Mitigating Adversarial Attacks in Federated Learning with Trusted
Execution Environments

Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments

13 September 2023

Papers citing "Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments"

9 / 9 papers shown

Title
Poisoning Prevention in Federated Learning and Differential Privacy via Stateful Proofs of Execution Norrathep Rattanavipanon Ivan de Oliviera Nunes 80 0 0 28 Jan 2025
Contrastive Learning Rivals Masked Image Modeling in Fine-tuning via Feature Distillation Yixuan Wei Han Hu Zhenda Xie Zheng-Wei Zhang Yue Cao Jianmin Bao Dong Chen B. Guo CLIP 88 124 0 27 May 2022
A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologies Zhuang Qian Kaizhu Huang Qiufeng Wang Xu-Yao Zhang OOD AAML ObjD 49 71 0 26 Mar 2022
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer Sachin Mehta Mohammad Rastegari ViT 215 1,213 0 05 Oct 2021
Recent Advances in Adversarial Training for Adversarial Robustness Tao Bai Jinqi Luo Jun Zhao B. Wen Qian Wang AAML 73 473 0 02 Feb 2021
A New Defense Against Adversarial Images: Turning a Weakness into a Strength Tao Yu Shengyuan Hu Chuan Guo Wei-Lun Chao Kilian Q. Weinberger AAML 55 101 0 16 Oct 2019
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism M. Shoeybi M. Patwary Raul Puri P. LeGresley Jared Casper Bryan Catanzaro MoE 245 1,821 0 17 Sep 2019
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 182 1,032 0 29 Nov 2018
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 296 39,198 0 01 Sep 2014