Federated Learning with MLX and Flower (Quickstart Example)#

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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#

Clone the project#

Start by cloning the example project:

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

[!TIP] For a more detailed walk-through check our quickstart MLX tutorial

Run with the Deployment Engine#

[!NOTE] An update to this example will show how to run this Flower project with the Deployment Engine and TLS certificates, or with Docker.