A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
-
Updated
Jun 2, 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.
Training PyTorch models with differential privacy
Everything about federated learning, including research papers, books, codes, tutorials, videos and beyond
An open framework for Federated Learning.
A Privacy-Preserving Framework Based on TensorFlow
Implementation of protocols in Falcon
Implementation of protocols in SecureNN.
Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.
Piranha: A GPU Platform for Secure Computation
Privacy Testing for Deep Learning
This is the research repository for Vid2Doppler: Synthesizing Doppler Radar Data from Videos for Training Privacy-Preserving Activity Recognition.
Advanced Privacy-Preserving Federated Learning framework
Privacy Preserving Convolutional Neural Network using Homomorphic Encryption for secure inference
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation (USENIX Security '23)
Extension of the MOTION2NX framework to implement neural network inferencing task where the data is supplied to the “secure compute servers” by the “data providers”.
Secure Linear Regression in the Semi-Honest Two-Party Setting.
Fast, memory-efficient, scalable optimization of deep learning with differential privacy
[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".
Privacy-Preserving Bandits (MLSys'20)
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."