On Sharing Models Instead of Data using Mimic learning for Smart Health Applications

On Sharing Models Instead of Data using Mimic learning for Smart Health Applications Mohamed Baza, Andrew Salazar †, Mohamed Mahmoud, Mohamed Abdallah ‡, Kemal Akkaya ‡ Department of Computer Science, Tennessee Tech University, Cookeville, TN, USA ‡ Department of Information and Decision Sciences, California State San Bernardino, San Bernardino, CA, USA ‡ division of Information and Computing Technology, College of Science and Engineering, HBKU, Doha, Qatar § Department of Electrical and Computer Engineering, Florida International University, Miami, FL, USA Abstract --Electronic health records (EHR) systems contain vast amounts of medical information about patients. These data can be used to train machine learning models that can predict health status, as well as to help prevent future diseases or disabilities. However, getting patients' medical data to obtain well-trained machine learning models is a challenging task. This is because sharing the patients' medical records is prohibited by law in most countries due to patients privacy concerns. In this paper, we tackle this problem by sharing the models instead of the original sensitive data by using the mimic learning approach. The idea is first to train a model on the original sensitive data, called the teacher model. Then, using this model, we can transfer its knowledge to another model, called the student model, without the need to learn the original data used in training the teacher model.