DBA
Federated attack generator
A tool for demonstrating and analyzing attacks on federated learning systems by introducing backdoors into distributed machine learning models.
DBA: Distributed Backdoor Attacks against Federated Learning (ICLR 2020)
179 stars
2 watching
45 forks
Language: Python
last commit: over 3 years ago federated-learning
Related projects:
Repository | Description | Stars |
---|---|---|
| An implementation of a game-theoretic defense against backdoor attacks in federated learning. | 6 |
| This project provides an implementation of backdoor attacks in federated learning frameworks using Python and PyTorch. | 277 |
| A framework for attacking federated learning systems with adaptive backdoor attacks | 23 |
| This project presents a framework for robust federated learning against backdoor attacks. | 71 |
| An implementation of a federated learning attack method known as Neurotoxin, which introduces backdoors into machine learning models during the training process. | 65 |
| A backdoor defense system for federated learning, designed to protect against data poisoning attacks by isolating subspace training and aggregating models with robust consensus fusion. | 18 |
| This project presents an attack on federated learning systems to compromise their privacy-preserving mechanisms. | 8 |
| This repository provides a setup and framework for investigating irreversible backdoor attacks in Federated Learning systems. | 31 |
| An implementation of a defense against model inversion attacks in federated learning | 55 |
| A PyTorch implementation of an attack-tolerant federated learning system to train robust local models against malicious attacks from adversaries. | 10 |
| Develops and evaluates a framework for detecting attacks on federated learning systems | 11 |
| Researchers develop an attack method to measure the effectiveness of federated learning privacy defenses by generating leakage in gradients | 58 |
| A PyTorch implementation of an attack and defense mechanism against Federated Recommendation Systems | 21 |
| Researchers investigate vulnerabilities in Federated Learning systems by introducing new backdoor attacks and exploring methods to defend against them. | 66 |
| An implementation of a framework for learning how to attack federated learning systems | 15 |