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

• URL: http://arxiv.org/abs/2005.10406v2
• Date: Thu, 4 Jun 2020 17:52:52 GMT
• Title: Training Keyword Spotting Models on Non-IID Data with Federated Learning
• Authors: Andrew Hard, Kurt Partridge, Cameron Nguyen, Niranjan Subrahmanya, Aishanee Shah, Pai Zhu, Ignacio Lopez Moreno, Rajiv Mathews
• Abstract summary: We show that a production-quality keyword-spotting model can be trained on-device using federated learning. To overcome the algorithmic constraints associated with fitting on-device data, we conduct thorough empirical studies of optimization algorithms. We label examples (given the zero visibility into on-device data) to explore teacher-student training.
• Score: 6.784774147680782
• License: http://creativecommons.org/licenses/by/4.0/
• 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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