Situation-Aware Cluster and Quantization Level Selection Algorithm for Fast Federated Learning

Sangwon Seo, Jaewook Lee, Haneul Ko, Sangheon Pack

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

In federated learning (FL), which clients and quantization levels are selected for the deep model parameters has a significant impact on learning time as well as learning accuracy. This is not a trivial issue because it is also significantly affected by factors, such as computational power, communication capacity, and data distribution. Considering these factors, we formulate a joint optimization problem for clustering and selecting clusters with quantization levels. Due to the high complexity of the formulated problem, we propose a situation-aware cluster and quantization level selection (SITUA-CQ) algorithm. In this algorithm, the FL server first assembles clients into clusters to mitigate the impact of biased data distributions and determines the most suitable clusters and quantization levels based on their computing power and channel quality. Extensive simulation results show that SITUA-CQ can reduce the round time by up to 80.3% compared to conventional algorithms.

Original languageEnglish
Pages (from-to)13292-13302
Number of pages11
JournalIEEE Internet of Things Journal
Volume10
Issue number15
DOIs
Publication statusPublished - 2023 Aug 1

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation (NRF) of Korea Grant funded by the Korean Government (MSIT) under Grant 2021R1A4A3022102 and Grant 2020R1A2C3006786. This article was presented in part at IEEE Wireless Communications and Networking Conference (WCNC), Austin, TX, USA, 2022 [DOI: 10.1109/WCNC51071.2022.9771600].

Publisher Copyright:
© 2014 IEEE.

Keywords

  • Cluster and quantization level selection
  • clustering
  • federated learning (FL)

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Science Applications

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