4 citations · 10 across the 6 of their papers we have counts for
6 papers
Federated Pruning: Improving Neural Network Efficiency with Federated Learning
Rongmei Lin, Yonghui Xiao, Tien-Ju Yang +4
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 w…
Online Model Compression for Federated Learning with Large Models
Tien-Ju Yang, Yonghui Xiao, Giovanni Motta +3
This paper addresses the challenges of training large neural network models under federated learning settings: high on-device memory usage and communication cost. The proposed Onli…
Partial Variable Training for Efficient On-Device Federated Learning
Tien-Ju Yang, Dhruv Guliani, Françoise Beaufays +1
This paper aims to address the major challenges of Federated Learning (FL) on edge devices: limited memory and expensive communication. We propose a novel method, called Partial Va…
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…
Low-rank Gradient Approximation For Memory-Efficient On-device Training of Deep Neural Network
Mary Gooneratne, Khe Chai Sim, Petr Zadrazil +3
Training machine learning models on mobile devices has the potential of improving both privacy and accuracy of the models. However, one of the major obstacles to achieving this goa…
Personalization of End-to-end Speech Recognition On Mobile Devices For Named Entities
Khe Chai Sim, Françoise Beaufays, Arnaud Benard +9
We study the effectiveness of several techniques to personalize end-to-end speech models and improve the recognition of proper names relevant to the user. These techniques differ i…