Energy or Accuracy? Near-Optimal User Selection and Aggregator Placement for Federated Learning in MEC

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

4 Scopus Citations
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Author(s)

  • Zichuan Xu
  • Dongrui Li
  • Wenzheng Xu
  • Qiufen Xia
  • Pan Zhou
  • Omer F. Rana
  • Hao Li

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)2470-2485
Number of pages16
Journal / PublicationIEEE Transactions on Mobile Computing
Volume23
Issue number3
Online published29 Mar 2023
Publication statusPublished - Mar 2024

Abstract

To unveil the hidden value in the datasets of user equipments (UEs) while preserving user privacy, federated learning (FL) is emerging as a promising technique to train a machine learning model using the datasets of UEs locally without uploading the datasets to a central location. Customers require to train machine learning models based on different datasets of UEs, through issuing FL requests that are implemented by FL services in a mobile edge computing (MEC) network. A key challenge of enabling FL in MEC networks is how to minimize the energy consumption of implementing FL requests while guaranteeing the accuracy of machine learning models, given that the availabilities of UEs usually are uncertain. In this paper, we investigate the problem of energy minimization for FL in an MEC network with uncertain availabilities of UEs. We first consider the energy minimization problem for a single FL request in an MEC network. We then propose a novel optimization framework for the problem with a single FL request, which consists of (1) an online learning algorithm with a bounded regret for the UE selection, by considering various contexts (side information) that influence energy consumption; and (2) an approximation algorithm with an approximation ratio for the aggregator placement for a single FL request. We thirdly deal with the problem with multiple FL requests, for which we devise an online learning algorithm with a bounded regret. We finally evaluate the performance of the proposed algorithms by extensive experiments. Experimental results show that the proposed algorithms outperform their counterparts by reducing at least 13% of the total energy consumption while achieving the same accuracy. © 2023 IEEE.

Research Area(s)

  • Approximation algorithms, Base stations, Computational modeling, Energy consumption, Energy minimization, Federated learning, Machine learning based algorithms, Minimization, Mobile computing, Mobile edge computing, Training, UE selection and aggregator placement

Citation Format(s)

Energy or Accuracy? Near-Optimal User Selection and Aggregator Placement for Federated Learning in MEC. / Xu, Zichuan; Li, Dongrui; Liang, Weifa et al.
In: IEEE Transactions on Mobile Computing, Vol. 23, No. 3, 03.2024, p. 2470-2485.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review