Privacy Preserving Federated Anomaly Detection in IoT Edge Computing Using Bayesian Game Reinforcement Learning

Edge computing (EC) combined with the Internet of Things (IoT) provides a scalable and efficient solution for smart homes. The rapid proliferation of IoT devices poses real-time data processing and security challenges. EC has become a transformative paradigm for addressing these challenges, particularly in intrusion detection and anomaly mitigation. The widespread connectivity of IoT edge networks has exposed them to various security threats, necessitating robust strategies to detect malicious activities. This research presents a privacy-preserving federated anomaly detection framework combined with Bayesian game theory (BGT) and double deep Q-learning (DDQL). The proposed framework integrates BGT to model attacker and defender interactions for dynamic threat level adaptation and resource availability. It also models a strategic layout between attackers and defenders that takes into account uncertainty. DDQL is incorporated to optimize decision-making and aids in learning optimal defense policies at the edge, thereby ensuring policy and decision optimization. Federated learning (FL) enables decentralized and unshared anomaly detection for sensitive data between devices. Data collection has been performed from various sensors in a real-time EC-IoT network to identify irregularities that occurred due to different attacks. The results reveal that the proposed model achieves high detection accuracy of up to 98% while maintaining low resource consumption. This study demonstrates the synergy between game theory and FL to strengthen anomaly detection in EC-IoT networks.