  This white paper describes recent advances in Gboard(Google Keyboard)'s use
of federated learning, DP-Follow-the-Regularized-Leader (DP-FTRL) algorithm,
and secure aggregation techniques to train machine learning (ML) models for
suggestion, prediction and correction intelligence from many users' typing
data. Gboard's investment in those privacy technologies allows users' typing
data to be processed locally on device, to be aggregated as early as possible,
and to have strong anonymization and differential privacy where possible.
Technical strategies and practices have been established to allow ML models to
be trained and deployed with meaningfully formal DP guarantees and high
utility. The paper also looks ahead to how technologies such as trusted
execution environments may be used to further improve the privacy and security
of Gboard's ML models.
