CENTAUR-Privacy-Federated-Representation-Learning
Federated Learning Framework
A framework for Federated Learning with Differential Privacy using PyTorch
A PyTorch based repository for Federate Learning with Differential Privacy
13 stars
3 watching
2 forks
Language: Shell
last commit: almost 2 years ago Related projects:
Repository | Description | Stars |
---|---|---|
| A PyTorch-based framework for Federated Learning experiments | 40 |
| This repository provides an implementation of a cross-silo federated learning framework with differential privacy mechanisms. | 25 |
| A framework for private federated learning that provides differential privacy guarantees at the individual record level. | 7 |
| Evaluates various methods for federated learning on different models and tasks. | 19 |
| An implementation of a defense against model inversion attacks in federated learning | 55 |
| An implementation of Personalized Federated Learning with Moreau Envelopes and related algorithms using PyTorch for research and experimentation. | 291 |
| A method for personalizing machine learning models in federated learning settings with adaptive differential privacy to improve performance and robustness | 57 |
| A decentralized federated learning framework based on blockchain and PyTorch. | 243 |
| Simulates a federated learning setting to preserve individual data privacy | 365 |
| Develops a framework to balance competing goals in federated learning by decoupling generic and personalized prediction tasks. | 14 |
| A framework for federated representation learning with domain awareness in multi-model scenarios. | 2 |
| A framework that enables federated learning across multiple datasets while optimizing model performance with record similarities. | 25 |
| An algorithm for balancing utility and privacy in federated learning on heterogeneous data | 59 |
| A PyTorch implementation of an attack-tolerant federated learning system to train robust local models against malicious attacks from adversaries. | 10 |
| A Python framework for collaborative machine learning without sharing sensitive data | 738 |