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AuthorKang, Jiawen
AuthorXiong, Zehui
AuthorNiyato, Dusit
AuthorZou, Yuze
AuthorZhang, Yang
AuthorGuizani, Mohsen
Available date2022-12-13T10:12:46Z
Publication Date2020-04-01
Publication NameIEEE Wireless Communications
Identifierhttp://dx.doi.org/10.1109/MWC.001.1900119
CitationKang, J., Xiong, Z., Niyato, D., Zou, Y., Zhang, Y., & Guizani, M. (2020). Reliable federated learning for mobile networks. IEEE Wireless Communications, 27(2), 72-80.‏
ISSN15361284
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85079692164&origin=inward
URIhttp://hdl.handle.net/10576/37225
AbstractFederated learning, as a promising machine learning approach, has emerged to leverage a distributed personalized dataset from a number of nodes, for example, mobile devices, to improve performance while simultaneously providing privacy preservation for mobile users. In federated learning, training data is widely distributed and maintained on the mobile devices as workers. A central aggregator updates a global model by collecting local updates from mobile devices using their local training data to train the global model in each iteration. However, unreliable data may be uploaded by the mobile devices (i.e., workers), leading to frauds in tasks of federated learning. The workers may perform unreliable updates intentionally, for example, the data poisoning attack, or unintentionally, for example, low-quality data caused by energy constraints or high-speed mobility. Therefore, finding out trusted and reliable workers in federated learning tasks becomes critical. In this article, the concept of reputation is introduced as a metric. Based on this metric, a reliable worker selection scheme is proposed for federated learning tasks. Consortium blockchain is leveraged as a decentralized approach for achieving efficient reputation management of the workers without repudiation and tampering. By numerical analysis, the proposed approach is demonstrated to improve the reliability of federated learning tasks in mobile networks.
SponsorThis work was supported in part by Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure NSoE DeST-SCI2019-0007; A*STAR-NTU-SUTD Joint Research Grant Call on Artificial Intelligence for the Future of Manufacturing RGANS1906, WASP/NTU M4082187 (4080); Singapore MOE Tier 1 2017-T1-002-007 RG122/17, MOE Tier 2 MOE2014-T2-2-015 ARC4/15, Singapore NRF2015-NRF-ISF001-2277, and Singapore EMA Energy Resilience NRF2017EWT-EP003-041; and the National Natural Science Foundation of China under Grant 61601336.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectMobile Networks
TitleReliable Federated Learning for Mobile Networks
TypeArticle
Pagination72-80
Issue Number2
Volume Number27
dc.accessType Abstract Only


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