On the game‐theoretic analysis of distributed generative adversarial networks. Issue 1 (3rd September 2021)

• Record Type: Journal Article
• Authors: Li, Zhongguo; Dong, Zhen; Chen, Wen‐Hua; Ding, Zhengtao
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 1(2022)
• Page Start: 516
• Page End: 534
• Publication Date: 2021-09-03
• Subjects: consensus -- distributed algorithm -- game‐theoretic approach -- generative adversarial networks -- minmax optimisation -- Nash equilibrium -- zero‐sum game; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22637
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: In this paper, a distributed method is proposed for training multiple generative adversarial networks (GANs) with private data sets via a game‐theoretic approach. To facilitate the requirement of privacy protection, distributed training algorithms offer a promising solution to learn global models without sample exchanges. Existing studies have mainly concentrated on training neural networks using pure cooperation strategies, which are not suitable for GANs. This paper develops a new framework for distributed GANs, where two groups of discriminators and generators are involved in a zero‐sum game. Under connected graphs, such a framework is reformulated as a constrained minmax optimisation problem. Then, a fully distributed training algorithm is proposed without exchanging any private data samples. The convergence of the proposed algorithm is established via advanced consensus and optimisation techniques. Simulation studies are presented to validate the effectiveness of the proposed framework and algorithm.