The integration of federated learning (FL) in drug discovery is an approach to address challenges in data privacy and collaboration. This chapter explores the role of FL in allowing collaborative training of models among pharmaceutical companies, academic institutions, and research organizations without the need to share sensitive datasets. The work is structured to provide a comprehensive overview of FL in this domain. First, we introduce the fundamentals of FL. Next, we examine the diverse applications of FL in drug discovery, and finally, we address critical challenges such as handling non-IID data, managing large-scale datasets, and ensuring privacy. The findings underscore FL’s potential in drug discovery by allowing collaboration across siloed datasets, improving model generalization, and enabling privacy-preserving innovation.