IoMT: A COVID-19 Healthcare System Driven by Federated Learning and Blockchain

【Author】 Samuel, Omaji; Omojo, Akogwu Blessing; Onuja, Abdulkarim Musa; Sunday, Yunisa; Tiwari, Prayag; Gupta, Deepak; Hafeez, Ghulam; Yahaya, Adamu Sani; Fatoba, Oluwaseun Jumoke; Shamshirband, Shahab

【Source】IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS

【影响因子】7.021

【Abstract】Internet of medical things (IoMT) has made it possible to collect applications and medical devices to improve healthcare information technology. Since the advent of the pandemic of coronavirus (COVID-19) in 2019, public health information has become more sensitive than ever. Moreover, different news items incorporated have resulted in differing public perceptions of COVID-19, especially on the social media platform and infrastructure. In addition, the unprecedented virality and changing nature of COVID-19 makes call centres to be likely overstressed, which is due to a lack of authentic and unregulated public media information. Furthermore, the lack of data privacy has restricted the sharing of COVID-19 information among health institutions. To resolve the above-mentioned limitations, this paper is proposing a privacy infrastructure based on federated learning and blockchain. The proposed infrastructure has the potentials to enhance the trust and authenticity of public media to disseminate COVID-19 information. Also, the proposed infrastructure can effectively provide a shared model while preserving the privacy of data owners. Furthermore, information security and privacy analyses show that the proposed infrastructure is robust against information security-related attacks.

【Keywords】COVID-19; Blockchains; Artificial intelligence; Privacy; Security; Medical diagnostic imaging; Data privacy; Blockchain; epidemiology; federated learning; information security; reinforcing addition

【发表时间】2023 FEB

【收录时间】2023-03-23

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-联邦学习