17 citations · 62 across the 9 of their papers we have counts for
7 papers · 1 filter
Practical and Light-weight Secure Aggregation for Federated Submodel Learning
Jamie Cui, Cen Chen, Tiandi Ye +1
Recently, Niu, et. al. introduced a new variant of Federated Learning (FL), called Federated Submodel Learning (FSL). Different from traditional FL, each client locally trains the…
Privacy Threats Analysis to Secure Federated Learning
Yuchen Li, Yifan Bao, Liyao Xiang +4
Federated learning is emerging as a machine learning technique that trains a model across multiple decentralized parties. It is renowned for preserving privacy as the data never le…
Improving Federated Relational Data Modeling via Basis Alignment and Weight Penalty
Yilun Lin, Chaochao Chen, Cen Chen +1
Federated learning (FL) has attracted increasing attention in recent years. As a privacy-preserving collaborative learning paradigm, it enables a broader range of applications, esp…
A Theoretical Perspective on Differentially Private Federated Multi-task Learning
Huiwen Wu, Cen Chen, Li Wang
In the era of big data, the need to expand the amount of data through data sharing to improve model performance has become increasingly compelling. As a result, effective collabora…
Privacy-preserving Transfer Learning via Secure Maximum Mean Discrepancy
Bin Zhang, Cen Chen, Li Wang
The success of machine learning algorithms often relies on a large amount of high-quality data to train well-performed models. However, data is a valuable resource and are always h…
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics
Bingzhe Wu, Chaochao Chen, Shiwan Zhao +6
Bayesian deep learning is recently regarded as an intrinsic way to characterize the weight uncertainty of deep neural networks~(DNNs). Stochastic Gradient Langevin Dynamics~(SGLD)…