Boosting Classification Tasks with Federated Learning: Concepts, Experiments and Perspectives
Yan Hu, Ahmad Chaddad · 2023
This paper presents the use of federated learning (FL) in healthcare to improve the efficiency and accuracy of medical diagnosis while addressing privacy concerns related to medical data. FL allows data to remain local and trains models independently, with only model parameters communicated to the server. Creating FL models is a popular solution in healthcare systems now, particularly with the increasing use of Internet of Medical Things (IoMT) devices that enable the storage of large amounts of health data. This work provides a comprehensive analysis of the current FL models employed in various applications in healthcare. We applied the FL model to a skin cancer data set and achieved a remarkable result with a classification accuracy of 90% or higher, demonstrating the potential of FL in medical image classification tasks. In this context, we also discuss current bottlenecks and future research directions in FL healthcare.