PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities

• URL: http://arxiv.org/abs/2303.02532v1
• Date: Sun, 5 Mar 2023 00:26:10 GMT
• Title: PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities
• Authors: Zhuqing Liu, Xin Zhang, Songtao Lu, and Jia Liu
• Abstract summary: We show an adaptive multi-agent learning technique for min-max optimization problems. We also propose an algorithm called PRECISION that enjoys a reduction in the number of iterations.
• Score: 25.153506493249854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $\epsilon$-stationary point imply $O(\epsilon^{-2})$ communication complexity and $O(m\sqrt{n}\epsilon^{-2})$ 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 $O(\epsilon^{-2})$ 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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