Trustworthy AI Federated Learning Frameworks for Secure IoT Applications
Keywords:
Deep federated learning (DFL),, Artificial Intelligence, Internet of Things, Zero Knowledge Proofs, general data protection regulation (GDPR),Trust-Based Security·Abstract
This paper proposes a novel framework that unifies GDPR-compliant federated learning architectures with trust-based malicious node identification for IoT networks. By combining privacy preserving mechanisms with trust scoring, the framework ensures both legal compliance and resilience against adversarial participants. The proposed system leverages AI-driven federated learning enhanced with Zero-Knowledge Proofs (ZKPs) to validate model updates without exposing sensitive data. Experimental validation on IoT datasets demonstrates improved robustness,scalability, and compliance compared with existing FL approaches.
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References
J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, and W. Zhao, “A survey on Internet of Things: Architecture, enabling technologies, security and privacy, and applications,” IEEE Internet Things J., vol. 4, no. 5, pp. 1125–1142, Oct. 2017.
H. Lin, K. Kaur, X. Wang, G. Kaddoum, J. Hu, and M. M. Hassan, “Privacy-aware access control in IoT-enabled healthcare: A federated deep learning approach,” IEEE Internet Things J., vol. 10, no. 4, pp. 2893–2902, Feb. 2023.
Y. Liu, J. Wang, Z. Yan, Z. Wan, and R. Jäntti, “A survey on blockchain-based trust management for Internet of Things,” IEEE Internet Things J., vol. 10, no. 7, pp. 5898–5922, Apr. 2023.
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