{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T11:40:27Z","timestamp":1785930027679,"version":"3.56.0"},"reference-count":47,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62473047"],"award-info":[{"award-number":["62473047"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62471383"],"award-info":[{"award-number":["62471383"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Cloud Comput."],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1109\/tcc.2025.3574823","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T13:31:58Z","timestamp":1748525518000},"page":"895-909","source":"Crossref","is-referenced-by-count":1,"title":["Robin: An Efficient Hierarchical Federated Learning Framework via a Learning-Based Synchronization Scheme"],"prefix":"10.1109","volume":"13","author":[{"given":"Tianyu","family":"Qi","sequence":"first","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4528-1489","authenticated-orcid":false,"given":"Yufeng","family":"Zhan","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5303-0700","authenticated-orcid":false,"given":"Peng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5977-4911","authenticated-orcid":false,"given":"Yuanqing","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Institute of Technology, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref2","first-page":"19","article-title":"Oort: Efficient federated learning via guided participant selection","volume-title":"Proc. USENIX Symp. Operating Syst. Des. Implementation","author":"Lai"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO56248.2022.00049"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3466752.3480129"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155494"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2022.3215520"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3369583.3392686"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/3458817.3476211"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICC40277.2020.9148862"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICDCS51616.2021.00012"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM42981.2021.9488756"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2022.3205551"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3096076"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2022.3214234"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2021.3109910"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/RTSS46320.2019.00043"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2021.3099723"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/icdcs57875.2023.00047"},{"key":"ref19","article-title":"Toward understanding the impact of staleness in distributed machine learning","author":"Dai","year":"2018"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TWC.2022.3190512"},{"key":"ref21","article-title":"Stress-ng","year":"2017"},{"key":"ref22","article-title":"Powermonitor","year":"2011"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.12794\/metadc1505267"},{"key":"ref24","first-page":"1","article-title":"Fast and provably good seedings for k-means","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Bachem"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref26","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref27","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","volume":"2","author":"Li","year":"2020"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref29","article-title":"Differentially private federated learning: A client level perspective","author":"Geyer","year":"2017"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s00779-017-1067-4"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2898113"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2022.3230904"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2023.3272801"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1145\/3570361.3592517"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2025.3529487"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2944481"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2024.3350050"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2024.3452696"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TMLCN.2025.3534754"},{"key":"ref40","first-page":"60397","article-title":"FedFed: Feature distillation against data heterogeneity in federated learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Yang"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO50266.2020.00090"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2022.3184157"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2025.3539284"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/tmc.2025.3540566"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2024.3482865"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TCC.2024.3370688"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2024.3363916"}],"container-title":["IEEE Transactions on Cloud Computing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6245519\/11152304\/11017685.pdf?arnumber=11017685","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T05:28:27Z","timestamp":1757136507000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11017685\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":47,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tcc.2025.3574823","relation":{},"ISSN":["2168-7161","2372-0018"],"issn-type":[{"value":"2168-7161","type":"electronic"},{"value":"2372-0018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7]]}}}