1 citations · 3 across the 15 of their papers we have counts for
9 papers · 1 filter
A RAG-Enhanced Bi-Level Cognitive Orchestration Framework for LEO Satellite Networks
Yuhong Jiang, Zhishu Shen, Tong Yin +4
The rapid growth of remote sensing data in Low Earth Orbit (LEO) satellite networks is increasingly constrained by limited downlink capacity to terrestrial networks. Satellite edge…
ML-ECS: A Collaborative Multimodal Learning Framework for Edge-Cloud Synergies
Yuze Liu, Shibo Chu, Tiehua Zhang +5
Edge-cloud synergies provide a promising paradigm for privacy-preserving deployment of foundation models, where lightweight on-device models adapt to domain-specific data and cloud…
A Structure-Agnostic Co-Tuning Framework for LLMs and SLMs in Cloud-Edge Systems
Yuze Liu, Yunhan Wang, Tiehua Zhang +5
The surge in intelligent applications driven by large language models (LLMs) has made it increasingly difficult for bandwidth-limited cloud servers to process extensive LLM workloa…
A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks
Zhuocheng Liu, Zhishu Shen, Qiushi Zheng +3
Low Earth Orbit (LEO) satellites are emerging as key components of 6G networks, with many already deployed to support large-scale Earth observation and sensing related tasks. Feder…
Intelligent Task Management via Dynamic Multi-region Division in LEO Satellite Networks
Zixuan Song, Zhishu Shen, Xiaoyu Zheng +3
As a key complement to terrestrial networks and a fundamental component of future 6G systems, Low Earth Orbit (LEO) satellite networks are expected to provide high-quality communic…
CCRSat: A Collaborative Computation Reuse Framework for Satellite Edge Computing Networks
Ye Zhang, Zhishu Shen, Dawen Jiang +3
In satellite computing applications, such as remote sensing, tasks often involve similar or identical input data, leading to the same processing results. Computation reuse is an em…
FedHC: A Hierarchical Clustered Federated Learning Framework for Satellite Networks
Zhuocheng Liu, Zhishu Shen, Pan Zhou +2
With the proliferation of data-driven services, the volume of data that needs to be processed by satellite networks has significantly increased. Federated learning (FL) is well-sui…
Collaborative UAVs Multi-task Video Processing Optimization Based on Enhanced Distributed Actor-Critic Networks
Ziqi Rong, Qiushi Zheng, Zhishu Shen +4
With the rapid advancement of the Internet of Things (IoT) and Artificial Intelligence (AI), intelligent information services are being increasingly integrated across various secto…
Energy Efficient Offloading Policies in Multi-Access Edge Computing Systems with Task Handover
Ling Hou, Shi Li, Zhishu Shen +3
The rapid growth of mobile devices and the increasing complexity of tasks have made energy efficiency a critical challenge in Multi-Access Edge Computing (MEC) systems. This paper…