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On Convergence of Federated Averaging Langevin Dynamics. (arXiv:2112.05120v2 [stat.ML] UPDATED)
June 15, 2022, 1:11 a.m. | Wei Deng, Qian Zhang, Yi-An Ma, Zhao Song, Guang Lin
cs.LG updates on arXiv.org arxiv.org
We propose a federated averaging Langevin algorithm (FA-LD) for uncertainty
quantification and mean predictions with distributed clients. In particular, we
generalize beyond normal posterior distributions and consider a general class
of models. We develop theoretical guarantees for FA-LD for strongly log-concave
distributions with non-i.i.d data and study how the injected noise and the
stochastic-gradient noise, the heterogeneity of data, and the varying learning
rates affect the convergence. Such an analysis sheds light on the optimal
choice of local updates to …
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