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We examine the problem of learning a set of parameters from a distributed dataset. We assume the datasets are collected by agents over a distributed ad-hoc network, and that the communication of the actual raw data is prohibitive due to either privacy constraints or communication constraints. We propose a distributed algorithm for online learning that is proved to guarantee a bounded excess risk and the bound can be made arbitrary small for sufficiently small step-sizes. We apply our framework to the expert advice problem where nodes learn the weights for the trained experts distributively.
Volkan Cevher, Efstratios Panteleimon Skoulakis, Luca Viano
Ali H. Sayed, Stefan Vlaski, Virginia Bordignon