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

Aishanee Shah
Cameron Nguyen
Niranjan Subrahmanya
Pai Zhu
Interspeech (2020)
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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 augmentation and data labeling to overcome the physical limitations of on-device training.