Federated Gaussian Mixture Models
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Abstract (arXiv)
This paper introduces FedGenGMM, a novel one-shot federated learning approach for Gaussian Mixture Models (GMM) tailored for unsupervised learning scenarios. In federated learning (FL), where multiple decentralized clients collaboratively train models without sharing raw data, significant challenges include statistical heterogeneity, high communication costs, and privacy concerns. FedGenGMM addresses these issues by allowing local GMM models, trained independently on client devices, to be aggregated through a single communication round. This approach leverages the generative property of GMMs, enabling the creation of a synthetic dataset on the server side to train a global model efficiently. Evaluation across diverse datasets covering image, tabular, and time series data demonstrates that FedGenGMM consistently achieves performance comparable to non-federated and iterative federated methods, even under significant data heterogeneity. Additionally, FedGenGMM significantly reduces communication overhead, maintains robust performance in anomaly detection tasks, and offers flexibility in local model complexities, making it particularly suitable for edge computing environments.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 17 of its 44 references that are in the map. First posted on arXiv 2 Jun 2025.
Builds on
17 of 44 references.
References · 17
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- 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
5 more
- 2020Practical One-Shot Federated Learning for Cross-Silo Settingcited by 77
- 2023Personalized Federated Learning under Mixture of Distributionscited by 37
- 2024Parametric Feature Transfer: One-shot Federated Learning with Foundation Modelscited by 13
- 2022Anomaly Detection through Unsupervised Federated Learningcited by 8
- 2023Shuffled Differentially Private Federated Learning for Time Series Data Analyticscited by 1
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.