Privacy-Preserving Data Aggregation Scheme Based on Federated Learning for IIoT

Fan Hongbin and Zhou Zhi ()
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Fan Hongbin: College of Computer and Artificial Intelligence, Xiangnan University, Chenzhou 423000, China
Zhou Zhi: Institute of Intelligent Structural Systems, Dalian University of Technology, Dalian 116024, China

Mathematics, 2023, vol. 11, issue 1, 1-15

Abstract: The extensive application of the Internet of Things in the industrial field has formed the industrial Internet of Things (IIoT). By analyzing and training data from the industrial Internet of Things, intelligent manufacturing can be realized. Due to privacy concerns, the industrial data of various institutions cannot be shared, which forms data islands. To address this challenge, we propose a privacy-preserving data aggregation federated learning (PPDAFL) scheme for the IIoT. In federated learning, data aggregation is adopted to protect model changes and provide data security for industrial devices. By utilizing a practical Byzantine fault tolerance (PBFT) algorithm, each round selects an IIoT device from each aggregation area as the data aggregation and initialization node, and uses data aggregation to protect the model changes of a single user while resisting reverse analysis attacks from the industrial management center. The Paillier cryptosystem and secret sharing are combined to realize data security, fault tolerance, and data sharing. A security analysis and performance evaluation show that the scheme reduces computation and communication overheads while guaranteeing data privacy, message authenticity, and integrity.

Keywords: federated learning; IIoT; privacy-preserving; PBFT (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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