{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:04:39Z","timestamp":1770739479345,"version":"3.49.0"},"reference-count":57,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"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":["62376279"],"award-info":[{"award-number":["62376279"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1109\/tnnls.2025.3611832","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T17:37:31Z","timestamp":1759772251000},"page":"781-794","source":"Crossref","is-referenced-by-count":0,"title":["FedMPS: Federated Learning in a Synergy of Multi-Level Prototype-Based Contrastive Learning and Soft Label Generation"],"prefix":"10.1109","volume":"37","author":[{"given":"Wenxin","family":"Yang","sequence":"first","affiliation":[{"name":"Laboratory for Big Data and Decision, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6879-5266","authenticated-orcid":false,"given":"Xingchen","family":"Hu","sequence":"additional","affiliation":[{"name":"Laboratory for Big Data and Decision and the School of Computer, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7947-8749","authenticated-orcid":false,"given":"Xiubin","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Electro-Mechanical Engineering, Xidian University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1018-8106","authenticated-orcid":false,"given":"Rouwan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Systems Engineering, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9335-9930","authenticated-orcid":false,"given":"Witold","family":"Pedrycz","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9066-1475","authenticated-orcid":false,"given":"Xinwang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2937-3065","authenticated-orcid":false,"given":"Jincai","family":"Huang","sequence":"additional","affiliation":[{"name":"Laboratory for Big Data and Decision, National University of Defense Technology, Changsha, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3169347"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-022-02155-w"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3370297"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2019.2956615"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119036"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1002\/int.22606"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3275438"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3172316"},{"key":"ref9","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":"ref10","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. 3rd Mach. Learn. Syst. Conf.","volume":"2","author":"Li"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.01.019"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3213187"},{"key":"ref14","article-title":"Prototype guided federated learning of visual feature representations","author":"Michieli","year":"2021","journal-title":"arXiv:2105.08982"},{"key":"ref15","first-page":"5972","article-title":"No fear of heterogeneity: Classifier calibration for federated learning with non-iid data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Luo"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20819"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2024.3353455"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3320250"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICOIN56518.2023.10048999"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110542"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP49359.2023.10222143"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3089943"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/0010-0285(72)90014-X"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295163"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00823"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01126"},{"key":"ref27","first-page":"15060","article-title":"Few-shot class-incremental learning via training-free prototype calibration","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Wang"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3105377"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3190359"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2022.3164215"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3070085"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.100"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref34","article-title":"Prototypical contrastive learning of unsupervised representations","author":"Li","year":"2020","journal-title":"arXiv:2005.04966"},{"key":"ref35","first-page":"18661","article-title":"Supervised contrastive learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Khosla"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00075"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i1.25091"},{"key":"ref38","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. 37th Int. Conf. Mach. Learn.","volume":"119","author":"Karimireddy"},{"key":"ref39","article-title":"Federated learning based on dynamic regularization","author":"Alp Emre Acar","year":"2021","journal-title":"arXiv:2111.04263"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00987"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3129371"},{"key":"ref42","first-page":"12878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref43","first-page":"38461","article-title":"Preservation of the global knowledge by not-true distillation in federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Lee"},{"key":"ref44","first-page":"11058","article-title":"Multi-level branched regularization for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn. (PMLR)","volume":"162","author":"Kim"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1117\/12.2675419"},{"key":"ref46","article-title":"FedMD: Heterogenous federated learning via model distillation","author":"Li","year":"2019","journal-title":"arXiv:1910.03581"},{"key":"ref47","first-page":"1","article-title":"FedMix: Approximation of mixup under mean augmented federated learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Yoon"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00993"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29763-x"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20056-4_40"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2023.3315066"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1093\/mmy\/myac072.P456"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177730491.MR0022058.Zbl0041.26103"},{"issue":"11","key":"ref54","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1503.02531"},{"key":"ref56","article-title":"Enhancing federated domain adaptation with multi-domain prototype-based federated fine-tuning","author":"Zhang","year":"2024","journal-title":"arXiv:2410.07738"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3022358"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/5962385\/11372199\/11186177.pdf?arnumber=11186177","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T21:07:11Z","timestamp":1770671231000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11186177\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":57,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2025.3611832","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]}}}