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We examine the performance of stochastic-gradient learners over connected networks for global optimization problems involving risk functions that are not necessarily quadratic. We consider two well-studied classes of distributed schemes including consensus strategies and diffusion strategies. We quantify how the mean-square-error and the convergence rate of the network vary with the combination policy and with the fraction of informed agents. Several combination policies are considered including doubly-stochastic rules, the averaging rule, Metropolis rule, and the Hastings rule. It will be seen that the performance of the network does not necessarily improve with a larger proportion of informed agents. A strategy to counter the degradation in performance is presented.
Volkan Cevher, Efstratios Panteleimon Skoulakis, Luca Viano
Ali H. Sayed, Kun Yuan, Lucas Cesar Eduardo Cassano
Ping Hu, Stefan Vlaski, Virginia Bordignon