Clustered Federated Learning with Model Integration for Non-IID Data in Wireless Networks

Jingyi Wang, Zhongyuan Zhao, Wei Hong, Tony Q.S. Quek, Zhiguo Ding

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

As a typical distributed learning paradigm, federated learning has enabled network edge intelligence by making full use of the local data and the computing resources at edge devices without privacy leakage. However, due to the non-IID characteristics of data samples and the unreliability of transmission circumstances, deployment of federated learning in edge networks cannot be well guaranteed. To tackle these challenges, in this paper, a clustered federated learning paradigm with model integration is proposed. First, the detailed framework of our paradigm is introduced. The key idea is to divide the users into multiple individual user clusters by managing the scale and participants of each cluster, and the distribution divergence can be mitigated via cluster-based federated learning. Then, all the learning models are ensembled by model integration to generalize on various target tasks. Second, an upper bound on the accuracy loss of our proposed paradigm is derived, which provides some insights for the impact of data distributions and channel qualities on model performance. To further improve the accuracy performance in wireless networks, a user clustering algorithm is sophisticatedly designed. Finally, the simulation results are provided to verify the significant performance gains of our proposed framework.

Original languageBritish English
Title of host publication2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1634-1639
Number of pages6
ISBN (Electronic)9781665459754
DOIs
StatePublished - 2022
Event2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Virtual, Online, Brazil
Duration: 4 Dec 20228 Dec 2022

Publication series

Name2022 IEEE GLOBECOM Workshops, GC Wkshps 2022 - Proceedings

Conference

Conference2022 IEEE GLOBECOM Workshops, GC Wkshps 2022
Country/TerritoryBrazil
CityVirtual, Online
Period4/12/228/12/22

Keywords

  • Clustered federated learning
  • model integration
  • non-IID data
  • transmission unreliability
  • wireless networks

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