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Research and Innovation
Published October 31, 2023 | Version v1
Book chapter Open

Chapter 2: Architecture and design choices for federated learning in modern digital healthcare systems

Creators

  • 1. Qualtek
  • 2. 6G Health Institute GmbH
  • 3. ROR icon Agentscape (Germany)
  • 4. ROR icon Ubitech (Greece)
  • 5. Thales SIX GTS France SAS
  • 6. Thales SIXT GTS France SAS
  • 7. ROR icon IMT Atlantique
  • 8. Eurescom GmbH

Description

Adoption of Data Spaces practices by digital healthcare domains can create great potential for the evolution of machine learning solutions due to high availability of data sources. However, privacy protection is a key aspect to clearly addressed before any stakeholder engages with the concept. This chapter proposes an approach for the establishment of trust among participants by addressing all the phases of the data life-cycle based on the continuous attestation of the trustworthiness of the all involved functional components. The details of the attestation methodology are presented in the context of continuous provision and availability of immutable proofs. The mechanisms for timely detecting and averting misuse of data are also elaborated, and finally a proof of concept implementation is presented.

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Book_Paroma-FLD-1634591-chapter-2.pdf

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Additional details

Funding

European Commission
PAROMA-MED – Privacy Aware and Privacy Preserving Distributed and Robust Machine Learning for Medical Applications 101070222

Dates

Created
2024-07-05