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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 anddistributed across multiple agents, each of which is seeking to learn thedistribution of the data on its own. In such scenarios, the agents often do notwish to share their local data as it can cause communication overhead for largedatasets. In this paper, to address this multi-agent GAN problem, a novelbrainstorming GAN (BGAN) architecture is proposed using which multiple agentscan generate real-like data samples while operating in a fully distributedmanner. BGAN allows the agents to gain information from other agents withoutsharing their real datasets but by ``brainstorming'' via the sharing of theirgenerated data samples. In contrast to existing distributed GAN solutions, theproposed BGAN architecture is designed to be fully distributed, and it does notneed any centralized controller. Moreover, BGANs are shown to be scalable andnot 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 Nashequilibrium is derived. Experimental results show that BGAN can generatereal-like data samples with higher quality and lower Jensen-Shannon divergence(JSD) and Fr\`echet Inception distance (FID) compared to other distributed GANarchitectures.

Brainstorming Generative Adversarial Networks (BGANs): Towards Multi-Agent Generative Models with Distributed Private Datasets