Towards Optimal Communication Complexity in Distributed Non-Convex Optimization
Kumar Kshitij Patel · Lingxiao Wang · Blake Woodworth · Brian Bullins · Nati Srebro
Keywords:
Distributed Optimization
Stochastic Optimization
Non-Convex Optimization
federated learning
Intermittent Communication Setting
lower bounds
2022 Poster
Abstract
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.
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