From R&D to real-world federated learning

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

Secure AI

In the AI landscape, securing machine learning models and their data is crucial.

  • Cybersecurity & Defence

    Federated learning, a dual-use technology, is especially valuable for secure AI in defence tech applications. We have several exciting projects under development, enhancing both security and efficiency.

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  • Cybersecurity & AI

    Integrating federated learning with additional security techniques, can significantly enhance the protection of AI applications, ensuring data privacy, regulatory compliance, and robust defence against threats.

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Applied federated learning

From enhanced 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.

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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.

  • What deployment options does FEDn offer?

    FEDn offers two main deployment options to cater to different organizational needs and project stages. The fully-managed SaaS (Software as a Service) model simplifies access to federated learning technology, making it ideal for early-stage projects, pilots, and proof-of-value phases. For organizations with strict cybersecurity requirements, FEDn can be deployed on private clouds or on-premise, providing full control over deployment, security, and privacy. This self-managed option is particularly suitable for advanced security needs, such as protecting IP-sensitive ML models.

  • 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.

  • What capabilities does FEDn offer to support my organization's federated learning needs?

    FEDn supports a range of capabilities to meet diverse organizational demands. It offers both multi-tenant and single-tenant options, allowing organizations to choose the configuration that best suits their needs. Additionally, FEDn takes care of complex operations management, including server aggregation, data storage, user authentication, network configuration, and system monitoring. This reduces the operational burden on users, enabling them to focus on developing and refining their federated learning.

FEDn SDK version 0.9 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.
Federated Multi-task Learning

Federated Multi-task Learning (FMTL) combines federated and multi-task learning. Clients train local models and share only necessary parameters to maintain privacy. This approach handles data and system heterogeneity, ideal for scenarios like hospitals collaborating on different medical tasks while keeping data private.

Enhancing data security with trusted execution environments

Trusted Execution Environments (TEEs) provide secure hardware-based protection for code and data, ensuring confidentiality and integrity. They are used to secure ML model serving, federated learning, and attestation processes. Performance benchmarks show Intel SGX excels with smaller applications, while AMD SEV handles larger workloads better.

Email Spam Detection with FEDn and Hugging Face

Our project uses the Hugging Face 'Transformers' library in FEDn to fine-tune a BERT-tiny model for spam detection on the Enron email dataset. Federated learning ensures data privacy by splitting the dataset between two clients. Training results in high accuracy (~99%) after a few rounds.