  We propose a new belief update rule for Distributed Non-Bayesian learning in
time-varying directed graphs, where a group of agents tries to collectively
identify a hypothesis that best describes a sequence of 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.
