27 citations · 49 across the 10 of their papers we have counts for
6 papers · 1 filter
Quality-of-Service Aware LLM Routing for Edge Computing with Multiple Experts
Jin Yang, Qiong Wu, Zhiying Feng +3
Large Language Models (LLMs) have demonstrated remarkable capabilities, leading to a significant increase in user demand for LLM services. However, cloud-based LLM services often s…
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association
Qiong Wu, Xu Chen, Tao Ouyang +4
Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally. However, for ma…
Olive Branch Learning: A Topology-Aware Federated Learning Framework for Space-Air-Ground Integrated Network
Qingze Fang, Zhiwei Zhai, Shuai Yu +3
The space-air-ground integrated network (SAGIN), one of the key technologies for next-generation mobile communication systems, can facilitate data transmission for users all over t…
Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control
Qiong Wu, Xu Chen, Zhi Zhou +2
To meet the ever increasing mobile traffic demand in 5G era, base stations (BSs) have been densely deployed in radio access networks (RANs) to increase the network coverage and cap…
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring
Qiong Wu, Xu Chen, Zhi Zhou +1
In-home health monitoring has attracted great attention for the ageing population worldwide. With the abundant user health data accessed by Internet of Things (IoT) devices and rec…
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework
Qiong Wu, Kaiwen He, Xu Chen
Internet of Things (IoT) have widely penetrated in different aspects of modern life and many intelligent IoT services and applications are emerging. Recently, federated learning is…