{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T15:12:04Z","timestamp":1759331524767,"version":"build-2065373602"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission","award":["KJZD-K202300515"],"award-info":[{"award-number":["KJZD-K202300515"]}]},{"name":"Chongqing Natural Science Foundation Innovation and Development Joint Fund","award":["CSTB2024NSCQ-LZX0088"],"award-info":[{"award-number":["CSTB2024NSCQ-LZX0088"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3610089","type":"journal-article","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T17:35:04Z","timestamp":1758044104000},"page":"162903-162914","source":"Crossref","is-referenced-by-count":0,"title":["Federated Learning With Dual-End Gradient Correction and Proxy-Free Self-Distillation"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4175-4257","authenticated-orcid":false,"given":"Haomin","family":"Wei","sequence":"first","affiliation":[{"name":"College of Computer and Information Science, Chongqing Normal University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujiang","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Faculty of Natural, Mathematical and Engineering Sciences, King&#x2019;s College London, Strand Campus, London, U.K."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0467-5422","authenticated-orcid":false,"given":"Tao","family":"Xie","sequence":"additional","affiliation":[{"name":"Faculty of Education, Southwest University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6597-2968","authenticated-orcid":false,"given":"Yunong","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Chongqing Normal University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. Artif. Intell. Statist.","author":"McMahan"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12091972"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3701724"},{"key":"ref4","article-title":"Personalised federated learning on heterogeneous feature spaces","author":"Rakotomamonjy","year":"2023","journal-title":"arXiv:2301.11447"},{"key":"ref5","article-title":"Hybrid federated learning for feature & sample heterogeneity: Algorithms and implementation","author":"Zhang","year":"2024","journal-title":"Trans. Mach. Learn. Res."},{"key":"ref6","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn. Syst. (MLSys)","author":"Li"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2020.05.137"},{"key":"ref8","first-page":"5132","article-title":"SCAFFOLD: Stochastic controlled averaging for federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Karimireddy"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106854"},{"key":"ref10","article-title":"On the convergence of FedAvg on non-IID data","author":"Li","year":"2019","journal-title":"arXiv:1907.02189"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2023.3315066"},{"key":"ref12","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017","journal-title":"arXiv:1708.07747"},{"key":"ref13","first-page":"2351","article-title":"Ensemble distillation for robust model fusion in federated learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lin"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3129371"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.07.098"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00987"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01057"},{"key":"ref18","article-title":"Federated learning based on dynamic regularization","author":"Acar","year":"2021","journal-title":"arXiv:2111.04263"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2025.3531773"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102481"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.32604\/cmes.2024.055596"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.001.2200204"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2024.3517658"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3046250"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3321594"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3162322"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3510135"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00409"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1503.02531"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00533"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1017\/9781108966559.019"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01453-z"},{"key":"ref33","first-page":"1607","article-title":"Born again neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Furlanello"},{"key":"ref34","first-page":"5714","article-title":"Self-supervised label augmentation via input transformations","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Lee"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015565"},{"key":"ref36","first-page":"12878","article-title":"Data-free knowledge distillation for heterogeneous federated learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00650"},{"key":"ref38","article-title":"FedCM: Federated learning with client-level momentum","author":"Xu","year":"2021","journal-title":"arXiv:2106.10874"},{"key":"ref39","first-page":"14068","article-title":"Group knowledge transfer: Federated learning of large CNNs at the edge","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"He"},{"key":"ref40","article-title":"Federated learning with non-IID data","author":"Zhao","year":"2018","journal-title":"arXiv:1806.00582"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"Krizhevsky","key":"ref41"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3164264"},{"key":"ref43","article-title":"LEAF: A benchmark for federated settings","author":"Caldas","year":"2018","journal-title":"arXiv:1812.01097"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/tnse.2025.3610626"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/ICC51166.2024.10622956"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11165039.pdf?arnumber=11165039","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T12:29:44Z","timestamp":1759235384000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11165039\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3610089","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2025]]}}}