论文标题
通过联合学习的非IID数据培训关键字发现模型
Training Keyword Spotting Models on Non-IID Data with Federated Learning
论文作者
论文摘要
我们证明，可以使用联合学习并实现对中央训练模型的可比较的虚假接受率和虚假拒绝率的生产质量关键字启动模型。为了克服与拟合在设备数据（固有非独立且相同分布的算法）相关的算法约束，我们使用大型联合模拟进行了对优化算法和超级参数构型的彻底经验研究。为了克服资源限制，我们用规格替换了内存密集的MTR数据增强，这将错误的拒绝率降低了56％。最后，为了标记示例（鉴于对设备数据的零可见性），我们探索了教师培训。
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.
