A Multi-Rank Federated Distillation Framework for Data-Imbalance Fault Diagnosis of Multi-Railway High-Speed Train Bogies

Abstract

To address the challenge of secure federated modeling in fault diagnosis under imbalanced data scenarios for multi-railway high-speed train bogies, this study proposes a multi-rank federated distillation (MFD) framework aimed at enhancing the generalization capacity of clients with limited sample sizes. First, the MFD framework is designed to perform multiple distillation tasks, with each task’s loss function decoupled into two components to balance losses between target and non-target classes. Second, an adaptive weight adjustment strategy is introduced to efficiently train models by coordinating the loss outputs across these tasks. Third, to mitigate the learning costs associated with the MFD, clients share a foundational shallow network via model transfer while incorporating personalized modules to improve adaptability. By validating the proposed framework on datasets from high-speed train bogies across multiple railways, this study demonstrates its effectiveness in addressing challenges associated with secure federated modeling while maintaining satisfactory diagnostic performance. The findings present a viable solution for implementing federated learning among clients with imbalanced data in industrial applications.