FederaMed

Soft Federated Learning with heterogeneous sources for the preservation of the security and data privacy: applications to health (FederaMed)

September 1, 2022 - August 31, 2025 | PID2021-123960OB-I00

Team

UGRITAI-Lab (Department of Computer Science and A.I.), Department of Criminal Law, Hospitals: Costa del Sol and Hospital Clínico Universitario San Cecilio

Principal Investigators

María José Martín Bautista

María Dolores Ruiz Jiménez

About the project

In the era of big data, the exponential growth in data generation requires improved analysis, exploitation and prediction, which is particularly critical in various sectors such as healthcare, banking and industry. However, there are challenges in sharing and centralising data due to concerns over competitiveness, confidentiality and security. This is exacerbated in areas such as healthcare, where large and diverse datasets are stored with strict privacy measures. Traditional business intelligence methods often fall short when analysing such complex, distributed data. The FederaMed project addresses these issues through the use of artificial intelligence, specifically federated learning and flexible computing techniques. Federated Learning facilitates collaborative model training without centralising data, thus ensuring privacy, while Flexible Computing improves data modelling and communication of results. The project aims to apply intelligent algorithms and natural language processing to manage and analyse heterogeneous textual data. It will also validate its solutions in real-world hospital environments, proving their viability and facilitating knowledge transfer.

Funding

Grant PID2021-123960OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU

(MICIU: Ministry of Science, Innovation and Universities, AEI: State Research Agency, ERDF: European Regional Development Fund, Next Generation EU: EU Recovery, Transformation and Resilience Plan)

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