Experimental Comparison of Clustering Approaches for Personalized Federated Learning

Seohee Choi; Minjung Park; Sangmi Chai

doi:10.1007/978-3-031-37129-5_16

Experimental Comparison of Clustering Approaches for Personalized Federated Learning

Seohee Choi, Minjung Park, Sangmi Chai

Division of Business Administration

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Federated learning allows clients to collaboratively train a global model while preserving data privacy. However, personalized the global model is necessary for each device to prevent performance degradation caused by the heterogeneity of each client’s local data. In this paper, we discuss previously studied client clustering techniques for personalized federated learning. We also evaluate the performance of personalized models generated by various federated learning and client clustering algorithms. Our research aims to bridge the research gap by evaluating the effectiveness of personalized models when applied to users.

Original language	English
Title of host publication	Computational Science and Its Applications – ICCSA 2023 Workshops, Proceedings
Editors	Osvaldo Gervasi, Beniamino Murgante, Francesco Scorza, Ana Maria A. C. Rocha, Chiara Garau, Yeliz Karaca, Carmelo M. Torre
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	187-193
Number of pages	7
ISBN (Print)	9783031371288
DOIs	https://doi.org/10.1007/978-3-031-37129-5_16
State	Published - 2023
Event	23rd International Conference on Computational Science and Its Applications, ICCSA 2023 - Athens, Greece Duration: 3 Jul 2023 → 6 Jul 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14112 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Computational Science and Its Applications, ICCSA 2023
Country/Territory	Greece
City	Athens
Period	3/07/23 → 6/07/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Clustering
Federated Learning
Personalized Federated Learning

Access to Document

10.1007/978-3-031-37129-5_16

Cite this

Choi, S., Park, M., & Chai, S. (2023). Experimental Comparison of Clustering Approaches for Personalized Federated Learning. In O. Gervasi, B. Murgante, F. Scorza, A. M. A. C. Rocha, C. Garau, Y. Karaca, & C. M. Torre (Eds.), Computational Science and Its Applications – ICCSA 2023 Workshops, Proceedings (pp. 187-193). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14112 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-37129-5_16

Choi, Seohee ; Park, Minjung ; Chai, Sangmi. / Experimental Comparison of Clustering Approaches for Personalized Federated Learning. Computational Science and Its Applications – ICCSA 2023 Workshops, Proceedings. editor / Osvaldo Gervasi ; Beniamino Murgante ; Francesco Scorza ; Ana Maria A. C. Rocha ; Chiara Garau ; Yeliz Karaca ; Carmelo M. Torre. Springer Science and Business Media Deutschland GmbH, 2023. pp. 187-193 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Experimental Comparison of Clustering Approaches for Personalized Federated Learning",

abstract = "Federated learning allows clients to collaboratively train a global model while preserving data privacy. However, personalized the global model is necessary for each device to prevent performance degradation caused by the heterogeneity of each client{\textquoteright}s local data. In this paper, we discuss previously studied client clustering techniques for personalized federated learning. We also evaluate the performance of personalized models generated by various federated learning and client clustering algorithms. Our research aims to bridge the research gap by evaluating the effectiveness of personalized models when applied to users.",

keywords = "Clustering, Federated Learning, Personalized Federated Learning",

author = "Seohee Choi and Minjung Park and Sangmi Chai",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 23rd International Conference on Computational Science and Its Applications, ICCSA 2023 ; Conference date: 03-07-2023 Through 06-07-2023",

year = "2023",

doi = "10.1007/978-3-031-37129-5_16",

language = "English",

isbn = "9783031371288",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "187--193",

editor = "Osvaldo Gervasi and Beniamino Murgante and Francesco Scorza and Rocha, {Ana Maria A. C.} and Chiara Garau and Yeliz Karaca and Torre, {Carmelo M.}",

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Choi, S, Park, M & Chai, S 2023, Experimental Comparison of Clustering Approaches for Personalized Federated Learning. in O Gervasi, B Murgante, F Scorza, AMAC Rocha, C Garau, Y Karaca & CM Torre (eds), Computational Science and Its Applications – ICCSA 2023 Workshops, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14112 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 187-193, 23rd International Conference on Computational Science and Its Applications, ICCSA 2023, Athens, Greece, 3/07/23. https://doi.org/10.1007/978-3-031-37129-5_16

Experimental Comparison of Clustering Approaches for Personalized Federated Learning. / Choi, Seohee; Park, Minjung; Chai, Sangmi.
Computational Science and Its Applications – ICCSA 2023 Workshops, Proceedings. ed. / Osvaldo Gervasi; Beniamino Murgante; Francesco Scorza; Ana Maria A. C. Rocha; Chiara Garau; Yeliz Karaca; Carmelo M. Torre. Springer Science and Business Media Deutschland GmbH, 2023. p. 187-193 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14112 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Federated learning allows clients to collaboratively train a global model while preserving data privacy. However, personalized the global model is necessary for each device to prevent performance degradation caused by the heterogeneity of each client’s local data. In this paper, we discuss previously studied client clustering techniques for personalized federated learning. We also evaluate the performance of personalized models generated by various federated learning and client clustering algorithms. Our research aims to bridge the research gap by evaluating the effectiveness of personalized models when applied to users.

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Choi S, Park M, Chai S. Experimental Comparison of Clustering Approaches for Personalized Federated Learning. In Gervasi O, Murgante B, Scorza F, Rocha AMAC, Garau C, Karaca Y, Torre CM, editors, Computational Science and Its Applications – ICCSA 2023 Workshops, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 187-193. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-37129-5_16

Experimental Comparison of Clustering Approaches for Personalized Federated Learning

Abstract

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Bibliographical note

Keywords

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Cite this