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cs.LG2024
Federated Learning of Large ASR Models in the Real World
Yonghui Xiao, Yuxin Ding, Changwan Ryu +2
Federated learning (FL) has shown promising results on training machine learning models with privacy preservation. However, for large models with over 100 million parameters, the t…
cs.LG2021
Enabling On-Device Training of Speech Recognition Models with Federated Dropout
Dhruv Guliani, Lillian Zhou, Changwan Ryu +5
Federated learning can be used to train machine learning models on the edge on local data that never leave devices, providing privacy by default. This presents a challenge pertaini…