Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154439
Title: Reliable federated learning for mobile networks
Authors: Kang, Jiawen
Xiong, Zehui
Niyato, Dusit
Zou, Yuze
Zhang, Y.
Guizani, M.
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Kang, 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. https://dx.doi.org/10.1109/MWC.001.1900119
Project: NSoE DeST-SCI2019-0007
RGANS1906
WASP/NTU M4082187 (4080)
2017-T1-002-007 RG122/17
MOE2014-T2-2-015 ARC4/15
NRF2015-NRF-ISF001-2277
NRF2017EWT-EP003-041
Journal: IEEE Wireless Communications
Abstract: Federated 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.
URI: https://hdl.handle.net/10356/154439
ISSN: 1556-6013
DOI: 10.1109/MWC.001.1900119
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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