Split learning for health: Distributed deep learning without sharing raw patient data
This addresses privacy concerns in healthcare data sharing for collaborative AI model training, though it appears incremental as it builds on existing distributed learning concepts.
The paper tackles the problem of enabling health entities to collaboratively train deep learning models without sharing sensitive raw patient data by proposing several configurations of SplitNN, a distributed deep learning method, and shows highly encouraging results compared to other methods like federated learning.
Can health entities collaboratively train deep learning models without sharing sensitive raw data? This paper proposes several configurations of a distributed deep learning method called SplitNN to facilitate such collaborations. SplitNN does not share raw data or model details with collaborating institutions. The proposed configurations of splitNN cater to practical settings of i) entities holding different modalities of patient data, ii) centralized and local health entities collaborating on multiple tasks and iii) learning without sharing labels. We compare performance and resource efficiency trade-offs of splitNN and other distributed deep learning methods like federated learning, large batch synchronous stochastic gradient descent and show highly encouraging results for splitNN.