57 citations · 159 across the 36 of their papers we have counts for
57 papers
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…
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…