Council Post: Split learning—Distributed deep learning method without sensitive data sharing

Data sharing is one of the major challenges in machine learning models. The advent of techniques like federated learning, differential privacy and split learning have addressed data silos, privacy and regulation issues in a big way. In this article, we will look at split learning, a new technique developed at the MIT Media Lab that allows training of machine learning models without sharing any raw data. The technique solves challenges like data silos, data sharing, etc. Most importantly, Split Neural Networks (SplitNN) does not share raw data or model details with collaborating institutions. The configurations caters to practical settings of entities holding different modalities of patient data; centralised and local health entities collaborating on multiple tasks; and learning witho