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 ...
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 ...
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

Conference Tutorial: Can federated learning solve our data privacy problems? State of the art and open challenges

Abstract: Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Data privacy and regulations prevent the free ...
InfoWorld

Federated learning: The killer use case for generative AI

Let’s imagine a fictional company, Global Retail Corporation, a multinational retail chain struggling with its initial approach to AI integration. They built custom generative AI applications on their ...