SecureMed: Federated learning empowered approach for securing medical data

In today’s era, the world is suffering from an infectious disease COVID-19 which proved to be health predicament. The COVID-19 testing data is being produced by the healthcare center at preeminent scale. The efficient handling of such data by World Health Organization (WHO) team and medical practitioners is the considerable challenge. The researchers have introduced many deep learning approaches to detect COVID-19, but security remained challenging. In this paper, we present federated learning based approach ‘SecureMed,’ which allows to securely process the data. In this approach, two parameters, ‘client selection’ and ‘aggregation method’ are considered. The proposed scheme inherits Markov model for designing the these two parameters. The approach is been validated by using publicly available COVID-19 dataset of size 771, 634 Genomes (source: NCBI, U.S. National Library of Medicine). Taking into account the probability computations, the proposed approach succeeds in client local training by 50%.