In today’s era healthcare industry is growing rapidly. It is now interconnected with IOMT (internet of medical things) and capable of monitoring real time patient data. This real time monitoring allows us early detection and personalized interventions of health issues in the patient which in turn helps in patient centered care. Devices such as smart watches, biosensors collect real time health data. These data can be further used for training AI models for promoting automation in health sector. However, with increasing use of these technologies raise many challenges and concerns related to privacy, security of personal data of patients. The sharing of patient data for AI model training increases the risk of data breachment. Federated Learning comes as a powerful solution for this issue, allowing learning of these AI models at local centers without sharing data to other devices. When combined with federated Learning, IOMT devices can revolutionize the field of healthcare by promising privacy, trusted learning models with safe data transfer. In this chapter, we will explore potential applications of IOMT devices along with their challenges and ethical considerations.