4 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…
Closing the Generalization Gap in Parameter-efficient Federated Edge Learning
Xinnong Du, Zhonghao Lyu, Xiaowen Cao +3
Federated edge learning (FEEL) provides a promising foundation for edge artificial intelligence (AI) by enabling collaborative model training while preserving data privacy. However…
Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning
Dingzhu Wen, Sijing Xie, Xiaowen Cao +4
This paper studies an over-the-air federated edge learning (Air-FEEL) system with integrated sensing, communication, and computation (ISCC), in which one edge server coordinates mu…