3 citations · 6 across the 9 of their papers we have counts for
9 papers
Prioritizing Gradient Sign Over Modulus: An Importance-Aware Framework for Wireless Federated Learning
Yiyang Yue, Jiacheng Yao, Wei Xu +3
Wireless federated learning (FL) facilitates collaborative training of artificial intelligence (AI) models to support ubiquitous intelligent applications at the wireless edge. Howe…
Task-Oriented Low-Label Semantic Communication With Self-Supervised Learning
Run Gu, Wei Xu, Zhaohui Yang +2
Task-oriented semantic communication enhances transmission efficiency by conveying semantic information rather than exact messages. Deep learning (DL)-based semantic communication…
Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture
Jiacheng Yao, Wei Shi, Wei Xu +3
Over-the-air computation (AirComp) has emerged as an essential approach for enabling communication-efficient federated learning (FL) over wireless networks. Nonetheless, the inhere…
Quantized Analog Beamforming Enabled Multi-task Federated Learning Over-the-air
Jiacheng Yao, Wei Xu, Guangxu Zhu +3
Over-the-air computation (AirComp) has recently emerged as a pivotal technique for communication-efficient federated learning (FL) in resource-constrained wireless networks. Though…
Multi-Cell Coordinated Beamforming for Integrate Communication and Multi-TMT Localization
Meidong Xia, Wei Xu, Jindan Xu +3
This paper investigates integrated localization and communication in a multi-cell system and proposes a coordinated beamforming algorithm to enhance target localization accuracy wh…
On Privacy, Security, and Trustworthiness in Distributed Wireless Large AI Models (WLAM)
Zhaohui Yang, Wei Xu, Le Liang +3
Combining wireless communication with large artificial intelligence (AI) models can open up a myriad of novel application scenarios. In sixth generation (6G) networks, ubiquitous c…
Stacked Intelligent Metasurfaces for Task-Oriented Semantic Communications
Guojun Huang, Jiancheng An, Zhaohui Yang +3
Semantic communication (SemCom) leveraging advanced deep learning (DL) technologies enhances the efficiency and reliability of information transmission. Emerging stacked intelligen…
Wireless Federated Learning over Resource-Constrained Networks: Digital versus Analog Transmissions
Jiacheng Yao, Wei Xu, Zhaohui Yang +3
To enable wireless federated learning (FL) in communication resource-constrained networks, two communication schemes, i.e., digital and analog ones, are effective solutions. In thi…
Digital versus Analog Transmissions for Federated Learning over Wireless Networks
Jiacheng Yao, Wei Xu, Zhaohui Yang +3
In this paper, we quantitatively compare these two effective communication schemes, i.e., digital and analog ones, for wireless federated learning (FL) over resource-constrained ne…