4 citations · 5 across the 6 of their papers we have counts for
7 papers
Placement Delivery Array for Cache-Aided MIMO Systems
Yifei Huang, Kai Wan, Minquan Cheng +2
We consider a cache-aided multiple-input multiple-output (MIMO) network, where a server equipped with antennas and a library of equal-size files communicates…
Fundamental Limits of Coded Caching with Fixed Subpacketization
Minquan Cheng, Yifei Huang, Youlong Wu +1
Coded caching is a promising technique to create coded multicast opportunities for cache-aided networks. By splitting each file into equal packets (i.e., the subpacketization l…
An Out-Of-Distribution Membership Inference Attack Approach for Cross-Domain Graph Attacks
Jinyan Wang, Liu Yang, Yuecen Wei +5
Graph Neural Network-based methods face privacy leakage risks due to the introduction of topological structures about the targets, which allows attackers to bypass the target's pri…
FedRGL: Robust Federated Graph Learning for Label Noise
De Li, Haodong Qian, Qiyu Li +4
Federated Graph Learning (FGL) is a distributed machine learning paradigm based on graph neural networks, enabling secure and collaborative modeling of local graph data among clien…
Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective
De Li, Xianxian Li, Zeming Gan +3
Graph neural networks based on message-passing mechanisms have achieved advanced results in graph classification tasks. However, their generalization performance degrades when nois…
Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation
Yuecen Wei, Xingcheng Fu, Qingyun Sun +4
Social networks are considered to be heterogeneous graph neural networks (HGNNs) with deep learning technological advances. HGNNs, compared to homogeneous data, absorb various aspe…
SSGD: A safe and efficient method of gradient descent
Jinhuan Duan, Xianxian Li, Shiqi Gao +2
With the vigorous development of artificial intelligence technology, various engineering technology applications have been implemented one after another. The gradient descent metho…