research-article

Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization

Authors:

Zhihua WuAuthors Info & Claims

ACM Transactions on Sensor Networks, Volume 20, Issue 2

Article No.: 48, Pages 1 - 32

https://doi.org/10.1145/3638052

Published: 16 February 2024 Publication History

Abstract

Federated learning (FL) has been proposed as a privacy-preserving distributed learning paradigm, which differs from traditional distributed learning in two main aspects: the systems heterogeneity, meaning that clients participating in training have significant differences in systems performance including CPU frequency, dataset size, and transmission power, and the statistical heterogeneity, indicating that the data distribution among clients exhibits Non-Independent Identical Distribution. Therefore, the random selection of clients will significantly reduce the training efficiency of FL. In this article, we propose a client selection mechanism considering both systems and statistical heterogeneity, which aims to improve the time-to-accuracy performance by trading off the impact of systems performance differences and data distribution differences among the clients on training efficiency. First, client selection is formulated as a combinatorial optimization problem that jointly optimizes systems and statistical performance. Then, we generalize it to a submodular maximization problem with knapsack constraint, and propose the Iterative Greedy with Partial Enumeration (IGPE) algorithm to greedily select the suitable clients. Then, the approximation ratio of IGPE is analyzed theoretically. Extensive experiments verify that the time-to-accuracy performance of the IGPE algorithm outperforms other compared algorithms in a variety of heterogeneous environments.

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Cited By

Sabah FChen YYang ZRaheem AAzam MAhmad NSarwar R(2025)FairDPFL-SCS: Fair Dynamic Personalized Federated Learning with strategic client selection for improved accuracy and fairnessInformation Fusion10.1016/j.inffus.2024.102756115(102756)Online publication date: Mar-2025
https://doi.org/10.1016/j.inffus.2024.102756
Nikolaidis CEfraimidis P(2025)Advancing elderly social care dropout prediction with federated learning: client selection and imbalanced data managementCluster Computing10.1007/s10586-024-04850-428:2Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10586-024-04850-4
Kim DWoo HLee Y(2024)Addressing Bias and Fairness Using Fair Federated Learning: A Synthetic ReviewElectronics10.3390/electronics1323466413:23(4664)Online publication date: 26-Nov-2024
https://doi.org/10.3390/electronics13234664
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Index Terms

Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
2. Networks
  1. Network services
    1. Cloud computing

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cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 20, Issue 2

March 2024

572 pages

EISSN:1550-4867

DOI:10.1145/3618080

Editor:
Wen Hu
University of New South Wales, Australia

Issue’s Table of Contents

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 16 February 2024

Online AM: 22 December 2023

Accepted: 15 December 2023

Revised: 21 November 2023

Received: 04 September 2023

Published in TOSN Volume 20, Issue 2

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National Natural Science Foundation of China
Jiangsu Provincial Key Laboratory of Network and Information Security
Key Project of Natural Science Research in Jiangsu Provincial Colleges and Universities
Key Laboratory of Computer Network and Information Integration of Ministry of Education of China
Collaborative Innovation Center of Novel Software Technology and Industrialization, the Fundamental Research Funds for the Central Universities, CCF-Baidu Open Fund
Future Network Scientific Research Fund Project

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Cited By

Sabah FChen YYang ZRaheem AAzam MAhmad NSarwar R(2025)FairDPFL-SCS: Fair Dynamic Personalized Federated Learning with strategic client selection for improved accuracy and fairnessInformation Fusion10.1016/j.inffus.2024.102756115(102756)Online publication date: Mar-2025
https://doi.org/10.1016/j.inffus.2024.102756
Nikolaidis CEfraimidis P(2025)Advancing elderly social care dropout prediction with federated learning: client selection and imbalanced data managementCluster Computing10.1007/s10586-024-04850-428:2Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10586-024-04850-4
Kim DWoo HLee Y(2024)Addressing Bias and Fairness Using Fair Federated Learning: A Synthetic ReviewElectronics10.3390/electronics1323466413:23(4664)Online publication date: 26-Nov-2024
https://doi.org/10.3390/electronics13234664
Esquivel JAldous BAbdelmoniem AAlhilal AYou L(2024)Performance Profiling of Federated Learning Across Heterogeneous Mobile Devices2024 IEEE 24th International Conference on Software Quality, Reliability, and Security Companion (QRS-C)10.1109/QRS-C63300.2024.00053(363-372)Online publication date: 1-Jul-2024
https://doi.org/10.1109/QRS-C63300.2024.00053
Castillo J AKaya EYe LHashemi A(2024)Equitable Client Selection in Federated Learning via Truncated Submodular Maximization2024 IEEE 63rd Conference on Decision and Control (CDC)10.1109/CDC56724.2024.10886563(5496-5502)Online publication date: 16-Dec-2024
https://doi.org/10.1109/CDC56724.2024.10886563
Putra MKarna NZainudin AKim DLee J(2024)TSFed: A three-stage optimization mechanism for secure and efficient federated learning in industrial IoT networksInternet of Things10.1016/j.iot.2024.10128727(101287)Online publication date: Oct-2024
https://doi.org/10.1016/j.iot.2024.101287

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