  We introduce a novel method to compute a rank $m$ approximation of the
inverse of the Hessian matrix in the distributed regime. By leveraging the
differences in gradients and parameters of multiple Workers, we are able to
efficiently implement a distributed approximation of the Newton-Raphson method.
We also present preliminary results which underline advantages and challenges
of second-order methods for large stochastic optimization problems. In
particular, our work suggests that novel strategies for combining gradients
provide further information on the loss surface.
