Minimax Optimization

Recently, min-max optimization problems have received increasing attention due to their wide range of applications in machine learning …

Federated min-max learning has received increasing attention in recent years thanks to its wide range of applications in various learning paradigms. In this paper, we propose a new algorithmic framework called stochastic sampling averaging gradient descent ascent (SAGDA), which yields an O(ε−2) communication complexity that is orders of magnitude lower than the state of the art.