Enhanced DASS-CARE 2.0: a blockchain-based and decentralized FL framework

Abstract

The emergence of the Cognitive Internet of Medical Things (CIoMT) during the COVID-19 pandemic has been transformational. The CIoMT is a rapidly evolving technology that uses artificial intelligence, big data, and the Internet of Things (IoT) to provide personalized patient care. The CIoMT can be used to monitor and track vital signs, such as temperature, blood pressure, and heart rate, thus giving healthcare providers real-time information about a patient’s health. However, in such systems, the problem of privacy during data processing or sharing remains. Therefore, federated learning (FL) plays an important role in the Cognitive Internet of Medical Things (CIoMT) by allowing multiple medical devices to securely collaborate in a distributed and privacy-preserving manner. On the other hand, classical centralized FL models have several limitations, such as single points of failure and malicious servers. This paper presents an enhancement of the existing DASS-CARE 2.0 framework by using a blockchain-based federated learning framework. The proposed solution provides a secure and reliable distributed learning platform for medical data sharing and analytics in a multi-organizational environment. The blockchain-based federated learning framework offrs an innovative solution to overcome the challenges encountered in traditional FL. Furthermore, we provide a comprehensive discussion of the advantages of the proposed framework through a comparative study between our DASS-CARE 2.0 and the traditional centralized FL model while taking the aforementioned security challenges into consideration. Overall, the performance of the proposed framework shows significant advantages compared to traditional methods.