Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31526
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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