5 citations · 11 across the 5 of their papers we have counts for
5 papers
FedBA: Non-IID Federated Learning Framework in UAV Networks
Pei Li, Zhijun Liu, Luyi Chang +2
With the development and progress of science and technology, the Internet of Things(IoT) has gradually entered people's lives, bringing great convenience to our lives and improving…
Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition
Kan Xie, Zhe Zhang, Bo Li +4
With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules…
6G-enabled Edge AI for Metaverse: Challenges, Methods, and Future Research Directions
Luyi Chang, Zhe Zhang, Pei Li +8
6G-enabled edge intelligence opens up a new era of Internet of Everything and makes it possible to interconnect people-devices-cloud anytime, anywhere. More and more next-generatio…
Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones
Zhe Zhang, Shiyao Ma, Zhaohui Yang +5
Air access networks have been recognized as a significant driver of various Internet of Things (IoT) services and applications. In particular, the aerial computing network infrastr…
Semi-Supervised Federated Learning with non-IID Data: Algorithm and System Design
Zhe Zhang, Shiyao Ma, Jiangtian Nie +4
Federated Learning (FL) allows edge devices (or clients) to keep data locally while simultaneously training a shared high-quality global model. However, current research is general…