Automatic Speech Recognition models require large amount of speech data for training, and the collection of such data often leads to privacy concerns. Federated learning has been widely used and is considered to be an effective decentralized technique by collaboratively learning a shared prediction model while keeping the data local on different clients devices. However, the limited computation and communication resources on clients devices present practical difficulties for large models. To overcome such challenges, we propose Federated Pruning to train a reduced model under the federated setting, while maintaining similar performance compared to the full model. Moreover, the vast amount of clients data can also be leveraged to improve the pruning results compared to centralized training. We explore different pruning schemes and provide empirical evidence of the effectiveness of our methods.

翻译：联邦学习已被广泛使用,并被视为一种有效的分散技术,通过合作学习共同预测模型,同时将不同客户设备的数据保留在本地;然而,客户设备上有限的计算和通信资源对大型模型来说是实际困难的。为了克服这些挑战,我们建议Freed Pruning在联邦环境下培训一个规模缩小的模型,同时保持与完整模型相似的性能。此外,大量客户数据也可以用来改进与集中培训相比的速成结果。我们探索不同的速成计划,并提供我们方法有效性的经验证据。