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

Feasibility Study of Multi-Site Split Learning for Privacy-Preserving  Medical Systems under Data Imbalance Constraints in COVID-19, X-Ray, and  Cholesterol Dataset