103 citations · 155 across the 4 of their papers we have counts for
5 papers
CoCoFL: Continual Computing for Federated Learning over Intermittent Satellite-Ground Links
Yun Shen, Kun Guo, Xi Yang +3
Low earth orbit (LEO) satellite constellations enable geographically distributed ground devices to collaboratively train a global model via federated learning (FL) without sharing…
Model Stealing Attacks Against Inductive Graph Neural Networks
Yun Shen, Xinlei He, Yufei Han +1
Many real-world data come in the form of graphs. Graph neural networks (GNNs), a new family of machine learning (ML) models, have been proposed to fully leverage graph data to buil…
Empowering the Edge Intelligence by Air-Ground Integrated Federated Learning
Yuben Qu, Chao Dong, Jianchao Zheng +4
Ubiquitous intelligence has been widely recognized as a critical vision of the future sixth generation (6G) networks, which implies the intelligence over the whole network from the…
UAVs as a Service: Boosting Edge Intelligence for Air-Ground Integrated Networks
Chao Dong, Yun Shen, Yuben Qu +3
The air-ground integrated network is a key component of future sixth generation (6G) networks to support seamless and near-instant super-connectivity. There is a pressing need to i…
Gradient Boosting Survival Tree with Applications in Credit Scoring
Miaojun Bai, Yan Zheng, Yun Shen
Credit scoring plays a vital role in the field of consumer finance. Survival analysis provides an advanced solution to the credit-scoring problem by quantifying the probability of…