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• W1769809944 abstract "In this work, we analyze the learning ability of diffusion-based distributed learners that receive a continuous stream of data arising from the same distribution. We establish four distinctive advantages for these learners relative to other decentralized schemes. First, we obtain closed-form expressions for the evolution of their excess-risk for strongly-convex risk functions under a diminishing step-size rule. Using the result, we then show that the distributed strategy can improve the asymptotic convergence rate of the excess-risk by a factor of N relative to non-cooperative schemes, where N is the number of learners in the ad-hoc network. We further show that the fastest attainable rate" @default.
• W1769809944 created "2016-06-24" @default.
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• W1769809944 date "2013-02-05" @default.
• W1769809944 modified "2023-09-26" @default.
• W1769809944 title "Excess-Risk Analysis of Distributed Stochastic Learners" @default.
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