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, CIFAR10 and CelebA datasets. MD-GAN exhibits a reduction by a factor of two of the learning complexity on each worker node, while providing better or identical performances with the adaptation of federated learning. We finally discuss the practical implications of distributing GANs.

In large scale multihop wireless networks, flat architectures are not scalable. In order to overcome this major drawback, clusterization is introduced to support selforganization and to enable hierarchical routing. When dealing with multihop wireless networks the robustness is a main issue due to the dynamicity of such networks. Several algorithms have been designed for the clusterization process. As far as we know, very few studies check the robustness feature of their clusterization protocols. Moreover, when it is the case, the evaluation is driven by simulations and never by a theoretical approach.In this paper, we show that a clusterization algorithm, that seems to present good properties of robustness, is self-stabilizing. We propose several enhancements to reduce the stabilization time and to improve stability. The use of a Directed Acyclic Graph ensures that the selfstabilizing properties always hold regardless of the underlying topology. These extra criterion are tested by simulations.