Model training generally requires collecting data from its source and transporting it to a centralized location where the model training algorithm runs. This becomes particularly problematic when the training data consists of personally identifiable information. We're encouraged by the emergence of federated learning as a privacy-preserving method for training on a large diverse set of data relating to individuals. Federated learning techniques allow the data to remain on the users' device, under their control, yet contribute to an aggregate corpus of training data. In one such technique, each user device updates a model independently; then the model parameters, rather than the data itself, are combined into a centralized view. Network bandwidth and device computational limitations present some significant technical challenges, but we like the way federated learning leaves users in control of their own personal information.