  Generative Adversarial Networks (GANs) are a powerful class of generative
models in the deep learning community. Current practice on large-scale GAN
training utilizes large models and distributed large-batch training strategies,
and is implemented on deep learning frameworks (e.g., TensorFlow, PyTorch,
etc.) designed in a centralized manner. In the centralized network topology,
every worker needs to either directly communicate with the central node or
indirectly communicate with all other workers in every iteration. However, when
the network bandwidth is low or network latency is high, the performance would
be significantly degraded. Despite recent progress on decentralized algorithms
for training deep neural networks, it remains unclear whether it is possible to
train GANs in a decentralized manner. The main difficulty lies at handling the
nonconvex-nonconcave min-max optimization and the decentralized communication
simultaneously. In this paper, we address this difficulty by designing the
\textbf{first gradient-based decentralized parallel algorithm} which allows
workers to have multiple rounds of communications in one iteration and to
update the discriminator and generator simultaneously, and this design makes it
amenable for the convergence analysis of the proposed decentralized algorithm.
Theoretically, our proposed decentralized algorithm is able to solve a class of
non-convex non-concave min-max problems with provable non-asymptotic
convergence to first-order stationary point. Experimental results on GANs
demonstrate the effectiveness of the proposed algorithm.
