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Failure prediction in production line based on federated learning: an empirical study

Author

Listed:
  • Ning Ge

    (Beihang University
    Ministry of Industry and Information Technology)

  • Guanghao Li

    (Beihang University)

  • Li Zhang

    (Beihang University)

  • Yi Liu

    (Beihang University)

Abstract

Data protection across organizations is limiting the application of centralized learning (CL) techniques. Federated learning (FL) enables multiple participants to build a learning model without sharing data. Nevertheless, there is very few research works on FL in intelligent manufacturing. This paper presents the results of an empirical study on failure prediction in the production line based on FL. This paper (1) designs Federated Support Vector Machine and federated random forest algorithms for the horizontal FL and vertical FL scenarios, respectively; (2) proposes an experiment process for evaluating the effectiveness between the FL and CL algorithms; (3) finds that the performance of FL and CL are not significantly different on the global testing data, on the random partial testing data, and on the estimated unknown Bosch data, respectively. The fact that the testing data is heterogeneous enhances our findings. Our study reveals that FL can replace CL for failure prediction.

Suggested Citation

  • Ning Ge & Guanghao Li & Li Zhang & Yi Liu, 2022. "Failure prediction in production line based on federated learning: an empirical study," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2277-2294, December.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:8:d:10.1007_s10845-021-01775-2
    DOI: 10.1007/s10845-021-01775-2
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    References listed on IDEAS

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    1. Zhenyu Liu & Donghao Zhang & Weiqiang Jia & Xianke Lin & Hui Liu, 2020. "An adversarial bidirectional serial–parallel LSTM-based QTD framework for product quality prediction," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1511-1529, August.
    2. Toyotaro Suzumura & Yi Zhou & Natahalie Baracaldo & Guangnan Ye & Keith Houck & Ryo Kawahara & Ali Anwar & Lucia Larise Stavarache & Yuji Watanabe & Pablo Loyola & Daniel Klyashtorny & Heiko Ludwig & , 2019. "Towards Federated Graph Learning for Collaborative Financial Crimes Detection," Papers 1909.12946, arXiv.org, revised Oct 2019.
    3. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
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