LDGAN: Latent Determined Ensemble Helps Removing IID Data Assumption and Cross-node Sampling in Distributed GANs

Generative Adversarial Networks (GANs) have received a lot of attention due to their powerful generative ability, and many related studies have been carried out. Among them, deploying GANs in distributed scenarios has become a hot research topic due to the sharp increase in the capacity of training data. However, distributed GANs face many challenges, such as different data distribution of each node, limitation of cross-node data sampling, etc. The previous methods defaulted to some strong assumptions to alleviate these problems, but the performance would be degraded once they encountered a natural scene. In this paper, we propose Latent Determined Generative Adversarial Network (LDGAN), a network introducing latent determined ensemble to guide the training of the generator without specific assumptions. LDGAN measures the difference in the latent space of data distribution at each node and uses this as the weight of the feedback information of each discriminator, and there is no need for any cross-node data sampling and the independent and identical distribution (iid) data assumption. Our experiments show that on the MNIST, Fashion-MNIST, and CIFAR-10 datasets, the images generated by LDGAN are more realistic and diverse, and the achieved Fréchet Inception Distance (FID) is 15.0%, 22.8%, and 15.3% smaller than the state-of-the-art models, respectively.