Privacy-preserving gradient boosting tree: Vertical federated learning for collaborative bearing fault diagnosis

Liqiao Xia, Pai Zheng (Corresponding Author), Jinjie Li, Wangchujun Tang, Xiangying Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)208-219
Number of pages12
JournalIET Collaborative Intelligent Manufacturing
Issue number3
Publication statusPublished - 9 Sept 2022

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Science Applications
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence


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