# Dynamically Weighted Federated k-Means

arXiv: https://arxiv.org/abs/2310.14858 · HTML: https://mlsanity.com/paper/2310.14858/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 63 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 27 of 27)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [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)
- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Attack of the Tails: Yes, You Really Can Backdoor Federated Learning](https://mlsanity.com/paper/2007.05084/) (2020)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Can You Really Backdoor Federated Learning?](https://mlsanity.com/paper/1911.07963/) (2019)
- [Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning](https://mlsanity.com/paper/1812.00535/) (2018)
- [Federated learning with hierarchical clustering of local updates to improve training on non-IID data](https://mlsanity.com/paper/2004.11791/) (2020)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [A Survey of Privacy Attacks in Machine Learning](https://mlsanity.com/paper/2007.07646/) (2020)
- [Revisiting Distributed Synchronous SGD](https://mlsanity.com/paper/1702.05800/) (2017)
- [Federated Learning for Keyword Spotting](https://mlsanity.com/paper/1810.05512/) (2018)
- [Robust Federated Learning in a Heterogeneous Environment](https://mlsanity.com/paper/1906.06629/) (2019)
- [Heterogeneity for the Win: One-Shot Federated Clustering](https://mlsanity.com/paper/2103.00697/) (2021)
- [Privacy Preserving Machine Learning: Threats and Solutions](https://mlsanity.com/paper/1804.11238/) (2018)
- [Federated Learning Of Out-Of-Vocabulary Words](https://mlsanity.com/paper/1903.10635/) (2019)
- [Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)](https://mlsanity.com/paper/2201.07316/) (2022)
- [Dynamic Clustering in Federated Learning](https://mlsanity.com/paper/2012.03788/) (2020)
- [Differentially Private Vertical Federated Clustering](https://mlsanity.com/paper/2208.01700/) (2022)
- [Federated K-means Clustering](https://mlsanity.com/paper/2310.01195/) (2023)
- [Federated Matrix Factorization: Algorithm Design and Application to Data Clustering](https://mlsanity.com/paper/2002.04930/) (2020)

## Built on by (most cited first, 2 of 2)

- [Distributed clustering in partially overlapping feature spaces](https://mlsanity.com/paper/2510.09799/) (2025)
- [Fed-BRDECS: Privacy-Preserving and Heterogeneity-Aware Federated Deep Embedded Clustering](https://mlsanity.com/paper/2610.07399/) (2026)

## Abstract (arXiv)

Federated clustering, an integral aspect of federated machine learning, enables multiple data sources to collaboratively cluster their data, maintaining decentralization and preserving privacy. In this paper, we introduce a novel federated clustering algorithm named Dynamically Weighted Federated k-means (DWF k-means) based on Lloyd's method for k-means clustering, to address the challenges associated with distributed data sources and heterogeneous data. Our proposed algorithm combines the benefits of traditional clustering techniques with the privacy and scalability benefits offered by federated learning. The algorithm facilitates collaborative clustering among multiple data owners, allowing them to cluster their local data collectively while exchanging minimal information with the central coordinator. The algorithm optimizes the clustering process by adaptively aggregating cluster assignments and centroids from each data source, thereby learning a global clustering solution that reflects the collective knowledge of the entire federated network. We address the issue of empty clusters, which commonly arises in the context of federated clustering. We conduct experiments on multiple datasets and data distribution settings to evaluate the performance of our algorithm in terms of clustering score, accuracy, and v-measure. The results demonstrate that our approach can match the performance of the centralized classical k-means baseline, and outperform existing federated clustering methods like k-FED in realistic scenarios.