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A Systematic Literature Review on Client Selection in Federated Learning

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Jingyue LiAuthors Info & Claims

EASE '23: Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering

Pages 2 - 11

https://doi.org/10.1145/3593434.3593438

Published: 14 June 2023 Publication History

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Abstract

With the arising concerns of privacy within machine learning, federated learning (FL) was invented in 2017, in which the clients, such as mobile devices, train a model and send the update to the centralized server. Choosing clients randomly for FL can harm learning performance due to different reasons. Many studies have proposed approaches to address the challenges of client selection of FL. However, no systematic literature review (SLR) on this topic existed. This SLR investigates the state of the art of client selection in FL and answers the challenges, solutions, and metrics to evaluate the solutions. We systematically reviewed 47 primary studies. The main challenges found in client selection are heterogeneity, resource allocation, communication costs, and fairness. The client selection schemes aim to improve the original random selection algorithm by focusing on one or several of the aforementioned challenges. The most common metric used is testing accuracy versus communication rounds, as testing accuracy measures the successfulness of the learning and preferably in as few communication rounds as possible, as they are very expensive. Although several possible improvements can be made with the current state of client selection, the most beneficial ones are evaluating the impact of unsuccessful clients and gaining a more theoretical understanding of the impact of fairness in FL.

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Cited By

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https://doi.org/10.1016/j.cosrev.2024.100697
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Index Terms

A Systematic Literature Review on Client Selection in Federated Learning
1. Computing methodologies

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EASE '23: Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering

June 2023

544 pages

ISBN:9798400700446

DOI:10.1145/3593434

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EASE '23: The International Conference on Evaluation and Assessment in Software Engineering

June 14 - 16, 2023

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Cited By

Dubey PKumar M(2025)Integrating Explainable AI with Federated Learning for Next-Generation IoT: A comprehensive review and prospective insightsComputer Science Review10.1016/j.cosrev.2024.10069756(100697)Online publication date: May-2025
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Barros AVeiga RMorais RRosário DCerqueira E(2024)MESFLA: Model Efficiency through Selective Federated Learning AlgorithmJournal of Internet Services and Applications10.5753/jisa.2024.404415:1(495-507)Online publication date: 22-Oct-2024
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Veiga RSousa JMorais RBastos LLobato WRosário DCerqueira E(2024)A Robust Client Selection Mechanism for Federated Learning EnvironmentsJournal of the Brazilian Computer Society10.5753/jbcs.2024.432530:1(444-455)Online publication date: 8-Oct-2024
https://doi.org/10.5753/jbcs.2024.4325
Trindade Sda Fonseca N(2024)Client Selection in Hierarchical Federated LearningIEEE Internet of Things Journal10.1109/JIOT.2024.340308211:17(28480-28495)Online publication date: 1-Sep-2024
https://doi.org/10.1109/JIOT.2024.3403082
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https://doi.org/10.1109/ICOIN59985.2024.10572056
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https://doi.org/10.1109/CloudNet62863.2024.10815742
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