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