AI RESEARCH
Decentralized EM Algorithm for Gaussian Mixtures under Data Heterogeneity and Partial Labeling
arXiv CS.LG
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ArXi:2411.05591v2 Announce Type: replace-cross We systematically study several network-based Expectation-Maximization (EM) algorithms for the Gaussian mixture model within decentralized federated learning (DFL). Our theoretical investigation shows that directly extending the classic EM algorithm to DFL leads to a biased estimator when data are heterogeneously distributed across sites. To address this, we