Federated Learning with Tensorflow/Keras and Flower (Quickstart Example)¶

This introductory example to Flower uses Tensorflow/Keras but deep knowledge of this frameworks is 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 to download, partition and preprocess the CIFAR-10 dataset.

Set up the project¶

Clone the project¶

Start by cloning the example project:

git clone --depth=1 https://github.com/adap/flower.git _tmp \
        && mv _tmp/examples/quickstart-tensorflow . \
        && rm -rf _tmp \
        && cd quickstart-tensorflow

This will create a new directory called quickstart-tensorflow with the following structure:

quickstart-tensorflow
├── tfexample
│   ├── __init__.py
│   ├── client_app.py   # Defines your ClientApp
│   ├── server_app.py   # Defines your ServerApp
│   └── task.py         # Defines your model, training and data loading
├── pyproject.toml      # Project metadata like dependencies and configs
└── README.md

Install dependencies and project¶

Install the dependencies defined in pyproject.toml as well as the tfhexample package.

pip install -e .

Run the project¶

You can run your Flower project in both simulation and deployment mode without making changes to the code. If you are starting with Flower, we recommend you using the simulation mode as it requires fewer components to be launched manually. By default, flwr run will make use of the Simulation Engine.

Run with the Simulation Engine¶

flwr run .

You can also override some of the settings for your ClientApp and ServerApp defined in pyproject.toml. For example:

flwr run . --run-config "num-server-rounds=5 learning-rate=0.05"

[!TIP] For a more detailed walk-through check our quickstart TensorFlow tutorial

Run with the Deployment Engine¶

[!NOTE] An update to this example will show how to run this Flower application with the Deployment Engine and TLS certificates, or with Docker.