Abstract
Coronavirus (COVID-19) has caused a global disaster with adverse effects on global health and the economy. Early detection of COVID-19 symptoms will help to reduce the severity of the disease. As a result, establishing a method for the initial recognition of COVID-19 is much needed. Artificial Intelligence (AI) plays a vital role in detection of COVID-19 cases. In the process of COVID-19 detection, AI requires access to patient personal records which are sensitive. The data shared can pose a threat to the privacy of patients. This necessitates a technique that can accurately detect the COVID-19 patients in a privacy preserving manner. Federated Learning (FL) is a promising solution, which can detect the COVID-19 disease at early stages without compromising the sensitive information of the patients. In this paper, we propose a novel hybrid algorithm named genetic clustered FL (Genetic CFL), that groups edge devices based on the hypertuned parameters and modifies the parameters cluster wise genetically. The experimental results proved that the proposed Genetic CFL approach performed better than conventional AI approaches.
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Cited by
24 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献