Unlock AI, break data silos,
and protect privacy with our Federated Learning software

Owkin Connect provides the infrastructure and AI technology that open the possibility for an unprecedented breadth of collaboration in healthcare while protecting patient privacy and data governance. Our distributed architecture and federated learning software allow data scientists to securely connect to decentralised, multi-party datasets and train AI models without having to pool data. Data remains local, only algorithms travel.

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Aggregate models & insights, not data:

Owkin Connect technology is centered on three core principles — collaboration, confidentiality, and compliance. Life science companies deploy Owkin Connect in a wide variety of settings for clinical research, drug discovery and development.

Build secure research networks

Build research

Create AI consortia where you can implement and manage data partnerships with your network of medical research partners.
Data access

Expand access to connected partner data

By eliminating the requirement to share data, partners can contribute to training AI without giving up competitive assets or compromising patient privacy.
Data compliance

Meet global compliance requirements

With our federated learning capabilities, life science companies can more easily leverage the insights across their international data assets meeting GDPR, HIPAA, and other regulatory requirements.

In consideration of the GDPR application, federated learning is a good tool to protect personal data and help in data privacy compliance. In a federated learning setting, each site keeps control of its data, which isn’t centralized, preserving data confidentiality and safety. As data isn’t merged, the security responsibility is handled by each site, and in case of a security issue, it will be easier to control the impact on data.

Owkin Connect
Federated Network

Owkin Connect is used in collaborative and competitive settings, when protecting confidential, proprietary or sensitive data and insights extracted from the data through the models are essential.

Case Studies

Owkin Connect is currently deployed and operating multiple federated networks
around the world, including:

Unparalleled collaboration between pharma competitors in Drug Discovery

Now more than ever innovation and optimization are crucial to increase the pharmaceutical industry’s productivity and sustainability. The MELLODDY Project, a three-year, multi-million euro and 17 partner large research study funded by the IMI 2 Joint Undertaking, has set out to understand how competitive parties in collaboration can benefit from joint machine learning efforts at scale on pre-clinical datasets.

The endeavor has the potential to accelerate drug discovery, without compromising proprietary information or data privacy. Enabling Federated Learning for the MELLODDY project, Owkin Connect helped to secure the first Federated Learning model for drug discovery at scale. Owkin’s proprietary structure has been audited and has been deployed at 10 pharmaceutical companies.

This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 831472. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.

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Breast Cancer Treatment Evaluation with Owkin Connect

Alongside clinical, research, and technology partners we established that machine learning models can be trained successfully on histology images, siloed at different clinical centers, to predict treatment responses in breast cancer. The model trained with Owkin Connect can help oncologists to choose the most effective breast cancer treatment for each patient based on a single biopsy. Learn more about the HealthChain project here.
Image caption: In January 2020, Connect enabled the first-ever federated deep learning model trained on distributed histology images stored behind hospital firewalls.

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Key Elements of our
Federated Learning Software

Key elements
Distributed ledger technology for traceability of all actions and exchanges
Model encryption
Model encryption and secure aggregation for privacy-preserving ML
Data library
Library of FL strategies and ML algorithms for healthcare
Granular user and object permissions

Recent publications

The Future of Digital Health with Federated Learning

Nature Partner Journals (NPJ) Digital Medicine 14th September 2020
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Federated Survival Analysis with Discrete-Time Cox Models

ICML Federated Learning Workshop 2020 16th June 2020
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Siloed Federated Learning for Multi-Centric Histopathology Datasets

Awarded Best Paper at MICCAI 2020 DCL Workshop 17th August 2020
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