Federated Learning in Healthcare: A Privacy Preserving Approach
Abstract
A need to enhance healthcare sector amidst pandemic arises. Many technological developments in Artificial Intelligence (AI) are being constantly leveraged in different fields of healthcare. One such advancement, Federated Learning(FL) has acquired recognition primarily due to its decentralized, collaborative nature of building AI models. The most significant feature in FL is that, raw data remain with the data sources throughout the training process and thus preventing its exposure. Hence, FL is more suitable and inevitable in healthcare domain as it deals with private sensitive data which needs to be protected. However, privacy threats still exist in FL, necessitating a requirement for further improvement in privacy protection This paper discusses about the concepts and applications of FL in healthcare and presents a novel approach for enhancing privacy preservation in Federated Learning.
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