9 papers
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…
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…