@flwrlabs/quickstart-mlx

Quickstart

flwr new @flwrlabs/quickstart-mlx

Readme

Federated Learning with MLX and Flower (Quickstart Example)

This introductory example to Flower uses MLX, but you don't need deep knowledge of MLX to run it. The example will help you understand how to adapt Flower to your specific use case, and running it is quite straightforward.

MLX is a NumPy-like array framework designed for efficient and flexible machine learning on Apple Silicon. In this example, we will train a simple 2-layer MLP on the MNIST dataset (handwritten digits recognition). The data will be downloaded and partitioned using Flower Datasets.

Set up the project

Fetch the app

Install Flower:

pip install flwr

Fetch the app:

flwr new @flwrlabs/quickstart-mlx

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

quickstart-mlx
├── mlxexample
│   ├── __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 mlxexample 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 learning-rate=0.05"

Run with the Deployment Engine

Follow this how-to guide to run the same app in this example but with Flower's Deployment Engine. After that, you might be intersted in setting up secure TLS-enabled communications and SuperNode authentication in your federation.

If you are already familiar with how the Deployment Engine works, you may want to learn how to run it using Docker. Check out the Flower with Docker documentation.