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

In this example, we demonstrate how to train a model with sample-level differential privacy (DP) using Flower. We employ TensorFlow and integrate the tensorflow-privacy engine to achieve sample-level differential privacy. This setup ensures robust privacy guarantees during the client training phase.

For more information about DP in Flower please refer to the tutorial. For additional information about tensorflow-privacy, 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/tensorflow-privacy . \
        && rm -rf flower \
        && cd tensorflow-privacy

This will create a new directory called tensorflow-privacy containing the following files:

tensorflow-privacy
├── tf_privacy
│   ├── 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

[!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.

Install dependencies and project¶

Install the dependencies defined in pyproject.toml as well as the tf_privacy 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 "l2-norm-clip=1.5 num-server-rounds=5"