| dc.creator | Salmeron, Jose L. | |
| dc.creator | Arévalo, Irina | |
| dc.date | 2024-05-21T07:24:04Z | |
| dc.date | 2024-05-21T07:24:04Z | |
| dc.date | 2024 | |
| dc.date.accessioned | 2024-07-17T21:14:45Z | |
| dc.date.available | 2024-07-17T21:14:45Z | |
| dc.identifier | 10.1186/s40537-024-00911-y | |
| dc.identifier | 21961115 | |
| dc.identifier | https://hdl.handle.net/20.500.12728/11281 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/9509732 | |
| dc.description | Federated learning is an emerging machine learning approach that allows the construction of a model between several participants who hold their own private data. This method is secure and privacy-preserving, suitable for training a machine learning model using sensitive data from different sources, such as hospitals. In this paper, the authors propose two innovative methodologies for Particle Swarm Optimisation-based federated learning of Fuzzy Cognitive Maps in a privacy-preserving way. In addition, one relevant contribution this research includes is the lack of an initial model in the federated learning process, making it effectively blind. This proposal is tested with several open datasets, improving both accuracy and precision. © The Author(s) 2024. | |
| dc.description | Artificial Intelligence for Healthy Aging; Misiones de I+D en Inteligencia Artificial, (MIA.2021, M02.0007) | |
| dc.format | application/pdf | |
| dc.language | en | |
| dc.publisher | Springer Nature | |
| dc.subject | Federated learning | |
| dc.subject | Fuzzy Cognitive Maps | |
| dc.subject | Privacy-preserving machine learning | |
| dc.title | Blind Federated Learning without initial model | |
| dc.type | Article | |
