9 citations · 13 across the 4 of their papers we have counts for
3 papers
cs.CR2024
A Learning-Based Attack Framework to Break SOTA Poisoning Defenses in Federated Learning
Yuxin Yang, Qiang Li, Chenfei Nie +3
Federated Learning (FL) is a novel client-server distributed learning framework that can protect data privacy. However, recent works show that FL is vulnerable to poisoning attacks…
cs.LG2024★ 1 cited
Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence
Shuya Feng, Meisam Mohammady, Hanbin Hong +4
Differentially private federated learning (DP-FL) is a promising technique for collaborative model training while ensuring provable privacy for clients. However, optimizing the tra…
cs.CR2016★ 3 cited
Privacy Preserving Linear Programming
Yuan Hong, Jaideep Vaidya, Nicholas Rizzo +1
With the rapid increase in computing, storage and networking resources, data is not only collected and stored, but also analyzed. This creates a serious privacy problem which often…