collaborators

9 papers

cs.DC2025

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…

cs.SI2025

Do We Really Need SFT? Prompt-as-Policy over Knowledge Graphs for Cold-start Next POI Recommendation

Jinze Wang, Lu Zhang, Yiyang Cui +5

Next point-of-interest (POI) recommendation is a key component of smart urban services, yet it remains challenging under cold-start conditions with sparse user-POI interactions. Re…

cs.LG2025

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)…

cs.DC2025

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…

cs.DC2025

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…

cs.DC2025

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…