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In this paper, we propose a data privacy-preserving and communicationefficient distributed GAN learning framework named Distributed AsynchronizedDiscriminator GAN (AsynDGAN). Our proposed framework aims to train a centralgenerator learns from distributed discriminator, and use the generatedsynthetic image solely to train the segmentation model.We validate the proposedframework on the application of health entities learning problem which is knownto be privacy sensitive. Our experiments show that our approach: 1) could learnthe real image's distribution from multiple datasets without sharing thepatient's raw data. 2) is more efficient and requires lower bandwidth thanother distributed deep learning methods. 3) achieves higher performancecompared to the model trained by one real dataset, and almost the sameperformance compared to the model trained by all real datasets. 4) has provableguarantees that the generator could learn the distributed distribution in anall important fashion thus is unbiased.

Synthetic Learning: Learn From Distributed Asynchronized Discriminator GAN Without Sharing Medical Image Data