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OpenMined Featured Contributor: October 2019

Interview with Daniele Gadler, OpenMined's Featured Contributor for October 2019!

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

OpenMined Featured Contributor: September 2019

Interview with Marianne Monteiro, OpenMined's Featured Contributor for September 2019!

OpenMined Featured Contributor: August 2019

Interview with Nick Duddy, OpenMined's Featured Contributor for August 2019!

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.

OpenMined Featured Contributor: July 2019

Interview with Théo Ryffel, OpenMined's Featured Contributor for July 2019!

Encrypted Training with PyTorch + PySyft

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

RAAIS Grant Announcement

Announcing the very first round of RAAIS OpenMined Scholarships, sponsored by very generous support from The RAAIS Foundation

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

Training a CNN using SPDZ

An implementation of Convolutional Neural Networks (CNNs) using secure Multi-Party Computation (MPC).

Weekly Digs #10

Small but good: we only dug up one paper this week but it comes with very interesting claims.

Weekly Digs #9

If anyone had any doubt that private machine learning is a growing area then this might take care of that: one week with papers on MPC, HE, SGX, and DP.

Weekly Digs #7

While academia may still be a bit busy with submission deadlines, industry reported interesting stories this week regarding secure computation

Weekly Digs #6

A slightly slower period yet still new work on differential privacy and covert channels!

Weekly Digs #5

Good mix of approaches this time, including custom secure computation, secure enclaves, peer-to-peer gossip, and differential privacy.

Welcome to the OpenMined Blog!

The OpenMined blog is your source for all official announcements, tutorials, and general news within the community.

Weekly Digs #4

Shorter but still interesting mix this week with two pillars of private machine learning: homomorphic encryption and differential privacy!

Weekly Digs #3

Big news this week with a good mix of everything: guides to help you explore, practical tools, and interesting new ideas! Enjoy.

Weekly Digs #2

This week saw updates on training logistic and boosting models on encrypted data, and an update to the student-teacher approach.

Weekly Digs #1

We are very happy to finally kick off our weekly dig into what's currently going on in private machine learning!