FL-GWO: A Federated Learning and Grey Wolf Optimization Framework for Blockchain-Assisted Trust Management in Decentralized IoT-Edge Systems
Abstract
Abstract Trust management in decentralized IoT-edge systems faces significant challenges due to dynamic network behaviors and the risk of malicious nodes. Existing approaches often rely on static trust models or centralized aggregation, which fail to adapt to evolving conditions and compromise privacy. We propose FL-GWO, a novel framework that integrates federated learning with Grey Wolf Optimization for blockchain-assisted trust management. In this system, each participating node trains a local trust model on its private behavioral data without sharing raw information, thereby preserving data privacy. The key innovation lies in replacing conventional gradient-based aggregation with a swarm intelligence mechanism during the global model update phase. Specifically, the Grey Wolf Optimization algorithm treats the search for optimal trust parameters as a multi-dimensional hunting problem, where the three best candidate solutions guide the pack toward superior parameter configurations. These optimized parameters are then broadcast back to local nodes, creating a closed-loop feedback system that continuously refines trust evaluation. The final trust score for each node is computed as a weighted non-linear function of six behavioral features, including communication reliability, packet delivery, latency, energy consumption, reputation, and anomaly detection. This mathematical structure ensures that trust weights adapt dynamically to changing network conditions rather than…
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.