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!
Federated Learning 2 months ago

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
PySyft 4 months ago

When data sharing is a Problem, PySyft 0.9 is the Solution

Data is key for research! But sharing data is never simple! The new PySyft 0.9 is key to unlock the full potential of research data by bridging the gap between data holders and researchers. In this post we will discover how!
PySyft 4 months ago

OpenMined's R5Q3 Padawan Program Graduates

Congratulations to all of our R5Q3 Padawan Program Graduates! We are thrilled to recognize our Graduates of OpenMined's Padawan Program
Padawan Program a year ago

OpenMined's R4Q2 Padawan Program Graduates

Congratulations to all of our R4Q2 Padawan Program Graduates! We are thrilled to recognize our Graduates of OpenMined's Padawan Program
Padawan Program 2 years ago

OpenMined's R3Q1 Padawan Program Graduates

Congratulations to all of our R3Q1 Padawan Program Graduates!‌ We are thrilled to recognize our Graduates of OpenMined's Padawan Progr
Announcements 2 years ago

A Survey of Differential Privacy Frameworks

A comprehensive overview of various libraries and frameworks for differential privacy and their use cases.
Differential Privacy 2 years ago

Padawan Program Alumni

OpenMined's Padawan Program continues to grow and support a diverse group of learners. We recognize our Padawan Program Alumni, acknowledge
PySyft 2 years ago

OpenMined's R2Q4 Padawan Program Graduates

Congratulations to all of our R2Q4 Padawan Program Graduates!
Announcements 2 years ago

Case Study - Federated privacy preserving analytics for secure collaboration among Telco and partners to improve customer engagement

MotivationWhile consumers expect better customer experience and personalization from businesses, they are increasingly sensitive to privacy
Federated Learning 2 years ago

Remote Data Science Part 2: Introduction to PySyft and PyGrid

This post is a continuation of "Remote Data Science Part 1: Today’s privacy challenges in BigData". The previous blog talks about
Remote Data Science 3 years ago

Install PySyft using Conda

PySyft is a Python library for secure and private Deep Learning. It uses Federated Learning, Differential Privacy, and Encrypyted Computatio
PySyft 3 years ago

Installing PySyft v0.5.0 with PyGrid on a Raspberry Pi 4

Introduction Install PySyft PyTorch v1.8.1 (1 min) Install other dependencies (6 min) Install syft 0.5.0 (6 min) Testing the environment In
Raspberry PI 4 years ago

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

Update as of November 18, 2021: The version of PySyft mentioned in this post has been deprecated. Any implementations using this older versi
PriCon2020 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

Speech Command Prediction with Federated Learning

Photo by Jason Rosewell on UnsplashUpdate as of November 18, 2021: The version of PySyft mentioned in this post has been deprecated. An
Federated Learning 4 years ago

CrypTen Integration into PySyft

CrypTen integrated in PySyft: a fast SMPC backend for secure computation between servers.
Multi-Party Computation 4 years ago

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.
Use Cases 5 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 5 years ago

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.
Deep Learning 5 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

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

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.
Announcements 5 years ago

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!
Announcements 5 years ago