  It seems as though progressively more people are in the race to upload
content, data, and information online; and hospitals haven't neglected this
trend either. Hospitals are now at the forefront for multi-site medical data
sharing to provide groundbreaking advancements in the way health records are
shared and patients are diagnosed. Sharing of medical data is essential in
modern medical research. Yet, as with all data sharing technology, the
challenge is to balance improved treatment with protecting patient's personal
information. This paper provides a novel split learning algorithm coined the
term, "multi-site split learning", which enables a secure transfer of medical
data between multiple hospitals without fear of exposing personal data
contained in patient records. It also explores the effects of varying the
number of end-systems and the ratio of data-imbalance on the deep learning
performance. A guideline for the most optimal configuration of split learning
that ensures privacy of patient data whilst achieving performance is
empirically given. We argue the benefits of our multi-site split learning
algorithm, especially regarding the privacy preserving factor, using CT scans
of COVID-19 patients, X-ray bone scans, and cholesterol level medical data.
