Thu-2-8-2 Training Keyword Spotting Models on Non-IID Data with Federated Learning

Andrew Hard(Google Inc.), Kurt Partridge(Google Inc.), Cameron Nguyen(Google Inc.), Niranjan Subrahmanya(Google Inc.), Aishanee Shah(Google Inc.), Pai Zhu(Google Inc.), Ignacio Moreno(Google Inc.) and Rajiv Mathews(Google Inc.)
Abstract: 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.
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