Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory
With the increasing importance of data privacy protection, various privacy-preserving machine learning methods have been proposed. In the clustering domain, various algorithms with a federated learning framework (i.e., federated clustering) have been actively studied and showed high clustering perfo...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10689590/ |
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| author | Naoki Masuyama Yusuke Nojima Yuichiro Toda Chu Kiong Loo Hisao Ishibuchi Naoyuki Kubota |
| author_facet | Naoki Masuyama Yusuke Nojima Yuichiro Toda Chu Kiong Loo Hisao Ishibuchi Naoyuki Kubota |
| author_sort | Naoki Masuyama |
| collection | DOAJ |
| description | With the increasing importance of data privacy protection, various privacy-preserving machine learning methods have been proposed. In the clustering domain, various algorithms with a federated learning framework (i.e., federated clustering) have been actively studied and showed high clustering performance while preserving data privacy. However, most of the base clusterers (i.e., clustering algorithms) used in existing federated clustering algorithms need to specify the number of clusters in advance. These algorithms, therefore, are unable to deal with data whose distributions are unknown or continually changing. To tackle this problem, this paper proposes a privacy-preserving continual federated clustering algorithm. In the proposed algorithm, an adaptive resonance theory-based clustering algorithm capable of continual learning is used as a base clusterer. Therefore, the proposed algorithm inherits the ability of continual learning. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to state-of-the-art federated clustering algorithms while realizing data privacy protection and continual learning ability. The source code is available at <uri>https://github.com/Masuyama-lab/FCAC</uri>. |
| format | Article |
| id | doaj-art-fbfa15c0d902444ab5a134f9c0c55b93 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fbfa15c0d902444ab5a134f9c0c55b932025-01-16T00:02:11ZengIEEEIEEE Access2169-35362024-01-011213969213971010.1109/ACCESS.2024.346711410689590Privacy-Preserving Continual Federated Clustering via Adaptive Resonance TheoryNaoki Masuyama0https://orcid.org/0000-0002-2886-1588Yusuke Nojima1https://orcid.org/0000-0003-4853-1305Yuichiro Toda2https://orcid.org/0000-0003-4170-2300Chu Kiong Loo3https://orcid.org/0000-0001-7867-2665Hisao Ishibuchi4https://orcid.org/0000-0001-9186-6472Naoyuki Kubota5https://orcid.org/0000-0001-8829-037XDepartment of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University, Sakai, Osaka, JapanDepartment of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University, Sakai, Osaka, JapanFaculty of Environmental, Life, Natural Science and Technology, Okayama University, Okayama, JapanDepartment of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, ChinaDepartment of Mechanical Systems Engineering, Graduate School of Systems Design, Tokyo Metropolitan University, Asahigaoka, Hino, Tokyo, JapanWith the increasing importance of data privacy protection, various privacy-preserving machine learning methods have been proposed. In the clustering domain, various algorithms with a federated learning framework (i.e., federated clustering) have been actively studied and showed high clustering performance while preserving data privacy. However, most of the base clusterers (i.e., clustering algorithms) used in existing federated clustering algorithms need to specify the number of clusters in advance. These algorithms, therefore, are unable to deal with data whose distributions are unknown or continually changing. To tackle this problem, this paper proposes a privacy-preserving continual federated clustering algorithm. In the proposed algorithm, an adaptive resonance theory-based clustering algorithm capable of continual learning is used as a base clusterer. Therefore, the proposed algorithm inherits the ability of continual learning. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to state-of-the-art federated clustering algorithms while realizing data privacy protection and continual learning ability. The source code is available at <uri>https://github.com/Masuyama-lab/FCAC</uri>.https://ieeexplore.ieee.org/document/10689590/Self-organizing feature mapsadaptive resonance theorycontinual learningfederated clusteringlocal ϵ-differential privacy |
| spellingShingle | Naoki Masuyama Yusuke Nojima Yuichiro Toda Chu Kiong Loo Hisao Ishibuchi Naoyuki Kubota Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory IEEE Access Self-organizing feature maps adaptive resonance theory continual learning federated clustering local ϵ-differential privacy |
| title | Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory |
| title_full | Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory |
| title_fullStr | Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory |
| title_full_unstemmed | Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory |
| title_short | Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory |
| title_sort | privacy preserving continual federated clustering via adaptive resonance theory |
| topic | Self-organizing feature maps adaptive resonance theory continual learning federated clustering local ϵ-differential privacy |
| url | https://ieeexplore.ieee.org/document/10689590/ |
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