26 citations · 46 across the 8 of their papers we have counts for
4 papers · 1 filter
Generalizable Black-Box Adversarial Attack with Meta Learning
Fei Yin, Yong Zhang, Baoyuan Wu +4
In the scenario of black-box adversarial attack, the target model's parameters are unknown, and the attacker aims to find a successful adversarial perturbation based on query feedb…
sqSGD: Locally Private and Communication Efficient Federated Learning
Yan Feng, Tao Xiong, Ruofan Wu +2
Federated learning (FL) is a technique that trains machine learning models from decentralized data sources. We study FL under local notions of privacy constraints, which provides s…
Toward Adversarial Robustness via Semi-supervised Robust Training
Yiming Li, Baoyuan Wu, Yan Feng +4
Adversarial examples have been shown to be the severe threat to deep neural networks (DNNs). One of the most effective adversarial defense methods is adversarial training (AT) thro…
An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning
Xue Yang, Yan Feng, Weijun Fang +4
Although federated learning improves privacy of training data by exchanging local gradients or parameters rather than raw data, the adversary still can leverage local gradients and…