3 citations · 3 across the 5 of their papers we have counts for
5 papers
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks
Yong Xie, Weijie Zheng, Hanxun Huang +2
As deep learning models are increasingly deployed in safety-critical applications, evaluating their vulnerabilities to adversarial perturbations is essential for ensuring their rel…
FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
Liuzhi Zhou, Yu He, Kun Zhai +6
Federated learning (FL) has emerged as a prominent approach for collaborative training of machine learning models across distributed clients while preserving data privacy. However,…
White-box Multimodal Jailbreaks Against Large Vision-Language Models
Ruofan Wang, Xingjun Ma, Hanxu Zhou +3
Recent advancements in Large Vision-Language Models (VLMs) have underscored their superiority in various multimodal tasks. However, the adversarial robustness of VLMs has not been…
FedEGG: Federated Learning with Explicit Global Guidance
Kun Zhai, Yifeng Gao, Difan Zou +4
Federated Learning (FL) holds great potential for diverse applications owing to its privacy-preserving nature. However, its convergence is often challenged by non-IID data distribu…
VFLAIR: A Research Library and Benchmark for Vertical Federated Learning
Tianyuan Zou, Zixuan Gu, Yu He +3
Vertical Federated Learning (VFL) has emerged as a collaborative training paradigm that allows participants with different features of the same group of users to accomplish coopera…