42 citations · 91 across the 11 of their papers we have counts for
5 papers · 1 filter
Modeling Global Distribution for Federated Learning with Label Distribution Skew
Tao Sheng, Chengchao Shen, Yuan Liu +3
Federated learning achieves joint training of deep models by connecting decentralized data sources, which can significantly mitigate the risk of privacy leakage. However, in a more…
On the Convergence of Multi-Server Federated Learning with Overlapping Area
Zhe Qu, Xingyu Li, Jie Xu +3
Multi-server Federated learning (FL) has been considered as a promising solution to address the limited communication resource problem of single-server FL. We consider a typical mu…
Perception-Aware Attack: Creating Adversarial Music via Reverse-Engineering Human Perception
Rui Duan, Zhe Qu, Shangqing Zhao +3
Recently, adversarial machine learning attacks have posed serious security threats against practical audio signal classification systems, including speech recognition, speaker reco…
Generalized Federated Learning via Sharpness Aware Minimization
Zhe Qu, Xingyu Li, Rui Duan +3
Federated Learning (FL) is a promising framework for performing privacy-preserving, distributed learning with a set of clients. However, the data distribution among clients often e…
LoMar: A Local Defense Against Poisoning Attack on Federated Learning
Xingyu Li, Zhe Qu, Shangqing Zhao +3
Federated learning (FL) provides a high efficient decentralized machine learning framework, where the training data remains distributed at remote clients in a network. Though FL en…