27 citations · 71 across the 11 of their papers we have counts for
10 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…
Joint Computing, Pushing, and Caching Optimization for Mobile Edge Computing Networks via Soft Actor-Critic Learning
Xiangyu Gao, Yaping Sun, Hao Chen +2
Mobile edge computing (MEC) networks bring computing and storage capabilities closer to edge devices, which reduces latency and improves network performance. However, to further re…
Pushing AI to Wireless Network Edge: An Overview on Integrated Sensing, Communication, and Computation towards 6G
Guangxu Zhu, Zhonghao Lyu, Xiang Jiao +5
Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelli…
Personalizing or Not: Dynamically Personalized Federated Learning with Incentives
Zichen Ma, Yu Lu, Wenye Li +1
Personalized federated learning (FL) facilitates collaborations between multiple clients to learn personalized models without sharing private data. The mechanism mitigates the stat…
Service Delay Minimization for Federated Learning over Mobile Devices
Rui Chen, Dian Shi, Xiaoqi Qin +3
Federated learning (FL) over mobile devices has fostered numerous intriguing applications/services, many of which are delay-sensitive. In this paper, we propose a service delay eff…
Joint LED Selection and Precoding Optimization for Multiple-User Multiple-Cell VLC Systems
Yang Yang, Yujie Yang, Mingzhe Chen +4
This paper proposes a hybrid dimming scheme based on joint LED selection and precoding design (TASP-HD) for multiple-user (MU) multiple-cell (MC) visible light communications (VLC)…