@article{6a58608a24b64e17b818575b558333cb,
title = "Privacy-preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis",
abstract = "Data-driven fault diagnosis approaches have been widely adopted due to their persuasive performance. However, data are always insufficient to develop effective fault diagnosis models in real manufacturing scenarios. Despite numerous approaches that have been offered to mitigate the negative effects of insufficient data, the most challenging issue lies in how to break down the data silos to enlarge data volume while preserving data privacy. To address this issue, a vertical federated learning (FL) model, privacy-preserving boosting tree, has been developed for collaborative fault diagnosis of industrial practitioners while maintaining anonymity. Only the model information will be shared under the homomorphic encryption protocol, safeguarding data privacy while retaining high accuracy. Besides, an Autoencoder model is provided to encourage practitioners to contribute and then improve model performance. Two bearing fault case studies are conducted to demonstrate the superiority of the proposed approach by comparing it with typical scenarios. This present study's findings offer industrial practitioners insights into investigating the vertical FL in fault diagnosis.",
author = "Liqiao Xia and Pai Zheng and Jinjie Li and Wangchujun Tang and Xiangying Zhang",
note = "Funding Information: This research is partially funded by the Mainland-Hong Kong Joint Funding Scheme (MHX/001/20), Innovation and Technology Commission (ITC), Hong Kong Special Administration Region, National Key R&D Programs of Cooperation on Science and Technology Innovation with Hong Kong, Macao and Taiwan (SQ2020YFE020182), Ministry of Science and Technology (MOST) of the People's Republic of China, Centre for Advances in Reliability and Safety (CAiRS) admitted under AIR@InnoHK Research Cluster, and the Shanghai Rising-Star Plan (Yangfan Program) from the Science and Technology Commission of Shanghai Municipality (22YF1400200). Funding Information: This research is partially funded by the Mainland‐Hong Kong Joint Funding Scheme (MHX/001/20), Innovation and Technology Commission (ITC), Hong Kong Special Administration Region, National Key R&D Programs of Cooperation on Science and Technology Innovation with Hong Kong, Macao and Taiwan (SQ2020YFE020182), Ministry of Science and Technology (MOST) of the People's Republic of China, Centre for Advances in Reliability and Safety (CAiRS) admitted under AIR@InnoHK Research Cluster, and the Shanghai Rising‐Star Plan (Yangfan Program) from the Science and Technology Commission of Shanghai Municipality (22YF1400200). Publisher Copyright: {\textcopyright} 2022 The Authors. IET Collaborative Intelligent Manufacturing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.",
year = "2022",
month = sep,
day = "9",
doi = "10.1049/cim2.12057",
language = "English",
volume = "4",
pages = "208--219",
journal = "IET Collaborative Intelligent Manufacturing",
issn = "2516-8398",
publisher = "John Wiley & Sons Inc.",
number = "3",
}