AI training without
moving data
Get started with FEDn, a federated learning framework enabling secure AI model creation without sharing data
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Distributed, scalable, and accessible

The federated learning framework FEDn is built from the ground up to ensure that projects can go from R&D to production seamlessly.

Truly distributed

FEDn is inherently distributed, eliminating the need for simulations of distributed environments. This design allows projects to be genuinely decentralized from start, ensuring real-world applicability.

Proven scalability

The architecture is designed for scalability, capable of supporting massive machine learning projects with tens of thousands of clients. Its been proven in extensive, large-scale applications.

Simplified Development

FEDn Studio abstracts away the complexities of infrastructure management. This allows developers to concentrate fully on the development aspect.

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.

Applied federated learning

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

Cybersecurity & Defence

In the modern cybersecurity and defence landscape, strong machine learning models directly rely on access to data. Federated learning is a key technology to solve the data challenge where data cannot be shared between parties or moved off site.

Learn more
Smartphone cross-device AI
Cross-Device AI
Improvement of AI models while maintaining data privacy and compliance.
Fleet Intelligence
On-vehicle machine learning for predictive maintenance of truck fleets.
Data federations
Data collaborations for unlocking collective value in air traffic management.

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.

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.

Truecaller
Northerly
Safespring
Finplustech

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.

Join our interactive federated learning workshop online!

Learn more about the FEDn framework with a live step-by-step demo on setting up a machine learning federation. Sign up now for the upcoming session.
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.

Understanding the Scaleout Software Suite

Diving into the world of federated learning, this text introduces and explores Scaleout's innovative software suite, specifically its key components, FEDn and Studio, and how they collaboratively deliver a powerful solution for federated machine learning initiatives.

Transforming System Developers into Smart Service Providers

Federated machine learning provides a solution to stringent data privacy regulations by allowing model training without centralizing data, turning developers into smart service providers.