ModelPoisoning
Model Poisoning Attack Library
An implementation of model poisoning attacks in federated learning
Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470
146 stars
6 watching
37 forks
Language: Python
last commit: over 2 years ago Related projects:
Repository | Description | Stars |
---|---|---|
| A defense mechanism against model poisoning attacks in federated learning | 37 |
| This implementation allows an attacker to directly obtain user data from federated learning gradient updates by modifying the shared model architecture. | 23 |
| A PyTorch implementation of an attack and defense mechanism against Federated Recommendation Systems | 21 |
| A PyTorch framework for analyzing vulnerabilities in federated learning models and predicting data breaches | 274 |
| A tool for demonstrating and analyzing attacks on federated learning systems by introducing backdoors into distributed machine learning models. | 179 |
| A tool for generating adversarial examples to attack text classification and inference models | 496 |
| A framework for analyzing and exploiting vulnerabilities in federated learning models using surrogate model attacks | 9 |
| A framework for attacking federated learning systems with adaptive backdoor attacks | 23 |
| Develops and evaluates a framework for detecting attacks on federated learning systems | 11 |
| A tool for extracting machine learning models from cloud-based services using prediction APIs | 344 |
| This project enables personalized learning models by collaborating on learning the best strategy for each client | 19 |
| An implementation of a framework for learning how to attack federated learning systems | 15 |
| An implementation of an adversarial example generation method for deep learning segmentation models. | 58 |
| This project presents an attack on federated learning systems to compromise their privacy-preserving mechanisms. | 8 |
| This project provides an implementation of backdoor attacks in federated learning frameworks using Python and PyTorch. | 277 |