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 …

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. And we explore techniques for utterance …