Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory
This work addresses the need for clustering algorithms that can handle dynamic data distributions without pre-specifying cluster numbers, benefiting domains with evolving data and privacy concerns, though it is incremental as it builds on existing federated clustering frameworks.
The paper tackled the problem of clustering data with unknown or changing distributions in a federated learning setting by proposing a privacy-preserving continual federated clustering algorithm using adaptive resonance theory, achieving superior clustering performance compared to state-of-the-art methods while maintaining data privacy and continual learning ability.
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 \url{https://github.com/Masuyama-lab/FCAC}.