Privacy in Federated Learning Models for Intrusion Detection Systems

This academic paper examines privacy concerns in federated learning models (a technique where AI systems train on data spread across multiple locations without centralizing it) used for intrusion detection systems (software that identifies unauthorized access attempts). The research, published in September 2026, appears to focus on understanding how privacy can be protected when building security AI systems across distributed networks.