210 citations · 1.1k across the 95 of their papers we have counts for
14 papers · 1 filter
Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systems
Jun Du, Bingqing Jiang, Chunxiao Jiang +2
To satisfy the expected plethora of computation-heavy applications, federated edge learning (FEEL) is a new paradigm featuring distributed learning to carry the capacities of low-l…
Task-Oriented Over-the-Air Computation for Multi-Device Edge AI
Dingzhu Wen, Xiang Jiao, Peixi Liu +3
Departing from the classic paradigm of data-centric designs, the 6G networks for supporting edge AI features task-oriented techniques that focus on effective and efficient executio…
Federated Learning via Unmanned Aerial Vehicle
Min Fu, Yuanming Shi, Yong Zhou
To enable communication-efficient federated learning (FL), this paper studies an unmanned aerial vehicle (UAV)-enabled FL system, where the UAV coordinates distributed ground devic…
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…
Robust Information Bottleneck for Task-Oriented Communication with Digital Modulation
Songjie Xie, Shuai Ma, Ming Ding +3
Task-oriented communications, mostly using learning-based joint source-channel coding (JSCC), aim to design a communication-efficient edge inference system by transmitting task-rel…
Trustworthy Federated Learning via Blockchain
Zhanpeng Yang, Yuanming Shi, Yong Zhou +2
The safety-critical scenarios of artificial intelligence (AI), such as autonomous driving, Internet of Things, smart healthcare, etc., have raised critical requirements of trustwor…
Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI
Dingzhu Wen, Peixi Liu, Guangxu Zhu +4
This paper studies a new multi-device edge artificial-intelligent (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) t…
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…
Multi-agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworks
Xiao Du, Ting Wang, Qiang Feng +4
The 6G network enables a subnetwork-wide evolution, resulting in a "network of subnetworks". However, due to the dynamic mobility of wireless subnetworks, the data transmission of…
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.…