# Asynchronous Federated Clustering with Unknown Number of Clusters

arXiv: https://arxiv.org/abs/2412.20341 · HTML: https://mlsanity.com/paper/2412.20341/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 49 references that are in the map.
- First posted on arXiv 29 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Heterogeneity for the Win: One-Shot Federated Clustering](https://mlsanity.com/paper/2103.00697/) (2021)
- [Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)](https://mlsanity.com/paper/2201.07316/) (2022)
- [Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis](https://mlsanity.com/paper/1908.09888/) (2019)
- [Self-similarity Driven Scale-invariant Learning for Weakly Supervised Person Search](https://mlsanity.com/paper/2302.12986/) (2023)
- [Hybrid Forest: A Concept Drift Aware Data Stream Mining Algorithm](https://mlsanity.com/paper/1902.03609/) (2019)
- [Unsupervised Clustered Federated Learning in Complex Multi-source Acoustic Environments](https://mlsanity.com/paper/2106.03671/) (2021)

## Built on by (most cited first, 11 of 11)

- [MEET-Sepsis: Multi-Endogenous-View Enhanced Time-Series Representation Learning for Early Sepsis Prediction](https://mlsanity.com/paper/2510.15985/) (2025)
- [Learning Unbiased Cluster Descriptors for Interpretable Imbalanced Concept Drift Detection](https://mlsanity.com/paper/2603.06757/) (2026)
- [A Federated Generalized Expectation-Maximization Algorithm for Mixture Models with an Unknown Number of Components](https://mlsanity.com/paper/2601.21160/) (2026)
- [Federated Deep Subspace Clustering](https://mlsanity.com/paper/2501.00230/) (2024)
- [Random Normed k-Means: A Paradigm-Shift in Clustering within Probabilistic Metric Spaces](https://mlsanity.com/paper/2504.03928/) (2025)
- [Parameter-Free Clustering via Self-Supervised Consensus Maximization (Extended Version)](https://mlsanity.com/paper/2511.09211/) (2025)
- [Federated Multi-Task Clustering](https://mlsanity.com/paper/2512.22897/) (2025)
- [Stitch the Fragments: One-Shot Hierarchical Federated Clustering](https://mlsanity.com/paper/2601.06404/) (2026)
- [One-Shot Federated Clustering of Non-Independent Completely Distributed Data](https://mlsanity.com/paper/2601.17512/) (2026)
- [Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numbers](https://mlsanity.com/paper/2603.12684/) (2026)
- [Federated Clustering with Unknown Local and Global Cluster Cardinalities](https://mlsanity.com/paper/2609.36762/) (2026)

## Abstract (arXiv)

Federated Clustering (FC) is crucial to mining knowledge from unlabeled non-Independent Identically Distributed (non-IID) data provided by multiple clients while preserving their privacy. Most existing attempts learn cluster distributions at local clients, and then securely pass the desensitized information to the server for aggregation. However, some tricky but common FC problems are still relatively unexplored, including the heterogeneity in terms of clients' communication capacity and the unknown number of proper clusters k^*. To further bridge the gap between FC and real application scenarios, this paper first shows that the clients' communication asynchrony and unknown k^* are complex coupling problems, and then proposes an Asynchronous Federated Cluster Learning (AFCL) method accordingly. It spreads the excessive number of seed points to the clients as a learning medium and coordinates them across the clients to form a consensus. To alleviate the distribution imbalance cumulated due to the unforeseen asynchronous uploading from the heterogeneous clients, we also design a balancing mechanism for seeds updating. As a result, the seeds gradually adapt to each other to reveal a proper number of clusters. Extensive experiments demonstrate the efficacy of AFCL.