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ClusterFL: A Clustering-based Federated Learning System for Human Activity Recognition

Published: 08 December 2022 Publication History

Abstract

Federated Learning (FL) has recently received significant interest, thanks to its capability of protecting data privacy. However, existing FL paradigms yield unsatisfactory performance for a wide class of human activity recognition (HAR) applications, since they are oblivious to the intrinsic relationship between data of different users. We propose ClusterFL, a clustering-based federated learning system that can provide high model accuracy and low communication overhead for HAR applications. ClusterFL features a novel clustered multi-task federated learning framework that minimizes the empirical training loss of multiple learned models while automatically capturing the intrinsic clustering relationship among the nodes. We theoretically prove the convergence of proposed FL framework for non-convex and strongly convex models and provide the guidance on selection of hyper-parameters for achieving such convergence. Based on the learned cluster relationship, ClusterFL can efficiently drop the nodes that converge slower or have little correlations with others in each cluster, significantly speeding up the convergence while maintaining the accuracy performance. We evaluate the performance of ClusterFL on an NVIDIA edge testbed using four new HAR datasets collected from 145 users. The results show that ClusterFL outperforms several state-of-the-art FL paradigms in terms of overall accuracy and can save more than 50% communication overhead.

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Published In

cover image ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks  Volume 19, Issue 1
February 2023
565 pages
ISSN:1550-4859
EISSN:1550-4867
DOI:10.1145/3561987
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

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Publication History

Published: 08 December 2022
Online AM: 04 August 2022
Accepted: 14 July 2022
Revised: 06 April 2022
Received: 28 September 2021
Published in TOSN Volume 19, Issue 1

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Author Tags

  1. Activity recognition
  2. federated learning
  3. clustering
  4. multi-task learning

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  • Research-article
  • Refereed

Funding Sources

  • Research Grants Council (RGC) of Hong Kong, China
  • Alzheimer’s Drug Discovery Foundation
  • Shenzhen Science and Technology
  • Guangdong Basic and Applied Basic Research Foundation
  • Shenzhen Institute of Artificial Intelligence and Robotics for Society

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  • (2025)FedDSHAR: A dual-strategy federated learning approach for human activity recognition amid noise label userFuture Generation Computer Systems10.1016/j.future.2025.107724166(107724)Online publication date: May-2025
  • (2025)FeL-MAR: Federated learning based multi resident activity recognition in IoT enabled smart homesFuture Generation Computer Systems10.1016/j.future.2024.107552163(107552)Online publication date: Feb-2025
  • (2024)Federated Learning for Mobility ApplicationsACM Computing Surveys10.1145/363786856:5(1-28)Online publication date: 12-Jan-2024
  • (2024)Mobile_FL: A streamlined FL framework for process optimisation via client clustering using rough c-means algorithmProceedings of the 10th ACM Cyber-Physical System Security Workshop10.1145/3626205.3659151(88-97)Online publication date: 2-Jul-2024
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  • (2024)Communication-Efficient and Privacy-Preserving Federated Learning via Joint Knowledge Distillation and Differential Privacy in Bandwidth-Constrained NetworksIEEE Transactions on Vehicular Technology10.1109/TVT.2024.342371873:11(17586-17601)Online publication date: Nov-2024
  • (2024)Democratizing Federated WiFi-Based Human Activity Recognition Using Hypothesis TransferIEEE Transactions on Mobile Computing10.1109/TMC.2024.345778823:12(15132-15148)Online publication date: Dec-2024
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