Web: http://arxiv.org/abs/2209.06359

Sept. 15, 2022, 1:11 a.m. | Rongmei Lin, Yonghui Xiao, Tien-Ju Yang, Ding Zhao, Li Xiong, Giovanni Motta, Françoise Beaufays

cs.LG updates on arXiv.org arxiv.org

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 …

arxiv efficiency federated learning network neural network pruning

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