A Unified Federated Framework for Trust Management and Privacy-Preserving Anomaly Detection in Zero Trust Edge Networks

Abstract

Edge computing and Internet of Things (IoT) have expanded the attack surface of modern networks. Security designs often tradeoff detection quality and privacy: centralized trust creates single points of failure, while distributed approaches may sacrifice accuracy or formal privacy guarantees. This article presents a federated trust modeling framework that integrates multimodal anomaly detection, Byzantine-resilient federated learning with $(\epsilon,\delta)$-differential privacy (DP), and context-aware zero trust architecture decision-making. The three-layer architecture comprises device-level trust learning with enhanced variational autoencoder, Isolation Forest, long short-term memory, and statistical process control modalities; a federated aggregation layer for robust aggregation with DP; and a trust scoring and decision layer that maps evidence to continuous trust and access levels using a subjective logic-inspired formulation. We provide detailed algorithmic implementations with pseudocode for each layer. Comprehensive evaluation demonstrates exceptional performance: high precision with low false positive rate, near-linear scalability achieving high efficiency, high accuracy with precision detecting most of attacks with zero false alarms, and sublinear time complexity. Privacy preservation is maintained through DP guarantees without accuracy degradation. The results support deployability studies for large-scale IoT and Artificial Intelligence of Things settings, with generalization to real telemetry left to future work.