How Robust is Federated Learning to Communication Error? A Comparison Study Between Uplink and Downlink Channels

Linping Qu, Shenghui Song, Chi Ying Tsui, Yuyi Mao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Because of its privacy-preserving capability, federated learning (FL) has attracted significant attention from both academia and industry. However, when being implemented over wireless networks, it is not clear how much communication error can be tolerated by FL. This paper investigates the robustness of FL to the uplink and downlink communication error. Our theoretical analysis reveals that the robustness depends on two critical parameters, namely the number of clients and the numerical range of model parameters. It is also shown that the uplink communication in FL can tolerate a higher bit error rate (BER) than downlink communication, and this difference is quantified by a proposed formula. The findings and theoretical analyses are further validated by extensive experiments.

Original languageEnglish
Title of host publication2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350303582
DOIs
Publication statusPublished - 3 Jul 2024
Event25th IEEE Wireless Communications and Networking Conference, WCNC 2024 - Dubai, United Arab Emirates
Duration: 21 Apr 202424 Apr 2024

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
ISSN (Print)1525-3511

Conference

Conference25th IEEE Wireless Communications and Networking Conference, WCNC 2024
Country/TerritoryUnited Arab Emirates
CityDubai
Period21/04/2424/04/24

Keywords

  • bit error rate (BER)
  • Federated learning (FL)
  • uplink and downlink

ASJC Scopus subject areas

  • General Engineering

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