{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T01:47:44Z","timestamp":1777945664254,"version":"3.51.4"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Sciences"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.ins.2026.123425","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T07:48:54Z","timestamp":1774424934000},"page":"123425","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Dawid-Skene-model-based label-noise mitigation for federated learning"],"prefix":"10.1016","volume":"745","author":[{"given":"Jia","family":"Dong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9944-0369","authenticated-orcid":false,"given":"Rui","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1147-1417","authenticated-orcid":false,"given":"Xinyi","family":"Shang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1174-610X","authenticated-orcid":false,"given":"Jing-Hao","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"12","key":"10.1016\/j.ins.2026.123425_bib0005","doi-asserted-by":"crossref","first-page":"9387","DOI":"10.1109\/TPAMI.2024.3418862","article-title":"Federated learning for generalization, robustness, fairness: a survey and benchmark","volume":"46","author":"Huang","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.ins.2026.123425_bib0010","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.ins.2023.03.033","article-title":"Federated Learning in smart Cities: privacy and security Survey","volume":"632","author":"Al-Huthaifi","year":"2023","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2026.123425_bib0015","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.future.2023.09.008","article-title":"Model aggregation techniques in federated Learning: a comprehensive survey","volume":"150","author":"Qi","year":"2024","journal-title":"Futur. Gener. Comput. Syst."},{"issue":"6","key":"10.1016\/j.ins.2026.123425_bib0020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3678182","article-title":"Federated Learning Survey: a multi-level taxonomy of aggregation techniques, experimental insights, and future frontiers","volume":"15","author":"Arbaoui","year":"2024","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.ins.2026.123425_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2024.110663","article-title":"A comprehensive survey on client selection strategies in federated Learning","volume":"251","author":"Li","year":"2024","journal-title":"Comput. Netw."},{"key":"10.1016\/j.ins.2026.123425_bib0030","author":"Liang"},{"key":"10.1016\/j.ins.2026.123425_bib0035","author":"Jiang"},{"issue":"3","key":"10.1016\/j.ins.2026.123425_bib0040","doi-asserted-by":"crossref","first-page":"5983","DOI":"10.1109\/TCE.2024.3385440","article-title":"A review of federated learning methods in heterogeneous scenarios","volume":"70","author":"Pei","year":"2024","journal-title":"IEEE Trans. Consum. Electron."},{"key":"10.1016\/j.ins.2026.123425_bib0045","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.future.2024.03.027","article-title":"Enhancing generalization in federated learning with heterogeneous data: a comparative literature review","volume":"157","author":"Mora","year":"2024","journal-title":"Futur. Gener. Comput. Syst."},{"key":"10.1016\/j.ins.2026.123425_bib0050","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"12830","article-title":"Fedfixer: mitigating heterogeneous label noise in federated learning","volume":"vol. 38","author":"Ji","year":"2024"},{"key":"10.1016\/j.ins.2026.123425_bib0055","series-title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","first-page":"5416","article-title":"Fedes: federated early-stopping for hindering memorizing heterogeneous label noise","author":"Zeng","year":"2024"},{"key":"10.1016\/j.ins.2026.123425_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2026.108889","article-title":"Federated learning with noisy labels: a comprehensive and concise review of current methodologies and future directions","author":"Dong","year":"2026","journal-title":"Neural Netw."},{"key":"10.1016\/j.ins.2026.123425_bib0065","series-title":"Federated Learning: Privacy and Incentive","first-page":"108","article-title":"Dealing with label quality disparity in federated learning","author":"Chen","year":"2020"},{"issue":"2","key":"10.1016\/j.ins.2026.123425_bib0070","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3706058","article-title":"Federated learning client pruning for noisy labels","volume":"10","author":"Morafah","year":"2025","journal-title":"ACM Trans. Model. Perform. Eval. Comput. Syst."},{"issue":"2","key":"10.1016\/j.ins.2026.123425_bib0075","doi-asserted-by":"crossref","first-page":"2193","DOI":"10.1109\/TVT.2021.3131852","article-title":"Client selection for federated learning with label noise","volume":"71","author":"Yang","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"1","key":"10.1016\/j.ins.2026.123425_bib0080","first-page":"20","article-title":"Maximum likelihood estimation of observer error-rates using the em algorithm","volume":"28","author":"Dawid","year":"1979","journal-title":"J. R. Stat. Soc. Ser. C Appl. Stat."},{"key":"10.1016\/j.ins.2026.123425_bib0085","series-title":"2023 9th International Conference on Computer and Communications (ICCC)","first-page":"2170","article-title":"A privacy preserving federated learning aggregation algorithm for noise label","author":"Han","year":"2023"},{"issue":"12","key":"10.1016\/j.ins.2026.123425_bib0090","doi-asserted-by":"crossref","first-page":"13820","DOI":"10.1109\/TII.2024.3435449","article-title":"Curriculum-based federated learning for machine fault diagnosis with noisy labels","volume":"20","author":"Sun","year":"2024","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.ins.2026.123425_bib0095","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"10184","article-title":"Fedcorr: multi-stage federated learning for label noise correction","author":"Xu","year":"2022"},{"issue":"12","key":"10.1016\/j.ins.2026.123425_bib0100","doi-asserted-by":"crossref","first-page":"17620","DOI":"10.1109\/TNNLS.2023.3306874","article-title":"Federated data quality assessment approach: robust learning with mixed label noise","volume":"35","author":"Zeng","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.ins.2026.123425_bib0105","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"3118","article-title":"Feddiv: collaborative noise filtering for federated learning with noisy labels","volume":"vol. 38","author":"Li","year":"2024"},{"issue":"8","key":"10.1016\/j.ins.2026.123425_bib0110","doi-asserted-by":"crossref","first-page":"10266","DOI":"10.1109\/JIOT.2024.3509962","article-title":"Feddc: label noise correction with dynamic clients for federated learning","volume":"12","author":"Giap","year":"2025","journal-title":"IEEE Internet Things J."},{"issue":"12","key":"10.1016\/j.ins.2026.123425_bib0115","doi-asserted-by":"crossref","first-page":"11406","DOI":"10.1109\/TMC.2024.3398801","article-title":"Overcoming noisy labels and non-iid data in edge federated learning","volume":"23","author":"Xu","year":"2024","journal-title":"IEEE Trans. Mob. Comput."},{"issue":"2","key":"10.1016\/j.ins.2026.123425_bib0120","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3626242","article-title":"Labeling chaos to learning harmony: federated learning with noisy labels","volume":"15","author":"Tsouvalas","year":"2024","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.ins.2026.123425_bib0125","author":"Yu"},{"key":"10.1016\/j.ins.2026.123425_bib0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107275","article-title":"Fedelr: when federated learning meets learning with noisy labels","volume":"187","author":"Pu","year":"2025","journal-title":"Neural Netw."},{"key":"10.1016\/j.ins.2026.123425_bib0135","series-title":"2025 27th International Conference on Advanced Communications Technology (ICACT)","first-page":"1","article-title":"Relaxed contrastive learning for robust federated models with noisy labels and limited clients","author":"Ejigu","year":"2025"},{"key":"10.1016\/j.ins.2026.123425_bib0140","article-title":"Mitigating label noise in federated learning with regularized features and robust loss","author":"Ejigu","year":"2025","journal-title":"IEEE Trans. Artif. Intell."},{"key":"10.1016\/j.ins.2026.123425_bib0145","series-title":"2020 25th International Conference on Pattern Recognition (ICPR)","first-page":"5020","article-title":"Overcoming noisy and irrelevant data in federated learning","author":"Tuor","year":"2021"},{"issue":"6","key":"10.1016\/j.ins.2026.123425_bib0150","first-page":"1942","article-title":"Fedprof: selective federated learning based on distributional representation profiling","volume":"34","author":"Wu","year":"2023","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.ins.2026.123425_bib0155","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"14184","article-title":"Federated learning with extremely noisy clients via negative distillation","volume":"vol. 38","author":"Lu","year":"2024"},{"issue":"2","key":"10.1016\/j.ins.2026.123425_bib0160","doi-asserted-by":"crossref","first-page":"366","DOI":"10.3390\/electronics14020366","article-title":"Federated learning for extreme label noise: enhanced knowledge distillation and particle swarm optimization","volume":"14","author":"Ouyang","year":"2025","journal-title":"Electronics"},{"key":"10.1016\/j.ins.2026.123425_bib0165","series-title":"Proceedings of the 20th International Conference on Artificial Intelligence and Statistics","first-page":"1273","article-title":"Communication-Efficient learning of deep networks from decentralized data","volume":"vol. 54","author":"McMahan","year":"2017"},{"issue":"1","key":"10.1016\/j.ins.2026.123425_bib0170","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the em algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc. Ser. B (Methodol.)"},{"key":"10.1016\/j.ins.2026.123425_bib0175","author":"LeCun"},{"key":"10.1016\/j.ins.2026.123425_bib0180","series-title":"Learning Multiple Layers of Features from Tiny Images","author":"Krizhevsky","year":"2009"},{"key":"10.1016\/j.ins.2026.123425_bib0185","author":"Hsu"},{"key":"10.1016\/j.ins.2026.123425_bib0190","author":"Simonyan"},{"key":"10.1016\/j.ins.2026.123425_bib0195","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.ins.2026.123425_bib0200","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume":"2","author":"Li","year":"2020","journal-title":"Proc. Mach. Learn. Syst."},{"key":"10.1016\/j.ins.2026.123425_bib0205","series-title":"International Conference on Machine Learning","first-page":"5132","article-title":"Scaffold: stochastic controlled averaging for federated learning","author":"Karimireddy","year":"2020"},{"key":"10.1016\/j.ins.2026.123425_bib0210","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"10713","article-title":"Model-contrastive federated learning","author":"Li","year":"2021"},{"key":"10.1016\/j.ins.2026.123425_bib0215","series-title":"Proceedings of the 29th International Coference on International Conference on Machine Learning","first-page":"17","article-title":"Truelabel + confusions: a spectrum of probabilistic models in analyzing multiple ratings","author":"Liu","year":"2012"},{"key":"10.1016\/j.ins.2026.123425_bib0220","series-title":"Proceedings 2025 Network and Distributed System Security Symposium","article-title":"Revisiting EM-based estimation for locally differentially private protocols","author":"Ye","year":"2025"}],"container-title":["Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526003567?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526003567?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T12:39:59Z","timestamp":1777725599000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0020025526003567"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":44,"alternative-id":["S0020025526003567"],"URL":"https:\/\/doi.org\/10.1016\/j.ins.2026.123425","relation":{},"ISSN":["0020-0255"],"issn-type":[{"value":"0020-0255","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Dawid-Skene-model-based label-noise mitigation for federated learning","name":"articletitle","label":"Article Title"},{"value":"Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ins.2026.123425","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"123425"}}