With the explosion of data generated across distributed environments, organizations face challenges in extracting insights while maintaining data privacy and regulatory compliance. Federated Learning (FL), a machine learning paradigm where models are trained across decentralized data sources without moving the data, has emerged as a promising solution. This paper explores the integration of FL with modern data warehousing architectures to enable secure, scalable, and privacy-preserving distributed analytics. We outline a federated data warehousing framework, highlight real-world use cases, evaluate system performance, and discuss future research directions.