This study proposes a novel framework that integrates federated deep learning and blockchain technology for preserving privacy in precision medicine in medical virology. As healthcare institutions increasingly utilize deep learning for tasks such as viral genome analysis and outbreak prediction, concerns about data privacy and security have increased. My framework addresses these challenges by proposing collaborative model training across multiple institutions without centralizing sensitive patient data. I also build a mathematical model that demonstrates how global model optimization can be achieved through distributed local computations, while blockchain technology ensures secure and transparent record-keeping of model updates. Smart contracts provide additional security through update validation. The framework shows potential for scalability and addresses data heterogeneity across institutions. While theoretical in nature, this research opens new avenues for privacy-compliant, large-scale data analysis in medical virology. It provides a blueprint for accelerating research while maintaining regulatory compliance and individual data sovereignty. Future work should focus on practical implementation and empirical validation in real-world healthcare settings.