IoT Security Enhancements in Smart Healthcare Using Federated Learning

Federated learning (FL) is a distributive machine learning (ML) approach that makes use of a centralised server to assist several Internet of Things (IoT) devices in cooperatively training an ML model. IoT device local data is safeguarded since it never leaves the device in FL. Since distributed IoT devices in FL often gather their local data on their own, each device's data set may naturally constitute a unique source domain. In this research work, there will be maintaining the security enhancements for smart healthcare using federated learning. There will be securing the data of IoT -based sensors which may lose local data while using the smart systems. Removing this issue will give importance to local data also using the FL method. Thus, this research involves considering different research work and gives the research methodology for the security-enhancing process using FL in IoT enhanced smart healthcare.