregation.secaggplus_utils import pseudo_rand_gen
+from flwr.server.client_proxy import ClientProxy
+from flwr.server.compat.legacy_context import LegacyContext
+from flwr.server.driver import Driver
+
+from ..constant import MAIN_CONFIGS_RECORD, MAIN_PARAMS_RECORD
+from ..constant import Key as WorkflowKey
+
+
+@dataclass
+class WorkflowState:  # pylint: disable=R0902
+    """The state of the SecAgg+ protocol."""
+
+    nid_to_proxies: Dict[int, ClientProxy] = field(default_factory=dict)
+    nid_to_fitins: Dict[int, RecordSet] = field(default_factory=dict)
+    sampled_node_ids: Set[int] = field(default_factory=set)
+    active_node_ids: Set[int] = field(default_factory=set)
+    num_shares: int = 0
+    threshold: int = 0
+    clipping_range: float = 0.0
+    quantization_range: int = 0
+    mod_range: int = 0
+    max_weight: float = 0.0
+    nid_to_neighbours: Dict[int, Set[int]] = field(default_factory=dict)
+    nid_to_publickeys: Dict[int, List[bytes]] = field(default_factory=dict)
+    forward_srcs: Dict[int, List[int]] = field(default_factory=dict)
+    forward_ciphertexts: Dict[int, List[bytes]] = field(default_factory=dict)
+    aggregate_ndarrays: NDArrays = field(default_factory=list)
+    legacy_results: List[Tuple[ClientProxy, FitRes]] = field(default_factory=list)
+
+
+class SecAggPlusWorkflow:
+    """The workflow for the SecAgg+ protocol.
+
+    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 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
+      key shares.
+    - 'collect masked vectors': Forward encrypted secret key shares to target clients
+      and collect masked model parameters.
+    - 'unmask': Collect secret key shares to decrypt and aggregate the model parameters.
+
+    Only the aggregated model parameters are exposed and passed to
+    `Strategy.aggregate_fit`, ensuring individual data privacy.
+
+    Parameters
+    ----------
+    num_shares : Union[int, float]
+        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.
+    reconstruction_threshold : Union[int, float]
+        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.
+    max_weight : Optional[float] (default: 1000.0)
+        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.
+    clipping_range : float, optional (default: 8.0)
+        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.
+    quantization_range : int, optional (default: 4194304, this equals 2**22)
+        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.
+    modulus_range : int, optional (default: 4294967296, this equals 2**32)
+        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.
+    timeout : Optional[float] (default: None)
+        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.
+
+    Notes
+    -----
+    - 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.
+    - Too large `max_weight` may compromise the precision of the quantization.
+    - `modulus_range` must be 2**n and larger than `quantization_range`.
+    - 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.
+    - 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.
+    - `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.
+    """
+
+    def __init__(  # pylint: disable=R0913
+        self,
+        num_shares: Union[int, float],
+        reconstruction_threshold: Union[int, float],
+        *,
+        max_weight: float = 1000.0,
+        clipping_range: float = 8.0,
+        quantization_range: int = 4194304,
+        modulus_range: int = 4294967296,
+        timeout: Optional[float] = None,
+    ) -> None:
+        self.num_shares = num_shares
+        self.reconstruction_threshold = reconstruction_threshold
+        self.max_weight = max_weight
+        self.clipping_range = clipping_range
+        self.quantization_range = quantization_range
+        self.modulus_range = modulus_range
+        self.timeout = timeout
+
+        self._check_init_params()
+
+    def __call__(self, driver: Driver, context: Context) -> None:
+        """Run the SecAgg+ protocol."""
+        if not isinstance(context, LegacyContext):
+            raise TypeError(
+                f"Expect a LegacyContext, but get {type(context).__name__}."
+            )
+        state = WorkflowState()
+
+        steps = (
+            self.setup_stage,
+            self.share_keys_stage,
+            self.collect_masked_vectors_stage,
+            self.unmask_stage,
+        )
+        log(INFO, "Secure aggregation commencing.")
+        for step in steps:
+            if not step(driver, context, state):
+                log(INFO, "Secure aggregation halted.")
+                return
+        log(INFO, "Secure aggregation completed.")
+
+    def _check_init_params(self) -> None:  # pylint: disable=R0912
+        # Check `num_shares`
+        if not isinstance(self.num_shares, (int, float)):
+            raise TypeError("`num_shares` must be of type int or float.")
+        if isinstance(self.num_shares, int):
+            if self.num_shares == 1:
+                self.num_shares = 1.0
+            elif self.num_shares <= 2:
+                raise ValueError("`num_shares` as an integer must be greater than 2.")
+            elif self.num_shares > self.modulus_range / self.quantization_range:
+                log(
+                    WARN,
+                    "A `num_shares` larger than `modulus_range / quantization_range` "
+                    "will potentially cause overflow when computing the aggregated "
+                    "model parameters.",
+                )
+        elif self.num_shares <= 0:
+            raise ValueError("`num_shares` as a float must be greater than 0.")
+
+        # Check `reconstruction_threshold`
+        if not isinstance(self.reconstruction_threshold, (int, float)):
+            raise TypeError("`reconstruction_threshold` must be of type int or float.")
+        if isinstance(self.reconstruction_threshold, int):
+            if self.reconstruction_threshold == 1:
+                self.reconstruction_threshold = 1.0
+            elif isinstance(self.num_shares, int):
+                if self.reconstruction_threshold >= self.num_shares:
+                    raise ValueError(
+                        "`reconstruction_threshold` must be less than `num_shares`."
+                    )
+        else:
+            if not 0 < self.reconstruction_threshold <= 1:
+                raise ValueError(
+                    "If `reconstruction_threshold` is a float, "
+                    "it must be greater than 0 and less than or equal to 1."
+                )
+
+        # Check `max_weight`
+        if self.max_weight <= 0:
+            raise ValueError("`max_weight` must be greater than 0.")
+
+        # Check `quantization_range`
+        if self.quantization_range <= 0:
+            raise ValueError("`quantization_range` must be greater than 0.")
+
+        # Check `quantization_range`
+        if not isinstance(self.quantization_range, int) or self.quantization_range <= 0:
+            raise ValueError(
+                "`quantization_range` must be an integer and greater than 0."
+            )
+
+        # Check `modulus_range`
+        if (
+            not isinstance(self.modulus_range, int)
+            or self.modulus_range <= self.quantization_range
+        ):
+            raise ValueError(
+                "`modulus_range` must be an integer and "
+                "greater than `quantization_range`."
+            )
+        if bin(self.modulus_range).count("1") != 1:
+            raise ValueError("`modulus_range` must be a power of 2.")
+
+    def _check_threshold(self, state: WorkflowState) -> bool:
+        for node_id in state.sampled_node_ids:
+            active_neighbors = state.nid_to_neighbours[node_id] & state.active_node_ids
+            if len(active_neighbors) < state.threshold:
+                log(ERROR, "Insufficient available nodes.")
+                return False
+        return True
+
+    def setup_stage(  # pylint: disable=R0912, R0914, R0915
+        self, driver: Driver, context: LegacyContext, state: WorkflowState
+    ) -> bool:
+        """Execute the 'setup' stage."""
+        # Obtain fit instructions
+        cfg = context.state.configs_records[MAIN_CONFIGS_RECORD]
+        current_round = cast(int, cfg[WorkflowKey.CURRENT_ROUND])
+        parameters = compat.parametersrecord_to_parameters(
+            context.state.parameters_records[MAIN_PARAMS_RECORD],
+            keep_input=True,
+        )
+        proxy_fitins_lst = context.strategy.configure_fit(
+            current_round, parameters, context.client_manager
+        )
+        if not proxy_fitins_lst:
+            log(INFO, "configure_fit: no clients selected, cancel")
+            return False
+        log(
+            INFO,
+            "configure_fit: strategy sampled %s clients (out of %s)",
+            len(proxy_fitins_lst),
+            context.client_manager.num_available(),
+        )
+
+        state.nid_to_fitins = {
+            proxy.node_id: compat.fitins_to_recordset(fitins, True)
+            for proxy, fitins in proxy_fitins_lst
+        }
+        state.nid_to_proxies = {proxy.node_id: proxy for proxy, _ in proxy_fitins_lst}
+
+        # Protocol config
+        sampled_node_ids = list(state.nid_to_fitins.keys())
+        num_samples = len(sampled_node_ids)
+        if num_samples < 2:
+            log(ERROR, "The number of samples should be greater than 1.")
+            return False
+        if isinstance(self.num_shares, float):
+            state.num_shares = round(self.num_shares * num_samples)
+            # If even
+            if state.num_shares < num_samples and state.num_shares & 1 == 0:
+                state.num_shares += 1
+            # If too small
+            if state.num_shares <= 2:
+                state.num_shares = num_samples
+        else:
+            state.num_shares = self.num_shares
+        if isinstance(self.reconstruction_threshold, float):
+            state.threshold = round(self.reconstruction_threshold * state.num_shares)
+            # Avoid too small threshold
+            state.threshold = max(state.threshold, 2)
+        else:
+            state.threshold = self.reconstruction_threshold
+        state.active_node_ids = set(sampled_node_ids)
+        state.clipping_range = self.clipping_range
+        state.quantization_range = self.quantization_range
+        state.mod_range = self.modulus_range
+        state.max_weight = self.max_weight
+        sa_params_dict = {
+            Key.STAGE: Stage.SETUP,
+            Key.SAMPLE_NUMBER: num_samples,
+            Key.SHARE_NUMBER: state.num_shares,
+            Key.THRESHOLD: state.threshold,
+            Key.CLIPPING_RANGE: state.clipping_range,
+            Key.TARGET_RANGE: state.quantization_range,
+            Key.MOD_RANGE: state.mod_range,
+            Key.MAX_WEIGHT: state.max_weight,
+        }
+
+        # The number of shares should better be odd in the SecAgg+ protocol.
+        if num_samples != state.num_shares and state.num_shares & 1 == 0:
+            log(WARN, "Number of shares in the SecAgg+ protocol should be odd.")
+            state.num_shares += 1
+
+        # Shuffle node IDs
+        random.shuffle(sampled_node_ids)
+        # Build neighbour relations (node ID -> secure IDs of neighbours)
+        half_share = state.num_shares >> 1
+        state.nid_to_neighbours = {
+            nid: {
+                sampled_node_ids[(idx + offset) % num_samples]
+                for offset in range(-half_share, half_share + 1)
+            }
+            for idx, nid in enumerate(sampled_node_ids)
+        }
+
+        state.sampled_node_ids = state.active_node_ids
+
+        # Send setup configuration to clients
+        cfgs_record = ConfigsRecord(sa_params_dict)  # type: ignore
+        content = RecordSet(configs_records={RECORD_KEY_CONFIGS: cfgs_record})
+
+        def make(nid: int) -> Message:
+            return driver.create_message(
+                content=content,
+                message_type=MessageType.TRAIN,
+                dst_node_id=nid,
+                group_id=str(cfg[WorkflowKey.CURRENT_ROUND]),
+                ttl="",
+            )
+
+        log(
+            DEBUG,
+            "[Stage 0] Sending configurations to %s clients.",
+            len(state.active_node_ids),
+        )
+        msgs = driver.send_and_receive(
+            [make(node_id) for node_id in state.active_node_ids], timeout=self.timeout
+        )
+        state.active_node_ids = {
+            msg.metadata.src_node_id for msg in msgs if not msg.has_error()
+        }
+        log(
+            DEBUG,
+            "[Stage 0] Received public keys from %s clients.",
+            len(state.active_node_ids),
+        )
+
+        for msg in msgs:
+            if msg.has_error():
+                continue
+            key_dict = msg.content.configs_records[RECORD_KEY_CONFIGS]
+            node_id = msg.metadata.src_node_id
+            pk1, pk2 = key_dict[Key.PUBLIC_KEY_1], key_dict[Key.PUBLIC_KEY_2]
+            state.nid_to_publickeys[node_id] = [cast(bytes, pk1), cast(bytes, pk2)]
+
+        return self._check_threshold(state)
+
+    def share_keys_stage(  # pylint: disable=R0914
+        self, driver: Driver, context: LegacyContext, state: WorkflowState
+    ) -> bool:
+        """Execute the 'share keys' stage."""
+        cfg = context.state.configs_records[MAIN_CONFIGS_RECORD]
+
+        def make(nid: int) -> Message:
+            neighbours = state.nid_to_neighbours[nid] & state.active_node_ids
+            cfgs_record = ConfigsRecord(
+                {str(nid): state.nid_to_publickeys[nid] for nid in neighbours}
+            )
+            cfgs_record[Key.STAGE] = Stage.SHARE_KEYS
+            content = RecordSet(configs_records={RECORD_KEY_CONFIGS: cfgs_record})
+            return driver.create_message(
+                content=content,
+                message_type=MessageType.TRAIN,
+                dst_node_id=nid,
+                group_id=str(cfg[WorkflowKey.CURRENT_ROUND]),
+                ttl="",
+            )
+
+        # Broadcast public keys to clients and receive secret key shares
+        log(
+            DEBUG,
+            "[Stage 1] Forwarding public keys to %s clients.",
+            len(state.active_node_ids),
+        )
+        msgs = driver.send_and_receive(
+            [make(node_id) for node_id in state.active_node_ids], timeout=self.timeout
+        )
+        state.active_node_ids = {
+            msg.metadata.src_node_id for msg in msgs if not msg.has_error()
+        }
+        log(
+            DEBUG,
+            "[Stage 1] Received encrypted key shares from %s clients.",
+            len(state.active_node_ids),
+        )
+
+        # Build forward packet list dictionary
+        srcs: List[int] = []
+        dsts: List[int] = []
+        ciphertexts: List[bytes] = []
+        fwd_ciphertexts: Dict[int, List[bytes]] = {
+            nid: [] for nid in state.active_node_ids
+        }  # dest node ID -> list of ciphertexts
+        fwd_srcs: Dict[int, List[int]] = {
+            nid: [] for nid in state.active_node_ids
+        }  # dest node ID -> list of src node IDs
+        for msg in msgs:
+            node_id = msg.metadata.src_node_id
+            res_dict = msg.content.configs_records[RECORD_KEY_CONFIGS]
+            dst_lst = cast(List[int], res_dict[Key.DESTINATION_LIST])
+            ctxt_lst = cast(List[bytes], res_dict[Key.CIPHERTEXT_LIST])
+            srcs += [node_id] * len(dst_lst)
+            dsts += dst_lst
+            ciphertexts += ctxt_lst
+
+        for src, dst, ciphertext in zip(srcs, dsts, ciphertexts):
+            if dst in fwd_ciphertexts:
+                fwd_ciphertexts[dst].append(ciphertext)
+                fwd_srcs[dst].append(src)
+
+        state.forward_srcs = fwd_srcs
+        state.forward_ciphertexts = fwd_ciphertexts
+
+        return self._check_threshold(state)
+
+    def collect_masked_vectors_stage(
+        self, driver: Driver, context: LegacyContext, state: WorkflowState
+    ) -> bool:
+        """Execute the 'collect masked vectors' stage."""
+        cfg = context.state.configs_records[MAIN_CONFIGS_RECORD]
+
+        # Send secret key shares to clients (plus FitIns) and collect masked vectors
+        def make(nid: int) -> Message:
+            cfgs_dict = {
+                Key.STAGE: Stage.COLLECT_MASKED_VECTORS,
+                Key.CIPHERTEXT_LIST: state.forward_ciphertexts[nid],
+                Key.SOURCE_LIST: state.forward_srcs[nid],
+            }
+            cfgs_record = ConfigsRecord(cfgs_dict)  # type: ignore
+            content = state.nid_to_fitins[nid]
+            content.configs_records[RECORD_KEY_CONFIGS] = cfgs_record
+            return driver.create_message(
+                content=content,
+                message_type=MessageType.TRAIN,
+                dst_node_id=nid,
+                group_id=str(cfg[WorkflowKey.CURRENT_ROUND]),
+                ttl="",
+            )
+
+        log(
+            DEBUG,
+            "[Stage 2] Forwarding encrypted key shares to %s clients.",
+            len(state.active_node_ids),
+        )
+        msgs = driver.send_and_receive(
+            [make(node_id) for node_id in state.active_node_ids], timeout=self.timeout
+        )
+        state.active_node_ids = {
+            msg.metadata.src_node_id for msg in msgs if not msg.has_error()
+        }
+        log(
+            DEBUG,
+            "[Stage 2] Received masked vectors from %s clients.",
+            len(state.active_node_ids),
+        )
+
+        # Clear cache
+        del state.forward_ciphertexts, state.forward_srcs, state.nid_to_fitins
+
+        # Sum collected masked vectors and compute active/dead node IDs
+        masked_vector = None
+        for msg in msgs:
+            res_dict = msg.content.configs_records[RECORD_KEY_CONFIGS]
+            bytes_list = cast(List[bytes], res_dict[Key.MASKED_PARAMETERS])
+            client_masked_vec = [bytes_to_ndarray(b) for b in bytes_list]
+            if masked_vector is None:
+                masked_vector = client_masked_vec
+            else:
+                masked_vector = parameters_addition(masked_vector, client_masked_vec)
+        if masked_vector is not None:
+            masked_vector = parameters_mod(masked_vector, state.mod_range)
+            state.aggregate_ndarrays = masked_vector
+
+        # Backward compatibility with Strategy
+        for msg in msgs:
+            fitres = compat.recordset_to_fitres(msg.content, True)
+            proxy = state.nid_to_proxies[msg.metadata.src_node_id]
+            state.legacy_results.append((proxy, fitres))
+
+        return self._check_threshold(state)
+
+    def unmask_stage(  # pylint: disable=R0912, R0914, R0915
+        self, driver: Driver, context: LegacyContext, state: WorkflowState
+    ) -> bool:
+        """Execute the 'unmask' stage."""
+        cfg = context.state.configs_records[MAIN_CONFIGS_RECORD]
+        current_round = cast(int, cfg[WorkflowKey.CURRENT_ROUND])
+
+        # Construct active node IDs and dead node IDs
+        active_nids = state.active_node_ids
+        dead_nids = state.sampled_node_ids - active_nids
+
+        # Send secure IDs of active and dead clients and collect key shares from clients
+        def make(nid: int) -> Message:
+            neighbours = state.nid_to_neighbours[nid]
+            cfgs_dict = {
+                Key.STAGE: Stage.UNMASK,
+                Key.ACTIVE_NODE_ID_LIST: list(neighbours & active_nids),
+                Key.DEAD_NODE_ID_LIST: list(neighbours & dead_nids),
+            }
+            cfgs_record = ConfigsRecord(cfgs_dict)  # type: ignore
+            content = RecordSet(configs_records={RECORD_KEY_CONFIGS: cfgs_record})
+            return driver.create_message(
+                content=content,
+                message_type=MessageType.TRAIN,
+                dst_node_id=nid,
+                group_id=str(current_round),
+                ttl="",
+            )
+
+        log(
+            DEBUG,
+            "[Stage 3] Requesting key shares from %s clients to remove masks.",
+            len(state.active_node_ids),
+        )
+        msgs = driver.send_and_receive(
+            [make(node_id) for node_id in state.active_node_ids], timeout=self.timeout
+        )
+        state.active_node_ids = {
+            msg.metadata.src_node_id for msg in msgs if not msg.has_error()
+        }
+        log(
+            DEBUG,
+            "[Stage 3] Received key shares from %s clients.",
+            len(state.active_node_ids),
+        )
+
+        # Build collected shares dict
+        collected_shares_dict: Dict[int, List[bytes]] = {}
+        for nid in state.sampled_node_ids:
+            collected_shares_dict[nid] = []
+        for msg in msgs:
+            res_dict = msg.content.configs_records[RECORD_KEY_CONFIGS]
+            nids = cast(List[int], res_dict[Key.NODE_ID_LIST])
+            shares = cast(List[bytes], res_dict[Key.SHARE_LIST])
+            for owner_nid, share in zip(nids, shares):
+                collected_shares_dict[owner_nid].append(share)
+
+        # Remove masks for every active client after collect_masked_vectors stage
+        masked_vector = state.aggregate_ndarrays
+        del state.aggregate_ndarrays
+        for nid, share_list in collected_shares_dict.items():
+            if len(share_list) < state.threshold:
+                log(
+                    ERROR, "Not enough shares to recover secret in unmask vectors stage"
+                )
+                return False
+            secret = combine_shares(share_list)
+            if nid in active_nids:
+                # The seed for PRG is the private mask seed of an active client.
+                private_mask = pseudo_rand_gen(
+                    secret, state.mod_range, get_parameters_shape(masked_vector)
+                )
+                masked_vector = parameters_subtraction(masked_vector, private_mask)
+            else:
+                # The seed for PRG is the secret key 1 of a dropped client.
+                neighbours = state.nid_to_neighbours[nid]
+                neighbours.remove(nid)
+
+                for neighbor_nid in neighbours:
+                    shared_key = generate_shared_key(
+                        bytes_to_private_key(secret),
+                        bytes_to_public_key(state.nid_to_publickeys[neighbor_nid][0]),
+                    )
+                    pairwise_mask = pseudo_rand_gen(
+                        shared_key, state.mod_range, get_parameters_shape(masked_vector)
+                    )
+                    if nid > neighbor_nid:
+                        masked_vector = parameters_addition(
+                            masked_vector, pairwise_mask
+                        )
+                    else:
+                        masked_vector = parameters_subtraction(
+                            masked_vector, pairwise_mask
+                        )
+        recon_parameters = parameters_mod(masked_vector, state.mod_range)
+        q_total_ratio, recon_parameters = factor_extract(recon_parameters)
+        inv_dq_total_ratio = state.quantization_range / q_total_ratio
+        # recon_parameters = parameters_divide(recon_parameters, total_weights_factor)
+        aggregated_vector = dequantize(
+            recon_parameters,
+            state.clipping_range,
+            state.quantization_range,
+        )
+        offset = -(len(active_nids) - 1) * state.clipping_range
+        for vec in aggregated_vector:
+            vec += offset
+            vec *= inv_dq_total_ratio
+
+        # Backward compatibility with Strategy
+        results = state.legacy_results
+        parameters = ndarrays_to_parameters(aggregated_vector)
+        for _, fitres in results:
+            fitres.parameters = parameters
+
+        # No exception/failure handling currently
+        log(
+            INFO,
+            "aggregate_fit: received %s results and %s failures",
+            len(results),
+            0,
+        )
+        aggregated_result = context.strategy.aggregate_fit(current_round, results, [])
+        parameters_aggregated, metrics_aggregated = aggregated_result
+
+        # Update the parameters and write history
+        if parameters_aggregated:
+            paramsrecord = compat.parameters_to_parametersrecord(
+                parameters_aggregated, True
+            )
+            context.state.parameters_records[MAIN_PARAMS_RECORD] = paramsrecord
+            context.history.add_metrics_distributed_fit(
+                server_round=current_round, metrics=metrics_aggregated
+            )
+        return True
diff --git a/src/py/flwr/simulation/__init__.py b/src/py/flwr/simulation/__init__.py
index 724ea9273916..d36d9977d1c5 100644
--- a/src/py/flwr/simulation/__init__.py
+++ b/src/py/flwr/simulation/__init__.py
@@ -17,6 +17,8 @@
 
 import importlib
 
+from flwr.simulation.run_simulation import run_simulation, run_simulation_from_cli
+
 is_ray_installed = importlib.util.find_spec("ray") is not None
 
 if is_ray_installed:
@@ -34,6 +36,4 @@ def start_simulation(*args, **kwargs):  # type: ignore
         raise ImportError(RAY_IMPORT_ERROR)
 
 
-__all__ = [
-    "start_simulation",
-]
+__all__ = ["start_simulation", "run_simulation_from_cli", "run_simulation"]
diff --git a/src/py/flwr/simulation/app.py b/src/py/flwr/simulation/app.py
index 0bb9290b6911..ff18f37664be 100644
--- a/src/py/flwr/simulation/app.py
+++ b/src/py/flwr/simulation/app.py
@@ -28,13 +28,13 @@
 from flwr.client import ClientFn
 from flwr.common import EventType, event
 from flwr.common.logger import log
-from flwr.server import Server
-from flwr.server.app import ServerConfig, init_defaults, run_fl
 from flwr.server.client_manager import ClientManager
 from flwr.server.history import History
+from flwr.server.server import Server, init_defaults, run_fl
+from flwr.server.server_config import ServerConfig
 from flwr.server.strategy import Strategy
 from flwr.simulation.ray_transport.ray_actor import (
-    DefaultActor,
+    ClientAppActor,
     VirtualClientEngineActor,
     VirtualClientEngineActorPool,
     pool_size_from_resources,
@@ -82,7 +82,7 @@ def start_simulation(
     client_manager: Optional[ClientManager] = None,
     ray_init_args: Optional[Dict[str, Any]] = None,
     keep_initialised: Optional[bool] = False,
-    actor_type: Type[VirtualClientEngineActor] = DefaultActor,
+    actor_type: Type[VirtualClientEngineActor] = ClientAppActor,
     actor_kwargs: Optional[Dict[str, Any]] = None,
     actor_scheduling: Union[str, NodeAffinitySchedulingStrategy] = "DEFAULT",
 ) -> History:
@@ -107,8 +107,8 @@ def start_simulation(
         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.
-    client_resources : Optional[Dict[str, float]] (default: `{"num_cpus": 1,
-        "num_gpus": 0.0}` CPU and GPU resources for a single client. Supported keys
+    client_resources : Optional[Dict[str, float]] (default: `{"num_cpus": 1, "num_gpus": 0.0}`)
+        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.
@@ -138,10 +138,10 @@ def start_simulation(
     keep_initialised: Optional[bool] (default: False)
         Set to True to prevent `ray.shutdown()` in case `ray.is_initialized()=True`.
 
-    actor_type: VirtualClientEngineActor (default: DefaultActor)
+    actor_type: VirtualClientEngineActor (default: ClientAppActor)
         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).
+        executing a ClientApp wrapping input argument `client_fn`.
 
     actor_kwargs: Optional[Dict[str, Any]] (default: None)
         If you want to create your own Actor classes, you might need to pass
@@ -160,7 +160,7 @@ def start_simulation(
     -------
     hist : flwr.server.history.History
         Object containing metrics from training.
-    """
+    """  # noqa: E501
     # pylint: disable-msg=too-many-locals
     event(
         EventType.START_SIMULATION_ENTER,
@@ -219,7 +219,7 @@ def start_simulation(
     log(
         INFO,
         "Optimize your simulation with Flower VCE: "
-        "https://flower.dev/docs/framework/how-to-run-simulations.html",
+        "https://flower.ai/docs/framework/how-to-run-simulations.html",
     )
 
     # Log the resources that a single client will be able to use
@@ -314,18 +314,30 @@ def update_resources(f_stop: threading.Event) -> None:
         log(ERROR, traceback.format_exc())
         log(
             ERROR,
-            "Your simulation crashed :(. This could be because of several reasons."
+            "Your simulation crashed :(. This could be because of several reasons. "
             "The most common are: "
+            "\n\t > Sometimes, issues in the simulation code itself can cause crashes. "
+            "It's always a good idea to double-check your code for any potential bugs "
+            "or inconsistencies that might be contributing to the problem. "
+            "For example: "
+            "\n\t\t - You might be using a class attribute in your clients that "
+            "hasn't been defined."
+            "\n\t\t - There could be an incorrect method call to a 3rd party library "
+            "(e.g., PyTorch)."
+            "\n\t\t - The return types of methods in your clients/strategies might be "
+            "incorrect."
             "\n\t > Your system couldn't fit a single VirtualClient: try lowering "
             "`client_resources`."
             "\n\t > All the actors in your pool crashed. This could be because: "
             "\n\t\t - You clients hit an out-of-memory (OOM) error and actors couldn't "
             "recover from it. Try launching your simulation with more generous "
             "`client_resources` setting (i.e. it seems %s is "
-            "not enough for your workload). Use fewer concurrent actors. "
+            "not enough for your run). Use fewer concurrent actors. "
             "\n\t\t - You were running a multi-node simulation and all worker nodes "
             "disconnected. The head node might still be alive but cannot accommodate "
-            "any actor with resources: %s.",
+            "any actor with resources: %s."
+            "\nTake a look at the Flower simulation examples for guidance "
+            "<https://flower.ai/docs/framework/how-to-run-simulations.html>.",
             client_resources,
             client_resources,
         )
diff --git a/src/py/flwr/simulation/ray_transport/ray_actor.py b/src/py/flwr/simulation/ray_transport/ray_actor.py
index 640817910396..08d0576e39f0 100644
--- a/src/py/flwr/simulation/ray_transport/ray_actor.py
+++ b/src/py/flwr/simulation/ray_transport/ray_actor.py
@@ -14,29 +14,22 @@
 # ==============================================================================
 """Ray-based Flower Actor and ActorPool implementation."""
 
-
+import asyncio
 import threading
 import traceback
 from abc import ABC
-from logging import ERROR, WARNING
+from logging import DEBUG, ERROR, WARNING
 from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type, Union
 
 import ray
 from ray import ObjectRef
 from ray.util.actor_pool import ActorPool
 
-from flwr import common
-from flwr.client import Client, ClientFn
-from flwr.client.workload_state import WorkloadState
+from flwr.client.client_app import ClientApp, LoadClientAppError
+from flwr.common import Context, Message
 from flwr.common.logger import log
-from flwr.simulation.ray_transport.utils import check_clientfn_returns_client
 
-# All possible returns by a client
-ClientRes = Union[
-    common.GetPropertiesRes, common.GetParametersRes, common.FitRes, common.EvaluateRes
-]
-# A function to be executed by a client to obtain some results
-JobFn = Callable[[Client], ClientRes]
+ClientAppFn = Callable[[], ClientApp]
 
 
 class ClientException(Exception):
@@ -53,48 +46,49 @@ class VirtualClientEngineActor(ABC):
 
     def terminate(self) -> None:
         """Manually terminate Actor object."""
-        log(WARNING, "Manually terminating %s}", self.__class__.__name__)
+        log(WARNING, "Manually terminating %s", self.__class__.__name__)
         ray.actor.exit_actor()
 
     def run(
         self,
-        client_fn: ClientFn,
-        job_fn: JobFn,
+        client_app_fn: ClientAppFn,
+        message: Message,
         cid: str,
-        state: WorkloadState,
-    ) -> Tuple[str, ClientRes, WorkloadState]:
-        """Run a client workload."""
-        # Execute tasks and return result
+        context: Context,
+    ) -> Tuple[str, Message, Context]:
+        """Run a client run."""
+        # Pass message through ClientApp and return a message
         # return also cid which is needed to ensure results
         # from the pool are correctly assigned to each ClientProxy
         try:
-            # Instantiate client (check 'Client' type is returned)
-            client = check_clientfn_returns_client(client_fn(cid))
-            # Inject state
-            client.set_state(state)
-            # Run client job
-            job_results = job_fn(client)
-            # Retrieve state (potentially updated)
-            updated_state = client.get_state()
+            # Load app
+            app: ClientApp = client_app_fn()
+
+            # Handle task message
+            out_message = app(message=message, context=context)
+
+        except LoadClientAppError as load_ex:
+            raise load_ex
+
         except Exception as ex:
             client_trace = traceback.format_exc()
-            message = (
-                "\n\tSomething went wrong when running your client workload."
+            mssg = (
+                "\n\tSomething went wrong when running your client run."
                 "\n\tClient "
                 + cid
                 + " crashed when the "
                 + self.__class__.__name__
-                + " was running its workload."
+                + " was running its run."
                 "\n\tException triggered on the client side: " + client_trace,
             )
-            raise ClientException(str(message)) from ex
+            raise ClientException(str(mssg)) from ex
 
-        return cid, job_results, updated_state
+        return cid, out_message, context
 
 
 @ray.remote
-class DefaultActor(VirtualClientEngineActor):
-    """A Ray Actor class that runs client workloads.
+class ClientAppActor(VirtualClientEngineActor):
+    """A Ray Actor class that runs client runs.
 
     Parameters
     ----------
@@ -237,18 +231,16 @@ def add_actors_to_pool(self, num_actors: int) -> None:
             self._idle_actors.extend(new_actors)
             self.num_actors += num_actors
 
-    def submit(
-        self, fn: Any, value: Tuple[ClientFn, JobFn, str, WorkloadState]
-    ) -> None:
-        """Take idle actor and assign it a client workload.
+    def submit(self, fn: Any, value: Tuple[ClientAppFn, Message, str, Context]) -> None:
+        """Take an idle actor and assign it to run a client app and Message.
 
         Submit a job to an actor by first removing it from the list of idle actors, then
-        check if this actor was flagged to be removed from the pool
+        check if this actor was flagged to be removed from the pool.
         """
-        client_fn, job_fn, cid, state = value
+        app_fn, mssg, cid, context = value
         actor = self._idle_actors.pop()
         if self._check_and_remove_actor_from_pool(actor):
-            future = fn(actor, client_fn, job_fn, cid, state)
+            future = fn(actor, app_fn, mssg, cid, context)
             future_key = tuple(future) if isinstance(future, List) else future
             self._future_to_actor[future_key] = (self._next_task_index, actor, cid)
             self._next_task_index += 1
@@ -257,7 +249,7 @@ def submit(
             self._cid_to_future[cid]["future"] = future_key
 
     def submit_client_job(
-        self, actor_fn: Any, job: Tuple[ClientFn, JobFn, str, WorkloadState]
+        self, actor_fn: Any, job: Tuple[ClientAppFn, Message, str, Context]
     ) -> None:
         """Submit a job while tracking client ids."""
         _, _, cid, _ = job
@@ -297,17 +289,17 @@ def _is_future_ready(self, cid: str) -> bool:
 
         return self._cid_to_future[cid]["ready"]  # type: ignore
 
-    def _fetch_future_result(self, cid: str) -> Tuple[ClientRes, WorkloadState]:
-        """Fetch result and updated state for a VirtualClient from Object Store.
+    def _fetch_future_result(self, cid: str) -> Tuple[Message, Context]:
+        """Fetch result and updated context for a VirtualClient from Object Store.
 
         The job submitted by the ClientProxy interfacing with client with cid=cid is
         ready. Here we fetch it from the object store and return.
         """
         try:
             future: ObjectRef[Any] = self._cid_to_future[cid]["future"]  # type: ignore
-            res_cid, res, updated_state = ray.get(
+            res_cid, out_mssg, updated_context = ray.get(
                 future
-            )  # type: (str, ClientRes, WorkloadState)
+            )  # type: (str, Message, Context)
         except ray.exceptions.RayActorError as ex:
             log(ERROR, ex)
             if hasattr(ex, "actor_id"):
@@ -324,7 +316,7 @@ def _fetch_future_result(self, cid: str) -> Tuple[ClientRes, WorkloadState]:
         # Reset mapping
         self._reset_cid_to_future_dict(cid)
 
-        return res, updated_state
+        return out_mssg, updated_context
 
     def _flag_actor_for_removal(self, actor_id_hex: str) -> None:
         """Flag actor that should be removed from pool."""
@@ -411,7 +403,7 @@ def process_unordered_future(self, timeout: Optional[float] = None) -> None:
 
     def get_client_result(
         self, cid: str, timeout: Optional[float]
-    ) -> Tuple[ClientRes, WorkloadState]:
+    ) -> Tuple[Message, Context]:
         """Get result from VirtualClient with specific cid."""
         # Loop until all jobs submitted to the pool are completed. Break early
         # if the result for the ClientProxy calling this method is ready
@@ -423,5 +415,92 @@ def get_client_result(
                 break
 
         # Fetch result belonging to the VirtualClient calling this method
-        # Return both result from tasks and (potentially) updated workload state
+        # Return both result from tasks and (potentially) updated run context
         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."""
+
+    def __init__(
+        self,
+        actor_type: Type[VirtualClientEngineActor],
+        client_resources: Dict[str, Union[int, float]],
+        actor_kwargs: Dict[str, Any],
+    ):
+        self.client_resources = client_resources
+
+        # Queue of idle actors
+        self.pool: "asyncio.Queue[Type[VirtualClientEngineActor]]" = asyncio.Queue(
+            maxsize=1024
+        )
+        self.num_actors = 0
+
+        # Resolve arguments to pass during actor init
+        actor_args = {} if actor_kwargs is None else actor_kwargs
+
+        # A function that creates an actor
+        self.create_actor_fn = lambda: actor_type.options(  # type: ignore
+            **client_resources
+        ).remote(**actor_args)
+
+        # Figure out how many actors can be created given the cluster resources
+        # and the resources the user indicates each VirtualClient will need
+        self.actors_capacity = pool_size_from_resources(client_resources)
+        self._future_to_actor: Dict[Any, Type[VirtualClientEngineActor]] = {}
+
+    def is_actor_available(self) -> bool:
+        """Return true if there is an idle actor."""
+        return self.pool.qsize() > 0
+
+    async def add_actors_to_pool(self, num_actors: int) -> None:
+        """Add actors to the pool.
+
+        This method may be executed also if new resources are added to your Ray cluster
+        (e.g. you add a new node).
+        """
+        for _ in range(num_actors):
+            await self.pool.put(self.create_actor_fn())  # type: ignore
+        self.num_actors += num_actors
+
+    async def terminate_all_actors(self) -> None:
+        """Terminate actors in pool."""
+        num_terminated = 0
+        while self.pool.qsize():
+            actor = await self.pool.get()
+            actor.terminate.remote()  # type: ignore
+            num_terminated += 1
+
+        log(DEBUG, "Terminated %i actors", num_terminated)
+
+    async def submit(
+        self, actor_fn: Any, job: Tuple[ClientAppFn, Message, str, Context]
+    ) -> Any:
+        """On idle actor, submit job and return future."""
+        # Remove idle actor from pool
+        actor = await self.pool.get()
+        # Submit job to actor
+        app_fn, mssg, cid, context = job
+        future = actor_fn(actor, app_fn, mssg, cid, context)
+        # Keep track of future:actor (so we can fetch the actor upon job completion
+        # and add it back to the pool)
+        self._future_to_actor[future] = actor
+        return future
+
+    async def fetch_result_and_return_actor_to_pool(
+        self, future: Any
+    ) -> Tuple[Message, Context]:
+        """Pull result given a future and add actor back to pool."""
+        # Get actor that ran job
+        actor = self._future_to_actor.pop(future)
+        await self.pool.put(actor)
+        # Retrieve result for object store
+        # Instead of doing ray.get(future) we await it
+        _, out_mssg, updated_context = await future
+        return out_mssg, updated_context
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 c6a63298dae6..c3493163ac52 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy.py
@@ -17,107 +17,27 @@
 
 import traceback
 from logging import ERROR
-from typing import Dict, Optional, cast
-
-import ray
+from typing import Optional
 
 from flwr import common
-from flwr.client import Client, ClientFn
-from flwr.client.client import (
-    maybe_call_evaluate,
-    maybe_call_fit,
-    maybe_call_get_parameters,
-    maybe_call_get_properties,
-)
+from flwr.client import ClientFn
+from flwr.client.client_app import ClientApp
 from flwr.client.node_state import NodeState
+from flwr.common import Message, Metadata, RecordSet
+from flwr.common.constant import MessageType, MessageTypeLegacy
 from flwr.common.logger import log
-from flwr.server.client_proxy import ClientProxy
-from flwr.simulation.ray_transport.ray_actor import (
-    ClientRes,
-    JobFn,
-    VirtualClientEngineActorPool,
+from flwr.common.recordset_compat import (
+    evaluateins_to_recordset,
+    fitins_to_recordset,
+    getparametersins_to_recordset,
+    getpropertiesins_to_recordset,
+    recordset_to_evaluateres,
+    recordset_to_fitres,
+    recordset_to_getparametersres,
+    recordset_to_getpropertiesres,
 )
-
-
-class RayClientProxy(ClientProxy):
-    """Flower client proxy which delegates work using Ray."""
-
-    def __init__(self, client_fn: ClientFn, cid: str, resources: Dict[str, float]):
-        super().__init__(cid)
-        self.client_fn = client_fn
-        self.resources = resources
-
-    def get_properties(
-        self, ins: common.GetPropertiesIns, timeout: Optional[float]
-    ) -> common.GetPropertiesRes:
-        """Return client's properties."""
-        future_get_properties_res = launch_and_get_properties.options(  # type: ignore
-            **self.resources,
-        ).remote(self.client_fn, self.cid, ins)
-        try:
-            res = ray.get(future_get_properties_res, timeout=timeout)
-        except Exception as ex:
-            log(ERROR, ex)
-            raise ex
-        return cast(
-            common.GetPropertiesRes,
-            res,
-        )
-
-    def get_parameters(
-        self, ins: common.GetParametersIns, timeout: Optional[float]
-    ) -> common.GetParametersRes:
-        """Return the current local model parameters."""
-        future_paramseters_res = launch_and_get_parameters.options(  # type: ignore
-            **self.resources,
-        ).remote(self.client_fn, self.cid, ins)
-        try:
-            res = ray.get(future_paramseters_res, timeout=timeout)
-        except Exception as ex:
-            log(ERROR, ex)
-            raise ex
-        return cast(
-            common.GetParametersRes,
-            res,
-        )
-
-    def fit(self, ins: common.FitIns, timeout: Optional[float]) -> common.FitRes:
-        """Train model parameters on the locally held dataset."""
-        future_fit_res = launch_and_fit.options(  # type: ignore
-            **self.resources,
-        ).remote(self.client_fn, self.cid, ins)
-        try:
-            res = ray.get(future_fit_res, timeout=timeout)
-        except Exception as ex:
-            log(ERROR, ex)
-            raise ex
-        return cast(
-            common.FitRes,
-            res,
-        )
-
-    def evaluate(
-        self, ins: common.EvaluateIns, timeout: Optional[float]
-    ) -> common.EvaluateRes:
-        """Evaluate model parameters on the locally held dataset."""
-        future_evaluate_res = launch_and_evaluate.options(  # type: ignore
-            **self.resources,
-        ).remote(self.client_fn, self.cid, ins)
-        try:
-            res = ray.get(future_evaluate_res, timeout=timeout)
-        except Exception as ex:
-            log(ERROR, ex)
-            raise ex
-        return cast(
-            common.EvaluateRes,
-            res,
-        )
-
-    def reconnect(
-        self, ins: common.ReconnectIns, timeout: Optional[float]
-    ) -> common.DisconnectRes:
-        """Disconnect and (optionally) reconnect later."""
-        return common.DisconnectRes(reason="")  # Nothing to do here (yet)
+from flwr.server.client_proxy import ClientProxy
+from flwr.simulation.ray_transport.ray_actor import VirtualClientEngineActorPool
 
 
 class RayActorClientProxy(ClientProxy):
@@ -127,171 +47,149 @@ def __init__(
         self, client_fn: ClientFn, cid: str, actor_pool: VirtualClientEngineActorPool
     ):
         super().__init__(cid)
-        self.client_fn = client_fn
+
+        def _load_app() -> ClientApp:
+            return ClientApp(client_fn=client_fn)
+
+        self.app_fn = _load_app
         self.actor_pool = actor_pool
         self.proxy_state = NodeState()
 
-    def _submit_job(self, job_fn: JobFn, timeout: Optional[float]) -> ClientRes:
-        # The VCE is not exposed to TaskIns, it won't handle multilple workloads
-        # For the time being, fixing workload_id is a small compromise
-        # This will be one of the first points to address integrating VCE + DriverAPI
-        workload_id = 0
+    def _submit_job(self, message: Message, timeout: Optional[float]) -> Message:
+        """Sumbit a message to the ActorPool."""
+        run_id = message.metadata.run_id
 
         # Register state
-        self.proxy_state.register_workloadstate(workload_id=workload_id)
+        self.proxy_state.register_context(run_id=run_id)
 
         # Retrieve state
-        state = self.proxy_state.retrieve_workloadstate(workload_id=workload_id)
+        state = self.proxy_state.retrieve_context(run_id=run_id)
 
         try:
             self.actor_pool.submit_client_job(
-                lambda a, c_fn, j_fn, cid, state: a.run.remote(c_fn, j_fn, cid, state),
-                (self.client_fn, job_fn, self.cid, state),
+                lambda a, a_fn, mssg, cid, state: a.run.remote(a_fn, mssg, cid, state),
+                (self.app_fn, message, self.cid, state),
+            )
+            out_mssg, updated_context = self.actor_pool.get_client_result(
+                self.cid, timeout
             )
-            res, updated_state = self.actor_pool.get_client_result(self.cid, timeout)
 
             # Update state
-            self.proxy_state.update_workloadstate(
-                workload_id=workload_id, workload_state=updated_state
-            )
+            self.proxy_state.update_context(run_id=run_id, context=updated_context)
 
         except Exception as ex:
             if self.actor_pool.num_actors == 0:
                 # At this point we want to stop the simulation.
-                # since no more client workloads will be executed
+                # since no more client runs will be executed
                 log(ERROR, "ActorPool is empty!!!")
             log(ERROR, traceback.format_exc())
             log(ERROR, ex)
             raise ex
 
-        return res
+        return out_mssg
+
+    def _wrap_recordset_in_message(
+        self,
+        recordset: RecordSet,
+        message_type: str,
+        timeout: Optional[float],
+        group_id: Optional[int],
+    ) -> Message:
+        """Wrap a RecordSet inside a Message."""
+        return Message(
+            content=recordset,
+            metadata=Metadata(
+                run_id=0,
+                message_id="",
+                group_id=str(group_id) if group_id is not None else "",
+                src_node_id=0,
+                dst_node_id=int(self.cid),
+                reply_to_message="",
+                ttl=str(timeout) if timeout else "",
+                message_type=message_type,
+                partition_id=int(self.cid),
+            ),
+        )
 
     def get_properties(
-        self, ins: common.GetPropertiesIns, timeout: Optional[float]
+        self,
+        ins: common.GetPropertiesIns,
+        timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.GetPropertiesRes:
         """Return client's properties."""
+        recordset = getpropertiesins_to_recordset(ins)
+        message = self._wrap_recordset_in_message(
+            recordset,
+            message_type=MessageTypeLegacy.GET_PROPERTIES,
+            timeout=timeout,
+            group_id=group_id,
+        )
 
-        def get_properties(client: Client) -> common.GetPropertiesRes:
-            return maybe_call_get_properties(
-                client=client,
-                get_properties_ins=ins,
-            )
-
-        res = self._submit_job(get_properties, timeout)
+        message_out = self._submit_job(message, timeout)
 
-        return cast(
-            common.GetPropertiesRes,
-            res,
-        )
+        return recordset_to_getpropertiesres(message_out.content)
 
     def get_parameters(
-        self, ins: common.GetParametersIns, timeout: Optional[float]
+        self,
+        ins: common.GetParametersIns,
+        timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.GetParametersRes:
         """Return the current local model parameters."""
+        recordset = getparametersins_to_recordset(ins)
+        message = self._wrap_recordset_in_message(
+            recordset,
+            message_type=MessageTypeLegacy.GET_PARAMETERS,
+            timeout=timeout,
+            group_id=group_id,
+        )
 
-        def get_parameters(client: Client) -> common.GetParametersRes:
-            return maybe_call_get_parameters(
-                client=client,
-                get_parameters_ins=ins,
-            )
+        message_out = self._submit_job(message, timeout)
 
-        res = self._submit_job(get_parameters, timeout)
+        return recordset_to_getparametersres(message_out.content, keep_input=False)
 
-        return cast(
-            common.GetParametersRes,
-            res,
-        )
-
-    def fit(self, ins: common.FitIns, timeout: Optional[float]) -> common.FitRes:
+    def fit(
+        self, ins: common.FitIns, timeout: Optional[float], group_id: Optional[int]
+    ) -> common.FitRes:
         """Train model parameters on the locally held dataset."""
+        recordset = fitins_to_recordset(
+            ins, keep_input=True
+        )  # This must stay TRUE since ins are in-memory
+        message = self._wrap_recordset_in_message(
+            recordset,
+            message_type=MessageType.TRAIN,
+            timeout=timeout,
+            group_id=group_id,
+        )
 
-        def fit(client: Client) -> common.FitRes:
-            return maybe_call_fit(
-                client=client,
-                fit_ins=ins,
-            )
-
-        res = self._submit_job(fit, timeout)
+        message_out = self._submit_job(message, timeout)
 
-        return cast(
-            common.FitRes,
-            res,
-        )
+        return recordset_to_fitres(message_out.content, keep_input=False)
 
     def evaluate(
-        self, ins: common.EvaluateIns, timeout: Optional[float]
+        self, ins: common.EvaluateIns, timeout: Optional[float], group_id: Optional[int]
     ) -> common.EvaluateRes:
         """Evaluate model parameters on the locally held dataset."""
+        recordset = evaluateins_to_recordset(
+            ins, keep_input=True
+        )  # This must stay TRUE since ins are in-memory
+        message = self._wrap_recordset_in_message(
+            recordset,
+            message_type=MessageType.EVALUATE,
+            timeout=timeout,
+            group_id=group_id,
+        )
 
-        def evaluate(client: Client) -> common.EvaluateRes:
-            return maybe_call_evaluate(
-                client=client,
-                evaluate_ins=ins,
-            )
+        message_out = self._submit_job(message, timeout)
 
-        res = self._submit_job(evaluate, timeout)
-
-        return cast(
-            common.EvaluateRes,
-            res,
-        )
+        return recordset_to_evaluateres(message_out.content)
 
     def reconnect(
-        self, ins: common.ReconnectIns, timeout: Optional[float]
+        self,
+        ins: common.ReconnectIns,
+        timeout: Optional[float],
+        group_id: Optional[int],
     ) -> common.DisconnectRes:
         """Disconnect and (optionally) reconnect later."""
         return common.DisconnectRes(reason="")  # Nothing to do here (yet)
-
-
-@ray.remote
-def launch_and_get_properties(
-    client_fn: ClientFn, cid: str, get_properties_ins: common.GetPropertiesIns
-) -> common.GetPropertiesRes:
-    """Exectue get_properties remotely."""
-    client: Client = _create_client(client_fn, cid)
-    return maybe_call_get_properties(
-        client=client,
-        get_properties_ins=get_properties_ins,
-    )
-
-
-@ray.remote
-def launch_and_get_parameters(
-    client_fn: ClientFn, cid: str, get_parameters_ins: common.GetParametersIns
-) -> common.GetParametersRes:
-    """Exectue get_parameters remotely."""
-    client: Client = _create_client(client_fn, cid)
-    return maybe_call_get_parameters(
-        client=client,
-        get_parameters_ins=get_parameters_ins,
-    )
-
-
-@ray.remote
-def launch_and_fit(
-    client_fn: ClientFn, cid: str, fit_ins: common.FitIns
-) -> common.FitRes:
-    """Exectue fit remotely."""
-    client: Client = _create_client(client_fn, cid)
-    return maybe_call_fit(
-        client=client,
-        fit_ins=fit_ins,
-    )
-
-
-@ray.remote
-def launch_and_evaluate(
-    client_fn: ClientFn, cid: str, evaluate_ins: common.EvaluateIns
-) -> common.EvaluateRes:
-    """Exectue evaluate remotely."""
-    client: Client = _create_client(client_fn, cid)
-    return maybe_call_evaluate(
-        client=client,
-        evaluate_ins=evaluate_ins,
-    )
-
-
-def _create_client(client_fn: ClientFn, cid: str) -> Client:
-    """Create a client instance."""
-    # Materialize client
-    return client_fn(cid)
diff --git a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
index b87418b671d3..22c5425cd9fd 100644
--- a/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
+++ b/src/py/flwr/simulation/ray_transport/ray_client_proxy_test.py
@@ -17,17 +17,29 @@
 
 from math import pi
 from random import shuffle
-from typing import List, Tuple, Type, cast
+from typing import Dict, List, Tuple, Type
 
 import ray
 
 from flwr.client import Client, NumPyClient
-from flwr.client.workload_state import WorkloadState
-from flwr.common import Code, GetPropertiesRes, Status
+from flwr.client.client_app import ClientApp
+from flwr.common import (
+    Config,
+    ConfigsRecord,
+    Context,
+    Message,
+    MessageTypeLegacy,
+    Metadata,
+    RecordSet,
+    Scalar,
+)
+from flwr.common.recordset_compat import (
+    getpropertiesins_to_recordset,
+    recordset_to_getpropertiesres,
+)
+from flwr.common.recordset_compat_test import _get_valid_getpropertiesins
 from flwr.simulation.ray_transport.ray_actor import (
-    ClientRes,
-    DefaultActor,
-    JobFn,
+    ClientAppActor,
     VirtualClientEngineActor,
     VirtualClientEngineActorPool,
 )
@@ -40,32 +52,24 @@ class DummyClient(NumPyClient):
     def __init__(self, cid: str) -> None:
         self.cid = int(cid)
 
+    def get_properties(self, config: Config) -> Dict[str, Scalar]:
+        """Return properties by doing a simple calculation."""
+        result = int(self.cid) * pi
+
+        # store something in context
+        self.context.state.configs_records["result"] = ConfigsRecord(
+            {"result": str(result)}
+        )
+        return {"result": result}
+
 
 def get_dummy_client(cid: str) -> Client:
     """Return a DummyClient converted to Client type."""
     return DummyClient(cid).to_client()
 
 
-# A dummy workload
-def job_fn(cid: str) -> JobFn:  # pragma: no cover
-    """Construct a simple job with cid dependency."""
-
-    def cid_times_pi(client: Client) -> ClientRes:  # pylint: disable=unused-argument
-        result = int(cid) * pi
-
-        # store something in state
-        client.numpy_client.state.state["result"] = str(result)  # type: ignore
-
-        # now let's convert it to a GetPropertiesRes response
-        return GetPropertiesRes(
-            status=Status(Code(0), message="test"), properties={"result": result}
-        )
-
-    return cid_times_pi
-
-
 def prep(
-    actor_type: Type[VirtualClientEngineActor] = DefaultActor,
+    actor_type: Type[VirtualClientEngineActor] = ClientAppActor,
 ) -> Tuple[List[RayActorClientProxy], VirtualClientEngineActorPool]:  # pragma: no cover
     """Prepare ClientProxies and pool for tests."""
     client_resources = {"num_cpus": 1, "num_gpus": 0.0}
@@ -100,51 +104,75 @@ def test_cid_consistency_one_at_a_time() -> None:
     Submit one job and waits for completion. Then submits the next and so on
     """
     proxies, _ = prep()
+
+    getproperties_ins = _get_valid_getpropertiesins()
+    recordset = getpropertiesins_to_recordset(getproperties_ins)
+
     # submit jobs one at a time
     for prox in proxies:
-        res = prox._submit_job(  # pylint: disable=protected-access
-            job_fn=job_fn(prox.cid), timeout=None
+        message = prox._wrap_recordset_in_message(  # pylint: disable=protected-access
+            recordset,
+            MessageTypeLegacy.GET_PROPERTIES,
+            timeout=None,
+            group_id=0,
+        )
+        message_out = prox._submit_job(  # pylint: disable=protected-access
+            message=message, timeout=None
         )
 
-        res = cast(GetPropertiesRes, res)
+        res = recordset_to_getpropertiesres(message_out.content)
+
         assert int(prox.cid) * pi == res.properties["result"]
 
     ray.shutdown()
 
 
-def test_cid_consistency_all_submit_first_workload_consistency() -> None:
+def test_cid_consistency_all_submit_first_run_consistency() -> None:
     """Test that ClientProxies get the result of client job they submit.
 
     All jobs are submitted at the same time. Then fetched one at a time. This also tests
-    NodeState (at each Proxy) and WorkloadState basic functionality.
+    NodeState (at each Proxy) and RunState basic functionality.
     """
     proxies, _ = prep()
-    workload_id = 0
+    run_id = 0
+
+    getproperties_ins = _get_valid_getpropertiesins()
+    recordset = getpropertiesins_to_recordset(getproperties_ins)
 
     # submit all jobs (collect later)
     shuffle(proxies)
     for prox in proxies:
         # Register state
-        prox.proxy_state.register_workloadstate(workload_id=workload_id)
+        prox.proxy_state.register_context(run_id=run_id)
         # Retrieve state
-        state = prox.proxy_state.retrieve_workloadstate(workload_id=workload_id)
+        state = prox.proxy_state.retrieve_context(run_id=run_id)
 
-        job = job_fn(prox.cid)
+        message = prox._wrap_recordset_in_message(  # pylint: disable=protected-access
+            recordset,
+            message_type=MessageTypeLegacy.GET_PROPERTIES,
+            timeout=None,
+            group_id=0,
+        )
         prox.actor_pool.submit_client_job(
-            lambda a, c_fn, j_fn, cid, state: a.run.remote(c_fn, j_fn, cid, state),
-            (prox.client_fn, job, prox.cid, state),
+            lambda a, a_fn, mssg, cid, state: a.run.remote(a_fn, mssg, cid, state),
+            (prox.app_fn, message, prox.cid, state),
         )
 
     # fetch results one at a time
     shuffle(proxies)
     for prox in proxies:
-        res, updated_state = prox.actor_pool.get_client_result(prox.cid, timeout=None)
-        prox.proxy_state.update_workloadstate(workload_id, workload_state=updated_state)
-        res = cast(GetPropertiesRes, res)
+        message_out, updated_context = prox.actor_pool.get_client_result(
+            prox.cid, timeout=None
+        )
+        prox.proxy_state.update_context(run_id, context=updated_context)
+        res = recordset_to_getpropertiesres(message_out.content)
+
         assert int(prox.cid) * pi == res.properties["result"]
         assert (
             str(int(prox.cid) * pi)
-            == prox.proxy_state.retrieve_workloadstate(workload_id).state["result"]
+            == prox.proxy_state.retrieve_context(run_id).state.configs_records[
+                "result"
+            ]["result"]
         )
 
     ray.shutdown()
@@ -156,20 +184,39 @@ def test_cid_consistency_without_proxies() -> None:
     num_clients = len(proxies)
     cids = [str(cid) for cid in range(num_clients)]
 
+    getproperties_ins = _get_valid_getpropertiesins()
+    recordset = getpropertiesins_to_recordset(getproperties_ins)
+
+    def _load_app() -> ClientApp:
+        return ClientApp(client_fn=get_dummy_client)
+
     # submit all jobs (collect later)
     shuffle(cids)
     for cid in cids:
-        job = job_fn(cid)
+        message = Message(
+            content=recordset,
+            metadata=Metadata(
+                run_id=0,
+                message_id="",
+                group_id=str(0),
+                src_node_id=0,
+                dst_node_id=12345,
+                reply_to_message="",
+                ttl="",
+                message_type=MessageTypeLegacy.GET_PROPERTIES,
+                partition_id=int(cid),
+            ),
+        )
         pool.submit_client_job(
             lambda a, c_fn, j_fn, cid_, state: a.run.remote(c_fn, j_fn, cid_, state),
-            (get_dummy_client, job, cid, WorkloadState(state={})),
+            (_load_app, message, cid, Context(state=RecordSet())),
         )
 
     # fetch results one at a time
     shuffle(cids)
     for cid in cids:
-        res, _ = pool.get_client_result(cid, timeout=None)
-        res = cast(GetPropertiesRes, res)
+        message_out, _ = pool.get_client_result(cid, timeout=None)
+        res = recordset_to_getpropertiesres(message_out.content)
         assert int(cid) * pi == res.properties["result"]
 
     ray.shutdown()
diff --git a/src/py/flwr/simulation/ray_transport/utils.py b/src/py/flwr/simulation/ray_transport/utils.py
index c8e6aa6cbe21..3861164998a4 100644
--- a/src/py/flwr/simulation/ray_transport/utils.py
+++ b/src/py/flwr/simulation/ray_transport/utils.py
@@ -15,9 +15,9 @@
 """Utilities for Actors in the Virtual Client Engine."""
 
 import traceback
+import warnings
 from logging import ERROR
 
-from flwr.client import Client
 from flwr.common.logger import log
 
 try:
@@ -26,7 +26,7 @@
     TF = None
 
 # Display Deprecation warning once
-# warnings.filterwarnings("once", category=DeprecationWarning)
+warnings.filterwarnings("once", category=DeprecationWarning)
 
 
 def enable_tf_gpu_growth() -> None:
@@ -37,7 +37,7 @@ def enable_tf_gpu_growth() -> None:
     # the same GPU.
     # Luckily we can disable this behavior by enabling memory growth
     # on the GPU. In this way, VRAM allocated to the processes grows based
-    # on the needs for the workload. (this is for instance the default
+    # on the needs for the run. (this is for instance the default
     # behavior in PyTorch)
     # While this behavior is critical for Actors, you'll likely need it
     # as well in your main process (where the server runs and might evaluate
@@ -59,25 +59,3 @@ def enable_tf_gpu_growth() -> None:
             log(ERROR, traceback.format_exc())
             log(ERROR, ex)
             raise ex
-
-
-def check_clientfn_returns_client(client: Client) -> Client:
-    """Warn once that clients returned in `clinet_fn` should be of type Client.
-
-    This is here for backwards compatibility. If a ClientFn is provided returning
-    a different type of client (e.g. NumPyClient) we'll warn the user but convert
-    the client internally to `Client` by calling `.to_client()`.
-    """
-    if not isinstance(client, Client):
-        # mssg = (
-        #     " Ensure your client is of type `Client`. Please convert it"
-        #     " using the `.to_client()` method before returning it"
-        #     " in the `client_fn` you pass to `start_simulation`."
-        #     " We have applied this conversion on your behalf."
-        #     " Not returning a `Client` might trigger an error in future"
-        #     " versions of Flower."
-        # )
-
-        # warnings.warn(mssg, DeprecationWarning, stacklevel=2)
-        client = client.to_client()
-    return client
diff --git a/src/py/flwr/simulation/run_simulation.py b/src/py/flwr/simulation/run_simulation.py
new file mode 100644
index 000000000000..56fce363726a
--- /dev/null
+++ b/src/py/flwr/simulation/run_simulation.py
@@ -0,0 +1,441 @@
+# 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 Simulation."""
+
+import argparse
+import asyncio
+import json
+import logging
+import threading
+import traceback
+from logging import DEBUG, ERROR, INFO, WARNING
+from time import sleep
+from typing import Dict, Optional
+
+import grpc
+
+from flwr.client import ClientApp
+from flwr.common import EventType, event, log
+from flwr.common.typing import ConfigsRecordValues
+from flwr.server.driver.driver import Driver
+from flwr.server.run_serverapp import run
+from flwr.server.server_app import ServerApp
+from flwr.server.superlink.driver.driver_grpc import run_driver_api_grpc
+from flwr.server.superlink.fleet import vce
+from flwr.server.superlink.state import StateFactory
+from flwr.simulation.ray_transport.utils import (
+    enable_tf_gpu_growth as enable_gpu_growth,
+)
+
+
+# Entry point from CLI
+def run_simulation_from_cli() -> None:
+    """Run Simulation Engine from the CLI."""
+    args = _parse_args_run_simulation().parse_args()
+
+    # Load JSON config
+    backend_config_dict = json.loads(args.backend_config)
+
+    _run_simulation(
+        server_app_attr=args.server_app,
+        client_app_attr=args.client_app,
+        num_supernodes=args.num_supernodes,
+        backend_name=args.backend,
+        backend_config=backend_config_dict,
+        app_dir=args.app_dir,
+        driver_api_address=args.driver_api_address,
+        enable_tf_gpu_growth=args.enable_tf_gpu_growth,
+        verbose_logging=args.verbose,
+    )
+
+
+# Entry point from Python session (script or notebook)
+# pylint: disable=too-many-arguments
+def run_simulation(
+    server_app: ServerApp,
+    client_app: ClientApp,
+    num_supernodes: int,
+    backend_name: str = "ray",
+    backend_config: Optional[Dict[str, ConfigsRecordValues]] = None,
+    enable_tf_gpu_growth: bool = False,
+    verbose_logging: bool = False,
+) -> None:
+    r"""Run a Flower App using the Simulation Engine.
+
+    Parameters
+    ----------
+    server_app : ServerApp
+        The `ServerApp` to be executed. It will send messages to different `ClientApp`
+        instances running on different (virtual) SuperNodes.
+
+    client_app : ClientApp
+        The `ClientApp` to be executed by each of the SuperNodes. It will receive
+        messages sent by the `ServerApp`.
+
+    num_supernodes : int
+        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.
+
+    backend_name : str (default: ray)
+        A simulation backend that runs `ClientApp`s.
+
+    backend_config : Optional[Dict[str, ConfigsRecordValues]]
+        'A dictionary, e.g {"<keyA>": <value>, "<keyB>": <value>} to configure a
+        backend. Values supported in <value> are those included by
+        `flwr.common.typing.ConfigsRecordValues`.
+
+    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 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.
+
+    verbose_logging : bool (default: False)
+        When diabled, only INFO, WARNING and ERROR log messages will be shown. If
+        enabled, DEBUG-level logs will be displayed.
+    """
+    _run_simulation(
+        num_supernodes=num_supernodes,
+        client_app=client_app,
+        server_app=server_app,
+        backend_name=backend_name,
+        backend_config=backend_config,
+        enable_tf_gpu_growth=enable_tf_gpu_growth,
+        verbose_logging=verbose_logging,
+    )
+
+
+# pylint: disable=too-many-arguments
+def run_serverapp_th(
+    server_app_attr: Optional[str],
+    server_app: Optional[ServerApp],
+    driver: Driver,
+    app_dir: str,
+    f_stop: asyncio.Event,
+    enable_tf_gpu_growth: bool,
+    delay_launch: int = 3,
+) -> threading.Thread:
+    """Run SeverApp in a thread."""
+
+    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.
+
+        Upon exception, trigger stop event for Simulation Engine.
+        """
+        try:
+            if tf_gpu_growth:
+                log(INFO, "Enabling GPU growth for Tensorflow on the main thread.")
+                enable_gpu_growth()
+
+            # Run ServerApp
+            run(**kwargs)
+        except Exception as ex:  # pylint: disable=broad-exception-caught
+            log(ERROR, "ServerApp thread raised an exception: %s", ex)
+            log(ERROR, traceback.format_exc())
+        finally:
+            log(DEBUG, "ServerApp finished running.")
+            # Upon completion, trigger stop event if one was passed
+            if stop_event is not None:
+                stop_event.set()
+                log(WARNING, "Triggered stop event for Simulation Engine.")
+
+    serverapp_th = threading.Thread(
+        target=server_th_with_start_checks,
+        args=(enable_tf_gpu_growth, f_stop),
+        kwargs={
+            "server_app_attr": server_app_attr,
+            "loaded_server_app": server_app,
+            "driver": driver,
+            "server_app_dir": app_dir,
+        },
+    )
+    sleep(delay_launch)
+    serverapp_th.start()
+    return serverapp_th
+
+
+# pylint: disable=too-many-locals
+def _main_loop(
+    num_supernodes: int,
+    backend_name: str,
+    backend_config_stream: str,
+    driver_api_address: str,
+    app_dir: str,
+    enable_tf_gpu_growth: bool,
+    client_app: Optional[ClientApp] = None,
+    client_app_attr: Optional[str] = None,
+    server_app: Optional[ServerApp] = None,
+    server_app_attr: Optional[str] = None,
+) -> 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
+    thread already contains a running Asyncio event loop. This is the case if running
+    the Simulation Engine on a Jupyter/Colab notebook.
+    """
+    # Initialize StateFactory
+    state_factory = StateFactory(":flwr-in-memory-state:")
+
+    # Start Driver API
+    driver_server: grpc.Server = run_driver_api_grpc(
+        address=driver_api_address,
+        state_factory=state_factory,
+        certificates=None,
+    )
+
+    f_stop = asyncio.Event()
+    serverapp_th = None
+    try:
+        # Initialize Driver
+        driver = Driver(
+            driver_service_address=driver_api_address,
+            root_certificates=None,
+        )
+
+        # Get and run ServerApp thread
+        serverapp_th = run_serverapp_th(
+            server_app_attr=server_app_attr,
+            server_app=server_app,
+            driver=driver,
+            app_dir=app_dir,
+            f_stop=f_stop,
+            enable_tf_gpu_growth=enable_tf_gpu_growth,
+        )
+
+        # SuperLink with Simulation Engine
+        event(EventType.RUN_SUPERLINK_ENTER)
+        vce.start_vce(
+            num_supernodes=num_supernodes,
+            client_app_attr=client_app_attr,
+            client_app=client_app,
+            backend_name=backend_name,
+            backend_config_json_stream=backend_config_stream,
+            app_dir=app_dir,
+            state_factory=state_factory,
+            f_stop=f_stop,
+        )
+
+    except Exception as ex:
+        log(ERROR, "An exception occurred !! %s", ex)
+        log(ERROR, traceback.format_exc())
+        raise RuntimeError("An error was encountered. Ending simulation.") from ex
+
+    finally:
+        # Stop Driver
+        driver_server.stop(grace=0)
+        driver.close()
+        # Trigger stop event
+        f_stop.set()
+
+        event(EventType.RUN_SUPERLINK_LEAVE)
+        if serverapp_th:
+            serverapp_th.join()
+
+    log(INFO, "Stopping Simulation Engine now.")
+
+
+# pylint: disable=too-many-arguments,too-many-locals
+def _run_simulation(
+    num_supernodes: int,
+    client_app: Optional[ClientApp] = None,
+    server_app: Optional[ServerApp] = None,
+    backend_name: str = "ray",
+    backend_config: Optional[Dict[str, ConfigsRecordValues]] = None,
+    client_app_attr: Optional[str] = None,
+    server_app_attr: Optional[str] = None,
+    app_dir: str = "",
+    driver_api_address: str = "0.0.0.0:9091",
+    enable_tf_gpu_growth: bool = False,
+    verbose_logging: bool = False,
+) -> None:
+    r"""Launch the Simulation Engine.
+
+    Parameters
+    ----------
+    num_supernodes : int
+        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.
+
+    client_app : Optional[ClientApp]
+        The `ClientApp` to be executed by each of the `SuperNodes`. It will receive
+        messages sent by the `ServerApp`.
+
+    server_app : Optional[ServerApp]
+        The `ServerApp` to be executed.
+
+    backend_name : str (default: ray)
+        A simulation backend that runs `ClientApp`s.
+
+    backend_config : Optional[Dict[str, ConfigsRecordValues]]
+        'A dictionary, e.g {"<keyA>":<value>, "<keyB>":<value>} to configure a
+        backend. Values supported in <value> are those included by
+        `flwr.common.typing.ConfigsRecordValues`.
+
+    client_app_attr : str
+        A path to a `ClientApp` module to be loaded: For example: `client:app` or
+        `project.package.module:wrapper.app`."
+
+    server_app_attr : str
+        A path to a `ServerApp` module to be loaded: For example: `server:app` or
+        `project.package.module:wrapper.app`."
+
+    app_dir : str
+        Add specified directory to the PYTHONPATH and load `ClientApp` from there.
+        (Default: current working directory.)
+
+    driver_api_address : str (default: "0.0.0.0:9091")
+        Driver API (gRPC) server address (IPv4, IPv6, or a domain name)
+
+    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
+        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
+        enabled, DEBUG-level logs will be displayed.
+    """
+    # Set logging level
+    if not verbose_logging:
+        logger = logging.getLogger("flwr")
+        logger.setLevel(INFO)
+
+    if backend_config is None:
+        backend_config = {}
+
+    if enable_tf_gpu_growth:
+        # Check that Backend config has also enabled using GPU growth
+        use_tf = backend_config.get("tensorflow", False)
+        if not use_tf:
+            log(WARNING, "Enabling GPU growth for your backend.")
+            backend_config["tensorflow"] = True
+
+    # Convert config to original JSON-stream format
+    backend_config_stream = json.dumps(backend_config)
+
+    simulation_engine_th = None
+    args = (
+        num_supernodes,
+        backend_name,
+        backend_config_stream,
+        driver_api_address,
+        app_dir,
+        enable_tf_gpu_growth,
+        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
+    # like Jupyter/Colab notebooks, there is an event loop present.
+    run_in_thread = False
+    try:
+        _ = (
+            asyncio.get_running_loop()
+        )  # Raises RuntimeError if no event loop is present
+        log(DEBUG, "Asyncio event loop already running.")
+
+        run_in_thread = True
+
+    except RuntimeError:
+        log(DEBUG, "No asyncio event loop runnig")
+
+    finally:
+        if run_in_thread:
+            log(DEBUG, "Starting Simulation Engine on a new thread.")
+            simulation_engine_th = threading.Thread(target=_main_loop, args=args)
+            simulation_engine_th.start()
+            simulation_engine_th.join()
+        else:
+            log(DEBUG, "Starting Simulation Engine on the main thread.")
+            _main_loop(*args)
+
+
+def _parse_args_run_simulation() -> argparse.ArgumentParser:
+    """Parse flower-simulation command line arguments."""
+    parser = argparse.ArgumentParser(
+        description="Start a Flower simulation",
+    )
+    parser.add_argument(
+        "--server-app",
+        required=True,
+        help="For example: `server:app` or `project.package.module:wrapper.app`",
+    )
+    parser.add_argument(
+        "--client-app",
+        required=True,
+        help="For example: `client:app` or `project.package.module:wrapper.app`",
+    )
+    parser.add_argument(
+        "--num-supernodes",
+        type=int,
+        required=True,
+        help="Number of simulated SuperNodes.",
+    )
+    parser.add_argument(
+        "--driver-api-address",
+        default="0.0.0.0:9091",
+        type=str,
+        help="For example: `server:app` or `project.package.module:wrapper.app`",
+    )
+    parser.add_argument(
+        "--backend",
+        default="ray",
+        type=str,
+        help="Simulation backend that executes the ClientApp.",
+    )
+    parser.add_argument(
+        "--backend-config",
+        type=str,
+        default='{"client_resources": {"num_cpus":2, "num_gpus":0.0}, "tensorflow": 0}',
+        help='A JSON formatted stream, e.g \'{"<keyA>":<value>, "<keyB>":<value>}\' to '
+        "configure a backend. Values supported in <value> are those included by "
+        "`flwr.common.typing.ConfigsRecordValues`. ",
+    )
+    parser.add_argument(
+        "--enable-tf-gpu-growth",
+        action="store_true",
+        help="Enables 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.",
+    )
+    parser.add_argument(
+        "--verbose",
+        action="store_true",
+        help="When unset, only INFO, WARNING and ERROR log messages will be shown. "
+        "If set, DEBUG-level logs will be displayed. ",
+    )
+    parser.add_argument(
+        "--app-dir",
+        default="",
+        help="Add specified directory to the PYTHONPATH and load"
+        "ClientApp and ServerApp from there."
+        " Default: current working directory.",
+    )
+
+    return parser
diff --git a/src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist_test.py b/src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist_test.py
index 1213410fbc34..f6b922b27eab 100644
--- a/src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist_test.py
+++ b/src/py/flwr_example/tensorflow_fashion_mnist/fashion_mnist_test.py
@@ -21,7 +21,7 @@
 
 
 def test_shuffle() -> None:
-    """Test if shuffle is deterministic depending on the the provided seed."""
+    """Test if shuffle is deterministic depending on the provided seed."""
     # Prepare
     x_tt = np.arange(8)
     y_tt = np.arange(8)
diff --git a/src/py/flwr_experimental/baseline/config/config.py b/src/py/flwr_experimental/baseline/config/config.py
index 5170ea7f7e26..16c144bb6a2f 100644
--- a/src/py/flwr_experimental/baseline/config/config.py
+++ b/src/py/flwr_experimental/baseline/config/config.py
@@ -23,7 +23,7 @@
 from flwr_experimental.ops.instance import Instance
 
 # We assume that devices which are older will have at most
-# ~80% of the the Samsung Galaxy Note 5 compute performance.
+# ~80% of the Samsung Galaxy Note 5 compute performance.
 SCORE_MISSING = int(226 * 0.80)
 
 DEVICE_DISTRIBUTION = [
diff --git a/src/py/flwr_experimental/baseline/tf_cifar/settings.py b/src/py/flwr_experimental/baseline/tf_cifar/settings.py
index 829856af0ace..ed1a72cafac9 100644
--- a/src/py/flwr_experimental/baseline/tf_cifar/settings.py
+++ b/src/py/flwr_experimental/baseline/tf_cifar/settings.py
@@ -120,9 +120,9 @@ def configure_clients(
             cid=str(i),
             partition=i,
             # Indices 0 to 49 fast, 50 to 99 slow
-            delay_factor=delay_factor_fast
-            if i < int(num_clients / 2)
-            else delay_factor_slow,
+            delay_factor=(
+                delay_factor_fast if i < int(num_clients / 2) else delay_factor_slow
+            ),
             # Shared
             iid_fraction=iid_fraction,
             num_clients=num_clients,
diff --git a/src/py/flwr_experimental/baseline/tf_fashion_mnist/settings.py b/src/py/flwr_experimental/baseline/tf_fashion_mnist/settings.py
index b0de9841de30..72adc1f0be04 100644
--- a/src/py/flwr_experimental/baseline/tf_fashion_mnist/settings.py
+++ b/src/py/flwr_experimental/baseline/tf_fashion_mnist/settings.py
@@ -141,9 +141,9 @@ def configure_clients(
             cid=str(i),
             partition=i,
             # Indices 0 to 49 fast, 50 to 99 slow
-            delay_factor=delay_factor_fast
-            if i < int(num_clients / 2)
-            else delay_factor_slow,
+            delay_factor=(
+                delay_factor_fast if i < int(num_clients / 2) else delay_factor_slow
+            ),
             # Shared
             iid_fraction=iid_fraction,
             num_clients=num_clients,
diff --git a/src/py/flwr_experimental/baseline/tf_hotkey/settings.py b/src/py/flwr_experimental/baseline/tf_hotkey/settings.py
index 18c58d1333a5..5bfb7b1e42ad 100644
--- a/src/py/flwr_experimental/baseline/tf_hotkey/settings.py
+++ b/src/py/flwr_experimental/baseline/tf_hotkey/settings.py
@@ -144,9 +144,9 @@ def configure_clients(
             cid=str(i),
             partition=i,
             # Indices 0 to 49 fast, 50 to 99 slow
-            delay_factor=delay_factor_fast
-            if i < int(num_clients / 2)
-            else delay_factor_slow,
+            delay_factor=(
+                delay_factor_fast if i < int(num_clients / 2) else delay_factor_slow
+            ),
             # Shared
             iid_fraction=iid_fraction,
             num_clients=num_clients,
diff --git a/src/py/flwr_experimental/logserver/server.py b/src/py/flwr_experimental/logserver/server.py
index dc729cebbf33..683b12b6db6c 100644
--- a/src/py/flwr_experimental/logserver/server.py
+++ b/src/py/flwr_experimental/logserver/server.py
@@ -69,9 +69,9 @@ def upload_file(local_filepath: str, s3_key: Optional[str]) -> None:
                 Bucket=CONFIG["s3_bucket"],
                 Key=s3_key,
                 ExtraArgs={
-                    "ContentType": "application/pdf"
-                    if s3_key.endswith(".pdf")
-                    else "text/plain"
+                    "ContentType": (
+                        "application/pdf" if s3_key.endswith(".pdf") else "text/plain"
+                    )
                 },
             )
         # pylint: disable=broad-except
diff --git a/src/py/flwr_experimental/ops/__init__.py b/src/py/flwr_experimental/ops/__init__.py
index b56c757e0207..bad31028e68c 100644
--- a/src/py/flwr_experimental/ops/__init__.py
+++ b/src/py/flwr_experimental/ops/__init__.py
@@ -13,4 +13,4 @@
 # limitations under the License.
 # ==============================================================================
 """Flower ops provides an opinionated way to provision necessary compute
-infrastructure for running Flower workloads."""
+infrastructure for running Flower runs."""
diff --git a/src/py/flwr_experimental/ops/cluster.py b/src/py/flwr_experimental/ops/cluster.py
index dfd0dd13727e..53a4e9617427 100644
--- a/src/py/flwr_experimental/ops/cluster.py
+++ b/src/py/flwr_experimental/ops/cluster.py
@@ -73,10 +73,10 @@ def ssh_connection(
     username, key_filename = ssh_credentials
 
     instance_ssh_port: int = cast(int, instance.ssh_port)
-    ignore_host_key_policy: Union[
-        Type[MissingHostKeyPolicy], MissingHostKeyPolicy
-    ] = cast(
-        Union[Type[MissingHostKeyPolicy], MissingHostKeyPolicy], IgnoreHostKeyPolicy
+    ignore_host_key_policy: Union[Type[MissingHostKeyPolicy], MissingHostKeyPolicy] = (
+        cast(
+            Union[Type[MissingHostKeyPolicy], MissingHostKeyPolicy], IgnoreHostKeyPolicy
+        )
     )
 
     client = SSHClient()
@@ -139,7 +139,7 @@ def group_instances_by_specs(instances: List[Instance]) -> List[List[Instance]]:
 
 
 class Cluster:
-    """Compute enviroment independend compute cluster."""
+    """Compute environment independend compute cluster."""
 
     def __init__(
         self,
diff --git a/src/py/flwr_tool/init_py_check.py b/src/py/flwr_tool/init_py_check.py
index 8cdc2e0ab5be..67425139f991 100755
--- a/src/py/flwr_tool/init_py_check.py
+++ b/src/py/flwr_tool/init_py_check.py
@@ -36,7 +36,7 @@ def check_missing_init_files(absolute_path: str) -> None:
 
 if __name__ == "__main__":
     if len(sys.argv) == 0:
-        raise Exception(
+        raise Exception(  # pylint: disable=W0719
             "Please provide at least one directory path relative to your current working directory."
         )
     for i, _ in enumerate(sys.argv):
diff --git a/src/py/flwr_tool/protoc.py b/src/py/flwr_tool/protoc.py
index 5d3ce942c1e0..b0b078c2eae4 100644
--- a/src/py/flwr_tool/protoc.py
+++ b/src/py/flwr_tool/protoc.py
@@ -51,7 +51,7 @@ def compile_all() -> None:
     exit_code = protoc.main(command)
 
     if exit_code != 0:
-        raise Exception(f"Error: {command} failed")
+        raise Exception(f"Error: {command} failed")  # pylint: disable=W0719
 
 
 if __name__ == "__main__":
diff --git a/src/py/flwr_tool/protoc_test.py b/src/py/flwr_tool/protoc_test.py
index 57ca3ff423c2..2d48582eb441 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) == 5
+    assert len(PROTO_FILES) == 7
diff --git a/src/py/flwr_tool/update_changelog.py b/src/py/flwr_tool/update_changelog.py
new file mode 100644
index 000000000000..e3cffff7e36c
--- /dev/null
+++ b/src/py/flwr_tool/update_changelog.py
@@ -0,0 +1,243 @@
+# mypy: ignore-errors
+# 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.
+# ==============================================================================
+"""This module is used to update the changelog."""
+
+
+import re
+from sys import argv
+
+from github import Github
+
+REPO_NAME = "adap/flower"
+CHANGELOG_FILE = "doc/source/ref-changelog.md"
+CHANGELOG_SECTION_HEADER = "### Changelog entry"
+
+
+def _get_latest_tag(gh_api):
+    """Retrieve the latest tag from the GitHub repository."""
+    repo = gh_api.get_repo(REPO_NAME)
+    tags = repo.get_tags()
+    return tags[0] if tags.totalCount > 0 else None
+
+
+def _get_pull_requests_since_tag(gh_api, tag):
+    """Get a list of pull requests merged into the main branch since a given tag."""
+    repo = gh_api.get_repo(REPO_NAME)
+    commits = {commit.sha for commit in repo.compare(tag.commit.sha, "main").commits}
+    prs = set()
+    for pr_info in repo.get_pulls(
+        state="closed", sort="created", direction="desc", base="main"
+    ):
+        if pr_info.merge_commit_sha in commits:
+            prs.add(pr_info)
+        if len(prs) == len(commits):
+            break
+    return prs
+
+
+def _format_pr_reference(title, number, url):
+    """Format a pull request reference as a markdown list item."""
+    return f"- **{title.replace('*', '')}** ([#{number}]({url}))"
+
+
+def _extract_changelog_entry(pr_info):
+    """Extract the changelog entry from a pull request's body."""
+    if not pr_info.body:
+        return None, "general"
+
+    entry_match = re.search(
+        f"{CHANGELOG_SECTION_HEADER}(.+?)(?=##|$)", pr_info.body, re.DOTALL
+    )
+    if not entry_match:
+        return None, None
+
+    entry_text = entry_match.group(1).strip()
+
+    # Remove markdown comments
+    entry_text = re.sub(r"<!--.*?-->", "", entry_text, flags=re.DOTALL).strip()
+
+    token_markers = {
+        "general": "<general>",
+        "skip": "<skip>",
+        "baselines": "<baselines>",
+        "examples": "<examples>",
+        "sdk": "<sdk>",
+        "simulations": "<simulations>",
+    }
+
+    # Find the token based on the presence of its marker in entry_text
+    token = next(
+        (token for token, marker in token_markers.items() if marker in entry_text), None
+    )
+
+    return entry_text, token
+
+
+def _update_changelog(prs):
+    """Update the changelog file with entries from provided pull requests."""
+    with open(CHANGELOG_FILE, "r+", encoding="utf-8") as file:
+        content = file.read()
+        unreleased_index = content.find("## Unreleased")
+
+        if unreleased_index == -1:
+            print("Unreleased header not found in the changelog.")
+            return
+
+        # Find the end of the Unreleased section
+        next_header_index = content.find("##", unreleased_index + 1)
+        next_header_index = (
+            next_header_index if next_header_index != -1 else len(content)
+        )
+
+        for pr_info in prs:
+            pr_entry_text, category = _extract_changelog_entry(pr_info)
+
+            # Skip if PR should be skipped or already in changelog
+            if category == "skip" or f"#{pr_info.number}]" in content:
+                continue
+
+            pr_reference = _format_pr_reference(
+                pr_info.title, pr_info.number, pr_info.html_url
+            )
+
+            # Process based on category
+            if category in ["general", "baselines", "examples", "sdk", "simulations"]:
+                entry_title = _get_category_title(category)
+                content = _update_entry(
+                    content,
+                    entry_title,
+                    pr_info,
+                    unreleased_index,
+                    next_header_index,
+                )
+
+            elif pr_entry_text:
+                content = _insert_new_entry(
+                    content, pr_info, pr_reference, pr_entry_text, unreleased_index
+                )
+
+            else:
+                content = _insert_entry_no_desc(content, pr_reference, unreleased_index)
+
+            next_header_index = content.find("##", unreleased_index + 1)
+            next_header_index = (
+                next_header_index if next_header_index != -1 else len(content)
+            )
+
+        # Finalize content update
+        file.seek(0)
+        file.write(content)
+        file.truncate()
+
+    print("Changelog updated.")
+
+
+def _get_category_title(category):
+    """Get the title of a changelog section based on its category."""
+    headers = {
+        "general": "General improvements",
+        "baselines": "General updates to Flower Baselines",
+        "examples": "General updates to Flower Examples",
+        "sdk": "General updates to Flower SDKs",
+        "simulations": "General updates to Flower Simulations",
+    }
+    return headers.get(category, "")
+
+
+def _update_entry(
+    content, category_title, pr_info, unreleased_index, next_header_index
+):
+    """Update a specific section in the changelog content."""
+    if (
+        section_index := content.find(
+            category_title, unreleased_index, next_header_index
+        )
+    ) != -1:
+        newline_index = content.find("\n", section_index)
+        closing_parenthesis_index = content.rfind(")", unreleased_index, newline_index)
+        updated_entry = f", [{pr_info.number}]({pr_info.html_url})"
+        content = (
+            content[:closing_parenthesis_index]
+            + updated_entry
+            + content[closing_parenthesis_index:]
+        )
+    else:
+        new_section = (
+            f"\n- **{category_title}** ([#{pr_info.number}]({pr_info.html_url}))\n"
+        )
+        insert_index = content.find("\n", unreleased_index) + 1
+        content = content[:insert_index] + new_section + content[insert_index:]
+    return content
+
+
+def _insert_new_entry(content, pr_info, pr_reference, pr_entry_text, unreleased_index):
+    """Insert a new entry into the changelog."""
+    if (existing_entry_start := content.find(pr_entry_text)) != -1:
+        pr_ref_end = content.rfind("\n", 0, existing_entry_start)
+        updated_entry = (
+            f"{content[pr_ref_end]}\n, [{pr_info.number}]({pr_info.html_url})"
+        )
+        content = content[:pr_ref_end] + updated_entry + content[existing_entry_start:]
+    else:
+        insert_index = content.find("\n", unreleased_index) + 1
+
+        # Split the pr_entry_text into paragraphs
+        paragraphs = pr_entry_text.split("\n")
+
+        # Indent each paragraph
+        indented_paragraphs = [
+            "    " + paragraph if paragraph else paragraph for paragraph in paragraphs
+        ]
+
+        # Join the paragraphs back together, ensuring each is separated by a newline
+        indented_pr_entry_text = "\n".join(indented_paragraphs)
+
+        content = (
+            content[:insert_index]
+            + "\n"
+            + pr_reference
+            + "\n\n"
+            + indented_pr_entry_text
+            + "\n"
+            + content[insert_index:]
+        )
+    return content
+
+
+def _insert_entry_no_desc(content, pr_reference, unreleased_index):
+    """Insert a changelog entry for a pull request with no specific description."""
+    insert_index = content.find("\n", unreleased_index) + 1
+    content = (
+        content[:insert_index] + "\n" + pr_reference + "\n" + content[insert_index:]
+    )
+    return content
+
+
+def main():
+    """Update changelog using the descriptions of PRs since the latest tag."""
+    # Initialize GitHub Client with provided token (as argument)
+    gh_api = Github(argv[1])
+    latest_tag = _get_latest_tag(gh_api)
+    if not latest_tag:
+        print("No tags found in the repository.")
+        return
+
+    prs = _get_pull_requests_since_tag(gh_api, latest_tag)
+    _update_changelog(prs)
+
+
+if __name__ == "__main__":
+    main()