We propose a distributed stochastic second-order proximal (St-SoPro) method that enables agents in network to cooperatively minimize the sum of their local loss functions without any centralized coordination. St-SoPro incorporates decentralized approximation into an augmented Lagrangian function, and randomly samples gradients Hessian matrices update, so it is efficient solving large-scale prob...