{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T19:41:34Z","timestamp":1774986094922,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T00:00:00Z","timestamp":1740528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Ministry of Economic Affairs and Digital Transformation","award":["TSI-063000-2021-3\/6\/7\u2013OPEN6G"],"award-info":[{"award-number":["TSI-063000-2021-3\/6\/7\u2013OPEN6G"]}]},{"name":"Spanish Ministry of Economic Affairs and Digital Transformation","award":["PID2022-137329OB-C41\/MCIN\/AEI\/10.13039\/50110001103"],"award-info":[{"award-number":["PID2022-137329OB-C41\/MCIN\/AEI\/10.13039\/50110001103"]}]},{"name":"European Union\u2014NextGenerationEU in the framework of the Recovery Plan, Transformation, and Resilience (PRTR)","award":["TSI-063000-2021-3\/6\/7\u2013OPEN6G"],"award-info":[{"award-number":["TSI-063000-2021-3\/6\/7\u2013OPEN6G"]}]},{"name":"European Union\u2014NextGenerationEU in the framework of the Recovery Plan, Transformation, and Resilience (PRTR)","award":["PID2022-137329OB-C41\/MCIN\/AEI\/10.13039\/50110001103"],"award-info":[{"award-number":["PID2022-137329OB-C41\/MCIN\/AEI\/10.13039\/50110001103"]}]},{"name":"Agencia Estatal de Investigaci\u00f3n of Ministerio de Ciencia e Innovaci\u00f3n of Spain","award":["TSI-063000-2021-3\/6\/7\u2013OPEN6G"],"award-info":[{"award-number":["TSI-063000-2021-3\/6\/7\u2013OPEN6G"]}]},{"name":"Agencia Estatal de Investigaci\u00f3n of Ministerio de Ciencia e Innovaci\u00f3n of Spain","award":["PID2022-137329OB-C41\/MCIN\/AEI\/10.13039\/50110001103"],"award-info":[{"award-number":["PID2022-137329OB-C41\/MCIN\/AEI\/10.13039\/50110001103"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The evolution of cellular networks from fifth-generation (5G) architectures to beyond 5G (B5G) and sixth-generation (6G) systems necessitates innovative solutions to overcome the limitations of traditional Radio Access Network (RAN) infrastructures. Existing monolithic and proprietary RAN components restrict adaptability, interoperability, and optimal resource utilization, posing challenges in meeting the stringent requirements of next-generation applications. The Open Radio Access Network (O-RAN) and Multi-Access Edge Computing (MEC) have emerged as transformative paradigms, enabling disaggregation, virtualization, and real-time adaptability\u2014which are key to achieving ultra-low latency, enhanced bandwidth efficiency, and intelligent resource management in future cellular systems. This paper presents a Federated Deep Reinforcement Learning (FDRL) framework for dynamic radio and edge computing resource allocation and slicing management in O-RAN environments. An Integer Linear Programming (ILP) model has also been developed, resulting in the proposed FDRL solution drastically reducing the system response time. On the other hand, unlike centralized Reinforcement Learning (RL) approaches, the proposed FDRL solution leverages Federated Learning (FL) to optimize performance while preserving data privacy and reducing communication overhead. Comparative evaluations against centralized models demonstrate that the federated approach improves learning efficiency and reduces bandwidth consumption. The system has been rigorously tested across multiple scenarios, including multi-client O-RAN environments and loss-of-synchronization conditions, confirming its resilience in distributed deployments. Additionally, a case study simulating realistic traffic profiles validates the proposed framework\u2019s ability to dynamically manage radio and computational resources, ensuring efficient and adaptive O-RAN slicing for diverse and high-mobility scenarios.<\/jats:p>","DOI":"10.3390\/fi17030106","type":"journal-article","created":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T04:46:37Z","timestamp":1740545197000},"page":"106","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Federated Learning System for Dynamic Radio\/MEC Resource Allocation and Slicing Control in Open Radio Access Network"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-9130-1493","authenticated-orcid":false,"given":"Mario","family":"Mart\u00ednez-Morfa","sequence":"first","affiliation":[{"name":"Department of Network Engineering, Universitat Politecnica de Catalunya, 08860 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1262-9952","authenticated-orcid":false,"given":"Carlos","family":"Ruiz de Mendoza","sequence":"additional","affiliation":[{"name":"Department of Network Engineering, Universitat Politecnica de Catalunya, 08860 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8056-0774","authenticated-orcid":false,"given":"Cristina","family":"Cervell\u00f3-Pastor","sequence":"additional","affiliation":[{"name":"Department of Network Engineering, Universitat Politecnica de Catalunya, 08860 Barcelona, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8232-6180","authenticated-orcid":false,"given":"Sebastia","family":"Sallent-Ribes","sequence":"additional","affiliation":[{"name":"Department of Network Engineering, Universitat Politecnica de Catalunya, 08860 Barcelona, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mollahasani, S., Erol-Kantarci, M., and Wilson, R. 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