Design of an iterative method for adaptive federated intrusion detection for energy-constrained edge-centric 6G IoT cyber-physical systems.

The increasing proliferation of 6G-enabled Internet of Things (IoT) in the Cyber-Physical Systems (CPS) domain has engendered requirements for distributed, intelligent, and energy-efficient Intrusion Detection Systems (IDS) operating to the edge. Thus, conventional IDS approaches are largely centralized and ignore some vital constraints of edge-centric CPS, such as limited energy, privacy preservation, and real-time responses to threats. Currently existing federated learning (FL)-based IDS solutions cannot optimize data relevance, model sparsity, or trade-offs for privacy efficiency, resulting in communications overhead and impaired performance under resource constraints. To this end, a Lightweight Federated Intrusion Detection Framework for Edge-Centric 6G IoT CPS is proposed in this paper, incorporating five novel analytical modules to achieve decentralized, adaptive, and resource-aware IDS operations. Foremost, Energy-Adaptive Federated Reinforcement Aggregation (EAFRA) will adjust model updates reasonably depending on local energy so that energy and accuracy can be optimized using reinforcement learning methods. Secondly, Spatio-Temporal Uncertainty-aware Federated Attention Filtering (STUFAF) applies Bayesian uncertainty with contextual metadata in giving priority for the informative updates while reducing false positives. Third, Lightweight Self-Evolving Edge Autoencoder Forest (LSE-EAF) assures low latency and high accuracy detection with minimal resource consumption using a hybrid of anomaly detectors. Fourth, Differentially Private Sparse Cluster Aggregation (DPSCA) does adaptive privacy-preserving sparse updates to contextually clustered nodes to balance privacy and communication costs. Finally, Federated Task-Aware Compression with Cyclical Consistency (FTAC