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Adaptive Dataset Management Scheme for Lightweight Federated Learning in Mobile Edge Computing.

Jingyeom Kim1, Juneseok Bang1, Joohyung Lee1

  • 1School of Computing, Gachon University, Seongnam 13120, Republic of Korea.

Sensors (Basel, Switzerland)
|April 27, 2024
PubMed
Summary
This summary is machine-generated.

Federated learning (FL) on mobile devices faces challenges due to limited computing power. This study introduces an adaptive dataset management (ADM) scheme to reduce local training burdens, improving participation in the Internet of Things.

Keywords:
dataset managementfederated learningmobile edge computing

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Area of Science:

  • Machine Learning
  • Mobile Edge Computing
  • Internet of Things

Background:

  • Federated learning (FL) enables collaborative model training across mobile devices (MDs) without data exposure.
  • FL alleviates central server burden but imposes significant local training computation on MDs with limited capabilities.

Purpose of the Study:

  • To propose an adaptive dataset management (ADM) scheme to reduce local training burden on MDs in FL.
  • To address the challenge of limited computing power on MDs hindering their contribution to FL.

Main Methods:

  • Empirical study on dataset size impact on accuracy gain over communication rounds.
  • Introduction of a discount factor representing the reduced impact of dataset size on accuracy.
  • Development of a theoretical framework for the ADM problem, considering the discount factor and Kullback-Leibler divergence (KLD).
  • Proposal of a greedy-based heuristic algorithm to solve the non-convex ADM optimization problem.

Main Results:

  • Confirmed that dataset size has a diminishing impact on accuracy gain in FL.
  • The proposed ADM scheme effectively reduces the training burden on MDs.
  • The heuristic algorithm provides a suboptimal solution with low complexity.

Conclusions:

  • The ADM scheme successfully alleviates local training load on MDs in FL.
  • Acceptable training accuracy is maintained while reducing computational demands on mobile devices.
  • The approach enhances the feasibility of FL in resource-constrained IoT environments.