  Distributed consensus optimization has received considerable attention in
recent years; several distributed consensus-based algorithms have been proposed
for (nonsmooth) convex and (smooth) nonconvex objective functions. However, the
behavior of these distributed algorithms on {\it nonconvex, nonsmooth and
stochastic} objective functions is not understood. This class of functions and
distributed setting are motivated by several applications, including problems
in machine learning and signal processing.
  This paper presents the first convergence analysis of the decentralized
stochastic subgradient method for such classes of problems, over networks
modeled as undirected, fixed, graphs.
