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Federated Learning brings intelligence to every device, enabling secure, scalable AI from the cloud to the edge, all while keeping your data safe.
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The FEDn framework

The FEDn Framework

FEDn enables federated learning development from local testing to full deployment. Projects can migrate from simulated environments to FEDn Studio deployment without code modifications.
Federated learning scenarios

What is Federated Learning?

Federated learning (FL) trains models on distributed data without centralization, enabling secure, scalable machine learning across edge devices in connected environments.

Industry Solutions

Discover how federated learning enables secure AI applications across automotive, defence, and cybersecurity while preserving data privacy.

A modern car
Automotive
Edge AI solutions that process large amounts of data on-vehicle in a privacy-preserving way will be critical to realizing the transformative potential of AI-powered vehicles.
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Defense
Federated learning, a dual-use technology, is especially valuable for secure AI in defense tech applications. Learn more about how federated learning can enable or enhance AI in cybersecurity and defense.
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Cybersecurity
Integrating federated learning with additional security techniques, can significantly enhance the protection of AI applications, ensuring data privacy, compliance, and defence against threats.

Video Series

Our videos  simplifies federated learning. Perfect for learners and experts alike. Watch here »
Federated Learning Explained

This video explains federated machine learning with a simple example for non-technical audiences.

It emphasizes privacy preservation as a main benefit and explores other advantages for various industries.

It discusses the need for federated learning, its basic mechanics, and additional benefits beyond privacy.

Get Started

Select your preferred way to start with federated learning and the FEDn framework: attend a workshop, book a personal session, or use our self-paced tutorial.
A workshop
Workshop
Get hands-on experience with the FEDn framework in our beginner-friendly workshop, where you'll learn the essentials of federated learning through practical examples.
A remote 1-on-1 session
1-on-1 Session
Book a personal federated learning consultation with our CTO to explore more about federated learning and how FEDn can benefit your organization.
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Tutorial
The best way for data scientists and ML professionals to learn FEDn is by registering for a free personal account and starting with the  Getting Started tutorial.

Collaborations & Partnerships

We are happy to serve a diverse range of federated learning use cases. This section highlights a few of the organizations we've had the privilege of working with.

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Federated Learning for Object Detection Using YOLO

YOLO object detection models can be integrated with federated learning to enable privacy-preserving machine learning across distributed devices. This combination allows for real-time object detection while keeping sensitive data local, making it ideal for applications like autonomous vehicles, medical imaging, and manufacturing quality control.

Hyperparameter tuning with Optuna and FEDn Python API

This post explains using Optuna to optimize the server-side learning rate of FedAdam in FEDn. It covers defining an objective function, tuning hyperparameters, and automating the process to improve model performance.

Enhancing Semiconductor Component Placement with Federated Learning

Mycronic is enhancing its semiconductor Pick and Place (PnP) machines with AI, but the effectiveness of these algorithms relies on access to large, sensitive data sets owned by their customers.