Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory

Naoki Masuyama1; Yusuke Nojima1; Yuichiro Toda2; Chu Kiong Loo3; Hisao Ishibuchi4; Naoyuki Kubota5

2024

10.1109/ACCESS.2024.3467114

IEEE Access   影响因子和分区

2169-3536

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 https://github.com/Masuyama-lab/FCAC.

其他

1.Department of Core Informatics, Graduate School of Informatics, Osaka Metropolitan University, Sakai, Osaka, Japan
2.Faculty of Environmental, Life, Natural Science and Technology, Okayama University, Okayama, Japan
3.Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia
4.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
5.Department of Mechanical Systems Engineering, Graduate School of Systems Design, Tokyo Metropolitan University, Asahigaoka, Hino, Tokyo, Japan

Naoki Masuyama,Yusuke Nojima,Yuichiro Toda,et al. Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory[J]. IEEE Access,2024,12.

Naoki Masuyama,Yusuke Nojima,Yuichiro Toda,Chu Kiong Loo,Hisao Ishibuchi,&Naoyuki Kubota.(2024).Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory.IEEE Access,12.

Naoki Masuyama,et al."Privacy-Preserving Continual Federated Clustering via Adaptive Resonance Theory".IEEE Access 12(2024).