A Privacy-Preserving Federated Grey Wolf Optimization and Blockchain Framework for Trust Management in Decentralized 6G Edge Networks
Abstract
Abstract Trust management in decentralized 6G edge networks faces significant challenges due to dynamic node behaviors, sparse observational data, and the risk of adversarial manipulation. We propose a novel framework that integrates a privacy-preserving federated learning architecture with a Grey Wolf Optimizer (GWO) and a permissioned blockchain subsystem to address these issues. In our approach, each edge node maintains a local trust model that maps behavioral features—such as packet delivery ratio, latency, energy consumption, and anomaly indicators—to a trust score. The key innovation lies in replacing conventional gradient-based weight updates in federated learning with the position-update mechanisms of the Grey Wolf Optimizer. This substitution allows the trust model to escape local minima that frequently arise from noisy or incomplete behavioral data in dynamic network environments. The GWO simulates the social hierarchy and hunting behavior of grey wolves, where the three best candidate solutions (alpha, beta, and delta wolves) guide the population toward optimal trust parameter configurations. Local models are optimized through iterative encircling and position updates, and only the updated model parameters—not raw behavioral data—are transmitted to the federated aggregator, thereby preserving node privacy. To ensure the integrity of this distributed process, we integrate a permissioned blockchain that records each trust update event as an immutable transaction.…
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