  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.
