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

【Author】 Zhang, Weishan; Lu, Qinghua; Yu, Qiuyu; Li, Zhaotong; Liu, Yue; Lo, Sin Kit; Chen, Shiping; Xu, Xiwei; Zhu, Liming

【Source】IEEE INTERNET OF THINGS JOURNAL

【Abstract】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.

【Keywords】Collaborative work; Data models; Blockchain; Servers; Computational modeling; Training; AI; blockchain; edge computing; failure detection; federated learning; IoT; machine learning

【标题】基于区块链的工业物联网设备故障检测联邦学习

【摘要】设备故障检测是工业物联网 (IIoT) 中最重要的问题之一。然而，在传统的 IIoT 设备故障检测中，客户端设备需要将原始数据上传到中央服务器进行模型训练，这可能导致敏感业务数据的泄露。因此，在本文中，为了确保客户端数据隐私，我们提出了一种基于区块链的联邦学习方法，用于 IIoT 中的设备故障检测。首先，我们提出了一种基于区块链的联邦学习系统的平台架构，用于 IIoT 中的故障检测，它可以验证客户端数据的完整性。在该架构中，每个客户端周期性地创建一个 Merkle 树，其中每个叶节点代表一个客户端数据记录，并将树根存储在区块链上。此外，为了解决 IIoT 故障检测中的数据异质性问题，我们提出了一种新颖的质心距离加权联合平均 (CDW_FedAvg) 算法，该算法考虑了每个客户端数据集的正类和负类之间的距离。此外，为了激励客户参与联邦学习，根据本地模型训练中使用的客户数据的大小和质心距离，设计了一种基于智能接触的激励机制。与我们的行业合作伙伴一起实施了所提议架构的原型，并在可行性、准确性和性能方面进行了评估。结果表明，该方法是可行的，具有令人满意的精度和性能。

【关键词】协作工作；数据模型；区块链；服务器；计算建模；训练;人工智能；区块链；边缘计算；故障检测；联邦学习；物联网；机器学习

【发表时间】2021

【收录时间】2022-07-06

【文献类型】Article

【论文大主题】区块链联邦学习

【论文小主题】联邦学习为主体

【影响因子】10.238

【翻译者】石东瑛