Duet Demo - How to do data science on data owned by a different organization

This is a summary of Duet Tutorial by Andrew Trask which was presented at OpenMined Privacy Conference 2020.Brief intro to federated learnin

Encrypted Inference using ResNet-18

Encrypted inference with ResNet-18 using PyTorch + PySyft on ants & bees images

Speech Command Prediction with Federated Learning

Photo by Jason Rosewell on UnsplashPrivate conversation is everywhere. If you talk to your wife, it’s obviously a private conversation.

CrypTen Integration into PySyft

CrypTen integrated in PySyft: a fast SMPC backend for secure computation between servers.

Sentiment Analysis on Multiple Datasets With SyferText - Demo

How can you do pre-processing if you are not allowed to have access to plaintext data? SyferText can help you! With SyferText, you can define pre-processing components to perform pre-processing remotely, blindly and in a completely secure fashion.

What is Federated Learning?

This post is part of our Privacy-Preserving Data Science, Explained series. In this article of the introductory series on Private ML, we wi

Towards privacy with RStudio: Encrypted deep learning with Syft and Keras

In this post, we introduce Syft, an open-source framework that integrates with PyTorch as well as TensorFlow, and show how to use it from R. In an example use case, we obtain private predictions from an R Keras model.

Privacy-Preserving AI Summary: MIT Deep Learning Series

This article briefly discusses the key concepts covered in Part 1 of this lecture by Andrew Trask, which is part of the MIT Deep Learning Se

Predictive Maintenance of Turbofan Engines using Federated Learning with PySyft and PyGrid

Is it possible to benefit from the wonders of machine learning without having direct access to data? Today, machine learning can be used to

OpenMined + apheris AI Partnership for PyTorch Mobile Federated Learning

Today, we’re very excited to announce our Use Case partnership with apheris AI to deploy the very first open-source system for private federated learning on server, web, and mobile at scale.

Announcing Spanish PySyft Tutorial Translations

As part of effort to make it easier for more people to access our projects and resources, we have translated our PySyft tutorials into Spanish!

Announcing Portuguese PySyft Tutorial Translations

Today we are pleased to announce the translation of our PySyft tutorials into Portuguese!

Introduction to Federated Learning and Privacy Preservation using PySyft and PyTorch

Federated Learning and Additive Secret Sharing using the PySyft framework

Split Neural Networks on PySyft and PyTorch

Summary: In this blog we are going to provide an introduction into a new decentralised learning methodology called, ‘Split Neural Networks’.

Meet OpenMined's new PyTorch-OpenMined Fellows

We’re very excited to announce the recipients of the latest round of open-source software development grants in the OpenMined community, gen

Introducing PySyft TensorFlow

Support for TensorFlow in PySyft!

Asynchronous Federated Learning in PySyft

In this post, we provide a showcase of applying federated learning using PySyft. PySyft is an open source python library for secure and priv

Privacy-Preserving AI in Medical Imaging: Federated Learning, Differential Privacy, and Encrypted Computation

In medical imaging, necessary privacy concerns limit us from fully maximizing the benefits of AI in our research. These modern privacy techniques could allow us to train our models on encrypted data from multiple institutions, hospitals, and clinics without sharing the patient data.

Encrypted Training with PyTorch + PySyft

Encrypted Training of Deep Learning models with PyTorch + PySyft on MNIST

Federated Learning of a Recurrent Neural Network on Raspberry PIs

In this article, you are going to learn how to setup PySyft on a Raspberry PI and how to train a Recurrent Neural Network in a federated way.

Encrypted Deep Learning Classification with PyTorch & PySyft

Encrypted Deep Learning Classification with PyTorch & PySyft in < 33ms on MNIST

Deep Learning -> Federated Learning in 10 Lines of PyTorch + PySyft

Deep Learning -> Federated Learning in 10 Lines of PyTorch + PySyft