64 citations · 279 across the 30 of their papers we have counts for
7 papers · 1 filter
Sparse Decentralized Federated Learning
Shan Sha, Shenglong Zhou, Lingchen Kong +1
Decentralized Federated Learning (DFL) enables collaborative model training without a central server but faces challenges in efficiency, stability, and trustworthiness due to commu…
Exact Penalty Method for Federated Learning
Shenglong Zhou, and Geoffrey Ye Li
Federated learning has burgeoned recently in machine learning, giving rise to a variety of research topics. Popular optimization algorithms are based on the frameworks of the (stoc…
0/1 Deep Neural Networks via Block Coordinate Descent
Hui Zhang, Shenglong Zhou, Geoffrey Ye Li +1
The step function is one of the simplest and most natural activation functions for deep neural networks (DNNs). As it counts 1 for positive variables and 0 for others, its intrinsi…
Test-time Batch Normalization
Tao Yang, Shenglong Zhou, Yuwang Wang +2
Deep neural networks often suffer the data distribution shift between training and testing, and the batch statistics are observed to reflect the shift. In this paper, targeting of…
FedGiA: An Efficient Hybrid Algorithm for Federated Learning
Shenglong Zhou, Geoffrey Ye Li
Federated learning has shown its advances recently but is still facing many challenges, such as how algorithms save communication resources and reduce computational costs, and whet…
Accretionary Learning with Deep Neural Networks
Xinyu Wei, Biing-Hwang Fred Juang, Ouya Wang +2
One of the fundamental limitations of Deep Neural Networks (DNN) is its inability to acquire and accumulate new cognitive capabilities. When some new data appears, such as new obje…
Communication-Efficient ADMM-based Federated Learning
Shenglong Zhou, Geoffrey Ye Li
Federated learning has shown its advances over the last few years but is facing many challenges, such as how algorithms save communication resources, how they reduce computational…