Federated Learning Use Cases

Explore documented applications of Federated Learning across various domains, including healthcare, finance, and scientific research. These real-world examples illustrate practical implementations, common challenges, and lessons learned from deployed projects.

Fl use cases

Federated Learning Tutorial Hands on Experince Using Flower

A hands-on tutorial for building and running a federated learning pipeline using the Flower framework and PyTorch, covering client, server, and utility scripts for practical FL experience.

Fl use cases

Federated Learning for Everyone (FL4E)

Overview of the FL4E framework, which simplifies and democratizes federated learning for clinical research, enabling flexible, scalable, and inclusive collaboration across healthcare stakeholders.

Fl use cases

The Journey of Data Within a Global Data Sharing Initiative - A Federated 3-Layer Data Analysis Pipeline to Scale Up Multiple Sclerosis Research

Describes a federated, three-layer data analysis pipeline for global MS research, integrating data from multiple sources while ensuring privacy, standardization, and collaborative analysis.

Fl use cases

Transforming Multiple Sclerosis Research - Pioneering Practical and Precise Approaches with Federated Learning using Real-World Data

Application of federated learning to real-world multiple sclerosis data, exploring optimal FL configurations and strategies for privacy-preserving, large-scale MS research.

Fl use cases

Tutorial on using the WiNGS REST API to query Federated Genomics Data

Step-by-step guide to querying federated genomics data using the WiNGS REST API, covering environment setup, API access, and data manipulation for secure, federated analysis.

Fl use cases

Tutorial running Federated Learning and Deep Learning models for predicting complex phenotypes

Tutorial on using federated and deep learning models to predict complex phenotypes from genetic data, including variant annotation, vectorization, and simulation setup.