Training with Sample-Level Differential Privacy using Opacus Privacy Engine¶

In this example, we demonstrate how to train a model with differential privacy (DP) using Flower. We employ PyTorch and integrate the Opacus Privacy Engine to achieve sample-level differential privacy. This setup ensures robust privacy guarantees during the client training phase. The code is adapted from the PyTorch Quickstart example.

For more information about DP in Flower please refer to the tutorial. For additional information about Opacus, visit the official website.

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/opacus . \
        && rm -rf flower \
        && cd opacus

This will create a new directory called opacus containing the following files:

opacus
├── opacus_fl
│   ├── 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 opacus_fl 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 "max-grad-norm=1.0 num-server-rounds=5"

[!NOTE] Please note that, at the current state, users cannot set NodeConfig for simulated ClientApps. For this reason, the hyperparameter noise_multiplier is set in the client_fn method based on a condition check on partition_id. This will be modified in a future version of Flower to allow users to set NodeConfig for simulated ClientApps.