A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning

Understand

In this paper, we study a large-scale multi-agent minimax optimization problem, which models many interesting applications in statistical learning and game theory, including Generative Adversarial Networks (GANs).

• The overall objective is a sum of agents' private local objective functions.
• We first analyze an important special case, empirical minimax problem, where the overall objective approximates a true population minimax risk by statistical samples.
• We provide generalization bounds for learning with this objective through Rademacher complexity analysis.