Many enterprise solutions can greatly benefit from Machine Learning (ML) models that are created from cross-domain enterprise data. However, many enterprises cannot share data freely across different locations due to regulatory restrictions, performance issues in moving large data volumes, or requirements to maintain autonomy. In such situations, the enterprise can benefit from the concept of federated learning in which ML models are created at multiple different geographic sites. These are combined together at a federation server without the need to share data. Motivated by the fact that web-services based architectures provide a means for robust integration of cross-domain information, in this paper, we describe a solution to the federated learning problem using such an architecture. We specifically focus on the problems enterprises encounter in using distributed data and discuss how we solved those problems through the solution architecture.