Graph-Regularized Learning of Gaussian Mixture Models
This addresses the challenge of distributed learning with data heterogeneity for applications like sensor networks or federated learning, though it is incremental as it builds on existing GMM and graph-based methods.
The paper tackled the problem of learning Gaussian Mixture Models in distributed settings with heterogeneous and limited local data by using a graph-regularized method that avoids raw data transfer, resulting in outperformance over centralized and locally trained models in low-sample regimes.
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.