Differentially Private Federated Learning with Secure Aggregation using Flower on MedMNIST

This project demonstrates an experiment in Federated Learning using the Flower framework, incorporating sample-level Differential Privacy and enabling Secure Aggregation through the SecAgg+ protocol. The experiment is conducted on the MedMNIST dataset collection.

Setup

This project is built and tested on Python 3.8.10.

In the project's main directory, run the following commands to create a virtual environment and install the required packages:

python -m venv env

source env/bin/activate

python -m pip install .

Key Features

1. Local Differential Privacy (LocalDP):

   • Differential privacy is implemented using Flower's LocalDpMod.

2. Secure Aggregation (SecAgg+ Protocol):

   • The SecAgg+ protocol is implemented via Flower's secaggplus_mod.

3. Easy Parameter Control:

   • Parameters related to federated learning settings and the SecAgg+ protocol can be controlled from the pyproject.toml file.