46 citations · 58 across the 5 of their papers we have counts for
5 papers
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning
Xiyu Zhao, Qimei Cui, Weicai Li +5
Personalized federated learning (PFL), e.g., the renowned Ditto, strikes a balance between personalization and generalization by conducting federated learning (FL) to guide persona…
A Novel Indicator for Quantifying and Minimizing Information Utility Loss of Robot Teams
Xiyu Zhao, Qimei Cui, Wei Ni +5
The timely exchange of information among robots within a team is vital, but it can be constrained by limited wireless capacity. The inability to deliver information promptly can re…
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
Xiyu Zhao, Qimei Cui, Ziqiang Du +6
Personalized federated learning (PFL) offers a solution to balancing personalization and generalization by conducting federated learning (FL) to guide personalized learning (PL). L…
UAV-Aided Lifelong Learning for AoI and Energy Optimization in Non-Stationary IoT Networks
Zhenzhen Gong, Omar Hashash, Yingze Wang +4
In this paper, a novel joint energy and age of information (AoI) optimization framework for IoT devices in a non-stationary environment is presented. In particular, IoT devices tha…
Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning
Jingheng Zheng, Hui Tian, Wanli Ni +2
Over-the-air federated learning (AirFL) allows devices to train a learning model in parallel and synchronize their local models using over-the-air computation. The integrity of Air…