  We consider the problem of distributed learning, where a network of agents
collectively aim to agree on a hypothesis that best explains a set of
distributed observations of conditionally independent random processes. We
propose a distributed algorithm and establish consistency, as well as a
non-asymptotic, explicit and geometric convergence rate for the concentration
of the beliefs around the set of optimal hypotheses. Additionally, if the
agents interact over static networks, we provide an improved learning protocol
with better scalability with respect to the number of nodes in the network.
