Privacy-Preserving Federated Multi-Agent AI for Intelligent Campus Management

A privacy-preserving federated multi-agent AI system for intelligent campus management.

A privacy-preserving federated multi-agent AI system for intelligent campus management. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from 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, cooperating agents with defined roles and tools, security and privacy controls such as encryption and access control, a realistic threat model, and the detection or protection mechanism itself. The reference build uses LLM APIs, Flower, Python, scikit-learn, and Wireshark, with a clear project structure, setup instructions and sample data so that it can be run and extended.