# Graph-Regularized Learning of Gaussian Mixture Models

arXiv: https://arxiv.org/abs/2509.13855 · HTML: https://mlsanity.com/paper/2509.13855/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 10 references that are in the map.
- First posted on arXiv 17 Sep 2025.

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

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [An Efficient Framework for Clustered Federated Learning](https://mlsanity.com/paper/2006.04088/) (2020)
- [Regularized EM Algorithms: A Unified Framework and Statistical Guarantees](https://mlsanity.com/paper/1511.08551/) (2015)
- [Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithms](https://mlsanity.com/paper/2310.15330/) (2023)
- [Distributed Learning of Finite Gaussian Mixtures](https://mlsanity.com/paper/2010.10412/) (2020)
- [Federated Expectation Maximization with heterogeneity mitigation and variance reduction](https://mlsanity.com/paper/2111.02083/) (2021)
- [Federated Gaussian Mixture Models](https://mlsanity.com/paper/2506.01780/) (2025)

## Abstract (arXiv)

We present a graph-regularized learning of Gaussian Mixture Models (GMMs) in distributed settings with heterogeneous and limited local data. The method exploits a provided similarity graph to guide parameter sharing among nodes, avoiding the transfer of raw data. The resulting model allows for flexible aggregation of neighbors' parameters and outperforms both centralized and locally trained GMMs in heterogeneous, low-sample regimes.