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Graph-Regularized Learning of Gaussian Mixture Models

arXiv 2509.13855 ↗First posted 17 Sep 2025cs.LG cs.DCcited by 0

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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.

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.

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References · 7

  1. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  2. 2020An Efficient Framework for Clustered Federated Learningcited by 511
  3. 2015Regularized EM Algorithms: A Unified Framework and Statistical Guaranteescited by 20
  4. 2023Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithmscited by 7
  5. 2020Distributed Learning of Finite Gaussian Mixturescited by 6
  6. 2021Federated Expectation Maximization with heterogeneity mitigation and variance reductioncited by 5
  7. 2025Federated Gaussian Mixture Modelscited by 2

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