Privacy in Federated Learning Models for Intrusion Detection Systems

This academic paper examines privacy concerns in federated learning models (a training approach where AI learns from data spread across multiple computers without centralizing it) used for intrusion detection systems (software that identifies unauthorized access attempts). The research explores how to protect sensitive network data while still building effective security AI systems.