On the Evaluation of Generative Models in Distributed Learning Tasks

The evaluation of deep generative models including generative adversarial networks (GANs) and diffusion models has been extensively studied in the literature. While the existing evaluation methods mainly target a centralized learning problem with training data stored by a single client, many applications of generative models concern distributed learning settings, e.g. the federated learning scenario, where training data are collected by and distributed among several clients. In this paper, we study the evaluation of generative models in distributed learning tasks with …

adversarial applications client cs.lg data deep generative models diffusion diffusion models distributed distributed learning evaluation gans generative generative adversarial networks generative models literature networks tasks training training data