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Adaptive Deadline Determination for Mobile Device Selection in Federated Learning

Authors
Lee, JaewookKo, HaneulPack, Sangheon
Issue Date
3월-2022
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Servers; Computational modeling; Training; Data models; Convergence; Adaptation models; Mobile handsets; Federated learning; mobile device selection; adaptive deadline
Citation
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, v.71, no.3, pp.3367 - 3371
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume
71
Number
3
Start Page
3367
End Page
3371
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/140277
DOI
10.1109/TVT.2021.3136308
ISSN
0018-9545
Abstract
Owing to dynamically changing resources and channel conditions of mobile devices (MDs), when a static deadline-based MD selection scheme is used for federated learning, resource utilization of MDs can be degraded. To mitigate this problem, we propose an adaptive deadline determination (ADD) algorithm for MD selection, where a deadline for each round is adaptively determined with the consideration of the performance disparity of MDs. Evaluation results demonstrate that ADD can achieve the fastest average convergence time among the comparison schemes.
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Pack, Sang heon
공과대학 (전기전자공학부)
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