Utilizing real-world health data for machine learning tasks necessitates addressing a number of practical issues, including distributed data silos, privacy concerns with creating a centralized database from person-specific sensitive data, resource constraints for transferring and integrating data from multiple sites, and the risk of a single point of failure. This invention established a privacy-preserving federated learning (PPFL) architecture capable of learning a global model using dispersed health data kept locally at many sites. Recent communication technology advancements have altered smart healthcare supported by artificial intelligence (AI). AI techniques have traditionally required centralized data gathering and processing, which may be impossible in realistic healthcare contexts due to the great scalability of modern healthcare networks and growing data privacy concerns. Federated Learning (FL), an emerging distributed collaborative AI paradigm, is especially appealing for smart healthcare because it coordinates several clients to complete AI training without requiring raw data sharing. It is critical to develop FL models in a privacy-preserving manner, especially in the setting of healthcare, where patient data is extremely sensitive. This paper highlights the significance of FL with Differential Privacy (DP) for smart healthcare. Federated Learning is one of the most accepted solutions for training machine learning models since, unlike other strategies, it has no effect on system speed.

Federated Learning, Differential Privacy, Smart Healthcare, Machine Learning, Artificial Intelligence.