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Robust federated learning with noisy communication
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Ang, Fan, Chen, Li, Zhao, Nan, Chen, Yunfei, Wang, Weidong and Yu, F. Richard (2020) Robust federated learning with noisy communication. IEEE Transactions on Communications, 68 (6). pp. 3452-3464. doi:10.1109/TCOMM.2020.2979149 ISSN 0090-6778.
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Official URL: https://doi.org/10.1109/TCOMM.2020.2979149
Abstract
Federated learning is a communication-efficient training process that alternate between local training at the edge devices and averaging of the updated local model at the center server. Nevertheless, it is impractical to achieve perfect acquisition of the local models in wireless communication due to the noise, which also brings serious effect on federated learning. To tackle this challenge in this paper, we propose a robust design for federated learning to decline the effect of noise. Considering the noise in two aforementioned steps, we first formulate the training problem as a parallel optimization for each node under the expectation-based model and worst-case model. Due to the non-convexity of the problem, regularizer approximation method is proposed to make it tractable. Regarding the worst-case model, we utilize the sampling-based successive convex approximation algorithm to develop a feasible training scheme to tackle the unavailable maxima or minima noise condition and the non-convex issue of the objective function. Furthermore, the convergence rates of both new designs are analyzed from a theoretical point of view. Finally, the improvement of prediction accuracy and the reduction of loss function value are demonstrated via simulation for the proposed designs.
Item Type: | Journal Article | |||||||||
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Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | |||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||
Library of Congress Subject Headings (LCSH): | Wireless communication systems , Signal theory (Telecommunication) , Electric noise | |||||||||
Journal or Publication Title: | IEEE Transactions on Communications | |||||||||
Publisher: | IEEE | |||||||||
ISSN: | 0090-6778 | |||||||||
Official Date: | June 2020 | |||||||||
Dates: |
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Volume: | 68 | |||||||||
Number: | 6 | |||||||||
Page Range: | pp. 3452-3464 | |||||||||
DOI: | 10.1109/TCOMM.2020.2979149 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Reuse Statement (publisher, data, author rights): | © 2020 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. | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 5 March 2020 | |||||||||
Date of first compliant Open Access: | 10 March 2020 | |||||||||
RIOXX Funder/Project Grant: |
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