Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning

18 September 2020

Papers citing "Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning"

6 / 6 papers shown

Title
FEDKIM: Adaptive Federated Knowledge Injection into Medical Foundation Models Xiaochen Wang Jiaqi Wang Houping Xiao J. Chen Fenglong Ma MedIm 61 7 0 17 Aug 2024
Delving into the Adversarial Robustness of Federated Learning Jie M. Zhang Bo-wen Li Chen Chen Lingjuan Lyu Shuang Wu Shouhong Ding Chao Wu FedML 30 34 0 19 Feb 2023
On the Convergence and Robustness of Adversarial Training Yisen Wang Xingjun Ma James Bailey Jinfeng Yi Bowen Zhou Quanquan Gu AAML 192 345 0 15 Dec 2021
Deep Co-Attention Network for Multi-View Subspace Learning Lecheng Zheng Y. Cheng Hongxia Yang Nan Cao Jingrui He 21 32 0 15 Feb 2021
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime Stéphane dÁscoli Maria Refinetti Giulio Biroli Florent Krzakala 88 152 0 02 Mar 2020
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 258 3,109 0 04 Nov 2016