wenzhu23333/Differential-Privacy-Based-Federated-Learning
Everything you want about DP-Based Federated Learning, including Papers and Code. (Mechanism: Laplace or Gaussian, Dataset: femnist, shakespeare, mnist, cifar-10 and fashion-mnist. )
This project helps machine learning researchers and data scientists studying federated learning implement and evaluate models with differential privacy. It provides code examples for common datasets and model architectures, allowing you to compare the performance of various privacy mechanisms. The output includes trained models and performance metrics, demonstrating the trade-offs between model accuracy and privacy guarantees.
423 stars. No commits in the last 6 months.
Use this if you are a researcher or practitioner exploring differentially private federated learning and need a structured collection of code implementations and research papers.
Not ideal if you are looking for a plug-and-play solution for production-ready private federated learning without needing to understand or modify the underlying mechanisms.
Stars
423
Forks
68
Language
Python
License
GPL-3.0
Category
Last pushed
Oct 26, 2024
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