Blind Federated Learning without initial model

Autor:	Jose L. Salmeron, Irina Arévalo
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	Federated learning Privacy-preserving machine learning Fuzzy Cognitive Maps Computer engineering. Computer hardware TK7885-7895 Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
Zdroj:	Journal of Big Data, Vol 11, Iss 1, Pp 1-31 (2024)
Druh dokumentu:	article
ISSN:	2196-1115
DOI:	10.1186/s40537-024-00911-y
Popis:	Abstract 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.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/95f4f1681ac84d4f80fb933b1403d2a3 Zobrazit plný text záznamu Full text from SpringerLink View record in DOAJ