  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.
