A privacy-preserving, distributed and cooperative FCM-based learning approach for cancer research
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913718845046784 |
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| author | Salmeron, Jose L. Arévalo, Irina |
| author_facet | Salmeron, Jose L. Arévalo, Irina |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_10102 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A privacy-preserving, distributed and cooperative FCM-based learning approach for cancer research Salmeron, Jose L. Arévalo, Irina Artificial Intelligence Distributed, Parallel, and Cluster Computing 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. |
| title | A privacy-preserving, distributed and cooperative FCM-based learning approach for cancer research |
| topic | Artificial Intelligence Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2402.10102 |