Adaptive Deadline Determination for Mobile Device Selection in Federated Learning

Jaewook Lee, Haneul Ko, Sangheon Pack

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)3367-3371
Number of pages5
JournalIEEE Transactions on Vehicular Technology
Issue number3
Publication statusPublished - 2022 Mar 1

Bibliographical note

Funding Information:
This work was supported by the Future Combat System Network Technology Research Center program of Defense Acquisition Program Administration and Agency for Defense Development under Grant UD190033ED. The review of this article was coordinated by Dr. Ai-Chun Pang.

Publisher Copyright:
© 1967-2012 IEEE.


  • Federated learning
  • adaptive deadline
  • mobile device selection

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics


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