  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.
