Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Sampling

Jiaxiang Geng, Yanzhao Hou, Xiaofeng Tao, Juncheng Wang, Bing Luo*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

Federated Learning (FL) algorithms commonly sample a random subset of clients to address the straggler issue and improve communication efficiency. While recent works have proposed various client sampling methods, they have limitations in joint system and data heterogeneity design, which may not align with practical heterogeneous wireless networks. In this work, we advocate a new independent client sampling strategy to minimize the wall-clock training time of FL, while considering data heterogeneity and system heterogeneity in both communication and computation. We first derive a new convergence bound for non-convex loss functions with independent client sampling and then propose an adaptive bandwidth allocation scheme. Furthermore, we propose an efficient independent client sampling algorithm based on the upper bounds on the convergence rounds and the expected per-round training time, to minimize the wall-clock time of FL, while considering both the data and system heterogeneity. Experimental results under practical wireless network settings with real-world prototype demonstrate that the proposed independent sampling scheme substantially outperforms the current best sampling schemes under various training models and datasets.

Original languageEnglish
Title of host publicationICC 2024 - IEEE International Conference on Communications
EditorsMatthew Valenti, David Reed, Melissa Torres
PublisherIEEE
Pages944-949
Number of pages6
ISBN (Electronic)9781728190549
ISBN (Print)9781728190556
DOIs
Publication statusPublished - 10 Jun 2024
Event2024 59th IEEE International Conference on Communications, ICC 2024: Scaling the Peaks of Global Communications - Sheraton Denver Downtown Hotel, Denver, United States
Duration: 9 Jun 202413 Jun 2024
https://icc2024.ieee-icc.org/ (Link to conference website)
https://icc2024.ieee-icc.org/program (Link to conference programme)

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607
ISSN (Electronic)1938-1883

Conference

Conference2024 59th IEEE International Conference on Communications, ICC 2024
Country/TerritoryUnited States
CityDenver
Period9/06/2413/06/24
Internet address

Scopus Subject Areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

User-Defined Keywords

  • client sampling
  • convergence analysis
  • Federated learning
  • optimization algorithm
  • wireless network

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