FedVLN
Privacy protector
An open-source implementation of a federated learning framework to protect data privacy in embodied agent learning for Vision-and-Language Navigation.
[ECCV 2022] Official pytorch implementation of the paper "FedVLN: Privacy-preserving Federated Vision-and-Language Navigation"
13 stars
3 watching
2 forks
Language: C++
last commit: about 2 years ago federated-learningprivacy-preserving-machine-learningvision-and-language-navigation
Related projects:
Repository | Description | Stars |
---|---|---|
dcalab-unipv/turning-privacy-preserving-mechanisms-against-federated-learning | This project presents an attack on federated learning systems to compromise their privacy-preserving mechanisms. | 8 |
shenzebang/centaur-privacy-federated-representation-learning | A framework for Federated Learning with Differential Privacy using PyTorch | 13 |
sap-samples/machine-learning-diff-private-federated-learning | Simulates a federated learning setting to preserve individual data privacy | 360 |
jeremy313/soteria | An implementation of a defense against model inversion attacks in federated learning | 55 |
maxencenoble/differential-privacy-for-heterogeneous-federated-learning | An algorithm for balancing utility and privacy in federated learning on heterogeneous data | 59 |
kenziyuliu/private-cross-silo-fl | This repository provides an implementation of a cross-silo federated learning framework with differential privacy mechanisms. | 25 |
git-disl/lockdown | A backdoor defense system against attacks in federated learning algorithms used for machine learning model training on distributed datasets. | 14 |
ai-secure/fedgame | An implementation of a game-theoretic defense against backdoor attacks in federated learning. | 5 |
eth-sri/bayes-framework-leakage | Develops and evaluates a framework for detecting attacks on federated learning systems | 11 |
zhuohangli/ggl | An attack implementation to test and evaluate the effectiveness of federated learning privacy defenses. | 57 |
xiyuanyang45/dynamicpfl | A method for personalizing machine learning models in federated learning settings with adaptive differential privacy to improve performance and robustness | 51 |
ai-secure/crfl | This project presents a framework for robust federated learning against backdoor attacks. | 71 |
enosair/federated-fdp | A framework for private federated learning that provides differential privacy guarantees at the individual record level. | 7 |
tensorflow/privacy | A Python library for training machine learning models while preserving the privacy of sensitive data | 1,943 |
jonasgeiping/breaching | A PyTorch framework for analyzing vulnerabilities in federated learning models and predicting data breaches | 269 |