{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:33:09Z","timestamp":1781368389373,"version":"3.54.1"},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T00:00:00Z","timestamp":1671753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Edge-based distributed intelligence techniques, such as federated learning (FL), have recently been used in many research fields thanks, in part, to their decentralized model training process and privacy-preserving features. However, because of the absence of effective deployment models for the radio access network (RAN), only a tiny number of FL apps have been created for the latest generation of public mobile networks (e.g., 5G and 6G). There is an attempt, in new RAN paradigms, to move toward disaggregation, hierarchical, and distributed network function processing designs. Open RAN (O-RAN), as a cutting-edge RAN technology, claims to meet 5G services with high quality. It includes integrated, intelligent controllers to provide RAN with the power to make smart decisions. This paper proposes a methodology for deploying and optimizing FL tasks in O-RAN to deliver distributed intelligence for 5G applications. To accomplish model training in each round, we first present reinforcement learning (RL) for client selection for each FL task and resource allocation using RAN intelligence controllers (RIC). Then, a slice is allotted for training depending on the clients chosen for the task. Our simulation results show that the proposed method outperforms state-of-art FL methods, such as the federated averaging algorithm (FedAvg), in terms of convergence and number of communication rounds.<\/jats:p>","DOI":"10.3390\/s23010133","type":"journal-article","created":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T04:24:53Z","timestamp":1671769493000},"page":"133","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["5G-Enabled Distributed Intelligence Based on O-RAN for Distributed IoT Systems"],"prefix":"10.3390","volume":"23","author":[{"given":"Ramin","family":"Firouzi","sequence":"first","affiliation":[{"name":"Department of Computer and Systems Sciences, Stockholm University, 16407 Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5924-5457","authenticated-orcid":false,"given":"Rahim","family":"Rahmani","sequence":"additional","affiliation":[{"name":"Department of Computer and Systems Sciences, Stockholm University, 16407 Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,23]]},"reference":[{"key":"ref_1","unstructured":"(2022, October 03). IoT Connected Devices Worldwide 2019\u20132030. Available online: https:\/\/www.statista.com\/statistics\/1183457\/iot-connected-devices-worldwide\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"205","DOI":"10.3390\/iot2020011","article-title":"A Conceptual Architecture in Decentralizing Computing, Storage, and Networking Aspect of IoT Infrastructure","volume":"2","author":"Oktian","year":"2021","journal-title":"IoT"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"85714","DOI":"10.1109\/ACCESS.2020.2991734","article-title":"An Overview on Edge Computing Research","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1109\/JSTSP.2021.3137669","article-title":"Private 5G Networks: Concepts, Architectures, and Research Landscape","volume":"16","author":"Wen","year":"2022","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107516","DOI":"10.1016\/j.comnet.2020.107516","article-title":"Open, Programmable, and Virtualized 5G Networks: State-of-the-Art and the Road Ahead","volume":"182","author":"Bonati","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107284","DOI":"10.1016\/j.comnet.2020.107284","article-title":"OpenAirInterface: Democratizing Innovation in the 5G Era","volume":"176","author":"Kaltenberger","year":"2020","journal-title":"Comput. Netw."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gomez-Miguelez, I., Garcia-Saavedra, A., Sutton, P.D., Serrano, P., Cano, C., and Leith, D.J. (2016). SrsLTE: An Open-Source Platform for LTE Evolution and Experimentation. Proceedings of the Tenth ACM International Workshop on Wireless Network Testbeds, Experimental Evaluation, and Characterization, Association for Computing Machinery.","DOI":"10.1145\/2980159.2980163"},{"key":"ref_8","unstructured":"(2022, October 07). GNU Radio\u2014The Free & Open Source Radio Ecosystem GNU Radio. Available online: https:\/\/www.gnuradio.org\/."},{"key":"ref_9","unstructured":"(2022, October 07). O-RAN ALLIANCE e.V. Available online: https:\/\/www.o-ran.org\/."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/MNET.2020.9277891","article-title":"A Perspective of O-RAN Integration with MEC, SON, and Network Slicing in the 5G Era","volume":"34","author":"Chen","year":"2020","journal-title":"IEEE Netw."},{"key":"ref_11","unstructured":"McMahan, B., and Ramage, D. (2022, October 10). Federated Learning: Collaborative Machine Learning without Centralized Training Data. Google AI Blog. Available online: http:\/\/ai.googleblog.com\/2017\/04\/federated-learning-collaborative.html."},{"key":"ref_12","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and Arcas, B.A. (2017, January 20\u201322). y Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR, Ft. Lauderdale, FL, USA. Available online: https:\/\/www.o-ran.org\/."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Nishio, T., and Yonetani, R. (2019, January 20\u201324). Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge. Proceedings of the ICC 2019\u20142019 IEEE International Conference on Communications (ICC), Shanghai, China.","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref_14","unstructured":"Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., and Chandra, V. (2018). Federated Learning with Non-IID Data. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/JSAC.2019.2904348","article-title":"Adaptive Federated Learning in Resource Constrained Edge Computing Systems","volume":"37","author":"Wang","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3400","DOI":"10.1109\/TNNLS.2019.2944481","article-title":"Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data","volume":"31","author":"Sattler","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, H., Kaplan, Z., Niu, D., and Li, B. (2020, January 6\u20139). Optimizing Federated Learning on Non-IID Data with Reinforcement Learning. Proceedings of the IEEE INFOCOM 2020\u2014IEEE Conference on Computer Communications, Toronto, ON, Canada.","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, S.Q., Lin, J., and Zhang, Q. (2022). A Multi-Agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learning. arXiv.","DOI":"10.1609\/aaai.v36i8.20894"},{"key":"ref_19","unstructured":"(2022, October 07). O-RAN Alliance white paper O-RAN: Towards an Open and Smart RAN. Available online: https:\/\/www.o-ran.org\/resources."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3367","DOI":"10.1109\/TMC.2020.2999852","article-title":"Machine Learning at the Edge: A Data-Driven Architecture With Applications to 5G Cellular Networks","volume":"20","author":"Polese","year":"2021","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_21","unstructured":"(2022, October 07). O-RAN Alliance white paper O-RAN Use Cases and Deployment Scenarios. Available online: https:\/\/www.o-ran.org\/resources."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2204","DOI":"10.1109\/JPROC.2019.2941458","article-title":"Wireless Network Intelligence at the Edge","volume":"107","author":"Park","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_23","unstructured":"(2022, December 15). Introduction\u2014Acumos 1.0 Documentation. Available online: https:\/\/docs.acumos.org\/en\/elpis\/architecture\/intro.html."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Banerji, R., Gupta, N., Kumar, S., Singh, S., Bhat, A., Sahu, B.J.R., and Yoon, S. (2020, January 25\u201328). ONAP Based Pro-Active Access Discovery and Selection for 5G Networks. Proceedings of the 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), Seoul, Republic of Korea.","DOI":"10.1109\/WCNCW48565.2020.9124724"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lee, H., Cha, J., Kwon, D., Jeong, M., and Park, I. (2020, January 7\u201311). Hosting AI\/ML Workflows on O-RAN RIC Platform. Proceedings of the 2020 IEEE Globecom Workshops (GC Wkshps), Virtual.","DOI":"10.1109\/GCWkshps50303.2020.9367572"},{"key":"ref_26","unstructured":"Smith, V., Chiang, C.-K., Sanjabi, M., and Talwalkar, A.S. (2017, January 4\u20139). Federated Multi-Task Learning. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"20889","DOI":"10.1109\/JIOT.2022.3176469","article-title":"FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning","volume":"9","author":"Wu","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_28","unstructured":"Osband, I., Blundell, C., Pritzel, A., and Van Roy, B. (2016, January 5\u201310). Deep Exploration via Bootstrapped DQN. Proceedings of the Advances in Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_29","unstructured":"Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013). Playing Atari with Deep Reinforcement Learning. arXiv."},{"key":"ref_30","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. (2017). Proximal Policy Optimization Algorithms. arXiv."},{"key":"ref_31","unstructured":"(2022, October 07). Magma\u2014Linux Foundation Project. Available online: https:\/\/magmacore.org\/."},{"key":"ref_32","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/133\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:49:04Z","timestamp":1760147344000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/133"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,23]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010133"],"URL":"https:\/\/doi.org\/10.3390\/s23010133","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,23]]}}}