Detect anomalies in network traffic data using Federated Machine Learning technique.
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Updated
May 17, 2024 - Jupyter Notebook
Detect anomalies in network traffic data using Federated Machine Learning technique.
SOON TO BE DEPRECATED - The TensorFlow bindings for 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
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.
Healthcare-Researcher-Connector Package: Federated Learning tool for bridging the gap between Healthcare providers and researchers
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.
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