SAGDA: Achieving O (null 2) Communication Complexity in Federated Min-Max Learning

Recently, min-max optimization has drawn considerable attention from the machine learning community. Compared with conventional minimization problems (e.g., empirical risk minimization), min-max optimization has a richer mathematical structure, thus being able to model more sophisticated learning problems that emerge from ever-emerging applications.