Blockchain and Edge Computing Enabled Federated Learning Fault Diagnosis Framework

被引：0
|
作者：
Shao H. [1 ]
Xiao Y. [1 ]
Min Z. [1 ]
Han S. [1 ]
Zhang H. [2 ]
机构：
[1] College of Mechanical and Vehicle Engineering, Hunan University, Changsha
[2] Nanjing Research Institute of Electronics Technology, Nanjing
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2023年 / 59卷 / 21期
关键词：
blockchain; edge computing; fault diagnosis; federated learning; industrial Internet of Things;
D O I：
10.3901/JME.2023.21.283
中图分类号：
学科分类号：
摘要：
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. © 2023 Editorial Office of Chinese Journal of Mechanical Engineering. All rights reserved.
引用
收藏
页码：283 / 292
页数：9
相关论文
共 22 条
  • [1] LEI Yaguo, Feng JIA, KONG Detong, Et al., Opportunities and challenges of machinery intelligent fault diagnosis in big data era[J], Journal of Mechanical Engineering, 54, 5, pp. 94-104, (2018)
  • [2] SHAO Haidong, XIAO Yiming, YAN Shen, Et al., Simulation data-driven enhanced unsupervised domain adaptation for bearing fault diagnosis [J], Journal of Mechanical Engineering, 59, 3, pp. 76-85, (2023)
  • [3] ZHANG W, LI X., Federated learning for machinery fault diagnosis with dynamic validation and self-supervision[J], Knowledge-Based Systems, 213, (2021)
  • [4] MCMAHAN H B, MOORE E, RAMAGE D, Et al., Communication-efficient learning of deep networks from decentralized data[C], Proceedings of the 20th International Conference on Machine Learning，(PMLR), 54, pp. 1273-1282, (2017)
  • [5] Chen FANG, GUO Yuanbo, WANG Yifeng, Et al., Edge computing privacy protection method based on blockchain and federated learning[J], Journal on Communications, 42, 11, pp. 28-40, (2021)
  • [6] QIU Tianchen, ZHENG Xiaoying, ZHU Yongxin, Et al., Federated learning architecture for non-IID data[J], Computer Engineering, 49, 7, pp. 110-117, (2023)
  • [7] MA Z, Et al., ShieldFL：Mitigating model poisoning attacks in privacy-preserving federated learning[J], IEEE Transactions Information Forensics Security, 17, pp. 1639-1654, (2022)
  • [8] ZHAO Y, ZHAO J, JIANG L, Et al., Privacy-preserving blockchain-based federated learning for IoT devices[J], IEEE Internet of Things Journal, 8, 3, pp. 1817-1829, (2021)
  • [9] ZHAO Y, LI M, LAI L, Et al., Federated learning with non-IID data[EB/OL], (2018)
  • [10] KARIMIREDDY S P, KALE S, Et al., Suresh. Scaffold：Stochastic controlled averaging for on-device federated learning[C], Proceedings of the 37th International Conference on Machine Learning. (PMLR), (2020)
123