  Federated learning is a distributed form of machine learning where both the
training data and model training are decentralized. In this paper, we use
federated learning in a commercial, global-scale setting to train, evaluate and
deploy a model to improve virtual keyboard search suggestion quality without
direct access to the underlying user data. We describe our observations in
federated training, compare metrics to live deployments, and present resulting
quality increases. In whole, we demonstrate how federated learning can be
applied end-to-end to both improve user experiences and enhance user privacy.
