Adaptive Deadline Determination for Mobile Device Selection in Federated Learning

Jaewook Lee, Haneul Ko, Sangheon Pack

Research output: Contribution to journalArticlepeer-review


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
JournalIEEE Transactions on Vehicular Technology
Publication statusAccepted/In press - 2021


  • Adaptation models
  • adaptive deadline
  • Computational modeling
  • Convergence
  • Data models
  • Federated learning
  • mobile device selection
  • Mobile handsets
  • Servers
  • Training

ASJC Scopus subject areas

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


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