ResSFL
Defense framework
Develops techniques to improve the resistance of split learning in federated learning against model inversion attacks
Official Repository for ResSFL (accepted by CVPR '22)
19 stars
1 watching
3 forks
Language: Shell
last commit: over 2 years ago federated-learningmachine-learningsplit-learning
Related projects:
Repository | Description | Stars |
---|---|---|
| An implementation of a robust federated learning method based on Shapley value to defend against various data and model poisoning attacks | 19 |
| This project presents a framework for robust federated learning against backdoor attacks. | 71 |
| An implementation of a personalized federated learning framework with decentralized sparse training and peer-to-peer communication protocol. | 72 |
| An implementation of a defense against model inversion attacks in federated learning | 55 |
| A method for personalizing machine learning models in federated learning settings with adaptive differential privacy to improve performance and robustness | 57 |
| A framework for personalized federated learning that improves shift-robustness with minimal extra training overhead | 3 |
| A framework for Federated Learning with Differential Privacy using PyTorch | 13 |
| Develops a framework to balance competing goals in federated learning by decoupling generic and personalized prediction tasks. | 14 |
| A framework for tackling heterogeneity and catastrophic forgetting in federated learning by leveraging cross-correlation and similarity learning | 97 |
| 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 repository provides an implementation of a differentially private federated learning algorithm designed to improve the robustness and performance of federated machine learning systems. | 42 |
| Provides a framework and theoretical foundation for Federated Reinforcement Learning with Byzantine Resilience in distributed systems | 85 |
| Researchers investigate vulnerabilities in Federated Learning systems by introducing new backdoor attacks and exploring methods to defend against them. | 66 |
| A framework for attacking federated learning systems with adaptive backdoor attacks | 23 |
| A framework for federated learning with partial model personalization | 2 |