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

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 task

Source: [1812.00564] Split learning for health: Distributed deep learning without sharing raw patient data

The paper describes how algorithm design (including training) can be shared across different organisations without each having access to each other’s resources.

This has important implications for the development of AI-based health applications, in that hospitals and other service providers need not share raw patient data with companies like Google/DeepMind. Health organisations could do the basic algorithm design in-house with the smaller, local data sets and then send the algorithm to organisations that have the massive data sets necessary for refining the algorithm, all without exposing the initial data and protecting patient privacy.

By Michael Rowe

I'm a lecturer in the Department of Physiotherapy at the University of the Western Cape in Cape Town, South Africa. I'm interested in technology, education and healthcare and look for places where these things meet.