5 papers
Efficient Entanglement Routing for Satellite-Aerial-Terrestrial Quantum Networks
Yu Zhang, Yanmin Gong, Lei Fan +3
In the era of 6G and beyond, space-aerial-terrestrial quantum networks (SATQNs) are shaping the future of the global-scale quantum Internet. This paper investigates the collaborati…
Quantum-Assisted Online Task Offloading and Resource Allocation in MEC-Enabled Satellite-Aerial-Terrestrial Integrated Networks
Yu Zhang, Yanmin Gong, Lei Fan +3
In the era of Internet of Things (IoT), multi-access edge computing (MEC)-enabled satellite-aerial-terrestrial integrated network (SATIN) has emerged as a promising technology to p…
FedPT: Federated Proxy-Tuning of Large Language Models on Resource-Constrained Edge Devices
Zhidong Gao, Yu Zhang, Zhenxiao Zhang +2
Despite demonstrating superior performance across a variety of linguistic tasks, pre-trained large language models (LMs) often require fine-tuning on specific datasets to effective…
Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
Zhidong Gao, Zhenxiao Zhang, Yu Zhang +3
Federated learning (FL) enables edge devices to collaboratively train a machine learning model without sharing their raw data. Due to its privacy-protecting benefits, FL has been d…
Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning
Zhidong Gao, Yu Zhang, Yanmin Gong +1
Federated Learning (FL) provides a privacy-preserving framework for training machine learning models on mobile edge devices. Traditional FL algorithms, e.g., FedAvg, impose a heavy…