In recent times, there has been a rapid spread of pandemics caused by rapidly mutating viruses, such as SARS-CoV-2 which has present significant challenges for healthcare systems worldwide. The global health crises like COVID-19 underscore the need for predictive models that support containment and resource management. Genomic data is very crucial in providing critical insights into viral evolution and the mechanics of dynamics. Genomic datasets contain information that requires such computational methods that protect privacy. We have used federated deep learning architecture using genomic data for pandemic prediction. We have achieved both data privacy by identifying key genomic features and implementing federated learning and robust model performance. Our results help in demonstrating the effectiveness of the method proposed by offering a scalable solution for the monitoring of pandemics.