# Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory

arXiv: https://arxiv.org/abs/2309.03487 · HTML: https://mlsanity.com/paper/2309.03487/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 78 references that are in the map.
- First posted on arXiv 7 Sep 2023.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Ensemble Distillation for Robust Model Fusion in Federated Learning](https://mlsanity.com/paper/2006.07242/) (2020)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Effective Diversity in Population Based Reinforcement Learning](https://mlsanity.com/paper/2002.00632/) (2020)
- [Heterogeneity for the Win: One-Shot Federated Clustering](https://mlsanity.com/paper/2103.00697/) (2021)
- [Local Differential Privacy based Federated Learning for Internet of Things](https://mlsanity.com/paper/2004.08856/) (2020)
- [Differentially Private k-Means with Constant Multiplicative Error](https://mlsanity.com/paper/1804.08001/) (2018)
- [Machine Unlearning of Federated Clusters](https://mlsanity.com/paper/2210.16424/) (2022)
- [Distributed dual vigilance fuzzy adaptive resonance theory learns online, retrieves arbitrarily-shaped clusters, and mitigates order dependence](https://mlsanity.com/paper/1901.00794/) (2018)
- [Differential Privacy of Hierarchical Census Data: An Optimization Approach](https://mlsanity.com/paper/2006.15673/) (2020)
- [Localizing Catastrophic Forgetting in Neural Networks](https://mlsanity.com/paper/1906.02568/) (2019)
- [Multi-label Classification via Adaptive Resonance Theory-based Clustering](https://mlsanity.com/paper/2103.01511/) (2021)
- [Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning](https://mlsanity.com/paper/2201.10713/) (2022)
- [A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning](https://mlsanity.com/paper/2305.01507/) (2023)

## Built on by (most cited first, 1 of 1)

- [An Adaptive Resonance Theory-based Topological Clustering Algorithm with a Self-Adjusting Vigilance Parameter](https://mlsanity.com/paper/2511.17983/) (2025)

## Abstract (arXiv)

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