Part of Advances in Neural Information Processing Systems 35 (NeurIPS 2022) Main Conference Track
Kumar Kshitij Patel, Lingxiao Wang, Blake E Woodworth, Brian Bullins, Nati Srebro
We study the problem of distributed stochastic non-convex optimization with intermittent communication. We consider the full participation setting where $M$ machines work in parallel over $R$ communication rounds and the partial participation setting where $M$ machines are sampled independently every round from some meta-distribution over machines. We propose and analyze a new algorithm that improves existing methods by requiring fewer and lighter variance reduction operations. We also present lower bounds, showing our algorithm is either $\textit{optimal}$ or $\textit{almost optimal}$ in most settings. Numerical experiments demonstrate the superior performance of our algorithm.