Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/31526
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dc.contributor.authorYou, J-
dc.contributor.authorYang, R-
dc.contributor.authorZhan, Y-
dc.contributor.authorSong, B-
dc.contributor.authorZhang, Y-
dc.contributor.authorWang, Z-
dc.date.accessioned2025-07-10T09:56:49Z-
dc.date.available2025-07-10T09:56:49Z-
dc.date.issued2025-03-12-
dc.identifierORCiD: Junxian You https://orcid.org/0009-0006-6157-7550-
dc.identifierORCiD: Rui Yang https://orcid.org/0000-0002-5634-5476-
dc.identifierORCiD: Yifan Zhan https://orcid.org/0009-0004-8781-4855-
dc.identifierORCiD: Baoye Song https://orcid.org/0000-0003-1631-5237-
dc.identifierORCiD: Yong Zhang https://orcid.org/0000-0002-1537-4588-
dc.identifierORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401-
dc.identifierArticle number: 3518713-
dc.identifier.citationYou, 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.en_US
dc.identifier.issn0018-9456-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/31526-
dc.description.abstractIn 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.en_US
dc.description.sponsorship10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62233012); Jiangsu Provincial Qinglan Project (2021); 10.13039/501100018528-Suzhou Science and Technology Programme (Grant Number: SYG202106).en_US
dc.format.extent1 - 13-
dc.format.mediumPrint-Electronic-
dc.languageEnglish-
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rightsCopyright © 2025 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works ( https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ ).-
dc.rights.urihttps://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/-
dc.subjecthigh-speed trainen_US
dc.subjectfault diagnosisen_US
dc.subjectfederated learningen_US
dc.subjectmulti-task learningen_US
dc.subjectByzantine resilienceen_US
dc.titleBR-MTFL: A Novel Byzantine Resilience-Enhanced Multitask Federated Learning Framework for High-Speed Train Fault Diagnosisen_US
dc.typeArticleen_US
dc.date.dateAccepted2025-01-09-
dc.identifier.doihttps://doi.org/10.1109/TIM.2025.3550635-
dc.relation.isPartOfIEEE Transactions on Instrumentation and Measurement-
pubs.publication-statusPublished-
pubs.volume74-
dc.identifier.eissn1557-9662-
dcterms.dateAccepted2025-01-09-
dc.rights.holderInstitute of Electrical and Electronics Engineers (IEEE)-
Appears in Collections:Dept of Computer Science Research Papers

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