@yan-gao/example-app

Quickstart

flwr new @yan-gao/example-app

Readme

Federated Learning with PyTorch and Flower

This introductory Flower example uses PyTorch for image classification, but deep knowledge of PyTorch is not required to run it. The example is easy to run and helps illustrate how Flower can be adapted to your own use case. It uses Flower Datasets to download, partition, and preprocess the CIFAR-10 dataset.

Fetch the App

Install Flower:

pip install flwr

Fetch the app:

flwr new @yan-gao/example-app

This will create a new directory called example-app with the following structure:

example-app
├── 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

Run the App

You can run your Flower App 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

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

cd example-app && pip install -e .

Run with default settings:

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 with the Deployment Engine

To run this App using Flower's Deployment Engine we recommend first creating some demo data using Flower Datasets. For example:

# Install Flower datasets
pip install "flwr-datasets['vision']"

# Create dataset partitions and save them to disk
flwr-datasets create uoft-cs/cifar10 --num-partitions 2 --out-dir demo_data

The above command will create two IID partitions of the CIFAR-10 dataset and save them in a demo_data directory. Next, you can pass one partition to each of your SuperNodes like this:

flower-supernode \
    --insecure \
    --superlink <SUPERLINK-FLEET-API> \
    --node-config="data-path=/path/to/demo_data/partition_0"

Finally, ensure the environment of each SuperNode has all dependencies installed. Then, launch the run via flwr run but pointing to a SuperLink connection that specifies the SuperLink your SuperNode is connected to:

flwr run . <SUPERLINK-CONNECTION> --stream