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.
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
How Substra works
Collaboratively train your machine learning models across distributed datasets and monitor performance
03/04
A data scientist trains the model: computation is done securely and autonomously through the Substra network
04/04
Everyone can easily examine performance and visualise the model performance at the end of the training process to select the best option
01/04
A data scientist registers data on their Substra servers for machine learning model training
02/04
A data scientist registers a model to be trained on Substra
03/04
A data scientist trains the model: computation is done securely and autonomously through the Substra network
04/04
Everyone can easily examine performance and visualise the model performance at the end of the training process to select the best option
01/04
A data scientist registers data on their Substra servers for machine learning model training
02/04
A data scientist registers a model to be trained on Substra
How Substra works
Collaboratively train your machine learning models across distributed datasets and monitor performance
01/04
A data scientist registers data on their Substra servers for machine learning model training
02/04
A data scientist registers a model to be trained on Substra
03/04
A data scientist trains the model: computation is done securely and autonomously through the Substra network
04/04
Everyone can easily examine performance and visualise the model performance at the end of the training process to select the best option
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
Discover how to use and deploy Substra in a real-world setting.
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