Recent Trends of Federated Learning for Smart Healthcare Systems

Abstract

The Internet of Things (IoT) has brought a revolutionary change in the healthcare system. Smart devices have helped people maintain their health by collecting and storing a wide range of data. Artificial intelligence (AI) has made its promising way in several areas. They help in the early diagnosis of various diseases along with storage and interpretation of health data. However, due to the lack of communication between devices and the risk of transmission of data, the efficiency of AI devices is questionable. To avoid the transmission of data, Federation learning (FL) was highlighted as an approach where issues related to the security of sensitive data can be reduced significantly. The combination of FL, AI, and Explainable Artificial Intelligence (XAI) techniques can minimize several limitations and challenges in the healthcare system. This chapter presents an overview of FL's application in healthcare. Different studies presented data about FL and its usage in healthcare. Currently, this paradigm approach is successfully used by specialists in diagnostic purposes.

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