Blockchain-Based Federated Learning for Device Failure Detection in Industrial IoT

• Published in: IEEE internet of things journal Vol. 8; no. 7; pp. 5926 - 5937
• Main Authors: Zhang, Weishan, Lu, Qinghua, Yu, Qiuyu, Li, Zhaotong, Liu, Yue, Lo, Sin Kit, Chen, Shiping, Xu, Xiwei, Zhu, Liming
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Blockchain; Blockchains; Centroids; Collaboration; Computational modeling; Computer architecture; Cryptography; Data models; edge computing; Failure detection; Feasibility; Federated learning; Heterogeneity; Industrial applications; Industrial Internet of Things; Internet of Things; IoT; machine learning; Servers; Training
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2020.3032544

Device failure detection is one of most essential problems in Industrial Internet of Things (IIoT). However, in conventional IIoT device failure detection, client devices need to upload raw data to the central server for model training, which might lead to disclosure of sensitive business data. Therefore, in this article, to ensure client data privacy, we propose a blockchain-based federated learning approach for device failure detection in IIoT. First, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT, which enables verifiable integrity of client data. In the architecture, each client periodically creates a Merkle tree in which each leaf node represents a client data record, and stores the tree root on a blockchain. Furthermore, to address the data heterogeneity issue in IIoT failure detection, we propose a novel centroid distance weighted federated averaging (CDW_FedAvg) algorithm taking into account the distance between positive class and negative class of each client data set. In addition, to motivate clients to participate in federated learning, a smart contact-based incentive mechanism is designed depending on the size and the centroid distance of client data used in local model training. A prototype of the proposed architecture is implemented with our industry partner, and evaluated in terms of feasibility, accuracy, and performance. The results show that the approach is feasible, and has satisfactory accuracy and performance.