Federated Learning with fastai and Flower (Quickstart Example)

This introductory example to Flower uses fastai, but deep knowledge of fastai is not necessarily required to run the example. The example will help you understand how to adapt Flower to your specific use case, and running it is quite straightforward.

fastai is a deep learning library built on PyTorch which provides practitioners with high-level components for building deep learning projects. In this example, we will train a SqueezeNet v1.1 model on the MNIST dataset. The data will be downloaded and partitioned using Flower Datasets.

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

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

quickstart-fastai
├── fastai_example
│   ├── 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 fastai_example 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.