Work

Enhancing Security and Privacy in Federated Learning

Year: 2025

Type: article

Abstract: The PAROMA-MED project advances privacy-preserving, federated machine learning for medical applications, ensuring sensitive health data is protected during model development and clinical research. Core to its strategy is the “code-to-data” approach, which ensures data never leaves its secure source. Analysis, AI training, and access are brought to the data via consent-driven, federated, and privacy-enhancing technologies. Among its innovative mechanisms, granular consent management stands out as a cornerstone for upholding patient autonomy and regulatory standards, such as GDPR and the European Health Data Space (EHDS).

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15745043

Authors: Ertl, Benjamin, Kotsoupolous, Kostas

Institution: Agentscape (Germany)

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: European Commission

Award: Privacy Aware and Privacy Preserving Distributed and Robust Machine Learning for Medical Applications