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Aligning Federated Learning with Existing Trust Structures in Health Care Systems

Patient-centered health care information systems (PHSs) on peer-to-peer (P2P) networks (e.g., decentralized personal health records) enable storing data locally at the edge to enhance data sovereignty and resilience to single points of failure. Nonetheless, these systems raise concerns on trust and...

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Autores principales: Abdullahi, Imrana Yari, Raab, René, Küderle, Arne, Eskofier, Björn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10094512/
https://www.ncbi.nlm.nih.gov/pubmed/37047992
http://dx.doi.org/10.3390/ijerph20075378
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author Abdullahi, Imrana Yari
Raab, René
Küderle, Arne
Eskofier, Björn
author_facet Abdullahi, Imrana Yari
Raab, René
Küderle, Arne
Eskofier, Björn
author_sort Abdullahi, Imrana Yari
collection PubMed
description Patient-centered health care information systems (PHSs) on peer-to-peer (P2P) networks (e.g., decentralized personal health records) enable storing data locally at the edge to enhance data sovereignty and resilience to single points of failure. Nonetheless, these systems raise concerns on trust and adoption in medical workflow due to non-alignment to current health care processes and stakeholders’ needs. The distributed nature of the data makes it more challenging to train and deploy machine learning models (using traditional methods) at the edge, for instance, for disease prediction. Federated learning (FL) has been proposed as a possible solution to these limitations. However, the P2P PHS architecture challenges current FL solutions because they use centralized engines (or random entities that could pose privacy concerns) for model update aggregation. Consequently, we propose a novel conceptual FL framework, CareNetFL, that is suitable for P2P PHS multi-tier and hybrid architecture and leverages existing trust structures in health care systems to ensure scalability, trust, and security. Entrusted parties (practitioners’ nodes) are used in CareNetFL to aggregate local model updates in the network hierarchy for their patients instead of random entities that could actively become malicious. Involving practitioners in their patients’ FL model training increases trust and eases access to medical data. The proposed concepts mitigate communication latency and improve FL performance through patient–practitioner clustering, reducing skewed and imbalanced data distributions and system heterogeneity challenges of FL at the edge. The framework also ensures end-to-end security and accountability through leveraging identity-based systems and privacy-preserving techniques that only guarantee security during training.
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spelling pubmed-100945122023-04-13 Aligning Federated Learning with Existing Trust Structures in Health Care Systems Abdullahi, Imrana Yari Raab, René Küderle, Arne Eskofier, Björn Int J Environ Res Public Health Perspective Patient-centered health care information systems (PHSs) on peer-to-peer (P2P) networks (e.g., decentralized personal health records) enable storing data locally at the edge to enhance data sovereignty and resilience to single points of failure. Nonetheless, these systems raise concerns on trust and adoption in medical workflow due to non-alignment to current health care processes and stakeholders’ needs. The distributed nature of the data makes it more challenging to train and deploy machine learning models (using traditional methods) at the edge, for instance, for disease prediction. Federated learning (FL) has been proposed as a possible solution to these limitations. However, the P2P PHS architecture challenges current FL solutions because they use centralized engines (or random entities that could pose privacy concerns) for model update aggregation. Consequently, we propose a novel conceptual FL framework, CareNetFL, that is suitable for P2P PHS multi-tier and hybrid architecture and leverages existing trust structures in health care systems to ensure scalability, trust, and security. Entrusted parties (practitioners’ nodes) are used in CareNetFL to aggregate local model updates in the network hierarchy for their patients instead of random entities that could actively become malicious. Involving practitioners in their patients’ FL model training increases trust and eases access to medical data. The proposed concepts mitigate communication latency and improve FL performance through patient–practitioner clustering, reducing skewed and imbalanced data distributions and system heterogeneity challenges of FL at the edge. The framework also ensures end-to-end security and accountability through leveraging identity-based systems and privacy-preserving techniques that only guarantee security during training. MDPI 2023-04-03 /pmc/articles/PMC10094512/ /pubmed/37047992 http://dx.doi.org/10.3390/ijerph20075378 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Perspective
Abdullahi, Imrana Yari
Raab, René
Küderle, Arne
Eskofier, Björn
Aligning Federated Learning with Existing Trust Structures in Health Care Systems
title Aligning Federated Learning with Existing Trust Structures in Health Care Systems
title_full Aligning Federated Learning with Existing Trust Structures in Health Care Systems
title_fullStr Aligning Federated Learning with Existing Trust Structures in Health Care Systems
title_full_unstemmed Aligning Federated Learning with Existing Trust Structures in Health Care Systems
title_short Aligning Federated Learning with Existing Trust Structures in Health Care Systems
title_sort aligning federated learning with existing trust structures in health care systems
topic Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10094512/
https://www.ncbi.nlm.nih.gov/pubmed/37047992
http://dx.doi.org/10.3390/ijerph20075378
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