Privacy-Preserving Federated Learning Framework

A privacy-preserving federated learning framework.

A privacy-preserving federated learning framework. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want practical security experience: understanding the threat, building the defence or detector, testing it against realistic attacks and reporting the results.

The main parts are privacy-preserving training across several clients, with only model updates shared, security and privacy controls such as encryption and access control, a realistic threat model, the detection or protection mechanism itself, and testing against sample attacks. The reference build uses Flower, Python, scikit-learn, Wireshark, and OpenSSL, with a clear project structure, setup instructions and sample data so that it can be run and extended.