Privacy-Preserving Federated Learning Framework for IoMT Smart Healthcare Applications

The conventional healthcare system has been trans-formed into a smart healthcare system through the use of the Internet of Medical Things (IoMT) due to recent advances in communication infrastructure and electronic gadgets. However, privacy concerns about the information shared between hospitals and end-users are brought up by mobile and wearable IoMT devices as a result of AI’s centralized training approach. The data transmitted by IoMT devices is extremely sensitive and vulnerable to interception by malicious parties. Therefore, the decentralized AI paradigm of Federated Learning (FL) has provided novel avenues for protecting participants’ privacy in IoMT without compromising their personal information. In this paper, we have proposed a privacy-preserving FL-based framework specifically tailored for smart healthcare applications. The framework uses a multilayered architecture to make model training safe and effective while protecting the privacy of patients. In addition, a detailed review of existing frameworks for smart healthcare applications, along with some research challenges, is presented.