14 citations · 28 across the 7 of their papers we have counts for
7 papers · 1 filter
GQFedWAvg: Optimization-Based Quantized Federated Learning in General Edge Computing Systems
Yangchen Li, Ying Cui, Vincent Lau
The optimal implementation of federated learning (FL) in practical edge computing systems has been an outstanding problem. In this paper, we propose an optimization-based quantized…
Structured Bayesian Compression for Deep Neural Networks Based on The Turbo-VBI Approach
Chengyu Xia, Danny H. K. Tsang, Vincent K. N. Lau
With the growth of neural network size, model compression has attracted increasing interest in recent research. As one of the most common techniques, pruning has been studied for a…
Optimization-Based GenQSGD for Federated Edge Learning
Yangchen Li, Ying Cui, Vincent Lau
Optimal algorithm design for federated learning (FL) remains an open problem. This paper explores the full potential of FL in practical edge computing systems where workers may hav…
An Optimization Framework for Federated Edge Learning
Yangchen Li, Ying Cui, Vincent Lau
The optimal design of federated learning (FL) algorithms for solving general machine learning (ML) problems in practical edge computing systems with quantized message passing remai…
Efficient Sparse Coding using Hierarchical Riemannian Pursuit
Ye Xue, Vincent Lau, Songfu Cai
Sparse coding is a class of unsupervised methods for learning a sparse representation of the input data in the form of a linear combination of a dictionary and a sparse code. This…
Online Orthogonal Dictionary Learning Based on Frank-Wolfe Method
Ye Xue, Vincent Lau
Dictionary learning is a widely used unsupervised learning method in signal processing and machine learning. Most existing works of dictionary learning are in an offline manner. Th…