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

PBFL: A Privacy-Preserving Blockchain-Based Federated Learning Framework With Homomorphic Encryption and Single Masking

Abstract: Federated learning (FL) has emerged as a promising paradigm for secure data sharing in Industrial Internet of Things (IIoT), enabling collaborative model training without direct exchange of ...
VentureBeat

How (and why) federated learning enhances cybersecurity

Each year, cyberattacks become more frequent and data breaches become more expensive. Whether companies seek to protect their AI system during development or use their algorithm to improve their ...