The Federated Learning Platform

Leverage siloed, business sensitive and regulated data in your machine learning pipelines.  

Our vendor agnostic platform lets you develop regulatory compliant solutions for computer vision, predictive maintenance, anomaly detection and more, without sharing and moving data.

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Secure federated learning for the Enterprise

Data in the Enterprise is increasingly distributed over multiple locations or devices. Pooling this data for machine learning is often not possible for regulatory or practical reasons.

Scaleout's software platform equips your team with a flexible and secure federated learning stack that integrates seamlessly with modern MLOps toolchains.

At Scaleout our mission is is to greatly reduce security risks in industrial machine learning by making the most promising  privacy preserving technologies accessible as production-grade cloud native services.

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  • Remove regulatory barriers in ML
    Enable collaboration between organisations and subsidiaries by removing regulatory barriers to data access (GDPR, sensitive data).

  • Reduce latency and cost in EdgeAI Train models and make predictions directly on edge nodes and devices using highly scalable federated learning.  

  • Improve security in machine learning
    Scaleout platform reduces risks in AI projects by removing the need for copying and moving datasets for model training. We also provide access control for model endpoints.

Scaleout Studio = FedML + MLOps
Scaleout's approach is both vendor agnostic and machine leaning framework agnostic. Studio is a SaaS that can be deployed on any public or private cloud (AWS, Azure, OpenStack, Kubernetes cluster).
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How does Federated Learning with Scaleout work? 

  • Data scientists provide a base-line machine learning model definition, specifying entrypoints for executing local model training, validation and inference. This "compute package" also encodes information such as how to read local data. The platform assists the data scientist to create the package in a secure manner so that it can be trusted to execute locally on clients.
  • Users register clients, approving them to connect to one or many aggregation servers. Security aspects such as client authentication, authorization and code distribution is handled by the platform.
  • Clients are started on each local site, and then receives requests to compute model updates. Data never leaves its original site - only model parameters are exchanged for aggregation. This process is iterated until convergence.

    Scaleout's software platform supports during each of these steps by managing scalable and resilient federated learning networks,  by securing the compute package, and by restricting access to model endpoints.

Learn more:

FEDn: open sourcefederated learning

Get started developing FL solutions today using our open source framework FEDn. Develop solutions for computer vision, NLP, fraud detection and more, with world-leading scalability and resilience.

Apache2 with community support in Discord.
Use Tensorflow, Torch, sklearn, or any machine learning framework.
A clear upgrade path to production and SaaS with Scaleout Studio.

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The Scaleout community is focused on decentralized AI and federated learning.

Our focus is on solving decentralized AI problems using the open source project FEDn, but other related projects and subjects are also discussed.

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