Our aim is to develop a platform based on Armoured Federated Learning for researchers and solution developers. 

The platform will enable solution developers to create tools for healthcare professionals that allow them to analyze their own patient data and compare it with data from other hospitals and research centers while maintaining patient privacy and anonymity in accordance with GDPR European policy.


Define the specific needs of stakeholders looking for federated learning solutions

Develop novel methods for enhancing the privacy of federated learning

Design the TRUMPET platform based on the armoured federated learning

Develop a privacy metric tool for the GDPR certification in federated learning implementations

Pilot and validate TRUMPET platform in the use case of federated clinical cancer data

Disseminate project outcomes in AI ecosystem and communities

Disseminate project outcomes in AI ecosystem and communities

Expand the use of TRUMPET platform to other sector and countries

Project Roadmap

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Federated Learning

Analysing the data, without collecting it

Federated learning is a machine learning technique that allows for training models on decentralized data without the need for data to be centrally collected and aggregated. This means that data can remain on the devices or servers where it is generated and the model can be trained using aggregated gradients from multiple devices.

Privacy and GDPR

The General Data Protection Regulation (GDPR) is a comprehensive privacy law that went into effect in the European Union (EU) in May 2018.

This regulation has a far-reaching impact on businesses of all sizes, as it applies to any organization that processes personal data of EU citizens, regardless of where the organization is located.