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- W4308014763 abstract "This paper focuses on Bayesian inference in a federated learning context (FL). While several distributed MCMC algorithms have been proposed, few consider the specific limitations of FL such as communication bottlenecks and statistical heterogeneity. Recently, Federated Averaging Langevin Dynamics (FALD) was introduced, which extends the Federated Averaging algorithm to Bayesian inference. We obtain a novel tight non-asymptotic upper bound on the Wasserstein distance to the global posterior for FALD. This bound highlights the effects of statistical heterogeneity, which causes a drift in the local updates that negatively impacts convergence. We propose a new algorithm VR-FALD* that uses control variates to correct the client drift. We establish non-asymptotic bounds showing that VR-FALD* is not affected by statistical heterogeneity. Finally, we illustrate our results on several FL benchmarks for Bayesian inference." @default.
- W4308014763 created "2022-11-07" @default.
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- W4308014763 date "2022-10-31" @default.
- W4308014763 modified "2023-09-25" @default.
- W4308014763 title "Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithms" @default.
- W4308014763 doi "https://doi.org/10.48550/arxiv.2211.00100" @default.
- W4308014763 hasPublicationYear "2022" @default.
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