On the Robustness of Graph Reduction Against GNN Backdoor

no code implementations • 2 Jul 2024 • Yuxuan Zhu, Michael Mandulak, Kerui Wu, George Slota, Yuseok Jeon, Ka-Ho Chow, Lei Yu

Meanwhile, graph reduction techniques, including coarsening and sparsification, which have long been employed to improve the scalability of large graph computational tasks, have recently emerged as effective methods for accelerating GNN training on large-scale graphs.

Computational Efficiency Data Poisoning

A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective

no code implementations • 6 Feb 2024 • Lei Yu, Meng Han, Yiming Li, Changting Lin, Yao Zhang, Mingyang Zhang, Yan Liu, Haiqin Weng, Yuseok Jeon, Ka-Ho Chow, Stacy Patterson

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 learning models.

Vertical Federated Learning