Federated Minimax Optimization: Improved Convergence Analyses and Algorithms

• URL: http://arxiv.org/abs/2203.04850v1
• Date: Wed, 9 Mar 2022 16:21:31 GMT
• Title: Federated Minimax Optimization: Improved Convergence Analyses and Algorithms
• Authors: Pranay Sharma, Rohan Panda, Gauri Joshi and Pramod K. Varshney
• Abstract summary: We consider non minimax optimization, is gaining prominence many modern machine learning applications such as GANs. We provide a novel and tighter analysis algorithm, improves convergence communication guarantees in the existing literature.
• Score: 32.062312674333775
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we consider nonconvex minimax optimization, which is gaining prominence in many modern machine learning applications such as GANs. Large-scale edge-based collection of training data in these applications calls for communication-efficient distributed optimization algorithms, such as those used in federated learning, to process the data. In this paper, we analyze Local stochastic gradient descent ascent (SGDA), the local-update version of the SGDA algorithm. SGDA is the core algorithm used in minimax optimization, but it is not well-understood in a distributed setting. We prove that Local SGDA has \textit{order-optimal} sample complexity for several classes of nonconvex-concave and nonconvex-nonconcave minimax problems, and also enjoys \textit{linear speedup} with respect to the number of clients. We provide a novel and tighter analysis, which improves the convergence and communication guarantees in the existing literature. For nonconvex-PL and nonconvex-one-point-concave functions, we improve the existing complexity results for centralized minimax problems. Furthermore, we propose a momentum-based local-update algorithm, which has the same convergence guarantees, but outperforms Local SGDA as demonstrated in our experiments.

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