Privacy and Robustness in Federated Learning: Attacks and Defenses

Privacy and Robustness in Federated Learning: Attacks and Defenses

7 December 2020

Lichao Sun

Jun Zhao

Philip S. Yu

Papers citing "Privacy and Robustness in Federated Learning: Attacks and Defenses"

8 / 58 papers shown

Title
Federated Learning for Malware Detection in IoT Devices Valerian Rey Pedro Miguel Sánchez Sánchez Alberto Huertas Celdrán Gérome Bovet Martin Jaggi FedML 39 252 0 15 Apr 2021
Towards Personalized Federated Learning A. Tan Han Yu Li-zhen Cui Qiang Yang FedML AI4CE 209 840 0 01 Mar 2021
FederBoost: Private Federated Learning for GBDT Zhihua Tian Rui Zhang Xiaoyang Hou Jian-wei Liu K. Ren Jian Liu Kui Ren FedML AI4CE 41 66 0 05 Nov 2020
Clean-Label Backdoor Attacks on Video Recognition Models Shihao Zhao Xingjun Ma Xiang Zheng James Bailey Jingjing Chen Yu-Gang Jiang AAML 193 274 0 06 Mar 2020
Threats to Federated Learning: A Survey Lingjuan Lyu Han Yu Qiang Yang FedML 199 434 0 04 Mar 2020
SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems Edward Chou Florian Tramèr Giancarlo Pellegrino AAML 168 287 0 02 Dec 2018
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 179 1,032 0 29 Nov 2018
Securing Distributed Gradient Descent in High Dimensional Statistical Learning Lili Su Jiaming Xu FedML 137 35 0 26 Apr 2018