Abstract

In recent years, Big Data Analytics (BDA) and Federated Learning (FL) have become increasingly essential in healthcare, potentially revolutionizing patient care and optimizing operational efficiency. Big data analytics has transformed the way the healthcare industry operates. It provides an opportunity to extract valuable insights from vast amounts of data that can lead to better healthcare outcomes and reduced healthcare costs. However, the use of big data in healthcare is often hindered by privacy concerns and the need to protect sensitive patient information. FL is an inventive machine learning scheme that addresses these concerns by enabling multiple organizations to collaboratively analyze large datasets without sharing sensitive patient information. This article offers a comprehensive review of the potential of FL to empower healthcare transformation through big data analytics. Furthermore, the article investigates the obstacles and possibilities related to healthcare FL, encompassing the requirement for uniformity, data quality, security, and trust and collaboration among healthcare stakeholders. Finally, the paper looks ahead to the prospects of FL in healthcare, including the potential for real-time monitoring, predictive modeling, and developing new healthcare models prioritizing prevention and wellness. This survey advances the state-of-the-art by comprehensively reviewing how FL can be effectively integrated with BDA to transform healthcare. It uniquely synthesizes current advancements, identifies key technological synergies, and outlines a robust framework for addressing privacy concerns and enhancing data interoperability in healthcare systems. This survey paper is intended for healthcare professionals, researchers, and policymakers interested in the potential of FL to transform the healthcare industry.