Restrictive rules for data sharing in many industries have led to the development of federated learning. Federated learning is a machine-learning technique that allows distributed clients to train models collaboratively without the need to share their respective training data with others. In this paper, we first explore the technical foundations of federated learning and its organizational opportunities. Second, we present a conceptual framework for the adoption of federated learning, mapping four types of organizations by their artificial intelligence capabilities and limits to data sharing. We then discuss why exemplary organizations in different contexts - including public authorities, financial service providers, manufacturing companies, as well as research and development consortia - might consider different approaches to federated learning. To conclude, we argue that federated learning presents organizational challenges with ample interdisciplinary opportunities for information systems researchers.