  Federated learning is a distributed, on-device computation framework that
enables training global models without exporting sensitive user data to
servers. In this work, we describe methods to extend the federation framework
to evaluate strategies for personalization of global models. We present tools
to analyze the effects of personalization and evaluate conditions under which
personalization yields desirable models. We report on our experiments
personalizing a language model for a virtual keyboard for smartphones with a
population of tens of millions of users. We show that a significant fraction of
users benefit from personalization.
