Federated Synthetic Learning from Multi-institutional and Heterogeneous Medical Data

Statistically and information-wise adequate data plays a critical role in training a robust deep learning model. However, collecting sufficient medical data to train a centralized model is still challenging due to various constraints such as privacy regulations and security. In this work, we develop a novel privacy-preserving federated-discriminator GAN, named FedD-GAN, that can learn and synthesize high-quality and various medical images regardless of their type, from heterogeneous datasets residing in multiple data centers whose data cannot be transferred or shared. We trained and evaluated FedD-GAN on three essential classes of medical data, each involving different types of medical images: cardiac CTA, brain MRI, and histopathology. We show that the synthesized images using our method have better quality than using a standard federated learning method and are realistic and accurate enough to train accurate segmentation models in downstream tasks. The segmentation model trained on the synthetic data only is comparable to that trained on an all-in-one real-image dataset shared from multiple data centers if possible. FedD-GAN can learn to generate a scalable and diverse synthetic database without compromising data privacy. This synthetic database could help to boost machine learning techniques in medical data analytics.