{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:44:33Z","timestamp":1760060673810,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T00:00:00Z","timestamp":1757376000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NExTWORKx","award":["101067652","CNS-2128448","ECCS-2335876","101019718"],"award-info":[{"award-number":["101067652","CNS-2128448","ECCS-2335876","101019718"]}]},{"name":"European Union\u2019s Horizon Europe research and innovation program under the Marie Sk\u0142odowska-Curie project FLASH","award":["101067652","CNS-2128448","ECCS-2335876","101019718"],"award-info":[{"award-number":["101067652","CNS-2128448","ECCS-2335876","101019718"]}]},{"name":"Ericsson Research Foundation","award":["101067652","CNS-2128448","ECCS-2335876","101019718"],"award-info":[{"award-number":["101067652","CNS-2128448","ECCS-2335876","101019718"]}]},{"name":"Hans Werth\u00e9n Foundation","award":["101067652","CNS-2128448","ECCS-2335876","101019718"],"award-info":[{"award-number":["101067652","CNS-2128448","ECCS-2335876","101019718"]}]},{"name":"U.S National Science Foundation","award":["101067652","CNS-2128448","ECCS-2335876","101019718"],"award-info":[{"award-number":["101067652","CNS-2128448","ECCS-2335876","101019718"]}]},{"name":"European Research Council (ERC) under the European Union\u2019s Horizon 2020 research and innovation program","award":["101067652","CNS-2128448","ECCS-2335876","101019718"],"award-info":[{"award-number":["101067652","CNS-2128448","ECCS-2335876","101019718"]}]},{"name":"Delft University of Technology","award":["101067652","CNS-2128448","ECCS-2335876","101019718"],"award-info":[{"award-number":["101067652","CNS-2128448","ECCS-2335876","101019718"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Many algorithms related to vehicular applications, such as enhanced perception of the environment, benefit from frequent updates and the use of data from multiple vehicles. Federated learning is a promising method to improve the accuracy of algorithms in the context of vehicular networks. However, limited communication bandwidth, varying wireless channel quality, and potential latency requirements may impact the number of vehicles selected for training per communication round and their assigned radio resources. In this work, we characterize the vehicles participating in federated learning based on their importance to the learning process and their use of wireless resources. We then address the joint vehicle selection and resource allocation problem, considering multi-cell networks with multi-user multiple-input multiple-output (MU-MIMO)-capable base stations and vehicles. We propose a \u201cvehicle-beam-iterative\u201d algorithm to approximate the solution to the resulting optimization problem. We then evaluate its performance through extensive simulations, using realistic road and mobility models, for the task of object classification of European traffic signs. Our results indicate that MU-MIMO improves the convergence time of the global model. Moreover, the application-specific accuracy targets are reached faster in scenarios where the vehicles have the same training data set sizes than in scenarios where the data set sizes differ.<\/jats:p>","DOI":"10.3390\/e27090941","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T12:04:55Z","timestamp":1757505895000},"page":"941","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Federated Learning over MU-MIMO Vehicular Networks"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1856-6996","authenticated-orcid":false,"given":"Maria","family":"Raftopoulou","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 CD Delft, The Netherlands"},{"name":"Department of Networks, Netherlands Organisation for Applied Scientific Research (TNO), 2595 DA The Hague, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4503-4242","authenticated-orcid":false,"suffix":"Jr.","given":"Jos\u00e9 Mairton B.","family":"da Silva","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Uppsala University, 751 05 Uppsala, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5423-5292","authenticated-orcid":false,"given":"Remco","family":"Litjens","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 CD Delft, The Netherlands"},{"name":"Department of Networks, Netherlands Organisation for Applied Scientific Research (TNO), 2595 DA The Hague, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2062-131X","authenticated-orcid":false,"given":"H. Vincent","family":"Poor","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3786-7922","authenticated-orcid":false,"given":"Piet","family":"Van Mieghem","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628 CD Delft, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1109\/COMST.2022.3149714","article-title":"A survey of collaborative machine learning using 5G vehicular communications","volume":"24","author":"Balkus","year":"2022","journal-title":"IEEE Commun. Surv. 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