AI RESEARCH
Fast convergence of a Federated Expectation-Maximization Algorithm
arXiv CS.LG
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ArXi:2408.05819v3 Announce Type: replace-cross Data heterogeneity has been a long-standing bottleneck in studying the convergence rates of Federated Learning algorithms. In order to better understand the issue of data heterogeneity, we study the convergence rate of the Expectation-Maximization (EM) algorithm for the Federated Mixture of $K$ Linear Regressions model (FMLR). We completely characterize the convergence rate of the EM algorithm under all regimes of number of clients and number of data points per client, with partial limits in the number of clients.