4 citations · 9 across the 6 of their papers we have counts for
6 papers
Boosting Gradient Leakage Attacks: Data Reconstruction in Realistic FL Settings
Mingyuan Fan, Fuyi Wang, Cen Chen +1
Federated learning (FL) enables collaborative model training among multiple clients without the need to expose raw data. Its ability to safeguard privacy, at the heart of FL, has r…
Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning
Mingyuan Fan, Zhanyi Hu, Fuyi Wang +1
Data heterogeneity and backdoor attacks rank among the most significant challenges facing federated learning (FL). For data heterogeneity, personalized federated learning (PFL) ena…
SemiAdv: Query-Efficient Black-Box Adversarial Attack with Unlabeled Images
Mingyuan Fan, Yang Liu, Cen Chen +1
Adversarial attack has garnered considerable attention due to its profound implications for the secure deployment of robots in sensitive security scenarios. To potentially push for…
Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
Mingyuan Fan, Cen Chen, Chengyu Wang +2
Recent works have brought attention to the vulnerability of Federated Learning (FL) systems to gradient leakage attacks. Such attacks exploit clients' uploaded gradients to reconst…
Defense against Backdoor Attacks via Identifying and Purifying Bad Neurons
Mingyuan Fan, Yang Liu, Cen Chen +2
The opacity of neural networks leads their vulnerability to backdoor attacks, where hidden attention of infected neurons is triggered to override normal predictions to the attacker…
Transferable Adversarial Examples with Bayes Approach
Mingyuan Fan, Cen Chen, Wenmeng Zhou +1
The vulnerability of deep neural networks (DNNs) to black-box adversarial attacks is one of the most heated topics in trustworthy AI. In such attacks, the attackers operate without…