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Research and Innovation
Published March 8, 2023 | Version v1
Conference paper Open

Federated machine learning through edge ready architectures with privacy preservation as a service

Creators

  • 1. Qualtek Hellas
  • 2. ROR icon Agentscape (Germany)
  • 3. ROR icon Université de Rennes
  • 4. ROR icon IMT Atlantique
  • 5. Eurescom GmbH
  • 6. ROR icon Thales (France)

Description

This paper presents the details of a novel approach, based on edge and advanced privacy preserving solutions, that tries to accelerate the adoption of personal data federation for the benefit of the evolution of valuable advanced AI models. The approach focuses on the establishment of high degree of trust between data owner and data management infrastructure so that consent in data processing is given by means of functional and enforceable options applicable at all levels of workloads and processes. The overall set of solutions will be delivered as an open-source set of implementations in the context of the PAROMA-MED project.

Files

PAROMA-MED-FNWF-2022.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