PRIVACY-AWARE FEDERATED LEARNING IN HYBRID CLOUD-BASED HEALTH SURVEILLANCE SYSTEMS

Abstract

In recent years, artificial intelligence (AI) and data analysis have begun to be widely used in the healthcare sector. However, the uniqueness of medical data lies in its sensitivity and personal nature. For this reason, ensuring confidentiality for information systems used in healthcare has become a top priority. Traditional data collection and centralized training methods pose serious risks to information security. To prevent such problems, solutions based on federated learning and hybrid cloud systems are being developed.