Detect anomalies in network traffic data using Federated Machine Learning technique.

SOON TO BE DEPRECATED - The TensorFlow bindings for PySyft

Integration of SplitNN for vertically partitioned data with OpenMined's PySyft

Credit Approval Classification Deep Learning Model using Differential Drivacy, Secure Multi-Party Computation, and Federated Learning

Material supporting the tutorial "Pursuing Privacy in Recommender Systems: The View of Users and Researchers from Regulations to Applications" held at the 15th ACM Conference on Recommender Systems in Amsterdam, Netherlands

Healthcare-Researcher-Connector Package: Federated Learning tool for bridging the gap between Healthcare providers and researchers

Federated learning with homomorphic encryption enables multiple parties to securely co-train artificial intelligence models in pathology and radiology, reaching state-of-the-art performance with privacy guarantees.

An implementation of Federated Learning using Pytorch and PySyft

A simple federated learning implementation on MNIST dataset using PySyft. Main goal of the project was to get used to the PySyft federated learning functionality instead of using traditional PyTorch features.

Demonstration of application of Distributed Computing in Federated Learning for our Semester-8 Course on Distributed and Cloud Computing

The project showcasing federated learning of model and testing on encrypted data and model

Securing Collaborative Medical AI by Using Differential Privacy

Project entry for the Secure and Private AI Challenge, hosted by Udacity and sponsored by Facebook (May - August, 2019)

Repo for project : smog detection project at Udacity Project Showcase

Implementations notebooks and scripts of secured and private ai scholarship challenge from facebook.

Repo including all the daily updates of #60daysofudacity Udacity Challenge

The implementation of the "Robust Federated Learning by Mixture of Experts" study.

All Things Deep Learning Projects

The premise of this challenge is to build a habit of practicing new skills by making a public commitment of practicing the topics of Secure and Private AI program every day for 60 days.