To achieve a high learning accuracy, generative adversarial networks (GANs) must be fed by large datasets that adequately represent the data space. However, in many scenarios, the available datasets may be limited and distributed across multiple agents, each of which is seeking to learn the distribution of the data on its own. In such scenarios, the agents often do not wish to share their local data as it can cause communication overhead for large datasets. In this paper, to address this multi-agent GAN problem, a novel brainstorming GAN (BGAN) architecture is proposed using which multiple agents can generate real-like data samples while operating in a fully distributed manner. BGAN allows the agents to gain information from other agents without sharing their real datasets but by ``brainstorming'' via the sharing of their generated data samples. In contrast to existing distributed GAN solutions, the proposed BGAN architecture is designed to be fully distributed, and it does not need any centralized controller. Moreover, BGANs are shown to be scalable and not dependent on the hyperparameters of the agents' deep neural networks (DNNs) thus enabling the agents to have different DNN architectures. Theoretically, the interactions between BGAN agents are analyzed as a game whose unique Nash equilibrium is derived. Experimental results show that BGAN can generate real-like data samples with higher quality and lower Jensen-Shannon divergence (JSD) and Fr\`echet Inception distance (FID) compared to other distributed GAN architectures.

翻译：为实现高学习精度，生成对抗网络（GANs）必须由充分代表数据空间的大规模数据集驱动。然而，在许多场景中，可用数据集可能有限且分布在不同智能体之间，每个智能体均试图独立学习其自身数据的分布。在此类场景中，智能体通常不愿共享本地数据，因为对于大规模数据集而言，这会导致通信开销。为解决这一多智能体GAN问题，本文提出了一种新颖的Brainstorming生成对抗网络（BGAN）架构，使多个智能体能够在完全分布式环境下生成逼真数据样本。BGAN允许智能体通过共享其生成的数据样本进行“头脑风暴”，从而在不共享真实数据集的情况下从其他智能体获取信息。与现有分布式GAN方案不同，所提出的BGAN架构被设计为完全分布式，无需任何集中控制器。此外，BGAN被证明具有可扩展性，且不依赖于智能体深度神经网络（DNN）的超参数，从而允许智能体采用不同的DNN架构。理论上，我们作为博弈分析了BGAN智能体之间的交互，并推导出了其唯一的纳什均衡。实验结果表明，与其他分布式GAN架构相比，BGAN能够生成更高质量、更低Jensen-Shannon散度（JSD）和Frèchet Inception距离（FID）的逼真数据样本。