PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities
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Recently, min-max optimization problems have received increasing attention due to their wide range of applications in machine learning (ML). However, most existing min-max solution techniques are either single-machine or distributed algorithms coordinated by a central server. In this paper, we focus on the decentralized min-max optimization for learning with domain constraints, where multiple agents collectively solve a nonconvex-strongly-concave min-max saddle point problem without coordination from any server. Decentralized min-max optimization problems with domain constraints underpins many important ML applications, including multi-agent ML fairness assurance, and policy evaluations in multi-agent reinforcement learning. We propose an algorithm called PRECISION (proximal gradient-tracking and stochastic recursive variance reduction) that enjoys a convergence rate of O(1/T), where T is the maximum number of iterations. To further reduce sample complexity, we propose PRECISION^+ with an adaptive batch size technique. We show that the fast O(1/T) convergence of PRECISION and PRECISION^+ to an ϵ-stationary point imply O(ϵ^-2) communication complexity and O(m√(n)ϵ^-2) sample complexity, where m is the number of agents and n is the size of dataset at each agent. To our knowledge, this is the first work that achieves O(ϵ^-2) in both sample and communication complexities in decentralized min-max learning with domain constraints. Our experiments also corroborate the theoretical results.
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