1.6k citations · 6.8k across the 149 of their papers we have counts for
10 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…
Over-The-Air Federated Learning under Byzantine Attacks
Houssem Sifaou, Geoffrey Ye Li
Federated learning (FL) is a promising solution to enable many AI applications, where sensitive datasets from distributed clients are needed for collaboratively training a global m…
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…
Efficient Wireless Federated Learning with Partial Model Aggregation
Zhixiong Chen, Wenqiang Yi, Arumugam Nallanathan +1
The data heterogeneity across devices and the limited communication resources, e.g., bandwidth and energy, are two of the main bottlenecks for wireless federated learning (FL). To…
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…
Robust Federated Learning via Over-The-Air Computation
Houssem Sifaou, Geoffrey Ye Li
This paper investigates the robustness of over-the-air federated learning to Byzantine attacks. The simple averaging of the model updates via over-the-air computation makes the lea…
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…
Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork
Chang-Jen Wang, Chao-Kai Wen, Shang-Ho +3
Phase retrieval (PR) is an important component in modern computational imaging systems. Many algorithms have been developed over the past half-century. Recent advances in deep lear…