Search Results for author: Chulin Xie

Found 8 papers, 4 papers with code

RVFR: Robust Vertical Federated Learning via Feature Subspace Recovery

no code implementations29 Sep 2021 Jing Liu, Chulin Xie, Krishnaram Kenthapadi, Oluwasanmi O Koyejo, Bo Li

Vertical Federated Learning (VFL) is a distributed learning paradigm that allows multiple agents to jointly train a global model when each agent holds a different subset of features for the same sample(s).

Federated Learning

Certified Robustness for Free in Differentially Private Federated Learning

no code implementations29 Sep 2021 Chulin Xie, Yunhui Long, Pin-Yu Chen, Krishnaram Kenthapadi, Bo Li

Federated learning (FL) provides an efficient training paradigm to jointly train a global model leveraging data from distributed users.

Federated Learning

Subnet Replacement: Deployment-stage backdoor attack against deep neural networks in gray-box setting

no code implementations15 Jul 2021 Xiangyu Qi, Jifeng Zhu, Chulin Xie, Yong Yang

We study the realistic potential of conducting backdoor attack against deep neural networks (DNNs) during deployment stage.

Backdoor Attack

CRFL: Certifiably Robust Federated Learning against Backdoor Attacks

1 code implementation15 Jun 2021 Chulin Xie, Minghao Chen, Pin-Yu Chen, Bo Li

Our method exploits clipping and smoothing on model parameters to control the global model smoothness, which yields a sample-wise robustness certification on backdoors with limited magnitude.

Federated Learning

Style-based Point Generator with Adversarial Rendering for Point Cloud Completion

1 code implementation CVPR 2021 Chulin Xie, Chuxin Wang, Bo Zhang, Hao Yang, Dong Chen, Fang Wen

In this paper, we proposed a novel Style-based Point Generator with Adversarial Rendering (SpareNet) for point cloud completion.

 Ranked #1 on Point Cloud Completion on ShapeNet (Earth Mover's Distance metric)

Point Cloud Completion

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

no code implementations18 Dec 2020 Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, Tom Goldstein

As machine learning systems grow in scale, so do their training data requirements, forcing practitioners to automate and outsource the curation of training data in order to achieve state-of-the-art performance.

Data Poisoning

DBA: Distributed Backdoor Attacks against Federated Learning

2 code implementations ICLR 2020 Chulin Xie, Keli Huang, Pin-Yu Chen, Bo Li

Compared to standard centralized backdoors, we show that DBA is substantially more persistent and stealthy against FL on diverse datasets such as finance and image data.

Backdoor Attack Feature Importance +1

Attack-Resistant Federated Learning with Residual-based Reweighting

2 code implementations24 Dec 2019 Shuhao Fu, Chulin Xie, Bo Li, Qifeng Chen

Federated learning has a variety of applications in multiple domains by utilizing private training data stored on different devices.

Federated Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.