Blockchain and Edge Computing Enabled Federated Learning Fault Diagnosis Framework

doi:10.3901/JME.2023.21.283

Abstract

Abstract: The industrial Internet of Things (IIoT) promotes mechanical fault diagnosis into the era of big data, however, privacy leakage caused by the need to share local private data among IIoT nodes is an urgent problem to be solved. Federated learning (FL) is expected to be applied to IIoT, which enables nodes to collaboratively train diagnostic models without making private data leave local storage. However, there are many challenges faced by the FL. Firstly, the centralized architecture of FL is highly susceptible to single point of failure. Moreover, the fault data of nodes in the IIoT are usually not independent and identically distributed (non-IID), which makes it difficult for the FL to converge. In addition, the FL lacks defense measures to prevent attacks conducted by malicious nodes. Finally, the FL needs incentive mechanisms to encourage nodes to share resources. Aiming at the challenges introduced above, a blockchain and edge computing enabled FL fault diagnosis framework is proposed, which adopts a decentralized mode to ensure the privacy and security of mechanical equipment fault data in the IIoT. In the proposed framework, a feature-contrastive loss function is constructed to address the non-IID problem. A Byzantine-tolerance scoring mechanism is designed to resist poisonous attacks. A reputation-based incentive algorithm is developed to evaluate the rewards owed to nodes. The proposed method is applied to a simulation scenario of planetary gearbox fault diagnosis for wind turbines in the IIoT, demonstrating its optimal overall performance without the disclosure of local private data.

Key words: blockchain, edge computing, fault diagnosis, federated learning, industrial Internet of Things

CLC Number:

TH17

SHAO Haidong, XIAO Yiming, MIN Zhishan, HAN Songyu, ZHANG Haizhou. Blockchain and Edge Computing Enabled Federated Learning Fault Diagnosis Framework[J]. Journal of Mechanical Engineering, 2023, 59(21): 283-292.

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