Federated AI for Cyber Defence: Enhancing Access Control Through Distributed Learning

Abstract: System security and the protection of sensitive data has become a need of the hour as fast as the development of cyber threats. A federated Artificial Intelligence (AI) presents a distributed learning framework that handles these issues through the provision of secure collaboration that preserves data privacy. This paper explores how Federated AI can enhance access control systems by making them suitable for detecting anomalies, enforcing policies, and adapting to changing threats in real-time. Centralized AI models of the traditional sort necessitate data aggregation at a single location, an avenue open to any breach and compliance concerns. By training models over the federation of decentralized nodes, Federated AI mitigates these risks by allowing data locality. This paper presents a decentralized learning paradigm which implements robust access control mechanisms using collective intelligence and simultaneously keeps sensitive information safe. Additionally, the combination of Federated AI with Zero Trust principles leads to a dynamic access control system that changes with user behavior and the settings external to the user. We discuss key advancements such as the utilization of edge devices for real-time anomaly detection, privacy-based techniques such as differential privacy and homomorphic encryption, and the inclusion of generative models that simulate and predict attack scenarios. Finally, the paper underscores the advantages and limits of the potential of Federated AI in cyber defence.