Flower Example on MNIST with Differential Privacy and Secure Aggregation

This example demonstrates a federated learning setup using the Flower, incorporating central differential privacy (DP) with client-side fixed clipping and secure aggregation (SA). It is intended for a small number of rounds for demonstration purposes.

This example is similar to the quickstart-pytorch example and extends it by integrating central differential privacy and secure aggregation. For more details on differential privacy and secure aggregation in Flower, please refer to the documentation here and here.

Set up the project

Clone the project

Start by cloning the example project:

git clone --depth=1 https://github.com/adap/flower.git && mv flower/examples/fl-dp-sa . && rm -rf flower && cd fl-dp-sa

This will create a new directory called fl-dp-sa containing the following files:

fl-dp-sa
├── fl_dp_sa
│   ├── 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 fl_dp_sa package.

# From a new python environment, run:
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 "noise-multiplier=0.1 clipping-norm=5"

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.