We consider distributed optimization where N nodes in a generic, connected network minimize the sum of their individual, locally known, convex costs. Existing literature proposes distributed gradient-like methods that are attractive due to computationally cheap iterations and provable resilience to random inter-node communication failures, but such methods have slow theoretical and empirical convergence rates. Building from the centralized Nesterov gradient methods, we propose accelerated distributed gradient-like methods and establish that they achieve strictly faster rates than existing distributed methods. At the same time, our methods maintain cheap iterations and resilience to random communication failures. Specifically, for convex, differentiable local costs with Lipschitz continuous and bounded derivative, we establish (with respect to the cost function optimality) convergence in probability and convergence rates in expectation and in second moment.
