Federated Learning in 10 Lines of code, with PySyft

In our last post, we ran a machine learning experiment using PySyft to study heart disease. Now, we’ll enhance that by implementing a Federated Learning example with the same medical datasets. The best part? We can do this in just 10 lines of code, thanks to a new gem in the PySyft API!

Need more medical data? A Python package and an email is all you need

Everybody working with data knows the unwritten rule in data science: the more data, the better! However, with medical data, accessing even

Padawan Program Alumni

OpenMined's Padawan Program continues to grow and support a diverse group of learners. We recognize our Padawan Program Alumni, acknowledge

OpenMined's R2Q4 Padawan Program Graduates

Congratulations to all of our R2Q4 Padawan Program Graduates!

CKKS explained, Part 5: Rescaling

Fifth part of the series CKKS explained where we see how to define rescaling

CKKS explained, Part 4: Multiplication and Relinearization

Fourth part of the series CKKS explained where we see how to define ciphertext multiplication and relinearization

How to setup PySyft on Windows 10

In this tutorial, you are going to learn how to setup PySyft, a privacy-preserving machine learning framework, on Windows 10.

CKKS explained, Part 3: Encryption and Decryption

Third part of the series CKKS explained where we see how to build an HE system from RLWE, implement encryption, decryption, addition and multiplication.

CKKS explained, Part 2: Full Encoding and Decoding

Second part of the series CKKS explained where we see how to implement CKKS encoder and decoder.

CKKS explained: Part 1, Vanilla Encoding and Decoding

First part of the series CKKS explained where we see how to implement a vanilla encoder and decoder.

Announcing 4 New Libraries for Federated Learning on Web and Mobile Devices

As part of the PyTorch/OpenMined grants we announced last December, the Web & Mobile team has been hard at work on developing 4 new libraries for model-centric federated learning.

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.

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.

Federated Learning for Credit Scoring

Want bureaus to score your credit without hoarding your data? Find out how FL can enable privacy-preserving, cross-border credit assessment.

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.

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

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

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.

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

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.

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.

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

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