  We study distributed stochastic nonconvex optimization in multi-agent
networks. We introduce a novel algorithmic framework for the distributed
minimization of the sum of the expected value of a smooth (possibly nonconvex)
function (the agents' sum-utility) plus a convex (possibly nonsmooth)
regularizer. The proposed method hinges on successive convex approximation
(SCA) techniques, leveraging dynamic consensus as a mechanism to track the
average gradient among the agents, and recursive averaging to recover the
expected gradient of the sum-utility function. Almost sure convergence to
(stationary) solutions of the nonconvex problem is established. Finally, the
method is applied to distributed stochastic training of neural networks.
Numerical results confirm the theoretical claims, and illustrate the advantages
of the proposed method with respect to other methods available in the
literature.
