  As deep learning technologies advance, increasingly more data is necessary to
generate general and robust models for various tasks. In the medical domain,
however, large-scale and multi-parties data training and analyses are
infeasible due to the privacy and data security concerns. In this paper, we
propose an extendable and elastic learning framework to preserve privacy and
security while enabling collaborative learning with efficient communication.
The proposed framework is named distributed Asynchronized Discriminator
Generative Adversarial Networks (AsynDGAN), which consists of a centralized
generator and multiple distributed discriminators. The advantages of our
proposed framework are five-fold: 1) the central generator could learn the real
data distribution from multiple datasets implicitly without sharing the image
data; 2) the framework is applicable for single-modality or multi-modality
data; 3) the learned generator can be used to synthesize samples for
down-stream learning tasks to achieve close-to-real performance as using actual
samples collected from multiple data centers; 4) the synthetic samples can also
be used to augment data or complete missing modalities for one single data
center; 5) the learning process is more efficient and requires lower bandwidth
than other distributed deep learning methods.
