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
On the Efficiency of Privacy Attacks in Federated Learning
Nawrin Tabassum, Ka-Ho Chow, Xuyu Wang +2
Recent studies have revealed severe privacy risks in federated learning, represented by Gradient Leakage Attacks. However, existing studies mainly aim at increasing the privacy att…
cs.CR2024
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…