4 citations · 5 across the 3 of their papers we have counts for
3 papers
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…
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…