164 citations · 499 across the 12 of their papers we have counts for
15 papers
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…
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model
Hao Zhang, You-Chi Cheng, Shankar Kumar +3
Capitalization normalization (truecasing) is the task of restoring the correct case (uppercase or lowercase) of noisy text. We propose a fast, accurate and compact two-level hierar…
Revealing and Protecting Labels in Distributed Training
Trung Dang, Om Thakkar, Swaroop Ramaswamy +3
Distributed learning paradigms such as federated learning often involve transmission of model updates, or gradients, over a network, thereby avoiding transmission of private data.…
Position-Invariant Truecasing with a Word-and-Character Hierarchical Recurrent Neural Network
Hao Zhang, You-Chi Cheng, Shankar Kumar +2
Truecasing is the task of restoring the correct case (uppercase or lowercase) of noisy text generated either by an automatic system for speech recognition or machine translation or…
A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter It
Trung Dang, Om Thakkar, Swaroop Ramaswamy +3
End-to-end Automatic Speech Recognition (ASR) models are commonly trained over spoken utterances using optimization methods like Stochastic Gradient Descent (SGD). In distributed s…
Communication-Efficient Agnostic Federated Averaging
Jae Ro, Mingqing Chen, Rajiv Mathews +2
In distributed learning settings such as federated learning, the training algorithm can be potentially biased towards different clients. Mohri et al. (2019) proposed a domain-agnos…