{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T21:19:18Z","timestamp":1778620758145,"version":"3.51.4"},"reference-count":34,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62502370"],"award-info":[{"award-number":["62502370"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2026205003"],"award-info":[{"award-number":["F2026205003"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Fusion"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.inffus.2026.104328","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T16:01:28Z","timestamp":1774972888000},"page":"104328","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Making federated learning forget less: Super teacher guidance for the non-IID world"],"prefix":"10.1016","volume":"133","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3888-8167","authenticated-orcid":false,"given":"Fangwei","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7903-2433","authenticated-orcid":false,"given":"Jiashuai","family":"Huo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2054-9215","authenticated-orcid":false,"given":"Changguang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4983-911X","authenticated-orcid":false,"given":"Yan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6043-4167","authenticated-orcid":false,"given":"Gaopan","family":"Hou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjing","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5420-2554","authenticated-orcid":false,"given":"Zhiyuan","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.inffus.2026.104328_bib0001","doi-asserted-by":"crossref","first-page":"7082","DOI":"10.1109\/TII.2024.3353920","article-title":"DecFFD: a personalized federated learning framework for cross-location fault diagnosis","volume":"20","author":"Deng","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"1","key":"10.1016\/j.inffus.2026.104328_bib0002","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-025-00337-3","article-title":"A secure and trustworthy blockchain-assisted edge computing architecture for industrial internet of things","volume":"15","author":"Asaithambi","year":"2025","journal-title":"Sci. Rep."},{"issue":"12","key":"10.1016\/j.inffus.2026.104328_bib0003","doi-asserted-by":"crossref","first-page":"14053","DOI":"10.1109\/TII.2024.3441626","article-title":"Explainable semantic federated learning enabled industrial edge network for fire surveillance","volume":"20","author":"Dong","year":"2024","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.inffus.2026.104328_bib0004","article-title":"Mitigating malicious model fusion in federated learning via confidence-aware defense","volume":"126","author":"Li","year":"2025","journal-title":"Inf. Fusion"},{"issue":"3","key":"10.1016\/j.inffus.2026.104328_bib0005","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1007\/s10115-024-02285-2","article-title":"Trustworthy federated learning: privacy, security, and beyond","volume":"67","author":"Chen","year":"2025","journal-title":"Know. Inform. Syst."},{"issue":"5","key":"10.1016\/j.inffus.2026.104328_bib0006","doi-asserted-by":"crossref","first-page":"3528","DOI":"10.1109\/TII.2025.3528569","article-title":"Secure federated learning for cloud-fog automation: vulnerabilities, challenges, solutions, and future directions","volume":"21","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.inffus.2026.104328_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103439","article-title":"Personalized federated learning for fault diagnosis with mixture of experts","volume":"125","author":"Zhuang","year":"2026","journal-title":"Inf. Fusion"},{"issue":"1","key":"10.1016\/j.inffus.2026.104328_bib0008","first-page":"1","article-title":"Federated learning for IoT: a survey of techniques, challenges, and applications","volume":"14","author":"Dritsas","year":"2025","journal-title":"J. Sens. Act. Netw."},{"key":"10.1016\/j.inffus.2026.104328_bib0009","series-title":"Proc. Artif. Intell. Stat. (AISTATS)","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"key":"10.1016\/j.inffus.2026.104328_bib0010","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."},{"issue":"7","key":"10.1016\/j.inffus.2026.104328_bib0011","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.1109\/TC.2021.3099723","article-title":"Adaptive federated learning on non-IID data with resource constraint","volume":"71","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Comput."},{"key":"10.1016\/j.inffus.2026.104328_bib0012","series-title":"Proc. 8th Int. Conf. Sig. Inf. Proc., Netw. Comput. (ICSINC)","first-page":"1261","article-title":"Improved federated average algorithm for non-independent and identically distributed data","author":"Zhou","year":"2022"},{"issue":"1","key":"10.1016\/j.inffus.2026.104328_bib0013","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TC.2023.3315066","article-title":"FedGKD: toward heterogeneous federated learning via global knowledge distillation","volume":"73","author":"Yao","year":"2023","journal-title":"IEEE Trans. Comput."},{"key":"10.1016\/j.inffus.2026.104328_bib0014","series-title":"Adv. Neural Inf. Process. Syst.","first-page":"38461","article-title":"Preservation of the global knowledge by not-true distillation in federated learning","volume":"35","author":"Lee","year":"2022"},{"key":"10.1016\/j.inffus.2026.104328_bib0015","unstructured":"C. Xu, Z. Hong, M. Huang, T. Jiang, Acceleration of federated learning with alleviated forgetting in local training,(2022) arXiv: 2203.02645."},{"key":"10.1016\/j.inffus.2026.104328_bib0016","series-title":"Proc. Conf. Lifelong Learn. Agents","first-page":"764","article-title":"Re-weighted softmax cross-entropy to control forgetting in federated learning","author":"Legate","year":"2023"},{"key":"10.1016\/j.inffus.2026.104328_bib0017","series-title":"Proc. AAAI Conf. Artif. Intel.","first-page":"21752","article-title":"Federated learning with sample-level client drift mitigation","volume":"39","author":"Xu","year":"2025"},{"key":"10.1016\/j.inffus.2026.104328_bib0018","unstructured":"T. Shen, Z. Li, D. Zhu, Z. Zhao, C. Wu, F. Wu, Fedeve: On bridging the client drift and period drift for cross-device federated learning,(2025) arXiv: 2508.14539."},{"key":"10.1016\/j.inffus.2026.104328_bib0019","series-title":"Proc. Mach. Learn. Syst.","first-page":"429","article-title":"Federated optimization in heterogeneous networks","author":"Li","year":"2020"},{"key":"10.1016\/j.inffus.2026.104328_bib0020","series-title":"Neur. Inf. Proc. Syst.","first-page":"7611","article-title":"Tackling the objective inconsistency problem in heterogeneous federated optimization","volume":"33","author":"Wang","year":"2020"},{"issue":"1","key":"10.1016\/j.inffus.2026.104328_bib0021","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1109\/TNSE.2025.3586778","article-title":"FedVaccine: robust federated learning in noisy and non-IID wireless network environments","volume":"13","author":"Lee","year":"2026","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"10.1016\/j.inffus.2026.104328_bib0022","series-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pat. Recog. (CVPR)","first-page":"10713","article-title":"Model-contrastive federated learning","author":"Li","year":"2021"},{"key":"10.1016\/j.inffus.2026.104328_bib0023","unstructured":"G. Serra, F. Buettner, Federated continual learning goes online: Leveraging uncertainty for modality-agnostic class-incremental learning,(2024) arXiv: 2405.18925."},{"key":"10.1016\/j.inffus.2026.104328_bib0024","series-title":"Proc. 36th AAAI Conf. Artif. Intell.","first-page":"3","article-title":"Class-wise adaptive self distillation for heterogeneous federated learning","volume":"22","author":"He","year":"2022"},{"issue":"6","key":"10.1016\/j.inffus.2026.104328_bib0025","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1109\/TBDATA.2022.3189703","article-title":"Learning critically: selective self-distillation in federated learning on non-IID data","volume":"10","author":"He","year":"2024","journal-title":"IEEE Trans. Big Data"},{"issue":"11","key":"10.1016\/j.inffus.2026.104328_bib0026","doi-asserted-by":"crossref","first-page":"16314","DOI":"10.1109\/JIOT.2025.3533003","article-title":"PFedKD: personalized federated learning via knowledge distillation using unlabeled pseudo data for internet of things","volume":"12","author":"Li","year":"2025","journal-title":"IEEE Internet Things J."},{"issue":"8","key":"10.1016\/j.inffus.2026.104328_bib0027","article-title":"FedAgent: federated learning on non-IID data via reinforcement learning and knowledge distillation","volume":"285","author":"Sun","year":"2025","journal-title":"Exp. Syst. Appl."},{"key":"10.1016\/j.inffus.2026.104328_bib0028","unstructured":"C. Song, D. Saxena, J. Cao, Y. Zhao, Feddistill: Global model distillation for local model de-biasing in non-IID federated learning,(2024) arXiv: 2404.09210."},{"issue":"1","key":"10.1016\/j.inffus.2026.104328_bib0029","article-title":"FedSam: enhancing federated learning accuracy with differential privacy and data heterogeneity mitigation","volume":"95","author":"Li","year":"2026","journal-title":"Comp. Stan. Inter."},{"issue":"10","key":"10.1016\/j.inffus.2026.104328_bib0030","doi-asserted-by":"crossref","first-page":"8137","DOI":"10.1007\/s10994-024-06524-z","article-title":"From MNIST to imagenet and back: benchmarking continual curriculum learning","volume":"113","author":"Faber","year":"2024","journal-title":"Mach. Learn."},{"key":"10.1016\/j.inffus.2026.104328_bib0031","unstructured":"Y. Zhang, H. Chen, Z. Lin, Z. Chen, J. Zhao, FedAC: An Adaptive Clustered Federated Learning Framework for Heterogeneous Data,(2024) arXiv: 2403.16460."},{"key":"10.1016\/j.inffus.2026.104328_bib0032","unstructured":"Y. Aperstein, A. Apartsin, Boosted Training of Lightweight Early Exits for Optimizing CNN Image Classification Inference,(2025) arXiv: 2509.08318."},{"issue":"8","key":"10.1016\/j.inffus.2026.104328_bib0033","first-page":"1200","article-title":"Hybrid architecture based on CNN and transformer for strip steel surface defect classification","volume":"11","author":"Li","year":"2022","journal-title":"Electronics (Basel)"},{"key":"10.1016\/j.inffus.2026.104328_bib0034","series-title":"Adv. Neur. Inf. Proc. Syst.","first-page":"5972","article-title":"No fear of heterogeneity: classifier calibration for federated learning with non-IID data","volume":"34","author":"Luo","year":"2021"}],"container-title":["Information Fusion"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1566253526002071?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1566253526002071?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T20:49:08Z","timestamp":1778618948000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1566253526002071"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":34,"alternative-id":["S1566253526002071"],"URL":"https:\/\/doi.org\/10.1016\/j.inffus.2026.104328","relation":{},"ISSN":["1566-2535"],"issn-type":[{"value":"1566-2535","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Making federated learning forget less: Super teacher guidance for the non-IID world","name":"articletitle","label":"Article Title"},{"value":"Information Fusion","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.inffus.2026.104328","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104328"}}