A Stochastic Proximal Gradient Framework for Decentralized Non-Convex Composite Optimization: Topology-Independent Sample Complexity and Communication Efficiency

Understand

Decentralized optimization is a promising parallel computation paradigm for large-scale data analytics and machine learning problems defined over a network of nodes.

• This paper is concerned with decentralized non-convex composite problems with population or empirical risk.
• In particular, the networked nodes are tasked to find an approximate stationary point of the average of local, smooth, possibly non-convex risk functions plus a possibly non-differentiable extended valued convex regularizer.
• Under this general formulation, we propose the first provably efficient, stochastic proximal gradient framework, called ProxGT.