10 citations · 13 across the 12 of their papers we have counts for
13 papers
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…
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…
Towards Heterogeneity-Aware and Energy-Efficient Topology Optimization for Decentralized Federated Learning in Edge Environment
Yuze Liu, Tiehua Zhang, Zhishu Shen +3
Federated learning (FL) has emerged as a promising paradigm within edge computing (EC) systems, enabling numerous edge devices to collaboratively train artificial intelligence (AI)…
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…
Farm-LightSeek: An Edge-centric Multimodal Agricultural IoT Data Analytics Framework with Lightweight LLMs
Dawen Jiang, Zhishu Shen, Qiushi Zheng +3
Amid the challenges posed by global population growth and climate change, traditional agricultural Internet of Things (IoT) systems is currently undergoing a significant digital tr…
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…
A Sharded Blockchain-Based Secure Federated Learning Framework for LEO Satellite Networks
Wenbo Wu, Cheng Tan, Kangcheng Yang +3
Low Earth Orbit (LEO) satellite networks are increasingly essential for space-based artificial intelligence (AI) applications. However, as commercial use expands, LEO satellite net…
Unleashing Collaborative Computing for Adaptive Video Streaming with Multi-objective Optimization in Satellite Terrestrial Networks
Zhishu Shen, Qiushi Zheng, Ziqi Rong +3
Satellite-terrestrial networks (STNs) are anticipated to deliver seamless IoT services across expansive regions. Given the constrained resources available for offloading computatio…
Collaborative Satellite Computing through Adaptive DNN Task Splitting and Offloading
Shifeng Peng, Xuefeng Hou, Zhishu Shen +4
Satellite computing has emerged as a promising technology for next-generation wireless networks. This innovative technology provides data processing capabilities, which facilitates…
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…