4 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…
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…
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…
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…