Abstract We propose a distributed and decentralized Generative Adversarial Networks (GANs) framework without the exchange of the training data. Each node contains local dataset, a discriminator and a generator, from which only the generator gradients are shared with other nodes. In this paper, we introduce a novel, distributed technique in which workers communicate directly with each other, having no central nodes. Our experimental results on the benchmark datasets demonstrate almost the same performance and accuracy compared with existing centralized GAN frameworks. The proposed framework addresses the lack of decentralized learning for GANs.

中文翻译：

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DEGAN：去中心化生成对抗网络

摘要 我们提出了一种分布式和分散的生成对抗网络 (GAN) 框架，无需交换训练数据。每个节点包含本地数据集、一个判别器和一个生成器，其中只有生成器梯度与其他节点共享。在本文中，我们介绍了一种新颖的分布式技术，在这种技术中，worker 之间直接通信，没有中心节点。与现有的集中式 GAN 框架相比，我们在基准数据集上的实验结果证明了几乎相同的性能和准确性。提议的框架解决了 GAN 缺乏去中心化学习的问题。