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- W2964283886 abstract "We propose a novel belief update algorithm for Distributed Non-Bayesian learning over time-varying directed graphs, where a group of agents tries to collectively select a distribution that best describes the observed data. We show that the proposed update rule, inspired by the Push-Sum algorithm, is consistent; moreover we provide an explicit characterization of its convergence rate. Our main result states that, after a transient time, all agents will concentrate their beliefs at a network independent rate. Network independent rates were not available for other consensus based distributed learning algorithms on time-varying directed graphs." @default.
- W2964283886 created "2019-07-30" @default.
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- W2964283886 date "2016-07-01" @default.
- W2964283886 modified "2023-09-23" @default.
- W2964283886 title "Network independent rates in distributed learning" @default.
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- W2964283886 doi "https://doi.org/10.1109/acc.2016.7525057" @default.
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