A Federated Multi-Agent Communication Framework for Autonomous Cyber Defense in Edge-Cloud IoT Environments
Abstract
With increasing system complexity and attack sophistication, autonomous cyber defense has become essential in edge-cloud Internet of Things (IoT) ecosystems, requiring intelligent, secure, and scalable agent communication frameworks. This paper proposes FMAD (Federated Multi-Agent Defense), a conceptual federated multi-agent framework that enables intelligent agent communication for autonomous cyber defense through collaborative sensing and federated learning, with a design emphasis on security and privacy preservation. The proposed framework defines a set of specialized intelligent agents responsible for distributed traffic sensing, threat intelligence analysis, federated model optimization, secure agent-to-agent communication, and adaptive mitigation. As an initial proof of concept, the Threat Intelligence Agent (TIA) was implemented using an xLSTM-based temporal intrusion detection model developed in this study and trained on the CIC-IoT-2023 dataset, achieving a detection accuracy of 99.89% while efficiently identifying temporal traffic anomalies at edge nodes. The FMAD framework integrates multiple collaborative agents for secure communication, federated learning, and coordinated cyber defense in edge-cloud IoT environments. The experimental evaluation focuses on the xLSTM-based Threat Intelligence Agent (TIA) for DDoS detection, while the remaining components are assessed at the design level. However, empirical validation in the current study is limited solely to the…
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