4 citations · 15 across the 24 of their papers we have counts for
4 papers · 1 filter
Deciphering the Interplay between Attack and Protection Complexity in Privacy-Preserving Federated Learning
Xiaojin Zhang, Mingcong Xu, Yiming Li +2
Federated learning (FL) offers a promising paradigm for collaborative model training while preserving data privacy. However, its susceptibility to gradient inversion attacks poses…
Fed-AugMix: Balancing Privacy and Utility via Data Augmentation
Haoyang Li, Wei Chen, Xiaojin Zhang
Gradient leakage attacks pose a significant threat to the privacy guarantees of federated learning. While distortion-based protection mechanisms are commonly employed to mitigate t…
Mitigating Data Reconstruction Attacks in Federated Learning: Conditions, Bounds, and Theoretical Guarantees
Xiaojin Zhang, Wei Chen, Mingcong Xu +1
Privacy attacks, particularly data reconstruction attacks, pose significant challenges to federated learning despite its promise in privacy-preserving collaborative training. This…
VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models
Yu Liu, Lang Gao, Mingxin Yang +4
Large Language Models (LLMs) have training corpora containing large amounts of program code, greatly improving the model's code comprehension and generation capabilities. However,…