{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T13:50:44Z","timestamp":1785160244882,"version":"3.55.0"},"reference-count":95,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T00:00:00Z","timestamp":1678233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009056","name":"University of West Bohemia","doi-asserted-by":"publisher","award":["SGS-2022-015"],"award-info":[{"award-number":["SGS-2022-015"]}],"id":[{"id":"10.13039\/100009056","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009056","name":"University of West Bohemia","doi-asserted-by":"publisher","award":["PE0000013-FAIR"],"award-info":[{"award-number":["PE0000013-FAIR"]}],"id":[{"id":"10.13039\/100009056","id-type":"DOI","asserted-by":"publisher"}]},{"name":"PNRR MUR project","award":["SGS-2022-015"],"award-info":[{"award-number":["SGS-2022-015"]}]},{"name":"PNRR MUR project","award":["PE0000013-FAIR"],"award-info":[{"award-number":["PE0000013-FAIR"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A further novelty resides in the fact that, differently from the other Federated Learning frameworks in the literature, ours can efficiently deal at the same time with two relevant issues in Machine Learning, i.e., data privacy and interpretability of the solutions. Our framework consists of a master\/slave approach in which each slave contains local data, protecting sensible private data, and exploits an evolutionary algorithm to generate prediction models. The master shares through the slaves the locally learned models that emerge on each slave. Sharing these local models results in global models. Being that data privacy and interpretability are very significant in the medical domain, the algorithm is tested to forecast future glucose values for diabetic patients by exploiting a Grammatical Evolution algorithm. The effectiveness of this knowledge-sharing process is assessed experimentally by comparing the proposed framework with another where no exchange of local models occurs. The results show that the performance of the proposed approach is better and demonstrate the validity of its sharing process for the emergence of local models for personal diabetes management, usable as efficient global models. When further subjects not involved in the learning process are considered, the models discovered by our framework show higher generalization capability than those achieved without knowledge sharing: the improvement provided by knowledge sharing is equal to about 3.03% for precision, 1.56% for recall, 3.17% for F1, and 1.56% for accuracy. Moreover, statistical analysis reveals the statistical superiority of model exchange with respect to the case of no exchange taking place.<\/jats:p>","DOI":"10.3390\/s23062957","type":"journal-article","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T02:01:47Z","timestamp":1678327307000},"page":"2957","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6127-1195","authenticated-orcid":false,"given":"Ivanoe","family":"De Falco","sequence":"first","affiliation":[{"name":"ICAR-National Research Council of Italy, Via P. Castellino, 80131 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4092-6102","authenticated-orcid":false,"given":"Antonio","family":"Della Cioppa","sequence":"additional","affiliation":[{"name":"ICAR-National Research Council of Italy, Via P. Castellino, 80131 Naples, Italy"},{"name":"Natural Computation Lab, DIEM, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7773-098X","authenticated-orcid":false,"given":"Tomas","family":"Koutny","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, New Technologies for Information Society, University of West Bohemia, Technicka 18, 330 01 Pilsen, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3269-0408","authenticated-orcid":false,"given":"Martin","family":"Ubl","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of West Bohemia, Technicka 18, 330 01 Pilsen, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1564-2503","authenticated-orcid":false,"given":"Michal","family":"Krcma","sequence":"additional","affiliation":[{"name":"Diabetology Center, First Department of Internal Medicine, University Hospital Pilsen, Alej Svobody 923\/80, 323 00 Pilsen, Czech Republic"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umberto","family":"Scafuri","sequence":"additional","affiliation":[{"name":"ICAR-National Research Council of Italy, Via P. Castellino, 80131 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ernesto","family":"Tarantino","sequence":"additional","affiliation":[{"name":"ICAR-National Research Council of Italy, Via P. Castellino, 80131 Naples, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,8]]},"reference":[{"key":"ref_1","unstructured":"Mitchell, T.M. (1997). Machine Learning, McGraw-Hill."},{"key":"ref_2","first-page":"1","article-title":"When machine learning meets privacy: A survey and outlook","volume":"54","author":"Liu","year":"2021","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_3","unstructured":"Kone\u010dn\u1ef3, J., McMahan, B., and Ramage, D. (2015). Federated optimization: Distributed optimization beyond the datacenter. arXiv."},{"key":"ref_4","unstructured":"Kone\u010dn\u1ef3, J., McMahan, H.B., Ramage, D., and Richt\u00e1rik, P. (2016). 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