Design a federated learning system in seven steps

What should you consider when building an enterprise federated learning system?Photo by Hunter Harritt on UnsplashIntroductionCompanies like
Federated Learning 3 years ago

Making autonomous vehicles robust with active learning, federated learning & V2X communication

When we think of driving in general, there are good drivers and bad drivers. So, on a 2D spectrum, we would picture a cluster of data of those drivers and realise that the good drivers’ data is clustered around a particular coordinate(x,y) while the bad drivers’ data is all over the place.
Use Cases 3 years ago

Tempered Sigmoid Activations for Deep Learning with Differential Privacy

Read to find out how tempered sigmoid activations help overcome the problem of exploding gradients and yield better accuracy under differentially private model training.
Differential Privacy 4 years ago

Encrypted Inference using ResNet-18

Encrypted inference with ResNet-18 using PyTorch + PySyft on ants & bees images
Private ML 4 years ago

Privacy Preserving AI Summary Part 2: MIT Deep Learning Series

This article briefly discusses the key ideas covered in Part 2 of this lecture by Andrew Trask, which is part of the MIT Deep Learning Serie
Private ML 4 years ago

What is Encrypted Machine Learning as a Service?

This post is part of our Privacy-Preserving Data Science, Explained series. In the era of XaaS(Anything as a Service), many companies provi
Private ML 4 years ago

Announcing the OpenMined-UCSF Data-Centric Federated Learning Fellowship

We’re very excited to announce the next round of open-source software development grants in the OpenMined community, generously sponsored by the University of California San Francisco! These grants will focus on bringing data-centric federated learning with differential privacy budgeting to PyGrid.
Announcements 4 years ago

What is Secure Multi-Party Computation?

This post is part of our Privacy-Preserving Data Science, Explained Simply series.
Multi-Party Computation 4 years ago

Privacy-Preserving Data Science, Explained

In this blog series, we’ll explain common topics in privacy-preserving data science, from a single sentence to code examples. We hope these posts serve as a useful resource for you to figure out the best techniques for your organization.
Private ML 4 years ago

What is Federated Learning?

This post is part of our Privacy-Preserving Data Science, Explained series. Update as of November 18, 2021: The version of PySyft mentioned
Federated Learning 4 years ago

Privacy-Preserving AI Summary: MIT Deep Learning Series

Update as of November 18, 2021: The version of PySyft mentioned in this post has been deprecated. Any implementations using this older versi
Private ML 5 years ago

PySyft, PyTorch and Intel SGX: Secure Aggregation on Trusted Execution Environments

When we talk about sensitive data and cloud computing, how can we guarantee the remote, secure and private execution of our applications? One possible solution to this problem is to run the application in a Trusted Execution Environment (TEE).
Private ML 5 years ago

PyGrid: A Peer-to-Peer Platform for Private Data Science and Federated Learning

What if you could train on all of the world’s data, without that data leaving the device, and while keeping that data private? PyGrid is a peer-to-peer platform for private data science and federated learning.
PyGrid 5 years ago

Inference privacy: what is it, and why do we care?

When you ask your home voice assistant to check the weather, it is listening to and saving not only your voice but also everything else tha
Medical Imaging 5 years ago

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.
Private ML 5 years ago

Autonomous Driving's Seat Belt Moment

Volvo made seatbelt patents readily available to all competitors to encourage adoption of this life saving innovation. Is there a way to promote sharing, or discovery, of safety critical innovations in autonomous vehicles?
Private ML 5 years ago

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
Federated Learning 5 years ago

Introduction to Federated Learning and Privacy Preservation using PySyft and PyTorch

Federated Learning and Additive Secret Sharing using the PySyft framework
Deep Learning 5 years ago

Split Neural Networks on PySyft and PyTorch

Update as of November 18, 2021: The version of PySyft mentioned in this post has been deprecated. Any implementations using this older versi
Private ML 5 years ago

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
Announcements 5 years ago

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.
Federated Learning 5 years ago

Introducing PySyft TensorFlow

Support for TensorFlow in PySyft!
Announcements 5 years ago

Asynchronous Federated Learning in PySyft

Update as of November 18, 2021: The version of PySyft mentioned in this post has been deprecated. Any implementations using this older versi
Federated Learning 5 years ago

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.
Deep Learning 5 years ago