4 citations · 8 across the 4 of their papers we have counts for
4 papers · 1 filter
EAB-FL: Exacerbating Algorithmic Bias through Model Poisoning Attacks in Federated Learning
Syed Irfan Ali Meerza, Jian Liu
Federated Learning (FL) is a technique that allows multiple parties to train a shared model collaboratively without disclosing their private data. It has become increasingly popula…
GLOCALFAIR: Jointly Improving Global and Local Group Fairness in Federated Learning
Syed Irfan Ali Meerza, Luyang Liu, Jiaxin Zhang +1
Federated learning (FL) has emerged as a prospective solution for collaboratively learning a shared model across clients without sacrificing their data privacy. However, the federa…
RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense
Yue Cui, Syed Irfan Ali Meerza, Zhuohang Li +3
Federated learning (FL) provides a variety of privacy advantages by allowing clients to collaboratively train a model without sharing their private data. However, recent studies ha…
Speech Privacy Leakage from Shared Gradients in Distributed Learning
Zhuohang Li, Jiaxin Zhang, Jian Liu
Distributed machine learning paradigms, such as federated learning, have been recently adopted in many privacy-critical applications for speech analysis. However, such frameworks a…