From R&D to real-world FL

The FEDn framework enables seamless development and deployment of federated learning applications, from local proofs-of-concept to distributed real-world settings.

Applied federated learning

From machine learning in cybersecurity and defence to privacy-centric AI in smartphones.

Artificial intelligence in cars

AI improves car performance and safety by learning from data to make smarter road decisions. Federated learning in edge computing processes data in vehicles for immediate responses, avoiding off-site data transmission.

Learn more
Edge learning
Federated learning streamlines autonomous vehicle training, saving time and money.
Fleet Intelligence
On-vehicle machine learning for predictive maintenance of truck fleets.
Data federations
Data collaborations for unlocking collective value in air traffic management.

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.


Get started with federated learning

Discover the ideal path for you and your team to ensure data privacy with privacy-preserving AI. Seamlessly move from R&D to a secure production environment with FEDn.


Join us for an upcoming workshop on federated learning, a great first touch for those looking to learn more about the FEDn framework.

Online workshop

An online workshop focusing on federated machine learning with a step-by-step demonstration. During this workshop, we will guide you through a typical workflow for establishing a cross-silo ML federation within an enterprise. Additionally, we will provide an in-depth exploration of the unique capabilities of Scaleout Studio and how it seamlessly integrates with MLOps workflows.


Questions & Answers

  • Why should we choose your FL framework over other options?

    Our framework offers an easy-to-use interface, visual aids, and collaboration tools for ML/FL projects, with features like distributed tracing and event logging for debugging and performance analysis. It ensures security through client identity management and authentication, and has scalable architecture with multiple servers and load-balancers. FEDn also allows flexible experimentation, session management, and deployment on any cloud or on-premises infrastructure.

  • Is this yet another ML platform we have to install?

    FEDn is a versatile framework that can be extended, configured, and integrated into existing systems to tailored to your environment. For effective Federated Learning (FL) management, deployment of server-side components and charts is necessary. It enhances rather than replaces your current setup.

  • Can we build our own IP using your framework?

    Absolutely. You can develop your own IP without any conflict. Utilize our framework and Scaleout’s expertise to accelerate your project. There's no risk of lock-in, as our Software Development Kit (SDK) for integration is licensed under Apache2. We're confident you'll find value in our support services, warranty, indemnification, and comprehensive toolkit.

  • How can I explore FL without deep technical expertise?

    We offer a cloud-hosted FL platform for easy FL exploration, optimized for cost and ideal for R&D. Scaleout enables data scientists to investigate FL without initial IT/DevOps resources. We provide a smooth transition to self-hosted production with enterprise integrations, ensuring your PoC is scalable, secure, and representative of real-world scenarios.

FEDn SDK version 0.8 and FEDn Studio version 0.9 released!

The latest update enhances operational efficiency, robustness, flexibility, and user experience with guided setup, dedicated pages for models/sessions, better event filtering, and more.
Input Privacy: Adversarial attacks and their impact on federated model training

Examination of the effects of label-flipping attacks on federated machine learning. Experiments show these attacks have a limited impact on the global model's accuracy compared to centralized training, due to the federated averaging process limiting malicious clients' influence.

Cross-device FL with FEDn Part 1

Using FEDn, this post demonstrates cross-device federated learning with intermittently connected clients. It sets up a local dev environment where clients randomly connect, train a model briefly, then disconnect - repeating the cycle. Results show FEDn can robustly train models under intermittent conditions, bridging research to production deployments.

Enhancing IoT security with federated learning

We're integrating federated learning to create an innovative intrusion detection system that enhances privacy and threat detection. This approach promises a secure, privacy-focused IoT, leveraging decentralized data without compromise. More details in the post and follow for updates.