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Machine learning requires a large volume of sample data, especially when itis used in high-accuracy medical applications. However, patient records are oneof the most sensitive private information that is not usually shared amonginstitutes. This paper presents spatio-temporal split learning, a distributeddeep neural network framework, which is a turning point in allowingcollaboration among privacy-sensitive organizations. Our spatio-temporal splitlearning presents how distributed machine learning can be efficiently conductedwith minimal privacy concerns. The proposed split learning consists of a numberof clients and a centralized server. Each client has only has one hidden layer,which acts as the privacy-preserving layer, and the centralized servercomprises the other hidden layers and the output layer. Since the centralizedserver does not need to access the training data and trains the deep neuralnetwork with parameters received from the privacy-preserving layer, privacy oforiginal data is guaranteed. We have coined the term, spatio-temporal splitlearning, as multiple clients are spatially distributed to cover diversedatasets from different participants, and we can temporally split the learningprocess, detaching the privacy preserving layer from the rest of the learningprocess to minimize privacy breaches. This paper shows how we can analyze themedical data whilst ensuring privacy using our proposed multi-sitespatio-temporal split learning algorithm on Coronavirus Disease-19 (COVID-19)chest Computed Tomography (CT) scans, MUsculoskeletal RAdiographs (MURA) X-rayimages, and cholesterol levels.

Spatio-Temporal Split Learning for Privacy-Preserving Medical Platforms: Case Studies with COVID-19 CT, X-Ray, and Cholesterol Data