AI-Enhanced Security Architecture for 6G Networks: A Federated Learning and Multi-Agent Approach

The coming 6G network is expected to provide better connectivity and network performance but will generate new security challenges. In this paper, we demonstrate a novel technological solution to address the security challenges of the 6G network by combining artificial intelligence with federated learning and multi-agent systems. Federated learning enables the distributed learning of data in the network without revealing the data’s confidentiality. In the meantime, multi-agent systems use intelligent autonomous agents that are equipped to quickly identify and handle any potential threat in a variety of network environments. In combination, these technologies constitute a system-on-system (SoS) type, end-to-end security architecture that is modular, dynamic, and self-contained, consistent with ultra-reliability requirements, high device count in the terahertz regime, and wide-ranging traffic data found in 6G. Through simulations and case studies, this paper demonstrates that the proposed method significantly enhances the accuracy of threat detection and response, as well as improves the efficiency of threat mitigation and the overall security compliance of the network for a more secure and robust 6G environment.