  We demonstrate that a production-quality keyword-spotting model can be
trained on-device using federated learning and achieve comparable false accept
and false reject rates to a centrally-trained model. To overcome the
algorithmic constraints associated with fitting on-device data (which are
inherently non-independent and identically distributed), we conduct thorough
empirical studies of optimization algorithms and hyperparameter configurations
using large-scale federated simulations. To overcome resource constraints, we
replace memory intensive MTR data augmentation with SpecAugment, which reduces
the false reject rate by 56%. Finally, to label examples (given the zero
visibility into on-device data), we explore teacher-student training.
