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Title: | BR-MTFL: A Novel Byzantine Resilience-Enhanced Multitask Federated Learning Framework for High-Speed Train Fault Diagnosis |
Authors: | You, J Yang, R Zhan, Y Song, B Zhang, Y Wang, Z |
Keywords: | high-speed train;fault diagnosis;federated learning;multi-task learning;Byzantine resilience |
Issue Date: | 12-Mar-2025 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Citation: | You, J. et al. (2025) 'BR-MTFL: A Novel Byzantine Resilience-Enhanced Multitask Federated Learning Framework for High-Speed Train Fault Diagnosis', IEEE Transactions on Instrumentation and Measurement, 74, 3518713, pp. 1 - 13. doi: 10.1109/TIM.2025.3550635. |
Abstract: | In high-speed train systems deployed across diverse geographical regions, robust fault diagnosis techniques are essential for ensuring operational safety. This article proposes the Byzantine resilience-enhanced multitask federated learning (BR-MTFL) framework, a novel framework tailored for the complexities of fault diagnosis in traction asynchronous motors under varying operational conditions. This framework innovatively introduces multitask federated learning (MTFL) to accommodate regional law restrictions and varying fault diagnosis requirements. In addition, the Byzantine resilience of our proposed framework is specifically enhanced to address the challenges posed by inconsistent and potentially misleading feature distributions across different train networks. BR-MTFL is practically validated through experiments conducted across nine clients, each representing a distinct set of fault types and operational conditions typical of high-speed trains. The experiments demonstrate the ability of BR-MTFL to outperform conventional federated learning frameworks in terms of accuracy and resilience to Byzantine threats. BR-MTFL establishes a new standard for federated learning applications in high-speed train fault diagnosis, particularly where data diversity and privacy dominate. |
URI: | https://bura.brunel.ac.uk/handle/2438/31526 |
DOI: | https://doi.org/10.1109/TIM.2025.3550635 |
ISSN: | 0018-9456 |
Other Identifiers: | ORCiD: Junxian You https://orcid.org/0009-0006-6157-7550 ORCiD: Rui Yang https://orcid.org/0000-0002-5634-5476 ORCiD: Yifan Zhan https://orcid.org/0009-0004-8781-4855 ORCiD: Baoye Song https://orcid.org/0000-0003-1631-5237 ORCiD: Yong Zhang https://orcid.org/0000-0002-1537-4588 ORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401 Article number: 3518713 |
Appears in Collections: | Dept of Computer Science Research Papers |
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