Towards Energy-Efficient Federated Learning over 5G+ Mobile Devices

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

10 Scopus Citations
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Author(s)

  • Dian Shi
  • Liang Li
  • Rui Chen
  • Pavana Prakash
  • Miao Pan

Detail(s)

Original languageEnglish
Pages (from-to)44-51
Journal / PublicationIEEE Wireless Communications
Volume29
Issue number5
Online published9 May 2022
Publication statusPublished - Oct 2022
Externally publishedYes

Abstract

The continuous convergence of machine learning algorithms, 5G and beyond (5G+) wireless communications, and artificial intelligence (AI) hardware implementation hastens the birth of federated learning (FL) over 5G+ mobile devices, which pushes AI functions to mobile devices and initiates a new era of on-device AI applications. Despite the remarkable progress made in FL, huge energy consumption is one of the most significant obstacles restricting the development of FL over battery-constrained 5G+ mobile devices. To address this issue, in this paper, we investigate how to develop energy efficient FL over 5G+ mobile devices by making a trade-off between energy consumption for "working" (i.e., local computing) and that for "talking" (i.e., wireless communications) in order to boost the overall energy efficiency. Specifically, we first examine energy consumption models for graphics processing unit (GPU) computation and wireless transmissions. Then, we overview the state of the art of integrating FL procedure with energyefficient learning techniques (e.g., gradient sparsification, weight quantization, pruning, etc.). Finally, we present several potential future research directions for FL over 5G+ mobile devices from the perspective of energy efficiency.

Research Area(s)

  • 5G mobile communication, Computational modeling, Energy consumption, Mobile handsets, Quantization (signal), Training, Wireless communication

Citation Format(s)

Towards Energy-Efficient Federated Learning over 5G+ Mobile Devices. / Shi, Dian; Li, Liang; Chen, Rui et al.
In: IEEE Wireless Communications, Vol. 29, No. 5, 10.2022, p. 44-51.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review