activity
20212024
most citedFrom Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection

3 citations · 3 across the 7 of their papers we have counts for

collaborators

5 papers

eess.SP2024

Data-Driven Online Resource Allocation for User Experience Improvement in Mobile Edge Clouds

Liqun Fu, Jingwen Tong, Tongtong Lin +1

As the cloud is pushed to the edge of the network, resource allocation for user experience improvement in mobile edge clouds (MEC) is increasingly important and faces multiple chal…

cs.LG20243 cited

From Learning to Analytics: Improving Model Efficacy with Goal-Directed Client Selection

Jingwen Tong, Zhenzhen Chen, Liqun Fu +2

Federated learning (FL) is an appealing paradigm for learning a global model among distributed clients while preserving data privacy. Driven by the demand for high-quality user exp…

cs.NI2023

Towards Scalable Wireless Federated Learning: Challenges and Solutions

Yong Zhou, Yuanming Shi, Haibo Zhou +3

The explosive growth of smart devices (e.g., mobile phones, vehicles, drones) with sensing, communication, and computation capabilities gives rise to an unprecedented amount of dat…

cs.NI2023

Millimeter Wave Full-Duplex Networks: MAC Design and Throughput Optimization

Shengbo Liu, Wen Wu, Liqun Fu +4

Full-duplex (FD) technique can remarkably boost the network capacity in the millimeter wave (mmWave) bands by enabling simultaneous transmission and reception. However, due to dire…

cs.IT2021

Energy-Efficient Trajectory Design for UAV-Aided Maritime Data Collection in Wind

Yifan Zhang, Jiangbin Lyu, Liqun Fu

Unmanned aerial vehicles (UAVs), especially fixed-wing ones that withstand strong winds, have great potential for oceanic exploration and research. This paper studies a UAV-aided m…