Federated Learning with PyTorch Lightning and Flower (Quickstart Example)¶

This introductory example to Flower uses PyTorch Lightning, 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 to download, partition and preprocess the MNIST dataset. The model being federated is a lightweight AutoEncoder as presented in Lightning in 15 minutes tutorial.

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:

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

This will create a new directory called quickstart-pytorch-lightning containing the following files:

quickstart-pytorch-lightning
├── pytorchlightning_example
│   ├── __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 pytorchlightning_example package.

pip install -e .

Run the Example¶

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 max-epochs=2"

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.