QuantumTrust-FedChain: A Blockchain-Aware Quantum-Tuned Federated Learning System for Cyber-Resilient Industrial IoT in 6G

Saleh Alharbi ()
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Saleh Alharbi: Information Technology Department, College of Computing and Information Technology, Shaqra University, Shaqra City 11961, Saudi Arabia

Future Internet, 2025, vol. 17, issue 11, 1-30

Abstract: Industrial Internet of Things (IIoT) systems face severe security and trust challenges, particularly under cross-domain data sharing and federated orchestration. We present QuantumTrust-FedChain, a cyber-resilient federated learning framework integrating quantum variational trust modeling, blockchain-backed provenance, and Byzantine-robust aggregation for secure IIoT collaboration in 6G networks. The architecture includes a Quantum Graph Attention Network (Q-GAT) for modeling device trust evolution using encrypted device logs. This consensus-aware federated optimizer penalizes adversarial gradients using stochastic contract enforcement, and a shard-based blockchain for real-time forensic traceability. Using datasets from SWaT and TON IoT, experiments show 98.3% accuracy in anomaly detection, 35% improvement in defense against model poisoning, and full ledger traceability with under 8.5% blockchain overhead. This framework offers a robust and explainable solution for secure AI deployment in safety-critical IIoT environments.

Keywords: federated learning; quantum computing; blockchain; industrial IoT; 6G networks; cybersecurity; trust management; byzantine fault tolerance (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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