73 citations · 84 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
Exploring Heterogeneous Characteristics of Layers in ASR Models for More Efficient Training
Lillian Zhou, Dhruv Guliani, Andreas Kabel +2
Transformer-based architectures have been the subject of research aimed at understanding their overparameterization and the non-uniform importance of their layers. Applying these a…
Training Speech Recognition Models with Federated Learning: A Quality/Cost Framework
Dhruv Guliani, Francoise Beaufays, Giovanni Motta
We propose using federated learning, a decentralized on-device learning paradigm, to train speech recognition models. By performing epochs of training on a per-user basis, federate…