As global privacy laws become more stringent, organizations face increasing challenges in collaborating with data across borders while adhering to data sovereignty regulations. Federated learning offers a promising solution by allowing machine learning models to be trained on local data without centralizing raw data, thereby reducing legal and ethical risks associated with data transfer. This decentralized approach aligns with key principles of major data protection laws, such as GDPR and HIPAA, which emphasize data minimization, local control, and privacy by design. Federated learning enables organizations in various sectors, including healthcare and financial services, to collaborate on shared models while respecting jurisdictional boundaries and maintaining data privacy. Despite its benefits, federated learning faces practical challenges such as communication overhead, system and data heterogeneity, and security vulnerabilities. Companies like Duality are advancing federated learning technologies by integrating privacy-enhancing tools to ensure compliance and protect data. As privacy regulations evolve, federated learning provides a viable pathway for organizations to glean insights from data without violating sovereignty rules.