  We develop a distributed algorithm for convex Empirical Risk Minimization,
the problem of minimizing large but finite sum of convex functions over
networks. The proposed algorithm is derived from directly discretizing the
second-order heavy-ball differential equation and results in an accelerated
convergence rate, i.e, faster than distributed gradient descent-based methods
for strongly convex objectives that may not be smooth. Notably, we achieve
acceleration without resorting to the well-known Nesterov's momentum approach.
We provide numerical experiments and contrast the proposed method with recently
proposed optimal distributed optimization algorithms.
