{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T18:16:15Z","timestamp":1774030575943,"version":"3.50.1"},"reference-count":47,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100008095","name":"Yanshan University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008095","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008970","name":"Hebei Normal University of Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008970","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972334"],"award-info":[{"award-number":["61972334"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Sciences"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.ins.2026.123284","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T16:14:19Z","timestamp":1771863259000},"page":"123284","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["From local bias to global consensus: Group-wise prototype federated learning under heterogeneous and cross-domain settings"],"prefix":"10.1016","volume":"741","author":[{"given":"Afei","family":"Li","sequence":"first","affiliation":[]},{"given":"JunHui","family":"Song","sequence":"additional","affiliation":[]},{"given":"Zhangqi","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Zhixin","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Jiadong","family":"Ren","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6891-340X","authenticated-orcid":false,"given":"Yongshan","family":"Liu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.ins.2026.123284_bib0005","doi-asserted-by":"crossref","first-page":"3799","DOI":"10.1109\/JIOT.2022.3174469","article-title":"Edge-based federated deep reinforcement learning for IoT traffic management","volume":"10","author":"Jarwan","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.ins.2026.123284_bib0010","article-title":"Adaptive federated learning with negative inner product aggregation","author":"Deng","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.ins.2026.123284_bib0015","article-title":"A flight arrival time prediction method based on cluster clustering-based modular with deep neural network","author":"Deng","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"2","key":"10.1016\/j.ins.2026.123284_bib0020","doi-asserted-by":"crossref","first-page":"3392","DOI":"10.1109\/JIOT.2023.3296460","article-title":"BFOD: blockchain-based privacy protection and security sharing scheme of flight operation data","volume":"11","author":"Li","year":"2024","journal-title":"IEEE Internet Things J."},{"issue":"19","key":"10.1016\/j.ins.2026.123284_bib0025","doi-asserted-by":"crossref","first-page":"16917","DOI":"10.1109\/JIOT.2023.3272334","article-title":"Toward federated Learning models resistant to adversarial attacks","volume":"10","author":"Hu","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.ins.2026.123284_bib0030","article-title":"Improving airport arrival flow prediction considering heterogeneous and dynamic network dependencies","volume":"100","author":"Zhen","year":"2023","journal-title":"Inf. Fusion."},{"issue":"5","key":"10.1016\/j.ins.2026.123284_bib0035","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1109\/TPDS.2023.3247541","article-title":"Shuffle differential private data aggregation for random population","volume":"34","author":"Wang","year":"2023","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.ins.2026.123284_bib0040","doi-asserted-by":"crossref","first-page":"1855","DOI":"10.1109\/TIFS.2023.3255171","article-title":"Privacy-preserving federated learning via functional encryption, revisited","volume":"18","author":"Chang","year":"2023","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"5","key":"10.1016\/j.ins.2026.123284_bib0045","doi-asserted-by":"crossref","first-page":"2864","DOI":"10.1109\/TNSE.2022.3185327","article-title":"Homomorphic encryption-based privacy-preserving federated learning in IoT-enabled healthcare system","volume":"10","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"10.1016\/j.ins.2026.123284_bib0050","series-title":"Proceedings of Machine Learning and Systems (MLSys)","first-page":"429","article-title":"Federated optimization in heterogeneous networks","author":"Li","year":"2020"},{"key":"10.1016\/j.ins.2026.123284_bib0055","series-title":"Proceedings of the International Conference on Machine Learning (ICML)","first-page":"5132","article-title":"SCAFFOLD: stochastic controlled averaging for federated learning","author":"Karimireddy","year":"2020"},{"issue":"7","key":"10.1016\/j.ins.2026.123284_bib0060","doi-asserted-by":"crossref","first-page":"3805","DOI":"10.1109\/TMC.2022.3147792","article-title":"Accelerating federated learning with cluster construction and hierarchical aggregation","volume":"22","author":"Wang","year":"2023","journal-title":"IEEE Trans. Mob. Comput."},{"key":"10.1016\/j.ins.2026.123284_bib0065","article-title":"Accelerating convergence of federated learning in MEC with dynamic community","volume":"23","author":"Sun","year":"2023","journal-title":"IEEE Trans. Mob. Comput."},{"key":"10.1016\/j.ins.2026.123284_bib0070","author":"Zhao"},{"key":"10.1016\/j.ins.2026.123284_bib0075","series-title":"Proceedings of the IEEE International Conference on Data Engineering (ICDE)","first-page":"965","article-title":"Federated learning on non-IID data silos: an experimental study","author":"Li","year":"2022"},{"issue":"9","key":"10.1016\/j.ins.2026.123284_bib0080","doi-asserted-by":"crossref","first-page":"3388","DOI":"10.1109\/TMC.2021.3056991","article-title":"User-level privacy-preserving federated learning: analysis and performance optimization","volume":"21","author":"Wei","year":"2022","journal-title":"IEEE Trans. Mob. Comput."},{"issue":"1\u20132","key":"10.1016\/j.ins.2026.123284_bib0085","first-page":"1","article-title":"Advances and open problems in federated learning, foundations and trends\u00ae","volume":"14","author":"Kairouz","year":"2021","journal-title":"Mach. Learn."},{"key":"10.1016\/j.ins.2026.123284_bib0090","first-page":"7611","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","volume":"33","author":"Wang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"10","key":"10.1016\/j.ins.2026.123284_bib0095","doi-asserted-by":"crossref","first-page":"5675","DOI":"10.1109\/TMC.2022.3186936","article-title":"Adaptive control of local updating and model compression for efficient federated learning","volume":"22","author":"Xu","year":"2023","journal-title":"IEEE Trans. Mob. Comput."},{"key":"10.1016\/j.ins.2026.123284_bib0100","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"10713","article-title":"Model-contrastive federated learning","author":"Li","year":"2021"},{"key":"10.1016\/j.ins.2026.123284_bib0105","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.future.2023.01.019","article-title":"FedProc: prototypical contrastive federated learning on non-IID data","volume":"143","author":"Mu","year":"2023","journal-title":"Futur. Gener. Comput. Syst."},{"key":"10.1016\/j.ins.2026.123284_bib0110","author":"Li"},{"key":"10.1016\/j.ins.2026.123284_bib0115","author":"Auerbach"},{"key":"10.1016\/j.ins.2026.123284_bib0120","first-page":"23309","article-title":"PartialFed: cross-domain personalized federated learning via partial initialization","volume":"34","author":"Sun","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ins.2026.123284_bib0125","first-page":"10752","article-title":"On convergence of FedProx: local dissimilarity invariant bounds, non-smoothness and beyond","volume":"35","author":"Yuan","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ins.2026.123284_bib0130","series-title":"Proceedings of the 2nd European Workshop on Machine Learning and Systems (EuroMLSys)","first-page":"1","article-title":"Empirical analysis of federated learning in heterogeneous environments","author":"Abdelmoniem","year":"2022"},{"key":"10.1016\/j.ins.2026.123284_bib0135","series-title":"Proceedings of the International Conference on Machine Learning (ICML)","first-page":"18250","article-title":"Generalized federated learning via sharpness aware minimization","author":"Qu","year":"2022"},{"key":"10.1016\/j.ins.2026.123284_bib0140","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"8397","article-title":"Local learning matters: rethinking data heterogeneity in federated learning","author":"Mendieta","year":"2022"},{"key":"10.1016\/j.ins.2026.123284_bib0145","series-title":"Proceedings of the International Conference on Machine Learning (ICML)","first-page":"9489","article-title":"Personalized federated learning using Hypernetworks","author":"Shamsian","year":"2021"},{"key":"10.1016\/j.ins.2026.123284_bib0150","author":"Oh"},{"key":"10.1016\/j.ins.2026.123284_bib0155","author":"Sun"},{"key":"10.1016\/j.ins.2026.123284_bib0160","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)","first-page":"8432","article-title":"FedProto: federated prototype learning across heterogeneous clients","author":"Tan","year":"2022"},{"key":"10.1016\/j.ins.2026.123284_bib0165","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"16312","article-title":"Rethinking federated learning with domain shift: a prototype view","author":"Huang","year":"2023"},{"issue":"3","key":"10.1016\/j.ins.2026.123284_bib0170","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1109\/TBDATA.2025.3527202","article-title":"Advances in robust federated learning: a survey with heterogeneity considerations","volume":"11","author":"Chen","year":"2025","journal-title":"IEEE Trans. Big Data"},{"key":"10.1016\/j.ins.2026.123284_bib0175","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"1013","article-title":"FedDG: federated domain generalization on medical image segmentation via episodic learning in continuous frequency space","author":"Liu","year":"2021"},{"key":"10.1016\/j.ins.2026.123284_bib0180","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"12077","article-title":"Fair federated learning under domain skew with local consistency and domain diversity","author":"Chen","year":"2024"},{"key":"10.1016\/j.ins.2026.123284_bib0185","article-title":"FedFA: federated learning with feature anchors to align features and classifiers for heterogeneous data","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Mob. Comput."},{"key":"10.1016\/j.ins.2026.123284_bib0190","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","first-page":"5041","article-title":"GPFL: simultaneously learning global and personalized feature information for personalized federated learning","author":"Zhang","year":"2023"},{"key":"10.1016\/j.ins.2026.123284_bib0195","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: a model-agnostic meta-learning approach","volume":"33","author":"Fallah","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ins.2026.123284_bib0200","series-title":"Proceedings of the International Conference on Machine Learning (ICML)","first-page":"6357","article-title":"Ditto: fair and robust federated learning through personalization","author":"Li","year":"2021"},{"key":"10.1016\/j.ins.2026.123284_bib0205","author":"Peng"},{"key":"10.1016\/j.ins.2026.123284_bib0210","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2025.110048","article-title":"Agricultural data privacy and federated learning: a review of challenges and opportunities","volume":"232","author":"Dembani","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.ins.2026.123284_bib0215","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.jmapro.2025.02.044","article-title":"A deep neural network approach to predict dimensional accuracy of thin-walled tubes in backward flow forming plasticity process","volume":"141","author":"Kocab\u0131\u00e7ak","year":"2025","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.ins.2026.123284_bib0220","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.jmapro.2024.11.067","article-title":"A deep neural network model for parameter identification in deep drawing metal forming process","volume":"133","author":"Guo","year":"2025","journal-title":"J. Manuf. Process."},{"key":"10.1016\/j.ins.2026.123284_bib0225","article-title":"A data-driven uncertainty quantification framework in probabilistic bio-inspired porous materials (material-UQ): an investigation for RotTMPS plates","volume":"435","author":"Guo","year":"2025","journal-title":"Comput. Methods Appl. Mech. Eng."},{"issue":"15","key":"10.1016\/j.ins.2026.123284_bib0230","doi-asserted-by":"crossref","first-page":"2300","DOI":"10.3390\/math12152300","article-title":"Deep neural network and evolved optimization algorithm for damage assessment in a truss bridge","volume":"12","author":"Nguyen-Ngoc","year":"2024","journal-title":"Mathematics"},{"key":"10.1016\/j.ins.2026.123284_bib0235","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1007\/s10483-025-3240-7","article-title":"Simultaneous imposition of initial and boundary conditions via decoupled physics-informed neural networks for solving initial-boundary value problems","volume":"46","author":"Luong","year":"2025","journal-title":"Appl. Math. Mech."}],"container-title":["Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S002002552600215X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S002002552600215X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T17:25:49Z","timestamp":1774027549000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S002002552600215X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":47,"alternative-id":["S002002552600215X"],"URL":"https:\/\/doi.org\/10.1016\/j.ins.2026.123284","relation":{},"ISSN":["0020-0255"],"issn-type":[{"value":"0020-0255","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"From local bias to global consensus: Group-wise prototype federated learning under heterogeneous and cross-domain settings","name":"articletitle","label":"Article Title"},{"value":"Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ins.2026.123284","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"123284"}}