Federated Learning with PyTorch and Flower (Quickstart Example)

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 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-pytorch . \
        && rm -rf _tmp \
        && cd quickstart-pytorch

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

quickstart-pytorch
├── pytorchexample
│   ├── __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 pytorchexample 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

[!TIP] This example might run faster when the ClientApps have access to a GPU. If your system has one, you can make use of it by configuring the backend.client-resources component in pyproject.toml. If you want to try running the example with GPU right away, use the local-simulation-gpu federation as shown below.

# Run with the default federation (CPU only)
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"

Run the project in the local-simulation-gpu federation that gives CPU and GPU resources to each ClientApp. By default, at most 5xClientApp will run in parallel in the available GPU. You can tweak the degree of parallelism by adjusting the settings of this federation in the pyproject.toml.

# Run with the `local-simulation-gpu` federation
flwr run . local-simulation-gpu

[!TIP] For a more detailed walk-through check our quickstart PyTorch 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.