210 citations · 1.1k across the 95 of their papers we have counts for
13 papers · 1 filter
Over-the-Air Federated Learning and Optimization
Jingyang Zhu, Yuanming Shi, Yong Zhou +3
Federated learning (FL), as an emerging distributed machine learning paradigm, allows a mass of edge devices to collaboratively train a global model while preserving privacy. In th…
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…
Federated Linear Bandit Learning via Over-the-Air Computation
Jiali Wang, Yuning Jiang, Xin Liu +2
In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the envi…
Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks
Junkai Qian, Yuning Jiang, Xin Liu +4
With the growing popularity of electric vehicles (EVs), maintaining power grid stability has become a significant challenge. To address this issue, EV charging control strategies h…
Integrated Sensing-Communication-Computation for Over-the-Air Edge AI Inference
Zeming Zhuang, Dingzhu Wen, Yuanming Shi +3
Edge-device co-inference refers to deploying well-trained artificial intelligent (AI) models at the network edge under the cooperation of devices and edge servers for providing amb…
Integrated Sensing-Communication-Computation for Edge Artificial Intelligence
Dingzhu Wen, Xiaoyang Li, Yong Zhou +3
Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twins, holographic projection, semantic commun…
Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization
Yuanming Shi, Shuhao Xia, Yong Zhou +3
Vertical federated learning (FL) is a collaborative machine learning framework that enables devices to learn a global model from the feature-partition datasets without sharing loca…
Green Federated Learning Over Cloud-RAN with Limited Fronthual Capacity and Quantized Neural Networks
Jiali Wang, Yijie Mao, Ting Wang +1
In this paper, we propose an energy-efficient federated learning (FL) framework for the energy-constrained devices over cloud radio access network (Cloud-RAN), where each device ad…
Task-Oriented Communications for 6G: Vision, Principles, and Technologies
Yuanming Shi, Yong Zhou, Dingzhu Wen +3
Driven by the interplay among artificial intelligence, digital twin, and wireless networks, 6G is envisaged to go beyond data-centric services to provide intelligent and immersive…
Features Disentangled Semantic Broadcast Communication Networks
Shuai Ma, Weining Qiao, Youlong Wu +6
Single-user semantic communications have attracted extensive research recently, but multi-user semantic broadcast communication (BC) is still in its infancy. In this paper, we prop…
Task-oriented Explainable Semantic Communications
Shuai Ma, Weining Qiao, Youlong Wu +6
Semantic communications utilize the transceiver computing resources to alleviate scarce transmission resources, such as bandwidth and energy. Although the conventional deep learnin…
Reconfigurable Intelligent Surface Empowered Rate-Splitting Multiple Access for Simultaneous Wireless Information and Power Transfer
Chengzhong Tian, Yijie Mao, Kangchun Zhao +2
Rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) have been both recognized as promising techniques for 6G. The benefits of combining the two techn…
STAR-RIS Assisted Over-the-Air Vertical Federated Learning in Multi-Cell Wireless Networks
Xiangyu Zeng, Yijie Mao, Yuanming Shi
Vertical federated learning (FL) is a critical enabler for distributed artificial intelligence services in the emerging 6G era, as it allows for secure and efficient collaboration…