FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective

26 October 2021

Yiran Chen

Papers citing "FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective"

23 / 23 papers shown

Title
Dual Defense: Enhancing Privacy and Mitigating Poisoning Attacks in Federated Learning Runhua Xu Shiqi Gao Chao Li J. Joshi Jianxin Li 79 3 0 08 Feb 2025
Poisoning Prevention in Federated Learning and Differential Privacy via Stateful Proofs of Execution Norrathep Rattanavipanon Ivan de Oliviera Nunes 130 0 0 28 Jan 2025
Gradient Purification: Defense Against Poisoning Attack in Decentralized Federated Learning Bin Li Xiaoye Miao Yongheng Shang Xinkui Zhao AAML 95 0 0 08 Jan 2025
LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasets Ang Li Jingwei Sun Binghui Wang Lin Duan Sicheng Li Yiran Chen H. Li FedML 82 127 0 07 Aug 2020
FedML: A Research Library and Benchmark for Federated Machine Learning Chaoyang He Songze Li Jinhyun So Xiao Zeng Mi Zhang ... Yang Liu Ramesh Raskar Qiang Yang M. Annavaram Salman Avestimehr FedML 231 577 0 27 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
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 277 834 0 19 Feb 2020
Robust Aggregation for Federated Learning Krishna Pillutla Sham Kakade Zaïd Harchaoui FedML 105 652 0 31 Dec 2019
Attack-Resistant Federated Learning with Residual-based Reweighting Shuhao Fu Chulin Xie Yue Liu Qifeng Chen FedML AAML 78 93 0 24 Dec 2019
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning Minghong Fang Xiaoyu Cao Jinyuan Jia Neil Zhenqiang Gong AAML OOD FedML 103 1,107 0 26 Nov 2019
Can You Really Backdoor Federated Learning? Ziteng Sun Peter Kairouz A. Suresh H. B. McMahan FedML 75 572 0 18 Nov 2019
Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging Luis Muñoz-González Kenneth T. Co Emil C. Lupu FedML 70 183 0 11 Sep 2019
On the Convergence of FedAvg on Non-IID Data Xiang Li Kaixuan Huang Wenhao Yang Shusen Wang Zhihua Zhang FedML 145 2,334 0 04 Jul 2019
On Evaluating Adversarial Robustness Nicholas Carlini Anish Athalye Nicolas Papernot Wieland Brendel Jonas Rauber Dimitris Tsipras Ian Goodfellow Aleksander Madry Alexey Kurakin ELM AAML 89 901 0 18 Feb 2019
A Little Is Enough: Circumventing Defenses For Distributed Learning Moran Baruch Gilad Baruch Yoav Goldberg FedML 57 503 0 16 Feb 2019
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 283 1,054 0 29 Nov 2018
Universal Multi-Party Poisoning Attacks Saeed Mahloujifar Mohammad Mahmoody Ameer Mohammed AAML 57 46 0 10 Sep 2018
Mitigating Sybils in Federated Learning Poisoning Clement Fung Chris J. M. Yoon Ivan Beschastnikh AAML 58 502 0 14 Aug 2018
How To Backdoor Federated Learning Eugene Bagdasaryan Andreas Veit Yiqing Hua D. Estrin Vitaly Shmatikov SILM FedML 97 1,913 0 02 Jul 2018
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates Dong Yin Yudong Chen Kannan Ramchandran Peter L. Bartlett OOD FedML 121 1,500 0 05 Mar 2018
The Hidden Vulnerability of Distributed Learning in Byzantium El-Mahdi El-Mhamdi R. Guerraoui Sébastien Rouault AAML FedML 67 748 0 22 Feb 2018
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 283 8,904 0 25 Aug 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 17,486 0 17 Feb 2016