210 citations · 607 across the 38 of their papers we have counts for
7 papers · 1 filter
UAV-Assisted Multi-Cluster Over-the-Air Computation
Min Fu, Yong Zhou, Yuanming Shi +2
In this paper, we study unmanned aerial vehicles (UAVs) assisted wireless data aggregation (WDA) in multicluster networks, where multiple UAVs simultaneously perform different WDA…
Over-the-Air Computation for 6G: Foundations, Technologies, and Applications
Zhibin Wang, Yapeng Zhao, Yong Zhou +3
The rapid advancement of artificial intelligence technologies has given rise to diversified intelligent services, which place unprecedented demands on massive connectivity and giga…
Interference Management for Over-the-Air Federated Learning in Multi-Cell Wireless Networks
Zhibin Wang, Yong Zhou, Yuanming Shi +1
Federated learning (FL) over resource-constrained wireless networks has recently attracted much attention. However, most existing studies consider one FL task in single-cell wirele…
Reconfigurable Intelligent Surfaces Empowered Green Wireless Networks with User Admission Control
Jinglian He, Yijie Mao, Yong Zhou +2
Reconfigurable intelligent surface (RIS) has emerged as a cost-effective and energy-efficient technique for 6G. By adjusting the phase shifts of passive reflecting elements, RIS is…
Differentially Private Federated Learning via Reconfigurable Intelligent Surface
Yuhan Yang, Yong Zhou, Youlong Wu +1
Federated learning (FL), as a disruptive machine learning paradigm, enables the collaborative training of a global model over decentralized local datasets without sharing them. It…
Over-the-Air Federated Learning via Second-Order Optimization
Peng Yang, Yuning Jiang, Ting Wang +3
Federated learning (FL) is a promising learning paradigm that can tackle the increasingly prominent isolated data islands problem while keeping users' data locally with privacy and…
Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning
Wenzhi Fang, Ziyi Yu, Yuning Jiang +3
Federated learning (FL), as an emerging edge artificial intelligence paradigm, enables many edge devices to collaboratively train a global model without sharing their private data.…