Machine learning over siloed data without the risks of data-pooling

Our technology enables you to connect data silos or edge devices into secure, managed ML federations.

With a cloud native approach we bridge production grade federated learning with MLSecOps. Our aim is to improve data protection and security in ML.

Scaleout Studio: Secure Federated Learning

Scaleout Studio is a comprehensive, vendor agnostic platform for data-private machine learning. Studio integrates leading open source federated learning with MLOps software into a platform designed to succeed with federated learning in the Enterprise.  Our approach is to build on and extend MLSecOps with federated learning. Studio supports both the the third-party trust-provider role as well as model development in federated learning.

Studio helps you to manage a scalable FL network of aggregation servers and lets you govern client connections. To connect local clients you make use of a light-weight client at each data site or device. In addition to enhanced input privacy from FL, Studio integrates leading open source tools for tracking and model serving, and adds role-based authentication and authorization to all deployed services and endpoints, providing a layer of protection against reverse engineering.

Studio is implemented as a highly modular Django application with a plug-in architecture for third-party applications specified via Helm-charts. This means that you can tailor the deployment to your particular needs, develop your own custom modules, and integrate with any toolchain you rely on for your MLOps pipelines. The platform is optimized for self-managed on-premise usage, and deploys to any standard Kubernetes cluster.

In the base-line distribution we have bundled leading open source tools for model development (Jupyter Lab, VSCode), model tracking and serving (MLFlow, TF Serving, Torch Serve) and storage mangement (MinIO, Kubernetes volumes). Studio provides project-based multi-tenancy, authentication and authorization, object level permissions, and access control for all managed applications and endpoints. Together, this provides an integrated end-to-end collaboration environment for federated learning operations. However, the deployment is composable and you can simply opt-out of any of the bundled MLOps tools if you have an existing infrastructure in place.

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Studio is available as an annual or monthly subscription with 9x5 support. We recommend combining this with our extended support subscription with direct access to all experts in our team. Contact us for more information.

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Get started with our open source developer tools

If you are a  ML-engineer, DevOps engineer or software developer, you can head over to GitHub right now and get started with our open source federated learning framework FEDn. FEDn has all features needed for research, with a clear upgrade path to secure production deployments with Scaleout Studio.

FEDn: The open core

FEDn is a modular and model agnostic framework developed by Scaleout Systems in collaboration with leading researchers at Uppsala University and with input from our customers and partners, including Scania, Zenseact and AI Sweden.

FEDn supports both cross-silo and cross-device (IoT, mobile, autonomous vehicles) use-cases over scalable FEDn networks. A flexible SDK lets you as a developer or researcher develop tailored clients in any programming language and create your own custom model aggregation schemes and global training strategies.

Key features:


Explore federated learning with Scaleout

Scaleout was created with the mission to build a next-generation platform for privacy-preserving AI for the distributed cloud.

More about federated learning
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