D
Differential-Privacy-Based-Federated-Learning
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. )
★425stars
Python
GPL-3.0
Updated: 2w ago
📋 Project at a Glance
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What's this?A , built with Python open-source project in the Security & Governance category, core strengths: deep-learning/differential-privacy
Who made it?Maintained by wenzhu23333 team, 425⭐ on GitHub, #209 out of 413 in Security & Governance
Why does it exist?As the Security & Governance landscape evolved, the wenzhu23333 team identified the need for better deep-learning solutions. Differential-Privacy-Based-Federated-Learning was created to simplify differential-privacy workflows.
What can it do?Key use cases: federated-learning, gaussian, laplace
How to install with AI?Use an AI coding assistant (Claude Code, Cursor, Copilot) to automatically set up pip dependencies and virtual env. Follow the README — the AI handles the rest.
🔗 github.com/wenzhu23333/Differential-Privacy-Based-Federated-Learning
🔗 github.com/wenzhu23333/Differential-Privacy-Based-Federated-Learning
Topics
deep-learningdifferential-privacyfederated-learninggaussianlaplaceprivacypytorch