Decentralized Stochastic Optimization

Decentralized stochastic optimization focuses on solving optimization problems collaboratively across multiple agents without a central coordinator, leveraging local data and communication to minimize a global objective function. Current research emphasizes developing efficient algorithms for various problem settings, including non-convex and nonsmooth objectives, multi-level compositional problems, and scenarios with communication constraints like quantization or directed networks. This field is crucial for advancing privacy-preserving machine learning, distributed control systems, and large-scale data analysis by enabling efficient and secure collaborative computation. Recent work highlights the development of algorithms achieving optimal convergence rates even under challenging conditions such as limited communication bandwidth and the need for inherent privacy protection.