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Title	A Privacy-Preserving Federated Learning Framework For Iot Environment Based On Secure Multi-Party Computation
ID_Doc	3807
Authors	Geng T.; Liu J.; Huang C.-T.
Year	2024
Published	Proceedings - 2024 IEEE Annual Congress on Artificial Intelligence of Things, AIoT 2024
DOI	http://dx.doi.org/10.1109/AIoT63253.2024.00032
Abstract	Artificial intelligence (AI) techniques, such as federated learning (FL), have been employed in Internet of Things (IoT) networks to harness the enormous amounts of data generated by ubiquitous IoT devices. However, using FL technique in IoT networks brings new security and privacy challenges like model inversion, data poisoning, and central aggregation failure. In this paper, we introduce an innovative framework that integrates blockchain technology and secure multi-party computation (MPC) with FL in IoT networks to address these challenges. Our proposed system enables decentralized and privacy-preserving FL across distributed IoT environments. Utilizing MPC protocols, our framework securely aggregates model updates from clients without revealing individual data, while blockchain technology ensures transparency and immutability of transactions. Security analysis shows that our framework can effectively address the three aforementioned security and privacy challenges and improve the reliability and robustness of FL processes in IoT networks. Our framework can be adapted and integrated with real-world AI-augmented IoT networks, such as smart cities and healthcare, to enhance their protection of security and privacy. © 2024 IEEE.
Author Keywords	Blockchain; Federated learning; IoT; Secure multi-party computation

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