bayes-framework-leakage
Federated Learning Detector
Develops and evaluates a framework for detecting attacks on federated learning systems
11 stars
6 watching
2 forks
Language: Python
last commit: almost 3 years ago Related projects:
Repository | Description | Stars |
---|---|---|
| A PyTorch framework for analyzing vulnerabilities in federated learning models and predicting data breaches | 274 |
| Researchers investigate vulnerabilities in Federated Learning systems by introducing new backdoor attacks and exploring methods to defend against them. | 66 |
| 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 |
| A framework for attacking federated learning systems with adaptive backdoor attacks | 23 |
| This project provides an implementation of backdoor attacks in federated learning frameworks using Python and PyTorch. | 277 |
| An implementation of Personalized Federated Learning with Gaussian Processes using Python. | 32 |
| An implementation of a framework for learning how to attack federated learning systems | 15 |
| Evaluates various methods for federated learning on different models and tasks. | 19 |
| A PyTorch implementation of an attack-tolerant federated learning system to train robust local models against malicious attacks from adversaries. | 10 |
| A tool for demonstrating and analyzing attacks on federated learning systems by introducing backdoors into distributed machine learning models. | 179 |
| An implementation of federated learning with prototype-based methods across heterogeneous clients | 134 |
| An experiment comparing different federated learning approaches for image classification tasks with non-iid datasets. | 8 |
| A framework for Federated Learning with Differential Privacy using PyTorch | 13 |
| An approach to heterogeneous federated learning allowing for model training on diverse devices with varying resources. | 61 |
| Simulates a federated learning setting to preserve individual data privacy | 365 |