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…
Joint Compression and Deadline Optimization for Wireless Federated Learning
Maojun Zhang, Yang Li, Dongzhu Liu +4
Federated edge learning (FEEL) is a popular distributed learning framework for privacy-preserving at the edge, in which densely distributed edge devices periodically exchange model…
Semantic Communications for Image Recovery and Classification via Deep Joint Source and Channel Coding
Zhonghao Lyu, Guangxu Zhu, Jie Xu +2
With the recent advancements in edge artificial intelligence (AI), future sixth-generation (6G) networks need to support new AI tasks such as classification and clustering apart fr…
An Overview on Over-the-Air Federated Edge Learning
Xiaowen Cao, Zhonghao Lyu, Guangxu Zhu +3
Over-the-air federated edge learning (Air-FEEL) has emerged as a promising solution to support edge artificial intelligence (AI) in future beyond 5G (B5G) and 6G networks. In Air-F…
Low-Latency Cooperative Spectrum Sensing via Truncated Vertical Federated Learning
Zezhong Zhang, Guangxu Zhu, Shuguang Cui
In recent years, the exponential increase in the demand of wireless data transmission rises the urgency for accurate spectrum sensing approaches to improve spectrum efficiency. The…
Toward Ambient Intelligence: Federated Edge Learning with Task-Oriented Sensing, Computation, and Communication Integration
Peixi Liu, Guangxu Zhu, Shuai Wang +4
In this paper, we address the problem of joint sensing, computation, and communication (SC) resource allocation for federated edge learning (FEEL) via a concrete case study o…
Towards Tailored Models on Private AIoT Devices: Federated Direct Neural Architecture Search
Chunhui Zhang, Xiaoming Yuan, Qianyun Zhang +3
Neural networks often encounter various stringent resource constraints while deploying on edge devices. To tackle these problems with less human efforts, automated machine learning…