2 papers
cs.LG2026
X-SAM: Boosting Sharpness-Aware Minimization with Dominant-Eigenvector Gradient Correction
Hongru Duan, Yongle Chen, Lei Guan
Sharpness-Aware Minimization (SAM) aims to improve generalization by minimizing a worst-case perturbed loss over a small neighborhood of model parameters. However, during training,…
cs.CR2024
Breaking Secure Aggregation: Label Leakage from Aggregated Gradients in Federated Learning
Zhibo Wang, Zhiwei Chang, Jiahui Hu +4
Federated Learning (FL) exhibits privacy vulnerabilities under gradient inversion attacks (GIAs), which can extract private information from individual gradients. To enhance privac…