{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T07:41:26Z","timestamp":1787902886227,"version":"build-2784847793"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,10,1]],"date-time":"2023-10-01T00:00:00Z","timestamp":1696118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,7,29]],"date-time":"2023-07-29T00:00:00Z","timestamp":1690588800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Communications"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1016\/j.comcom.2023.07.039","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T11:13:48Z","timestamp":1690802028000},"page":"356-375","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":44,"special_numbering":"C","title":["Enabling federated learning of explainable AI models within beyond-5G\/6G networks"],"prefix":"10.1016","volume":"210","author":[{"given":"Jos\u00e9 Luis","family":"Corcuera B\u00e1rcena","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pietro","family":"Ducange","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Marcelloni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanni","family":"Nardini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Noferi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0482-5048","authenticated-orcid":false,"given":"Alessandro","family":"Renda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabrizio","family":"Ruffini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessio","family":"Schiavo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giovanni","family":"Stea","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Virdis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.comcom.2023.07.039_b1","series-title":"First International Workshop on Artificial Intelligence in beyond 5G and 6G Wireless Networks","article-title":"Pervasive artificial intelligence in next generation wireless: The Hexa-X project perspective","author":"Miltiadis","year":"2022"},{"key":"10.1016\/j.comcom.2023.07.039_b2","series-title":"C-V2X Use Cases and Service Level Requirements Vol. I","year":"2020"},{"key":"10.1016\/j.comcom.2023.07.039_b3","series-title":"C-V2X Use Cases and Service Level Requirements Vol. II","year":"2021"},{"key":"10.1016\/j.comcom.2023.07.039_b4","series-title":"2018 IEEE International Conference on Communications","first-page":"1","article-title":"Predicting QoE factors with machine learning","author":"Vasilev","year":"2018"},{"key":"10.1016\/j.comcom.2023.07.039_b5","series-title":"2021 IEEE International Conference on Fuzzy Systems","first-page":"1","article-title":"XAI models for quality of experience prediction in wireless networks","author":"Renda","year":"2021"},{"key":"10.1016\/j.comcom.2023.07.039_b6","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/OJSP.2021.3099065","article-title":"Forecasting video QoE with deep learning from multivariate time-series","volume":"2","author":"Dinaki","year":"2021","journal-title":"IEEE Open J. Signal Process."},{"issue":"2","key":"10.1016\/j.comcom.2023.07.039_b7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3298981","article-title":"Federated machine learning: Concept and applications","volume":"10","author":"Yang","year":"2019","journal-title":"ACM Trans. Intell. Syst. Tech."},{"key":"10.1016\/j.comcom.2023.07.039_b8","series-title":"Ethics Guidelines for Trustworthy AI","year":"2019"},{"key":"10.1016\/j.comcom.2023.07.039_b9","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Barredo Arrieta","year":"2020","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.comcom.2023.07.039_b10","series-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume":"vol. 54","author":"McMahan","year":"2017"},{"key":"10.1016\/j.comcom.2023.07.039_b11","series-title":"2022 International Joint Conference on Neural Networks","first-page":"1","article-title":"Boosting the federation: Cross-silo federated learning without gradient descent","author":"Polato","year":"2022"},{"issue":"9","key":"10.1016\/j.comcom.2023.07.039_b12","doi-asserted-by":"crossref","first-page":"3537","DOI":"10.1109\/TFUZZ.2021.3118733","article-title":"Horizontal federated learning of Takagi\u2013Sugeno fuzzy rule-based models","volume":"30","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.comcom.2023.07.039_b13","series-title":"2021 IEEE International Conference on Fuzzy Systems","first-page":"1","article-title":"Towards a federated fuzzy learning system","author":"Wilbik","year":"2021"},{"key":"10.1016\/j.comcom.2023.07.039_b14","series-title":"IEEE WCCI 2022 (World Congress on Computational Intelligence)","article-title":"An approach to federated learning of explainable fuzzy regression models","author":"Corcuera B\u00e1rcena","year":"2022"},{"key":"10.1016\/j.comcom.2023.07.039_b15","unstructured":"J.L.C. B\u00e1rcena, F. Marcelloni, A. Renda, A. Bechini, P. Ducange, A federated fuzzy c-means clustering algorithm., in: Proceedings of the 13th International Workshop on Fuzzy Logic and Applications 2021, (WILF), 2021."},{"issue":"8","key":"10.1016\/j.comcom.2023.07.039_b16","doi-asserted-by":"crossref","first-page":"395","DOI":"10.3390\/info13080395","article-title":"Federated learning of explainable AI models in 6G systems: Towards secure and automated vehicle networking","volume":"13","author":"Renda","year":"2022","journal-title":"Information"},{"key":"10.1016\/j.comcom.2023.07.039_b17","series-title":"First International Workshop on Artificial Intelligence in beyond 5G and 6G Wireless Networks - AI6G2022, Vol. 3189","first-page":"1","article-title":"Towards trustworthy AI for QoE prediction in B5G\/6G networks","author":"B\u00e1rcena","year":"2022"},{"key":"10.1016\/j.comcom.2023.07.039_b18","series-title":"Expanded 6G Vision, Use Cases and Societal Values","year":"2021"},{"issue":"5","key":"10.1016\/j.comcom.2023.07.039_b19","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1109\/MWC.2017.1600304WC","article-title":"Intelligent 5G: When cellular networks meet artificial intelligence","volume":"24","author":"Li","year":"2017","journal-title":"IEEE Wirel. Commun."},{"key":"10.1016\/j.comcom.2023.07.039_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2020.107556","article-title":"Towards artificial intelligence enabled 6G: State of the art, challenges, and opportunities","volume":"183","author":"Zhang","year":"2020","journal-title":"Comput. Netw."},{"issue":"6","key":"10.1016\/j.comcom.2023.07.039_b21","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1109\/MNET.011.2000195","article-title":"Artificial-intelligence-enabled intelligent 6G networks","volume":"34","author":"Yang","year":"2020","journal-title":"IEEE Netw."},{"issue":"6","key":"10.1016\/j.comcom.2023.07.039_b22","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MCOM.001.1900461","article-title":"Federated learning for wireless communications: Motivation, opportunities, and challenges","volume":"58","author":"Niknam","year":"2020","journal-title":"IEEE Commun. Mag."},{"issue":"10","key":"10.1016\/j.comcom.2023.07.039_b23","doi-asserted-by":"crossref","first-page":"3609","DOI":"10.1109\/TMC.2021.3058627","article-title":"Fedpacket: A federated learning approach to mobile packet classification","volume":"21","author":"Bakopoulou","year":"2022","journal-title":"IEEE Trans. Mob. Comput."},{"key":"10.1016\/j.comcom.2023.07.039_b24","series-title":"2021 IEEE Globecom Workshops","first-page":"1","article-title":"Privacy preserving federated RSRP estimation for future mobile networks","author":"Haliloglu","year":"2021"},{"issue":"7","key":"10.1016\/j.comcom.2023.07.039_b25","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/MCOM.001.2001016","article-title":"Federated learning at the network edge: When not all nodes are created equal","volume":"59","author":"Malandrino","year":"2021","journal-title":"IEEE Commun. Mag."},{"key":"10.1016\/j.comcom.2023.07.039_b26","first-page":"1","article-title":"Lyapunov-based optimization of edge resources for energy-efficient adaptive federated learning","author":"Battiloro","year":"2022","journal-title":"IEEE Trans. Green Commun. Netw."},{"key":"10.1016\/j.comcom.2023.07.039_b27","doi-asserted-by":"crossref","first-page":"140699","DOI":"10.1109\/ACCESS.2020.3013541","article-title":"Federated learning: A survey on enabling technologies, protocols, and applications","volume":"8","author":"Aledhari","year":"2020","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.comcom.2023.07.039_b28","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1109\/COMST.2020.2986024","article-title":"Federated learning in mobile edge networks: A comprehensive survey","volume":"22","author":"Lim","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"issue":"2","key":"10.1016\/j.comcom.2023.07.039_b29","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/MWC.001.1900323","article-title":"Artificial intelligence-enabled cellular networks: A critical path to beyond-5G and 6G","volume":"27","author":"Shafin","year":"2020","journal-title":"IEEE Wirel. Commun."},{"issue":"6","key":"10.1016\/j.comcom.2023.07.039_b30","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/MCOM.001.2000050","article-title":"Explainable artificial intelligence for 6G: Improving trust between human and machine","volume":"58","author":"Guo","year":"2020","journal-title":"IEEE Commun. Mag."},{"issue":"4","key":"10.1016\/j.comcom.2023.07.039_b31","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1109\/TNET.2009.2037497","article-title":"Passive diagnosis for wireless sensor networks","volume":"18","author":"Liu","year":"2010","journal-title":"IEEE\/ACM Trans. Netw."},{"issue":"1","key":"10.1016\/j.comcom.2023.07.039_b32","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/JSTSP.2017.2785826","article-title":"Constrained Bayesian active learning of interference channels in cognitive radio networks","volume":"12","author":"Tsakmalis","year":"2018","journal-title":"IEEE J. Sel. Top. Sign. Proces."},{"issue":"4","key":"10.1016\/j.comcom.2023.07.039_b33","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1109\/TMM.2016.2525862","article-title":"A decision-tree-based perceptual video quality prediction model and its application in FEC for wireless multimedia communications","volume":"18","author":"Hameed","year":"2016","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.comcom.2023.07.039_b34","doi-asserted-by":"crossref","unstructured":"G. Dimopoulos, I. Leontiadis, P. Barlet-Ros, K. Papagiannaki, Measuring video QoE from encrypted traffic, in: Proc. of the 2016 Internet Measurement Conf., 2016, pp. 513\u2013526.","DOI":"10.1145\/2987443.2987459"},{"key":"10.1016\/j.comcom.2023.07.039_b35","series-title":"2017 IEEE Int\u2019L Conf. Communications","first-page":"1","article-title":"Machine learning for predicting QoE of video streaming in mobile networks","author":"Lin","year":"2017"},{"key":"10.1016\/j.comcom.2023.07.039_b36","series-title":"First Workshop on Online Learning from Uncertain Data Streams","article-title":"Hoeffding regression trees for forecasting quality of experience in B5G\/6G networks","author":"B\u00e1rcena","year":"2022"},{"issue":"1","key":"10.1016\/j.comcom.2023.07.039_b37","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TSMC.1985.6313399","article-title":"Fuzzy identification of systems and its applications to modeling and control","author":"Takagi","year":"1985","journal-title":"IEEE Trans. Syst. Man Cybern."},{"issue":"2","key":"10.1016\/j.comcom.2023.07.039_b38","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/S1568-4946(02)00032-7","article-title":"Design of adaptive Takagi\u2013Sugeno\u2013Kang fuzzy models","volume":"2","author":"Kukolj","year":"2002","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.comcom.2023.07.039_b39","series-title":"Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases, Vol. 19","author":"Cord","year":"2001"},{"issue":"12","key":"10.1016\/j.comcom.2023.07.039_b40","doi-asserted-by":"crossref","first-page":"3065","DOI":"10.1109\/TFUZZ.2020.2967282","article-title":"Optimize TSK fuzzy systems for classification problems: Minibatch gradient descent with uniform regularization and batch normalization","volume":"28","author":"Cui","year":"2020","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"5","key":"10.1016\/j.comcom.2023.07.039_b41","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1109\/TFUZZ.2019.2958559","article-title":"Optimize TSK fuzzy systems for regression problems: Minibatch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA)","volume":"28","author":"Wu","year":"2020","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.comcom.2023.07.039_b42","series-title":"Hexa-X project","year":"2022"},{"key":"10.1016\/j.comcom.2023.07.039_b43","doi-asserted-by":"crossref","first-page":"181176","DOI":"10.1109\/ACCESS.2020.3028550","article-title":"Simu5G\u2013An OMNeT++ library for end-to-end performance evaluation of 5G networks","volume":"8","author":"Nardini","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.comcom.2023.07.039_b44","series-title":"Multi-access edge computing (MEC); framework and reference architecture","author":"ETSI","year":"2022"},{"issue":"2","key":"10.1016\/j.comcom.2023.07.039_b45","doi-asserted-by":"crossref","first-page":"203","DOI":"10.3390\/network2020014","article-title":"Privacy-aware access protocols for MEC applications in 5G","volume":"2","author":"Akman","year":"2022","journal-title":"Network"},{"key":"10.1016\/j.comcom.2023.07.039_b46","doi-asserted-by":"crossref","first-page":"18706","DOI":"10.1109\/ACCESS.2021.3053233","article-title":"Multi-access edge computing architecture, data security and privacy: A review","volume":"9","author":"Ali","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.comcom.2023.07.039_b47","series-title":"2022 IEEE International Mediterranean Conference on Communications and Networking","first-page":"280","article-title":"Teleoperated support for remote driving over 5G mobile communications","author":"Kakkavas","year":"2022"},{"key":"10.1016\/j.comcom.2023.07.039_b48","series-title":"2022 IEEE Conference on Standards for Communications and Networking","first-page":"58","article-title":"Realistic field trial evaluation of a tele-operated support service for remote driving over 5G","author":"Kakkavas","year":"2022"},{"key":"10.1016\/j.comcom.2023.07.039_b49","series-title":"2022 IEEE International Systems Conference","first-page":"1","article-title":"System modeling and performance evaluation of predictive QoS for future tele-operated driving","author":"Schippers","year":"2022"},{"key":"10.1016\/j.comcom.2023.07.039_b50","article-title":"A multi-layer probing approach for video over 5G in vehicular scenarios","volume":"38","author":"Lopes","year":"2022","journal-title":"Veh. Commun."},{"issue":"2","key":"10.1016\/j.comcom.2023.07.039_b51","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1109\/MWC.2018.1700173","article-title":"Improving video streaming quality in 5G enabled vehicular networks","volume":"25","author":"Qiao","year":"2018","journal-title":"IEEE Wirel. Commun."},{"key":"10.1016\/j.comcom.2023.07.039_b52","series-title":"2022 Joint European Conference on Networks and Communications & 6G Summit","first-page":"393","article-title":"Evaluating 5G uplink performance in low latency video streaming","author":"Uitto","year":"2022"},{"key":"10.1016\/j.comcom.2023.07.039_b53","series-title":"OMNeT++ simulation framework website","year":"2022"},{"key":"10.1016\/j.comcom.2023.07.039_b54","article-title":"Deployment and configuration of MEC apps with Simu5G","author":"Noferi","year":"2021","journal-title":"CoRR"},{"key":"10.1016\/j.comcom.2023.07.039_b55","doi-asserted-by":"crossref","first-page":"148504","DOI":"10.1109\/ACCESS.2021.3123873","article-title":"Scalable real-time emulation of 5G networks with Simu5G","volume":"9","author":"Nardini","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.comcom.2023.07.039_b56","series-title":"INET library website","year":"2022"},{"key":"10.1016\/j.comcom.2023.07.039_b57","series-title":"Fifth International Conference on Hybrid Intelligent Systems","first-page":"6","article-title":"Feature selection with decision tree criterion","author":"Grabczewski","year":"2005"},{"issue":"20","key":"10.1016\/j.comcom.2023.07.039_b58","doi-asserted-by":"crossref","first-page":"4340","DOI":"10.1016\/j.ins.2011.02.021","article-title":"Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures","volume":"181","author":"Gacto","year":"2011","journal-title":"Inform. Sci."},{"key":"10.1016\/j.comcom.2023.07.039_b59","series-title":"Breakthroughs in Statistics","first-page":"196","article-title":"Individual comparisons by ranking methods","author":"Wilcoxon","year":"1992"},{"key":"10.1016\/j.comcom.2023.07.039_b60","unstructured":"3GPP TR 38.901 v16.1.0, \u201cStudy on Channel Model for Frequencies from 0.5 to 100 GHz\u201d, Tech. rep., 2020, January 2020."}],"container-title":["Computer Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0140366423002724?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0140366423002724?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T23:46:02Z","timestamp":1758843962000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0140366423002724"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10]]},"references-count":60,"alternative-id":["S0140366423002724"],"URL":"https:\/\/doi.org\/10.1016\/j.comcom.2023.07.039","relation":{},"ISSN":["0140-3664"],"issn-type":[{"value":"0140-3664","type":"print"}],"subject":[],"published":{"date-parts":[[2023,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enabling federated learning of explainable AI models within beyond-5G\/6G networks","name":"articletitle","label":"Article Title"},{"value":"Computer Communications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.comcom.2023.07.039","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}