8 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…