Privacy-Preserving Federated Learning for Collaborative Financial Fraud Detection

A privacy-preserving federated learning system for collaborative financial fraud detection.

A privacy-preserving federated learning system for collaborative financial fraud detection. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on 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.