区块链和边缘计算赋能的联邦学习故障诊断框架

doi:10.3901/JME.2023.21.283

机械工程学报 ›› 2023, Vol. 59 ›› Issue (21): 283-292.doi: 10.3901/JME.2023.21.283

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区块链和边缘计算赋能的联邦学习故障诊断框架

邵海东¹, 肖一鸣¹, 闵志闪¹, 韩淞宇¹, 张海舟²

1. 湖南大学机械与运载工程学院长沙 410082;
2. 南京电子技术研究所南京 210039

收稿日期:2022-12-26 修回日期:2023-07-01 出版日期:2023-11-05 发布日期:2024-01-15
通讯作者: 邵海东(通信作者)，男，1990年出生，博士，副教授，博士研究生导师。主要研究方向为故障诊断与寿命预测，数据挖掘与信息融合，工业大数据分析。E-mail：hdshao@hnu.edu.cn
作者简介:肖一鸣，男，1999年出生，博士研究生。主要研究方向为联邦学习故障诊断，不确定性分析。E-mail：xiaoym@hnu.edu.cn
基金资助:
国家自然科学基金(52275104)和湖南省自然科学基金优秀青年科学基金(2021JJ20017)资助项目。

Blockchain and Edge Computing Enabled Federated Learning Fault Diagnosis Framework

SHAO Haidong¹, XIAO Yiming¹, MIN Zhishan¹, HAN Songyu¹, ZHANG Haizhou²

1. College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082;
2. Nanjing Research Institute of Electronics Technology, Nanjing 210039

Received:2022-12-26 Revised:2023-07-01 Online:2023-11-05 Published:2024-01-15

摘要/Abstract

摘要： 工业物联网助推机械故障诊断步入大数据时代，然而因各节点需要共享本地的私有数据而造成隐私泄露是当前工业物联网亟需解决的问题。联邦学习有望应用于工业物联网以实现在私有数据不离开本地存储的前提下，协同各节点训练诊断模型。然而，联邦学习面临着以下诸多挑战。首先，联邦学习的中心化架构极易引发单点故障。其次，工业物联网中各节点的故障数据通常是非独立同分布的，以致联邦学习难以收敛。再次，联邦学习缺乏防御手段来阻止恶意节点的攻击。最后，联邦学习需要激励机制来鼓励节点分享资源。针对这些挑战，提出了一种区块链和边缘计算赋能的联邦学习故障诊断框架，采用去中心化的模式保障工业物联网中机械设备故障数据的隐私和安全。在此框架中，构造了一种特征对比损失函数来解决非独立同分布问题，设计了一种拜占庭容错的评分机制来抵抗投毒攻击，并开发了一种基于信誉的激励算法来评估应给予节点的奖励。所提方法被应用于工业物联网中风力发电机的行星齿轮箱故障诊断模拟场景，在私有本地数据不泄露的前提下，展现出最优的综合性能。

关键词: 区块链, 边缘计算, 故障诊断, 联邦学习, 工业物联网

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

中图分类号:

TH17

邵海东, 肖一鸣, 闵志闪, 韩淞宇, 张海舟. 区块链和边缘计算赋能的联邦学习故障诊断框架[J]. 机械工程学报, 2023, 59(21): 283-292.

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.

参考文献

[1] 雷亚国,贾峰,孔德同,等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报, 2018, 54(5):94-104. LEI Yaguo, JIA Feng, KONG Detong, et al. Opportunities and challenges of machinery intelligent fault diagnosis in big data era[J]. Journal of Mechanical Engineering, 2018, 54(5):94-104.
[2] 邵海东,肖一鸣,颜深,等. 仿真数据驱动的改进无监督域适应轴承故障诊断[J]. 机械工程学报, 2023, 59(3):76-85. SHAO Haidong, XIAO Yiming, YAN Shen, et al. Simulation data-driven enhanced unsupervised domain adaptation for bearing fault diagnosis[J]. Journal of Mechanical Engineering, 2023, 59(3):76-85.
[3] ZHANG W, LI X. Federated learning for machinery fault diagnosis with dynamic validation and self-supervision[J]. Knowledge-Based Systems, 2021, 213:106679.
[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), 2017, 54:1273-1282.
[5] 方晨,郭渊博,王一丰,等. 基于区块链和联邦学习的边缘计算隐私保护方法[J]. 通信学报, 2021, 42(11):28-40. FANG Chen, GUO Yuanbo, WANG Yifeng, et al. Edge computing privacy protection method based on blockchain and federated learning[J]. Journal on Communications, 2021, 42(11):28-40.
[6] 邱天晨,郑小盈,祝永新,等. 面向非独立同分布数据的联邦学习架构[J]. 计算机工程, 2023, 49(7):110-117. QIU Tianchen, ZHENG Xiaoying, ZHU Yongxin, et al. Federated learning architecture for non-IID data[J]. Computer Engineering, 2023, 49(7):110-117.
[7] MA Z, MA J, MIAO Y, et al. ShieldFL:Mitigating model poisoning attacks in privacy-preserving federated learning[J]. IEEE Transactions Information Forensics Security, 2022, 17:1639-1654.
[8] ZHAO Y, ZHAO J, JIANG L, et al. Privacy-preserving blockchain-based federated learning for IoT devices[J]. IEEE Internet of Things Journal, 2021, 8(3):1817-1829.
[9] ZHAO Y, LI M, LAI L, et al. Federated learning with non-IID data[EB/OL]. arXiv preprint arXiv:1806.00582, 2018.
[10] KARIMIREDDY S P, KALE S, MOHRI M, et al. Suresh. Scaffold:Stochastic controlled averaging for on-device federated learning[C]//Proceedings of the 37th International Conference on Machine Learning. (PMLR), 2020.
[11] ZHANG W, LU Q, YU Q, et al. Blockchain-based federated learning for device failure detection in industrial IoT[J]. IEEE Internet of Things Journal, 2021, 8(7):5926-5937.
[12] LI Y, CHEN C, LIU N, et al. A blockchain-based decentralized federated learning framework with committee consensus[J]. IEEE Network, 202135(1):234-241.
[13] XU Y, LU Z, GAI K, et al. BESIFL:Blockchain empowered secure and incentive federated learning paradigm in IoT[J]. IEEE Internet Things Journal, 2023, 10(8):6561-6573.
[14] LIU Y, PENG J, KANG J, et al. A secure federated learning framework for 5G networks[J]. IEEE Wireless Communications, 2020, 27(4):24-31.
[15] KANG J, XIONG Z, NIYATO D, et al. Incentive mechanism for reliable federated learning:A joint optimization approach to combining reputation and contract theory[J]. IEEE Internet of Things Journal, 2019, 6(6):10700-10714.
[16] FENG S, NIYATO D, WANG P, et al. Joint service pricing and cooperative relay communication for federated learning[C]//iThings/GreenCom/CPSCom/SmartData, 2019:815-820.
[17] LI Q, HE B, SONG D. Model-Contrastive federated learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021:10713-10722.
[18] ZHANG Y, SCHAAR M. Reputation-based incentive protocols in crowdsourcing applications[C]//2012 Proceedings IEEE INFOCOM, 2012:2140-2148.
[19] GILAD Y, HEMO R, MICALI S, et al. Algorand:Scaling Byzantine agreements for cryptocurrencies[C]//Proc ACM Symp Oper Syst Principles (SOSP), 2017:51-68.
[20] 陈晓磊,徐小力,吴国新. 物联网架构下风力发电机组远程状态监测系统设计[J]. 风机技术, 2013(1):63-66, 88. CHEN Xiaolei, XU Xiaoli, WU Guoxin. Design of Internet of Thing structured remote condition monitoring system for wind turbines[J]. Chinese Journal of Turbomachinery Chinese Journal of Turbomachinery, 2013(1):63-66, 88.
[21] LI T, ZHOU Z, LI S, et al. The emerging graph neural networks for intelligent fault diagnostics and prognostics:A guideline and a benchmark study[J]. Mechanical Systems and Signal Processing, 2022, 168:108653.
[22] YUROCHKIN M, AGARWAL M, GHOSH S, et al. Bayesian nonparametric federated learning of neural networks[C]//Proceedings of the 36th International Conference on Machine Learning, (PMLR), 2019, 97:7252-7261.

区块链和边缘计算赋能的联邦学习故障诊断框架

Blockchain and Edge Computing Enabled Federated Learning Fault Diagnosis Framework

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