Federated Variational Autoencoder with PyTorch and Flower¶

This example demonstrates how a variational autoencoder (VAE) can be trained in a federated way using the Flower framework. 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/pytorch-federated-variational-autoencoder . \
              && rm -rf _tmp && cd pytorch-federated-variational-autoencoder

This will create a new directory called pytorch-federated-variational-autoencoder with the following structure:

pytorch-federated-variational-autoencoder
├── README.md
├── fedvaeexample
│   ├── __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

Install dependencies and project¶

Install the dependencies defined in pyproject.toml as well as the fedvaeexample 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

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.