Merge branch 'main' into retire-xgboost-horizontal

.github/workflows/_docker-build.yml

@@ -98,7 +98,7 @@ jobs:
           touch "/tmp/digests/${digest#sha256:}"
 
       - name: Upload digest
-        uses: actions/upload-artifact@c7d193f32edcb7bfad88892161225aeda64e9392 # v4.0.0
+        uses: actions/upload-artifact@1eb3cb2b3e0f29609092a73eb033bb759a334595 # v4.1.0
         with:
           name: digests-${{ steps.build-id.outputs.id }}-${{ matrix.platform.name }}
           path: /tmp/digests/*
@@ -114,7 +114,7 @@ jobs:
       metadata: ${{ steps.meta.outputs.json }}
     steps:
       - name: Download digests
-        uses: actions/download-artifact@f44cd7b40bfd40b6aa1cc1b9b5b7bf03d3c67110 # v4.1.0
+        uses: actions/download-artifact@6b208ae046db98c579e8a3aa621ab581ff575935 # v4.1.1
         with:
           pattern: digests-${{ needs.build.outputs.build-id }}-*
           path: /tmp/digests

datasets/e2e/tensorflow/pyproject.toml

@@ -9,7 +9,7 @@ description = "Flower Datasets with TensorFlow"
 authors = ["The Flower Authors <[email protected]>"]
 
 [tool.poetry.dependencies]
-python = "^3.8"
+python = ">=3.8,<3.11"
 flwr-datasets = { path = "./../../", extras = ["vision"] }
 tensorflow-cpu = "^2.9.1, !=2.11.1"
 parameterized = "==0.9.0"

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.

pyproject.toml

@@ -82,7 +82,7 @@ rest = ["requests", "starlette", "uvicorn"]
 types-dataclasses = "==0.6.6"
 types-protobuf = "==3.19.18"
 types-requests = "==2.31.0.10"
-types-setuptools = "==68.2.0.0"
+types-setuptools = "==69.0.0.20240115"
 clang-format = "==17.0.4"
 isort = "==5.12.0"
 black = { version = "==23.10.1", extras = ["jupyter"] }