Flower Android Client Example with Kotlin and TensorFlow Lite 2022¶

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This example is similar to the Flower Android Example in Java:

This example demonstrates a federated learning setup with Android Clients. The training on Android is done on a CIFAR10 dataset using TensorFlow Lite. The setup is as follows:

  • The CIFAR10 dataset is randomly split across 10 clients. Each Android client holds a local dataset of 5000 training examples and 1000 test examples.

  • The FL server runs in Python but all the clients run on Android.

  • We use a strategy called FedAvgAndroid for this example.

  • The strategy is vanilla FedAvg with a custom serialization and deserialization to handle the Bytebuffers sent from Android clients to Python server.

Set up¶

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:

git clone --depth=1 https://github.com/adap/flower.git && mv flower/examples/android-kotlin . && rm -rf flower && cd android-kotlin

Download the training and testing data from https://www.dropbox.com/s/coeixr4kh8ljw6o/cifar10.zip?dl=1 and extract them to client/app/src/main/assets/data.

Download the TFLite model from https://github.com/FedCampus/dyn_flower_android_drf/files/11858642/cifar10.zip to client/app/src/main/assets/model/cifar10.tflite. Alternatively, see gen_tflite/README.md for information on how to convert the CIFAR10 models to a .tflite file.

Install dependencies¶

Project dependencies (such as tensorflow and flwr) are defined in pyproject.toml. We recommend Poetry to install those dependencies and manage your virtual environment (Poetry installation), but feel free to use a different way of installing dependencies and managing virtual environments if you have other preferences.

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:

poetry run python3 -c "import flwr"

If you don’t see any errors you’re good to go!

Alternatively, with Pip. 
python3 -m pip install -r requirements.txt

Run the demo¶

Start the Flower server at ./:

python3 server.py
Or without the "3" on windows. 
python server.py

Install the app on physical Android devices and launch it.

Note: the highest tested JDK version the app supports is 16; it fails to build using JDK 19 on macOS.

In the user interface, fill in:

  • Device number: a unique number among 1 ~ 10. This number is used to choose the partition of the training dataset.

  • Server IP: an IPv4 address of the computer your backend server is running on. You can probably find it in your system network settings.

  • Server port: 8080.

Push the first button and load the dataset. This may take a minute.

Push the second button and start the training.