Empowering Biomedical Health with Federated Learning: Addressing Privacy and Data Sharing for Enhanced Disease Detection and Diagnosis

The advent of big data and AI has resulted in a data revolution across many sectors, healthcare among them. However, the private nature of patient information frequently prevents its sharing, resulting in data storage that slow the development of AI in healthcare. Federated learning (FL) emerges as a possible answer to these problems. As a result of FL, reli-able illness detection models can be developed on distributed data held by diverse individuals without compromising data privacy. In this study, we investigate how FL may help solve problems associated with data sharing and privacy in biomed-ical health. This research seeks to contribute to the incorporation of artificial intelligence in biological health by reviewing relevant literature and case studies to provide a full know l-edge of FL's benefits and limitations in disease identification and diagnosis.