GitHub

TheAxeC/federated-learning-tutorial

data Folder: This folder contains slices of the original Wisconsin Breast Cancer dataset, labeled as "_1" and "_2". centralised Folder: Here you'll find an empty data dictionary CSV file corresponding ...
GitHub

Federated Learning in Chemical Engineering: A Tutorial on a Framework for Privacy-Preserving Collaboration Across Distributed Data Sources

Welcome to the Review on Federated Learning (FL) for Chemical Engineering repository. Federated Learning is where multiple decentralized devices collaboratively train a model without sharing their ...
IEEE

A Tutorial on Federated Learning from Theory to Practice: Foundations, Software Frameworks, Exemplary Use Cases, and Selected Trends

Abstract: When data privacy is imposed as a necessity, Federated learning (FL) emerges as a relevant artificial intelligence field for developing machine learning (ML) models in a distributed and ...
TNO

Federated learning: get to know privacy-preserving data analysis

Federated Learning is a decentralised and privacy-friendly form of machine learning. This means that there is no need for a central database to hold all of the sensitive data, so these data cannot be ...
IEEE

Tutorial: Federated Learning × Security for Network Monitoring

Abstract: Federated Learning (FL) is a Machine Learning paradigm that enables training models across distributed clients without accessing their data. In the context of network security, FL can be ...