  We present a novel Newton-type method for distributed optimization, which is
particularly well suited for stochastic optimization and learning problems. For
quadratic objectives, the method enjoys a linear rate of convergence which
provably \emph{improves} with the data size, requiring an essentially constant
number of iterations under reasonable assumptions. We provide theoretical and
empirical evidence of the advantages of our method compared to other
approaches, such as one-shot parameter averaging and ADMM.
