  A recent technical breakthrough in the domain of machine learning is the
discovery and the multiple applications of Generative Adversarial Networks
(GANs). Those generative models are computationally demanding, as a GAN is
composed of two deep neural networks, and because it trains on large datasets.
A GAN is generally trained on a single server.
  In this paper, we address the problem of distributing GANs so that they are
able to train over datasets that are spread on multiple workers. MD-GAN is
exposed as the first solution for this problem: we propose a novel learning
procedure for GANs so that they fit this distributed setup. We then compare the
performance of MD-GAN to an adapted version of Federated Learning to GANs,
using the MNIST and CIFAR10 datasets. MD-GAN exhibits a reduction by a factor
of two of the learning complexity on each worker node, while providing better
performances than federated learning on both datasets. We finally discuss the
practical implications of distributing GANs.
