96 citations · 124 across the 12 of their papers we have counts for
12 papers
Pruning-Aware Multi-Cluster Co-Inference for Large AI Models in AI-RANs
Xiaowen Cao, Zhonghao Lyu, Shicheng Chu +6
The increasing scale and computational demands of large artificial intelligence models (LAIMs) present significant challenges for efficient inference in resource-constrained distri…
Sense Smarter, Think Better: A Survey on Edge Perception for Next-Generation Networks
Zhonghao Lyu, Xiaowen Cao, Xianxin Song +11
Edge perception has emerged as a foundational capability for future wireless networks, enabling the network edge to proactively sense, interpret, and interact with the physical env…
OmniISR: A Unified Framework for Centralized and Federated Learning via Intermediate Supervision and Regularization
Wei-Bin Kou, Guangxu Zhu, Ming Tang +4
The global deployment of edge intelligence operates across heterogeneous legal frameworks. While some regions permit centralized learning (CL) via cloud data aggregation, others en…
Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN
Zezhong Zhang, Guangxu Zhu, Junting Chen +1
In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. This calls for fast and accurate estimation on the d…
Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving
Wei-Bin Kou, Shuai Wang, Guangxu Zhu +4
While federated learning (FL) improves the generalization of end-to-end autonomous driving by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence…
Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning
Yao Tang, Guangxu Zhu, Wei Xu +3
Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)-mounted e…