Towards Optimal Communication Complexity in Distributed Non-Convex Optimization
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 $ extit{optimal}$ or $ extit{almost optimal}$ in most settings. Numerical experiments demonstrate the superior performance of our algorithm.
