Learning-to-Attack-Federated-Learning
Federated Learning Attacker Framework
An implementation of a framework for learning how to attack federated learning systems
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Language: Python
last commit: about 2 years ago Related projects:
Repository | Description | Stars |
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hfzhang31/a3fl | A framework for attacking federated learning systems with adaptive backdoor attacks | 23 |
ebagdasa/backdoor_federated_learning | This project provides an implementation of backdoor attacks in federated learning frameworks using Python and PyTorch. | 277 |
ksreenivasan/ood_federated_learning | Researchers investigate vulnerabilities in Federated Learning systems by introducing new backdoor attacks and exploring methods to defend against them. | 66 |
jeremy313/fl-wbc | A defense mechanism against model poisoning attacks in federated learning | 37 |
ai-secure/crfl | This project presents a framework for robust federated learning against backdoor attacks. | 71 |
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aiot-mlsys-lab/fedrolex | An approach to heterogeneous federated learning allowing for model training on diverse devices with varying resources. | 61 |
deu30303/feddefender | A PyTorch implementation of an attack-tolerant federated learning system to train robust local models against malicious attacks from adversaries. | 10 |
xtra-computing/fedov | Develops a framework to address label skews in one-shot federated learning by partitioning data and adapting models. | 17 |
fangxiuwen/robust_fl | An implementation of a robust federated learning framework for handling noisy and heterogeneous clients in machine learning. | 43 |
lins-lab/fedbr | An implementation of federated learning algorithm to reduce local learning bias and improve convergence on heterogeneous data | 25 |
ai-secure/dba | A tool for demonstrating and analyzing attacks on federated learning systems by introducing backdoors into distributed machine learning models. | 179 |
haozzh/fedcr | Evaluates various methods for federated learning on different models and tasks. | 19 |
jeremy313/soteria | An implementation of a defense against model inversion attacks in federated learning | 55 |
xtra-computing/fedsim | A framework that enables federated learning across multiple datasets while optimizing model performance with record similarities. | 25 |