Using Privacy and Federated Learning in Recommendations - Part 1

Recommendation systems are everywhere in our everyday life online — they can be incredibly useful, time-saving, and aid in our discovery of things relevant to our interests. Privacy-preserving recommendation systems can use better signals to build better models.

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

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

Announcing the OpenMined-PyTorch Federated Learning Fellowships

We’re very excited to announce the next round of grants sponsored by the PyTorch team! This grant will focus on developing “worker libraries”, allowing PySyft code to be executed in other environments like a mobile phone or web browser.

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

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.

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!