{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T06:24:47Z","timestamp":1766298287185,"version":"3.41.0"},"reference-count":55,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100019065","name":"State Grid Corporation of China Science and Technology Program","doi-asserted-by":"publisher","award":["SGGR0000ZHJS2400733"],"award-info":[{"award-number":["SGGR0000ZHJS2400733"]}],"id":[{"id":"10.13039\/501100019065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Netw. Serv. Manage."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1109\/tnsm.2025.3560629","type":"journal-article","created":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T17:37:18Z","timestamp":1744738638000},"page":"2853-2865","source":"Crossref","is-referenced-by-count":1,"title":["MOHFL: Multi-Level One-Shot Hierarchical Federated Learning With Enhanced Model Aggregation Over Non-IID Data"],"prefix":"10.1109","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2184-765X","authenticated-orcid":false,"given":"Huili","family":"Liu","sequence":"first","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4732-4865","authenticated-orcid":false,"given":"Yinglong","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6204-3047","authenticated-orcid":false,"given":"Chenqi","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0000-4357","authenticated-orcid":false,"given":"Xiaofeng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8499-4851","authenticated-orcid":false,"given":"Tingdong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2023.3267463"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2024.3422376"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2023.3287393"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2023.3298220"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2024.3414417"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2022.3196736"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2023.3259431"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2023.3243003"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2023.3280515"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3186960"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3362972"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2022.100642"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2023.3329450"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3190512"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM53939.2023.10228954"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2024.3363916"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2020.3003744"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3162595"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2023.3338021"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3216981"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2020.3021008"},{"key":"ref23","first-page":"1","article-title":"Enhancing one-shot federated learning through data and ensemble co-boosting","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Dai"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3390\/app13179862"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s11554-024-01461-5"},{"key":"ref26","first-page":"21414","article-title":"DENSE: Data-free one-shot federated learning","volume-title":"Proc. 36th Int. Conf. Adv. Neural Inf. Process. Syst.","author":"Zhang"},{"key":"ref27","first-page":"1","article-title":"Data-free one-shot federated learning under very high statistical heterogeneity","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Heinbaugh"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3070013"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/6887040"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ATC55345.2022.9943034"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3161943"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2023.3238049"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2021.3053588"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3009406"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3366947"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2022.3216326"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00993"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3361087"},{"key":"ref39","first-page":"3483","article-title":"Learning structured output representation using deep conditional generative models","volume-title":"Proc. Int. Conf. Adv. Neural Inf. Process. Syst.","author":"Sohn"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3261988"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2024.3403229"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TGCN.2024.3425792"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3289814"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3276865"},{"key":"ref45","first-page":"17854","article-title":"DFRD: Data-free robustness distillation for heterogeneous federated learning","volume-title":"Proc. 37th Int. Conf. Adv. Neural Inf. Process. Syst.","author":"Luo"},{"key":"ref46","first-page":"79570","article-title":"One-for-all: Bridge the gap between heterogeneous architectures in knowledge distillation","volume-title":"Proc. 37th Int. Conf. Adv. Neural Inf. Process. Syst.","author":"Hao"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-023-01813-x"},{"key":"ref48","first-page":"18661","article-title":"Supervised contrastive learning","volume-title":"Proc. Int. Conf. Adv. Neural Inf. Process. Syst.","author":"Khosla"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref50","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017","journal-title":"arXiv:1708.07747"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.2118\/18761-MS"},{"key":"ref52","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. 20th Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2920407"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D19-1198"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3384842"}],"container-title":["IEEE Transactions on Network and Service Management"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/4275028\/11027486\/10965878.pdf?arnumber=10965878","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T04:24:45Z","timestamp":1749270285000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10965878\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":55,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tnsm.2025.3560629","relation":{},"ISSN":["1932-4537","2373-7379"],"issn-type":[{"type":"electronic","value":"1932-4537"},{"type":"electronic","value":"2373-7379"}],"subject":[],"published":{"date-parts":[[2025,6]]}}}