This repository accompanies the Federated Few-Shot Learning (FFSL) tutorial: From Theory to Practice and provides code and plotting utilities to transition from a basic Federated Learning (FL) ...

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 ...

This tutorial will guide you through the process of implementing a federated learning setup using the Scaleout Edge platform in combination with Ultralytics YOLOv8 models. Federated learning allows ...

One of the key challenges of machine learning is the need for large amounts of data. Gathering training datasets for machine learning models poses privacy, security, and processing risks that ...

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 ...