Performance Weighting for Robust Federated Learning Against Corrupted Sources

2 May 2022

Papers citing "Performance Weighting for Robust Federated Learning Against Corrupted Sources"

20 / 20 papers shown

Title
Robust Asymmetric Heterogeneous Federated Learning with Corrupted Clients Xiuwen Fang Mang Ye Di Lin FedML 167 1 0 12 Mar 2025
Support Vector Machines under Adversarial Label Contamination Huang Xiao Battista Biggio B. Nelson Han Xiao Claudia Eckert Fabio Roli AAML 56 231 0 01 Jun 2022
Covert Model Poisoning Against Federated Learning: Algorithm Design and Optimization Kang Wei Jun Li Ming Ding Chuan Ma Yo-Seb Jeon H. Vincent Poor FedML 47 8 0 28 Jan 2021
Auto-weighted Robust Federated Learning with Corrupted Data Sources Shenghui Li Edith C.H. Ngai Fanghua Ye Thiemo Voigt FedML 62 29 0 14 Jan 2021
Accelerating Federated Learning in Heterogeneous Data and Computational Environments Dimitris Stripelis J. Ambite FedML 47 11 0 25 Aug 2020
Data Poisoning Attacks Against Federated Learning Systems Vale Tolpegin Stacey Truex Mehmet Emre Gursoy Ling Liu FedML 118 653 0 16 Jul 2020
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning Hongyi Wang Kartik K. Sreenivasan Shashank Rajput Harit Vishwakarma Saurabh Agarwal Jy-yong Sohn Kangwook Lee Dimitris Papailiopoulos FedML 79 606 0 09 Jul 2020
Robust Aggregation for Federated Learning Krishna Pillutla Sham Kakade Zaïd Harchaoui FedML 107 658 0 31 Dec 2019
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 259 6,276 0 10 Dec 2019
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning Minghong Fang Xiaoyu Cao Jinyuan Jia Neil Zhenqiang Gong AAML OOD FedML 103 1,120 0 26 Nov 2019
Can You Really Backdoor Federated Learning? Ziteng Sun Peter Kairouz A. Suresh H. B. McMahan FedML 75 574 0 18 Nov 2019
Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detection Lingchen Zhao Shengshan Hu Qian Wang Jianlin Jiang Chao Shen Xiangyang Luo Pengfei Hu AAML 55 94 0 29 Oct 2019
Measure Contribution of Participants in Federated Learning Guan Wang Charlie Xiaoqian Dang Ziye Zhou FedML 93 200 0 17 Sep 2019
Federated Machine Learning: Concept and Applications Qiang Yang Yang Liu Tianjian Chen Yongxin Tong FedML 75 2,318 0 13 Feb 2019
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 286 1,057 0 29 Nov 2018
Mitigating Sybils in Federated Learning Poisoning Clement Fung Chris J. M. Yoon Ivan Beschastnikh AAML 58 504 0 14 Aug 2018
How To Backdoor Federated Learning Eugene Bagdasaryan Andreas Veit Yiqing Hua D. Estrin Vitaly Shmatikov SILM FedML 97 1,922 0 02 Jul 2018
Certified Defenses for Data Poisoning Attacks Jacob Steinhardt Pang Wei Koh Percy Liang AAML 108 756 0 09 Jun 2017
Deep Learning is Robust to Massive Label Noise David Rolnick Andreas Veit Serge J. Belongie Nir Shavit NoLa 79 557 0 30 May 2017
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 406 17,559 0 17 Feb 2016