# Distributed clustering in partially overlapping feature spaces

arXiv: https://arxiv.org/abs/2510.09799 · HTML: https://mlsanity.com/paper/2510.09799/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 9 of 9)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Cold Case: The Lost MNIST Digits](https://mlsanity.com/paper/1905.10498/) (2019)
- [Polynomial Matrix Completion for Missing Data Imputation and Transductive Learning](https://mlsanity.com/paper/1912.06989/) (2019)
- [k-POD: A Method for k-Means Clustering of Missing Data](https://mlsanity.com/paper/1411.7013/) (2014)
- [Federated K-means Clustering](https://mlsanity.com/paper/2310.01195/) (2023)
- [Federated Deep Multi-View Clustering with Global Self-Supervision](https://mlsanity.com/paper/2309.13697/) (2023)
- [Dynamically Weighted Federated k-Means](https://mlsanity.com/paper/2310.14858/) (2023)
- [Federated Incomplete Multi-View Clustering with Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2406.08524/) (2024)
- [Binary Random Projections with Controllable Sparsity Patterns](https://mlsanity.com/paper/2006.16180/) (2020)

## Abstract (arXiv)

We introduce and address a novel distributed clustering problem where each participant has a private dataset containing only a subset of all available features, and some features are included in multiple datasets. This scenario occurs in many real-world applications, such as in healthcare, where different institutions have complementary data on similar patients. We propose two different algorithms suitable for solving distributed clustering problems that exhibit this type of feature space heterogeneity. The first is a federated algorithm in which participants collaboratively update a set of global centroids. The second is a one-shot algorithm in which participants share a statistical parametrization of their local clusters with the central server, who generates and merges synthetic proxy datasets. In both cases, participants perform local clustering using algorithms of their choice, which provides flexibility and personalized computational costs. Pretending that local datasets result from splitting and masking an initial centralized dataset, we identify some conditions under which the proposed algorithms are expected to converge to the optimal centralized solution. Finally, we test the practical performance of the algorithms on three public datasets.