3 papers
cs.CR2024
DiffBreak: Is Diffusion-Based Purification Robust?
Andre Kassis, Urs Hengartner, Yaoliang Yu
Diffusion-based purification (DBP) has become a cornerstone defense against adversarial examples (AEs), regarded as robust due to its use of diffusion models (DMs) that project AEs…
cs.LG2024
Semi-Variance Reduction for Fair Federated Learning
Saber Malekmohammadi, Yaoliang Yu
Ensuring fairness in a Federated Learning (FL) system, i.e., a satisfactory performance for all of the participating diverse clients, is an important and challenging problem. There…
cs.LG2024
Noise-Aware Algorithm for Heterogeneous Differentially Private Federated Learning
Saber Malekmohammadi, Yaoliang Yu, Yang Cao
High utility and rigorous data privacy are of the main goals of a federated learning (FL) system, which learns a model from the data distributed among some clients. The latter has…