A Privacy-Preserving, Distributed and Cooperative FCM-Based Learning Approach for Cancer Research

Distributed Artificial Intelligence is attracting interest day by day. In this paper, the authors introduce an innovative methodology for distributed learning of Particle Swarm Optimization-based Fuzzy Cognitive Maps in a privacy-preserving way. The authors design a training scheme for collaborative...

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Autores principales: Salmeron, Jose L., Arévalo, Irina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338152/
http://dx.doi.org/10.1007/978-3-030-52705-1_35
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author Salmeron, Jose L.
Arévalo, Irina
author_facet Salmeron, Jose L.
Arévalo, Irina
author_sort Salmeron, Jose L.
collection PubMed
description Distributed Artificial Intelligence is attracting interest day by day. In this paper, the authors introduce an innovative methodology for distributed learning of Particle Swarm Optimization-based Fuzzy Cognitive Maps in a privacy-preserving way. The authors design a training scheme for collaborative FCM learning that offers data privacy compliant with the current regulation. This method is applied to a cancer detection problem, proving that the performance of the model is improved by the Federated Learning process, and obtaining similar results to the ones that can be found in the literature.
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spelling pubmed-73381522020-07-07 A Privacy-Preserving, Distributed and Cooperative FCM-Based Learning Approach for Cancer Research Salmeron, Jose L. Arévalo, Irina Rough Sets Article Distributed Artificial Intelligence is attracting interest day by day. In this paper, the authors introduce an innovative methodology for distributed learning of Particle Swarm Optimization-based Fuzzy Cognitive Maps in a privacy-preserving way. The authors design a training scheme for collaborative FCM learning that offers data privacy compliant with the current regulation. This method is applied to a cancer detection problem, proving that the performance of the model is improved by the Federated Learning process, and obtaining similar results to the ones that can be found in the literature. 2020-06-10 /pmc/articles/PMC7338152/ http://dx.doi.org/10.1007/978-3-030-52705-1_35 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Salmeron, Jose L.
Arévalo, Irina
A Privacy-Preserving, Distributed and Cooperative FCM-Based Learning Approach for Cancer Research
title A Privacy-Preserving, Distributed and Cooperative FCM-Based Learning Approach for Cancer Research
title_full A Privacy-Preserving, Distributed and Cooperative FCM-Based Learning Approach for Cancer Research
title_fullStr A Privacy-Preserving, Distributed and Cooperative FCM-Based Learning Approach for Cancer Research
title_full_unstemmed A Privacy-Preserving, Distributed and Cooperative FCM-Based Learning Approach for Cancer Research
title_short A Privacy-Preserving, Distributed and Cooperative FCM-Based Learning Approach for Cancer Research
title_sort privacy-preserving, distributed and cooperative fcm-based learning approach for cancer research
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338152/
http://dx.doi.org/10.1007/978-3-030-52705-1_35
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