5 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.LG2024
On the Robustness of Graph Reduction Against GNN Backdoor
Yuxuan Zhu, Michael Mandulak, Kerui Wu +4
Graph Neural Networks (GNNs) are gaining popularity across various domains due to their effectiveness in learning graph-structured data. Nevertheless, they have been shown to be su…
cs.CR2024
E-SAGE: Explainability-based Defense Against Backdoor Attacks on Graph Neural Networks
Dingqiang Yuan, Xiaohua Xu, Lei Yu +3
Graph Neural Networks (GNNs) have recently been widely adopted in multiple domains. Yet, they are notably vulnerable to adversarial and backdoor attacks. In particular, backdoor at…
cs.CR2024★ 5 cited
A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective
Lei Yu, Meng Han, Yiming Li +8
Vertical Federated Learning (VFL) is a federated learning paradigm where multiple participants, who share the same set of samples but hold different features, jointly train machine…