  In this work, we propose a method for training distributed GAN with
sequential temporary discriminators. Our proposed method tackles the challenge
of training GAN in the federated learning manner: How to update the generator
with a flow of temporary discriminators? We apply our proposed method to learn
a self-adaptive generator with a series of local discriminators from multiple
data centers. We show our design of loss function indeed learns the correct
distribution with provable guarantees. The empirical experiments show that our
approach is capable of generating synthetic data which is practical for
real-world applications such as training a segmentation model.
