64 citations · 234 across the 59 of their papers we have counts for
70 papers
M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-Distortion
Yangyi Liu, Stefano Rini, Sadaf Salehkalaibar +1
In federated learning (FL), the communication constraint between the remote learners and the Parameter Server (PS) is a crucial bottleneck. For this reason, model updates must be c…
Sharp asymptotics on the compression of two-layer neural networks
Mohammad Hossein Amani, Simone Bombari, Marco Mondelli +2
In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M<N nodes. More precisely, we consider the setting in which…
How to Attain Communication-Efficient DNN Training? Convert, Compress, Correct
Zhong-Jing Chen, Eduin E. Hernandez, Yu-Chih Huang +1
This paper introduces CO3 -- an algorithm for communication-efficient federated Deep Neural Network (DNN) training. CO3 takes its name from three processing applied which reduce th…
Convert, compress, correct: Three steps toward communication-efficient DNN training
Zhong-Jing Chen, Eduin E. Hernandez, Yu-Chih Huang +1
In this paper, we introduce a novel algorithm, , for communication-efficiency distributed Deep Neural Network (DNN) training. is a joint training/com…
A Perspective on Neural Capacity Estimation: Viability and Reliability
Farhad Mirkarimi, Stefano Rini, Nariman Farsad
Recently, several methods have been proposed for estimating the mutual information from sample data using deep neural networks. These estimators ar referred to as neural mutual inf…
Coded Demixing for Unsourced Random Access
Jamison R. Ebert, Vamsi K. Amalladinne, Stefano Rini +2
Unsourced random access (URA) is a recently proposed multiple access paradigm tailored to the uplink channel of machine-type communication networks. By exploiting a strong connecti…