Federated transfer learning in fault diagnosis under data privacy with target self-adaptation

Abstract

The past decades have witnessed great developments and applications of the data-driven machinery fault diagnosis methods. Due to the difficulties and significant expenses in collecting labeled data, multiple industrial users generally would like to collaborate for training powerful models. However, for data privacy concerns, the local data generally cannot be shared with others for centralized training. Moreover, the data at different users are usually obtained under different conditions. The model generalization problem is quite challenging since no simultaneous data access is permitted. To address these issues, a federated transfer learning method is proposed. Fake data of additional classes are generated to suppress the decision boundaries, and only the models are communicated across different clients. A prediction alignment scheme is proposed at the target client for self-adaptation. A model and instance-level consensus scheme is introduced to enhance model performance. The proposed method achieves promising knowledge transfer effects with only communications of models, and the challenging partial transfer learning problems can be also well addressed. Experiments on real-world machinery datasets validate the proposed method, which is promising for applications in the real industries.