TRUMPET
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.
Objectives
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.
