Federated Gaussian Mixture Models

• URL: http://arxiv.org/abs/2506.01780v1
• Date: Mon, 02 Jun 2025 15:23:53 GMT
• Title: Federated Gaussian Mixture Models
• Authors: Sophia Zhang Pettersson, Kuo-Yun Liang, Juan Carlos Andresen,
• Abstract summary: FedGenGMM is a novel one-shot federated learning approach for unsupervised learning scenarios.<n>It allows local GMM models, trained independently on client devices, to be aggregated through a single communication round.<n>It consistently achieves performance comparable to non-federated and iterative federated methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors [50.131271229165165]
  Federated Learning (FL) has emerged as a promising framework for distributed machine learning.<n>Data heterogeneity resulting from differences across user behaviors, preferences, and device characteristics poses a significant challenge for federated learning.<n>We propose Adaptive Weight Aggregation (FedAWA), a novel method that adaptively adjusts aggregation weights based on client vectors during the learning process.
  arXiv  Detail & Related papers  (2025-03-20T04:49:40Z)
• Client Contribution Normalization for Enhanced Federated Learning [4.726250115737579]
  Mobile devices, including smartphones and laptops, generate decentralized and heterogeneous data. Federated Learning (FL) offers a promising alternative by enabling collaborative training of a global model across decentralized devices without data sharing. This paper focuses on data-dependent heterogeneity in FL and proposes a novel approach leveraging mean latent representations extracted from locally trained models.
  arXiv  Detail & Related papers  (2024-11-10T04:03:09Z)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• FLASH: Federated Learning Across Simultaneous Heterogeneities [54.80435317208111]
  FLASH(Federated Learning Across Simultaneous Heterogeneities) is a lightweight and flexible client selection algorithm. It outperforms state-of-the-art FL frameworks under extensive sources of Heterogeneities. It achieves substantial and consistent improvements over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-02-13T20:04:39Z)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• Federated Learning with Neural Graphical Models [2.2721854258621064]
  Federated Learning (FL) addresses the need to create models based on proprietary data. We develop a FL framework which maintains a global NGM model that learns the averaged information from the local NGM models. We experimentally demonstrated the use of FedNGMs for extracting insights from CDC's Infant Mortality dataset.
  arXiv  Detail & Related papers  (2023-09-20T23:24:22Z)
• Federated cINN Clustering for Accurate Clustered Federated Learning [33.72494731516968]
  Federated Learning (FL) presents an innovative approach to privacy-preserving distributed machine learning. We propose the Federated cINN Clustering Algorithm (FCCA) to robustly cluster clients into different groups.
  arXiv  Detail & Related papers  (2023-09-04T10:47:52Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning [87.08902493524556]
  Federated learning(FL) has recently attracted increasing attention from academia and industry. We propose FedDM to build the global training objective from multiple local surrogate functions. In detail, we construct synthetic sets of data on each client to locally match the loss landscape from original data.
  arXiv  Detail & Related papers  (2022-07-20T04:55:18Z)
• Federated Multi-Task Learning under a Mixture of Distributions [10.00087964926414]
  Federated Learning (FL) is a framework for on-device collaborative training of machine learning models. First efforts in FL focused on learning a single global model with good average performance across clients, but the global model may be arbitrarily bad for a given client. We study federated MTL under the flexible assumption that each local data distribution is a mixture of unknown underlying distributions.
  arXiv  Detail & Related papers  (2021-08-23T15:47:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.