{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:51:49Z","timestamp":1775667109638,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T00:00:00Z","timestamp":1657670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In the last few years, federated learning (FL) has emerged as a novel alternative for analyzing data spread across different parties without needing to centralize them. In order to increase the adoption of FL, there is a need to develop more algorithms that can be deployed under this novel privacy-preserving paradigm. In this paper, we present our federated generalized linear model (GLM) for horizontally partitioned data. It allows generating models of different families (linear, Poisson, logistic) without disclosing privacy-sensitive individual records. We describe its algorithm (which can be implemented in the user\u2019s platform of choice) and compare the obtained federated models against their centralized counterpart, which were mathematically equivalent. We also validated their execution time with increasing numbers of records and involved parties. We show that our federated GLM is accurate enough to be used for the privacy-preserving analysis of horizontally partitioned data in real-life scenarios. Further development of this type of algorithm has the potential to make FL a much more common practice among researchers.<\/jats:p>","DOI":"10.3390\/a15070243","type":"journal-article","created":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T22:06:00Z","timestamp":1657749960000},"page":"243","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A Federated Generalized Linear Model for Privacy-Preserving Analysis"],"prefix":"10.3390","volume":"15","author":[{"given":"Matteo","family":"Cellamare","sequence":"first","affiliation":[{"name":"Netherlands Comprehensive Cancer Organization (IKNL), 5612 HZ Eindhoven, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9122-0110","authenticated-orcid":false,"given":"Anna J.","family":"van Gestel","sequence":"additional","affiliation":[{"name":"Netherlands Comprehensive Cancer Organization (IKNL), 5612 HZ Eindhoven, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7887-3926","authenticated-orcid":false,"given":"Hasan","family":"Alradhi","sequence":"additional","affiliation":[{"name":"Netherlands Comprehensive Cancer Organization (IKNL), 5612 HZ Eindhoven, The Netherlands"}]},{"given":"Frank","family":"Martin","sequence":"additional","affiliation":[{"name":"Netherlands Comprehensive Cancer Organization (IKNL), 5612 HZ Eindhoven, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7233-2117","authenticated-orcid":false,"given":"Arturo","family":"Moncada-Torres","sequence":"additional","affiliation":[{"name":"Netherlands Comprehensive Cancer Organization (IKNL), 5612 HZ Eindhoven, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sagiroglu, S., and Sinanc, D. 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