Privacy Leakage of Adversarial Training Models in Federated Learning
Systems

Privacy Leakage of Adversarial Training Models in Federated Learning Systems

21 February 2022

Jingyang Zhang

Yiran Chen

Papers citing "Privacy Leakage of Adversarial Training Models in Federated Learning Systems"

9 / 9 papers shown

Title
Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions Zeju Cai Jianguo Chen Yuting Fan Zibin Zheng Keqin Li 39 4 0 01 Mar 2024
SoK: Unintended Interactions among Machine Learning Defenses and Risks Vasisht Duddu S. Szyller Nadarajah Asokan AAML 47 2 0 07 Dec 2023
Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks Erfan Darzi N. Sijtsema P. V. Ooijen 16 2 0 12 Oct 2023
Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments Simon Queyrut V. Schiavoni Pascal Felber AAML FedML 18 6 0 13 Sep 2023
Federated Learning for Computer Vision Yassine Himeur Iraklis Varlamis Hamza Kheddar Abbes Amira Shadi Atalla Yashbir Singh F. Bensaali W. Mansoor FedML 18 20 0 24 Aug 2023
Pelta: Shielding Transformers to Mitigate Evasion Attacks in Federated Learning Simon Queyrut Yérom-David Bromberg V. Schiavoni FedML AAML 9 1 0 08 Aug 2023
Fairness and Privacy-Preserving in Federated Learning: A Survey Taki Hasan Rafi Faiza Anan Noor Tahmid Hussain Dong-Kyu Chae FedML 35 39 0 14 Jun 2023
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 217 675 0 19 Oct 2020
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 273 2,886 0 15 Sep 2016