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Training generative adversarial networks (GAN) in a distributed fashion is a promising technology since it is contributed to training GAN on a massive of data efficiently in real-world applications. However, GAN is known to be difficult to train by SGD-type methods (may fail to converge) and the distributed SGD-type methods may also suffer from massive amount of communication cost. In this paper, we propose a distributed GANs training algorithm with quantized gradient, dubbed DQGAN, which isarXiv:2010.13359v1 fatcat:qfjmj572mffw5phdavq6cfgjze