Edge Federated Learning for Smart HealthCare Systems: Applications and Challenges

The rapid advancement of technology in healthcare has led to an unprecedented increase in the amount of medical data being generated, from patient records to real-time monitoring data. While this data holds immense potential for improving patient care and outcomes, it also raises significant challenges in terms of security and privacy, especially within the framework of smart healthcare systems. Traditional data processing approaches often require the centralization of data, which can expose sensitive information to potential breaches. In smart healthcare systems, the integration of Edge Computing with Federated Learning offers a transformative approach to data analysis and processing. EFL leverages the computational capabilities allowing data to be processed closer to its source. The combination of these technologies holds the potential to revolutionize how healthcare data is managed, analyzed, and utilized, paving the way for more personalized and efficient healthcare delivery. This survey report delves into the evolution of FL and edge computing, exploring their convergence in the context of smart healthcare. It examines various applications of EFL within healthcare, from remote patient monitoring to predictive analytics and personalized medicine. Additionally, the report addresses the challenges and concerns associated with EFL, such as data heterogeneity, communication overhead, and the need for robust security measures. Finally, it outlines future research directions that could further enhance the effectiveness and adoption of EFL in smart healthcare systems, underscoring its potential to reshape the landscape of medical data processing.