{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T09:39:11Z","timestamp":1774949951952,"version":"3.50.1"},"reference-count":63,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"24","license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100008982","name":"NPRP-Standard (NPRP-S) Thirteen (13th) Cycle from the Qatar National Research Fund","doi-asserted-by":"publisher","award":["NPRP13S-0201-200219"],"award-info":[{"award-number":["NPRP13S-0201-200219"]}],"id":[{"id":"10.13039\/100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2022,12,15]]},"DOI":"10.1109\/jiot.2022.3194833","type":"journal-article","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T15:45:32Z","timestamp":1659023132000},"page":"25626-25642","source":"Crossref","is-referenced-by-count":25,"title":["Semi-Supervised Federated Learning Over Heterogeneous Wireless IoT Edge Networks: Framework and Algorithms"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6886-6500","authenticated-orcid":false,"given":"Abdullatif","family":"Albaseer","sequence":"first","affiliation":[{"name":"Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3261-7588","authenticated-orcid":false,"given":"Mohamed","family":"Abdallah","sequence":"additional","affiliation":[{"name":"Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0903-1204","authenticated-orcid":false,"given":"Ala","family":"Al-Fuqaha","sequence":"additional","affiliation":[{"name":"Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7565-5253","authenticated-orcid":false,"given":"Aiman","family":"Erbad","sequence":"additional","affiliation":[{"name":"Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8528-0512","authenticated-orcid":false,"given":"Octavia A.","family":"Dobre","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Applied Science, Memorial University, St. John&#x2019;s, Canada"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Communication-efficient federated distillation","author":"sattler","year":"2020","journal-title":"arXiv 2012 00632"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/PIMRC.2019.8904164"},{"key":"ref33","article-title":"FedMVT: Semi-supervised vertical federated learning with multiview training","author":"kang","year":"2020","journal-title":"arXiv 2008 10838"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3092015"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/BigData52589.2021.9671693"},{"key":"ref30","article-title":"Towards utilizing unlabeled data in federated learning: A survey and prospective","author":"jin","year":"2020","journal-title":"arXiv 2002 11545"},{"key":"ref37","article-title":"Distillation-based semi-supervised federated learning for communication-efficient collaborative training with non-iid private data","author":"itahara","year":"2020","journal-title":"arXiv 2008 06180"},{"key":"ref36","article-title":"Federated semi-supervised learning with inter-client consistency","author":"jeong","year":"2020","journal-title":"arXiv 2006 12097"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/IWCMC48107.2020.9148475"},{"key":"ref34","article-title":"Towards adaptive federated semi-supervised learning for visual recognition","author":"wen","year":"2021"},{"key":"ref60","first-page":"1","article-title":"On the convergence of fedavg on non-iid data","author":"li","year":"2020","journal-title":"Proc ICLR"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2949715"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/ICCWorkshops50388.2021.9473806"},{"key":"ref63","article-title":"Improving robustness using generated data","volume":"34","author":"gowal","year":"2021","journal-title":"Advances in neural information processing systems"},{"key":"ref28","article-title":"Delay minimization for federated learning over wireless communication networks","author":"yang","year":"2020","journal-title":"Proc Int Conf Mach Learn (ICML) Workshop on Federated Learn"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3037554"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3024629"},{"key":"ref2","article-title":"Federated learning for mobile keyboard prediction","author":"hard","year":"2018","journal-title":"arXiv 1811 03604"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2020.2986024"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9149138"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM46510.2021.9685938"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3025446"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2021.3108197"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488839"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3103715"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3095077"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3136308"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3022534"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3024629"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2020.3035770"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"ref56","author":"press","year":"2007","journal-title":"Numerical Recipes 3rd Edition The Art of Scientific Computing"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2022.3151466"},{"key":"ref54","article-title":"Good semi-supervised learning that requires a bad GAN","author":"dai","year":"2017","journal-title":"arXiv 1705 09783"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761315"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2016.2611964"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/5GWF52925.2021.00088"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3111624"},{"key":"ref40","article-title":"Improving semi-supervised federated learning by reducing the gradient diversity of models","author":"zhang","year":"2020","journal-title":"arXiv 2008 11364"},{"key":"ref12","article-title":"Federated learning: Strategies for improving communication efficiency","author":"konecny","year":"2017","journal-title":"arXiv 1610 05492"},{"key":"ref13","volume":"3","author":"mcmahan","year":"2017","journal-title":"Federated learning Collaborative machine learning without centralized training data"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICCWorkshops50388.2021.9473806"},{"key":"ref15","article-title":"Fine-grained data selection for improved energy efficiency of federated edge learning","author":"albaseer","year":"2021","journal-title":"IEEE Trans Netw Sci Eng"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3089054"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2019.2904348"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TCOMM.2019.2944169"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3042530"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3055767"},{"key":"ref3","first-page":"1","article-title":"Edge learning: The enabling technology for distributed big data analytics in the edge","volume":"54","author":"zhang","year":"2021","journal-title":"ACM Comput Surveys"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.001.1900103"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2967772"},{"key":"ref8","article-title":"Federated learning in mobile edge networks: A comprehensive survey","author":"lim","year":"2019","journal-title":"arXiv 1909 11875"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2020.2975749"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2019.8737464"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.2019.1800286"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148625"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICCWorkshops49005.2020.9145118"},{"key":"ref48","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Proc Artif Intell Stat"},{"key":"ref47","article-title":"Don&#x2019;t use large mini-batches, use local SGD","author":"lin","year":"2018","journal-title":"arXiv 1808 07217"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3092015"},{"key":"ref41","article-title":"SemiFL: Communication efficient semi-supervised federated learning with unlabeled clients","author":"diao","year":"2021","journal-title":"arXiv 2106 01432"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3103715"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2956615"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6488907\/9973413\/09844147.pdf?arnumber=9844147","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T14:34:29Z","timestamp":1672065269000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9844147\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,15]]},"references-count":63,"journal-issue":{"issue":"24"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2022.3194833","relation":{"has-preprint":[{"id-type":"doi","id":"10.36227\/techrxiv.19317632.v3","asserted-by":"object"},{"id-type":"doi","id":"10.36227\/techrxiv.19317632.v1","asserted-by":"object"},{"id-type":"doi","id":"10.36227\/techrxiv.19317632.v2","asserted-by":"object"},{"id-type":"doi","id":"10.36227\/techrxiv.19317632","asserted-by":"object"}]},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"value":"2327-4662","type":"electronic"},{"value":"2372-2541","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,15]]}}}