# Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numbers

arXiv: https://arxiv.org/abs/2603.12684 · HTML: https://mlsanity.com/paper/2603.12684/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 48 references that are in the map.
- First posted on arXiv 13 Mar 2026.

## Builds on (most cited first, 21 of 21)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints](https://mlsanity.com/paper/1910.01991/) (2019)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Federated learning with hierarchical clustering of local updates to improve training on non-IID data](https://mlsanity.com/paper/2004.11791/) (2020)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [Heterogeneity for the Win: One-Shot Federated Clustering](https://mlsanity.com/paper/2103.00697/) (2021)
- [k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)](https://mlsanity.com/paper/2004.04523/) (2020)
- [Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering](https://mlsanity.com/paper/2205.11506/) (2022)
- [FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer](https://mlsanity.com/paper/2306.15347/) (2023)
- [Machine Unlearning of Federated Clusters](https://mlsanity.com/paper/2210.16424/) (2022)
- [A review of mean-shift algorithms for clustering](https://mlsanity.com/paper/1503.00687/) (2015)
- [Federated Learning Challenges and Opportunities: An Outlook](https://mlsanity.com/paper/2202.00807/) (2022)
- [Asynchronous Federated Clustering with Unknown Number of Clusters](https://mlsanity.com/paper/2412.20341/) (2024)
- [Secure Federated Clustering](https://mlsanity.com/paper/2205.15564/) (2022)
- [Learning Self-Growth Maps for Fast and Accurate Imbalanced Streaming Data Clustering](https://mlsanity.com/paper/2404.09243/) (2024)
- [Differentially Private Federated Clustering over Non-IID Data](https://mlsanity.com/paper/2301.00955/) (2023)
- [FedGT: Federated Node Classification with Scalable Graph Transformer](https://mlsanity.com/paper/2401.15203/) (2024)
- [On the privacy of federated Clustering: A Cryptographic View](https://mlsanity.com/paper/2312.07992/) (2023)

## Abstract (arXiv)

Federated Clustering (FC) is an emerging and promising solution in exploring data distribution patterns from distributed and privacy-protected data in an unsupervised manner. Existing FC methods implicitly rely on the assumption that clients are with a known number of uniformly sized clusters. However, the true number of clusters is typically unknown, and cluster sizes are naturally imbalanced in real scenarios. Furthermore, the privacy-preserving transmission constraints in federated learning inevitably reduce usable information, making the development of robust and accurate FC extremely challenging. Accordingly, we propose a novel FC framework named Fed-k^*-HC, which can automatically determine an optimal number of clusters k^* based on the data distribution explored through hierarchical clustering. To obtain the global data distribution for k^* determination, we let each client generate micro-subclusters. Their prototypes are then uploaded to the server for hierarchical merging. The density-based merging design allows exploring clusters of varying sizes and shapes, and the progressive merging process can self-terminate according to the neighboring relationships among the prototypes to determine k^*. Extensive experiments on diverse datasets demonstrate the FC capability of the proposed Fed-k^*-HC in accurately exploring a proper number of clusters.