The most proven federated learning software designed for healthcare research.

Substra

Substra is a ready-to-use, open source federated learning (FL) software developed by Owkin, now hosted by the Linux Foundation for AI and Data. Substra enables the training and validation of machine learning models on distributed datasets. It includes a flexible Python interface and a web application to run federated learning training at scale.

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Aggregate models and insights - not data

Academic research centers and Biopharma companies deploy Substra (formerly Owkin Connect) in a wide variety of federated learning settings for clinical research, drug discovery and development.

Why use Substra?

Data agnostic

Can be used with many different data modalities such as video, images, audio, time series or tabular data

Framework agnostic

Compatible with any machine learning model from any framework including TensorFlow, Torch and Scikit-Learn

Infrastructure agnostic

Works seamlessly with any IT infrastructure setup - on premise or in the cloud

Flexible

Can be deployed in a federated production environment or used on a single machine to perform simulations and debug code

Traceable

Monitoring, traceability and fine grained permissions for complete control of data manipulations

Secure

Proven in strict privacy settings to meet global compliance requirements for hospitals and pharmaceutical companies

Data agnostic

Can be used with many different data modalities such as video, images, audio, time series or tabular data

Framework agnostic

Compatible with any machine learning model from any framework including TensorFlow, Torch and Scikit-Learn

Infrastructure agnostic

Works seamlessly with any IT infrastructure setup - on premise or in the cloud

Flexible

Can be deployed in a federated production environment or used on a single machine to perform simulations and debug code

Traceable

Monitoring, traceability and fine grained permissions for complete control of data manipulations

Secure

Proven in strict privacy settings to meet global compliance requirements for hospitals and pharmaceutical companies

Data agnostic

Can be used with many different data modalities such as video, images, audio, time series or tabular data

Framework agnostic

Compatible with any machine learning model from any framework including TensorFlow, Torch and Scikit-Learn

Infrastructure agnostic

Works seamlessly with any IT infrastructure setup - on premise or in the cloud

Flexible

Can be deployed in a federated production environment or used on a single machine to perform simulations and debug code

Traceable

Monitoring, traceability and fine grained permissions for complete control of data manipulations

Secure

Proven in strict privacy settings to meet global compliance requirements for hospitals and pharmaceutical companies

Data agnostic

Can be used with many different data modalities such as video, images, audio, time series or tabular data

Framework agnostic

Compatible with any machine learning model from any framework including TensorFlow, Torch and Scikit-Learn

Infrastructure agnostic

Works seamlessly with any IT infrastructure setup - on premise or in the cloud

Flexible

Can be deployed in a federated production environment or used on a single machine to perform simulations and debug code

Traceable

Monitoring, traceability and fine grained permissions for complete control of data manipulations

Secure

Proven in strict privacy settings to meet global compliance requirements for hospitals and pharmaceutical companies

Data agnostic

Can be used with many different data modalities such as video, images, audio, time series or tabular data

Framework agnostic

Compatible with any machine learning model from any framework including TensorFlow, Torch and Scikit-Learn

Infrastructure agnostic

Works seamlessly with any IT infrastructure setup - on premise or in the cloud

Flexible

Can be deployed in a federated production environment or used on a single machine to perform simulations and debug code

Traceable

Monitoring, traceability and fine grained permissions for complete control of data manipulations

Secure

Proven in strict privacy settings to meet global compliance requirements for hospitals and pharmaceutical companies

Data agnostic

Can be used with many different data modalities such as video, images, audio, time series or tabular data

Framework agnostic

Compatible with any machine learning model from any framework including TensorFlow, Torch and Scikit-Learn

Infrastructure agnostic

Works seamlessly with any IT infrastructure setup - on premise or in the cloud

Flexible

Can be deployed in a federated production environment or used on a single machine to perform simulations and debug code

Traceable

Monitoring, traceability and fine grained permissions for complete control of data manipulations

Secure

Proven in strict privacy settings to meet global compliance requirements for hospitals and pharmaceutical companies

Use cases

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FAQ

What is Substra?

Who owns Substra?

What kinds of data does Substra support?

What machine learning models can be used with Substra?

Is Substra limited to medical and biotech applications?

How can I be sure Substra is secure enough to be used with my private data?

How does Substra protect data privacy?

Which deep learning frameworks does Substra support?

Does Owkin offer federated learning consulting or deployment services?

What is the LFAI & Data Foundation?

Product documentation

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Discover how to use and deploy Substra in a real-world setting.

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