Split learning for health: Distributed deep learning without sharing raw patient data

Can health entities collaboratively train deep learning models without sensitive raw data? This paper proposes several configurations of a deep learning method called SplitNN to facilitate such. SplitNN does not share raw data or model details with institutions. The proposed configurations of splitNN cater to settings of i) entities holding different modalities of patient data, ) centralized and local health entities collaborating on multiple tasks and) learning without sharing labels. We compare performance and resource trade-offs of splitNN and other distributed deep learning methods federated learning, large batch synchronous stochastic gradient descent show highly encouraging results for splitNN.