machine-learning-diff-private-federated-learning
Federated Learning Simulator
Simulates a federated learning setting to preserve individual data privacy
Simulate a federated setting and run differentially private federated learning.
365 stars
16 watching
94 forks
Language: Python
last commit: 7 months ago differential-privacyfederated-learningmachine-learningsamplesample-codesecurity
Related projects:
Repository | Description | Stars |
---|---|---|
| Develops and evaluates federated learning algorithms for personalizing machine learning models across heterogeneous client data distributions. | 157 |
| An algorithm for balancing utility and privacy in federated learning on heterogeneous data | 59 |
| A platform for conducting high-performance federated learning simulations in Python. | 185 |
| An open-source project exploring Federated Learning model updates and their rank structure using data from various datasets. | 14 |
| A framework for Federated Learning with Differential Privacy using PyTorch | 13 |
| An implementation of a defense against model inversion attacks in federated learning | 55 |
| This project presents an attack on federated learning systems to compromise their privacy-preserving mechanisms. | 8 |
| An implementation of a federated learning algorithm for handling heterogeneous data | 6 |
| This project enables personalized federated learning with inferred collaboration graphs to improve the performance of machine learning models on non-IID (non-independent and identically distributed) datasets. | 26 |
| This repository provides an implementation of a cross-silo federated learning framework with differential privacy mechanisms. | 25 |
| A Python implementation of Personalized Federated Learning with Graph using PyTorch. | 49 |
| A method for personalizing machine learning models in federated learning settings with adaptive differential privacy to improve performance and robustness | 57 |
| An implementation of federated learning algorithm to reduce local learning bias and improve convergence on heterogeneous data | 25 |
| A library that provides an easy-to-use framework for simulating federated learning algorithms | 254 |
| Develops a framework to address label skews in one-shot federated learning by partitioning data and adapting models. | 17 |