Estrid He

FUGNN: Harmonizing Fairness and Utility in Graph Neural Networks

2024 · arXiv (Cornell University)

Fairness-aware Graph Neural Networks (GNNs) often face a challenging trade-off, where prioritizing fairness may require compromising utility. In this work, we re-examine fairness through the lens of spectral graph theory, aiming to reconcile fairness and …

Vertical Federated XGBoost with Privacy Preservation via Secure Multiparty Computation

2026 · Journal of Cybersecurity and Privacy

Gradient Boosted Decision Trees (GBDTs) are popular for their strong predictive performance. However, in domains like finance and healthcare, data are often distributed across organizations, making collaborative model training challenging due to privacy concerns. Vertical …