Split Learning for Distributed Collaborative Training of Deep Learning
  Models in Health Informatics
Zhuohang Li Chao Yan Xinmeng Zhang Gharib Gharibi Zhijun Yin Xiaoqian Jiang Bradley A. Malin
Deep learning continues to rapidly evolve and is now demonstrating remarkable
potential for numerous medical prediction tasks. However, realizing deep
learning models that generalize across healthcare organizations is challenging.
This is due, in part, to the inherent siloed nature of these organizations and
patient privacy requirements. To address this problem, we illustrate how split
learning can enable collaborative training of deep learning models across
disparate and privately maintained health datasets, while keeping the original
records and model parameters private. We introduce a new privacy-preserving
distributed learning framework that offers a higher level of privacy compared
to conventional federated learning. We use several biomedical imaging and
electronic health record (EHR) datasets to show that deep learning models
trained via split learning can achieve highly similar performance to their
centralized and federated counterparts while greatly improving computational
efficiency and reducing privacy risks.