{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T21:10:01Z","timestamp":1756156201965,"version":"3.44.0"},"reference-count":27,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"17","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100020196","name":"Shandong Provincial Natural Science Foundation Major Basic Research Program","doi-asserted-by":"publisher","award":["ZR2024ZD20"],"award-info":[{"award-number":["ZR2024ZD20"]}],"id":[{"id":"10.13039\/501100020196","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Data Open Innovative Application Laboratory"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072469"],"award-info":[{"award-number":["62072469"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Internet Things J."],"published-print":{"date-parts":[[2025,9,1]]},"DOI":"10.1109\/jiot.2025.3589756","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T17:39:58Z","timestamp":1752687598000},"page":"35025-35036","source":"Crossref","is-referenced-by-count":0,"title":["Toward Accurate Federated Graph Learning Via Layer-Wised Clustering for Social Internet of Things"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1929-0332","authenticated-orcid":false,"given":"Yuru","family":"Liu","sequence":"first","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil &#x0026; Gas Industrial Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6044-5392","authenticated-orcid":false,"given":"Yuange","family":"Liu","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil &#x0026; Gas Industrial Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9800-1068","authenticated-orcid":false,"given":"Weishan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil &#x0026; Gas Industrial Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5349-4598","authenticated-orcid":false,"given":"Qiao","family":"Qiao","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil &#x0026; Gas Industrial Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"}]},{"given":"Daobin","family":"Luo","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil &#x0026; Gas Industrial Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"}]},{"given":"Xiaohui","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6799-5212","authenticated-orcid":false,"given":"Chaoqun","family":"Zheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), and the Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8287-2942","authenticated-orcid":false,"given":"Shaohua","family":"Cao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3597-4150","authenticated-orcid":false,"given":"Lingzhao","family":"Meng","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil &#x0026; Gas Industrial Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3346-769X","authenticated-orcid":false,"given":"Tao","family":"Chen","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil &#x0026; Gas Industrial Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5235-0748","authenticated-orcid":false,"given":"Hongwei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Shandong Key Laboratory of Intelligent Oil &#x0026; Gas Industrial Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9418-7147","authenticated-orcid":false,"given":"Rui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Technology, Qingdao Haier Intelligent Technology RD Company, Qingdao, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2019.03.009"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00049"},{"key":"ref3","article-title":"FedGNN: Federated graph neural network for privacy-preserving recommendation","author":"Wu","year":"2021","journal-title":"arXiv:2102.04925"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2023.3315066"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.098"},{"key":"ref6","first-page":"4387","article-title":"The non-iid data quagmire of decentralized machine learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Hsieh"},{"key":"ref7","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Stat.","author":"McMahan"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ISCC58397.2023.10218066"},{"key":"ref9","first-page":"19586","article-title":"An efficient framework for clustered federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Ghosh"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00309"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/jiot.2024.3503274"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3056185"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/tcomm.2024.3523968"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.009.2400219"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3479158"},{"key":"ref16","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst.","volume":"2","author":"Li"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/223"},{"key":"ref18","first-page":"1","article-title":"Optimal client sampling for federated learning","volume-title":"Proc. Trans. Mach. Learn. Res.","author":"Chen"},{"key":"ref19","first-page":"3407","article-title":"Clustered sampling: Low-variance and improved representativity for clients selection in federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Fraboni"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3125565"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539112"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i14.29468"},{"key":"ref23","first-page":"1","article-title":"Class-wise adaptive self distillation for heterogeneous federated learning","volume-title":"Proc. 36th AAAI Conf. Artif. Intell. Virtual","author":"He"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i8.26187"},{"key":"ref25","first-page":"18839","article-title":"Federated graph classification over non-iid graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Xie"},{"key":"ref26","first-page":"1","article-title":"On the convergence of FedAvg on non-iid data","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Li"},{"key":"ref27","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy"}],"container-title":["IEEE Internet of Things Journal"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6488907\/11134518\/11082269.pdf?arnumber=11082269","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T20:48:18Z","timestamp":1756154898000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11082269\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,1]]},"references-count":27,"journal-issue":{"issue":"17"},"URL":"https:\/\/doi.org\/10.1109\/jiot.2025.3589756","relation":{},"ISSN":["2327-4662","2372-2541"],"issn-type":[{"type":"electronic","value":"2327-4662"},{"type":"electronic","value":"2372-2541"}],"subject":[],"published":{"date-parts":[[2025,9,1]]}}}