Volume 30 Issue 5
Sep.  2021
Turn off MathJax
Article Contents
MA Xue, WEN Chenglin. An Asynchronous Quasi-Cloud/Edge/Client Collaborative Federated Learning Mechanism for Fault Diagnosis[J]. Chinese Journal of Electronics, 2021, 30(5): 969-977. doi: 10.1049/cje.2021.07.008
Citation: MA Xue, WEN Chenglin. An Asynchronous Quasi-Cloud/Edge/Client Collaborative Federated Learning Mechanism for Fault Diagnosis[J]. Chinese Journal of Electronics, 2021, 30(5): 969-977. doi: 10.1049/cje.2021.07.008

An Asynchronous Quasi-Cloud/Edge/Client Collaborative Federated Learning Mechanism for Fault Diagnosis

doi: 10.1049/cje.2021.07.008

This work is supported by the Technology Project of China Electric Power Research Institute (No.SGHB0000KXJS1800375).

  • Received Date: 2020-12-17
    Available Online: 2021-09-02
  • Although the federated learning method has the ability to balance data and protect data privacy by means of model aggregation, while the existing methods are difficult to achieve the effectiveness of centralized learning under data sharing. The existing federated structure only has a certain degree of confidentiality for data privacy, that is to say, each client can reconstruct a part of the information of other clients based on the model parameters shared between the server and the clients under certain conditions. In order to make the federated learning mechanism more confidential, we breaks the existing mechanism that the parameters between the federated model and the client model are completely shared, and establishes a new asynchronous quasi-cloud/edge/client collaborative federated learning mechanism. We construct a hierarchical multi-level confidential communication network, where the network parameters are shared in a way of quasi-cloud/edge/client coordination without data communication. The cloud and the edges respectively use the sequential Kalman filter algorithm to perform an asynchronous fusion of the network parameters uploaded in their respective fusion centers for the next round of updates; The effectiveness of the proposed algorithm is verified on the data of a type of rotating machinery.
  • loading
  • Andrea, Calderaro, "Book review:Big data:A revolution that will transform how we live, work, and think", Media, Culture & Society, Vol.37, No.7, pp.1113-1115, 2015.
    V. Grover, R. H. L. Chiang, T. P. Liang, et al., "Creating strategic business value from big data analytics:A research framework", Journal of Management Information Systems, Vol.35, No.2, pp.388-423, 2018.
    J. Zhang and D. Tao, "Empowering things with intelligence:A survey of the progress, challenges, and opportunities in artificial intelligence of things", IEEE Internet of Things Journal, DOI:10.1109/JIOT.2020.3039359, 2020.
    H. Pan, J. Zheng, Y. Yang and B. Hong, "Research on intelligent fault diagnosis method based on CELCD and MFVPMCD", Acta Electronica Sinica, Vol.45, No.03, pp.546-551, 2017. (in Chinese)
    J. Zheng, H. Pan, X. Qi, et al., "Time-frequency analysis method based on improved empirical wavelet transform and its application in rolling bearing fault diagnosis", Acta Electronica Sinica, Vol.46, No.02, pp.358-364, 2018. (in Chinese)
    K. A. Houser and W. G. Voss, "GDPR:The end of google and facebook or a new paradigm in data privacy", Richmond Journal of Law and Technology, Vol.25, No.1, pp.1-109, 2018.
    H. Zhu and Y. Jin, "Multi-objective evolutionary federated learning", IEEE Transactions on Neural Networks, Vol.28, No.12, pp.1-13, 2019.
    E. C. Martins and F. G. Jota, "Design of networked control systems with explicit compensation for time-delay variations", IEEE Transactions on Systems Man & Cybernetics, Part, C, Vol.40, No.3, pp.308-318, 2010.
    Q. Yang, Y. Liu, T. Chen and Y. Tong, "Federated machine learning:Concept and applications", ACM Transactions on Intelligent Systems and Technology, Vol. 10, No. 2, pp.1-19, 2019.
    N. I. Mowla, N. H. Tran, I. Doh and K. Chae, "Federated learning-based cognitive detection of jamming attack in flying ad-hoc network", IEEE Access, Vol.8, No.1, pp.4338-4350, 2020.
    R. C. Geyer, T. Klein and M. Nabi, "Differentially private federated learning a client level perspective", 31st Conference on Neural Information Processing Systems, Vol.15, No.99, pp.911-926, 2017.
    S. Caldas, K. Jakub, H. B. Mcmahan, et al., "Expanding the reach of federated learning by reducing client resource requirements", IEEE Journal on Selected Areas in Communications, Vol.37, No.6, pp.1205-1221, 2018.
    M. Xia, T. Li, L. Xu, et al., "Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks", IEEE-ASME Transactions on Mechatronics, Vol.23, No.1, pp.101-110, 2017.
    A. Bharadwaj and S. Ghosh, "Data reconstruction at surface in immersed-boundary methods", Computers & Fluidsvol, Vol.196, No.1, pp.1-18, 2019.
    X. Hu, W. Pedrycz, G. Wu, et al., "Data reconstruction with information granules:An augmented method of fuzzy clustering", Applied Soft Computing, Vol.55, No.1, pp.523-532, 2017.
    S. Jeong, M. Ferguson, R. Hou, et al., "Sensor data reconstruction using bidirectional recurrent neural network with application to bridge monitoring", Advanced Engineering Informatics, Vol.42, No.1, pp.1-14, 2019.
    J. Zhang, J. Tian, T. Wen, et al., "Deep fault diagnosis for rotating machinery with scarce labeled samples", Chinese Journal of Electronics, Vol.29, No.04, pp.666-672, 2020.
    T. Wen, C. Constantinou, L. Chen, et al., "A practical access point deployment optimization strategy in communicationbased train control systems", IEEE Transactions on Intelligent Transportation Systems, Vol.20, No.8, pp.3156-3167, 2019.
    X. Guo, L. Chen and C. Shen, "Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis", Measurement, Vol.93, No.01, pp.490-502, 2016.
    Y. Lu, X. Huang, Y. Dai, et al., "Blockchain and federated learning for privacy-preserved data sharing in industrial iot", IEEE Transactions on Industrial Informatics, Vol.16, No.6, pp.4177-4186, 2020.
    G. Li, Z. Zhou, C. Hu, et al., "A new safety assessment model for complex system based on the conditional generalized minimum variance and the belief rule base", Safety Science, Vol.93, No.01, pp.108-120, 2017.
    W. S. Mcculloch and W. Pitts, "A logical calculus of the ideas immanent in nervous activity", IEEE Transactions on Industrial Informatics, Vol.5, No.4, pp.115-133, 1943.
    S. Gannot, D. Burshtein and E. Weinstein, "Iterative and sequential kalman filter-based speech enhancement algorithms", IEEE Transactions on Speech and Audio Processing, Vol.6, No.4, pp.373-385, 1998.
    V. D. M. Laurens and G. Hinton, "Visualizing data using TSNE", Journal of Machine Learning Research, Vol.9, No.1, pp.2579-2605, 2008.
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (149) PDF downloads(17) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint