A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
-
Updated
Dec 25, 2024
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
A curated list of awesome responsible machine learning resources.
Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond
Training PyTorch models with differential privacy
A Privacy-Preserving Framework Based on TensorFlow
Privacy Testing for Deep Learning
Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.
Implementation of protocols in SecureNN.
Fast, memory-efficient, scalable optimization of deep learning with differential privacy
Implementation of protocols in Falcon
Piranha: A GPU Platform for Secure Computation
Advanced Privacy-Preserving Federated Learning framework
Full stack service enabling decentralized machine learning on private data
Privacy Preserving Convolutional Neural Network using Homomorphic Encryption for secure inference
This is the research repository for Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition.
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation (USENIX Security '23)
This repository contains all the implementation of different papers on Federated Learning
[ICML 2022 / ICLR 2024] Source code for our papers "Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks" and "Be Careful What You Smooth For".
Secure Linear Regression in the Semi-Honest Two-Party Setting.
Privacy-Preserving Machine Learning (PPML) Tutorial
Add a description, image, and links to the privacy-preserving-machine-learning topic page so that developers can more easily learn about it.
To associate your repository with the privacy-preserving-machine-learning topic, visit your repo's landing page and select "manage topics."