{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T07:13:03Z","timestamp":1776150783180,"version":"3.50.1"},"reference-count":32,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100007314","name":"Minnan Normal University","doi-asserted-by":"publisher","award":["KJ18024"],"award-info":[{"award-number":["KJ18024"]}],"id":[{"id":"10.13039\/501100007314","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2023J01920"],"award-info":[{"award-number":["2023J01920"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Standards & Interfaces"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.csi.2026.104159","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T16:11:28Z","timestamp":1773159088000},"page":"104159","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["ASG-FU: An adaptive and secure grouping framework for federated unlearning"],"prefix":"10.1016","volume":"98","author":[{"given":"Hui","family":"Huang","sequence":"first","affiliation":[]},{"given":"Zhiming","family":"Huang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1233-3563","authenticated-orcid":false,"given":"Jian","family":"Ge","sequence":"additional","affiliation":[]},{"given":"Qilei","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0195-3260","authenticated-orcid":false,"given":"Jinsheng","family":"Fang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.csi.2026.104159_b1","series-title":"General Data Protection Regulation (GDPR)","author":"European Union","year":"2018"},{"issue":"1","key":"10.1016\/j.csi.2026.104159_b2","first-page":"1637","article-title":"California consumer privacy act","volume":"75","author":"Illman","year":"2019","journal-title":"Bus. Lawyer"},{"key":"10.1016\/j.csi.2026.104159_b3","unstructured":"N. Carlini, C. Liu, \u00da. Erlingsson, J. Kos, D. Song, The secret sharer: Evaluating and testing unintended memorization in neural networks, in: 28th USENIX Security Symposium, USENIX Security 19, 2019, pp. 267\u2013284."},{"key":"10.1016\/j.csi.2026.104159_b4","series-title":"2021 IEEE Symposium on Security and Privacy","first-page":"141","article-title":"Machine unlearning","author":"Bourtoule","year":"2021"},{"key":"10.1016\/j.csi.2026.104159_b5","doi-asserted-by":"crossref","unstructured":"M. Chen, Z. Zhang, T. Wang, M. Backes, M. Humbert, Y. Zhang, When machine unlearning jeopardizes privacy, in: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, 2021, pp. 896\u2013911.","DOI":"10.1145\/3460120.3484756"},{"key":"10.1016\/j.csi.2026.104159_b6","series-title":"Certified data removal from machine learning models","author":"Guo","year":"2019"},{"key":"10.1016\/j.csi.2026.104159_b7","first-page":"16025","article-title":"Variational bayesian unlearning","volume":"33","author":"Nguyen","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.csi.2026.104159_b8","first-page":"18075","article-title":"Remember what you want to forget: Algorithms for machine unlearning","volume":"34","author":"Sekhari","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.csi.2026.104159_b9","series-title":"Knowledge removal in sampling-based bayesian inference","author":"Fu","year":"2022"},{"key":"10.1016\/j.csi.2026.104159_b10","series-title":"2021 IEEE\/ACM 29th International Symposium on Quality of Service","first-page":"1","article-title":"Federaser: Enabling efficient client-level data removal from federated learning models","author":"Liu","year":"2021"},{"key":"10.1016\/j.csi.2026.104159_b11","series-title":"2015 IEEE Symposium on Security and Privacy","first-page":"463","article-title":"Towards making systems forget with machine unlearning","author":"Cao","year":"2015"},{"key":"10.1016\/j.csi.2026.104159_b12","doi-asserted-by":"crossref","unstructured":"Y. Lin, Z. Gao, H. Du, D. Niyato, J. Kang, X. Liu, Incentive and dynamic client selection for federated unlearning, in: Proceedings of the ACM Web Conference 2024, 2024, pp. 2936\u20132944.","DOI":"10.1145\/3589334.3645462"},{"key":"10.1016\/j.csi.2026.104159_b13","series-title":"Artificial Intelligence and Statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"key":"10.1016\/j.csi.2026.104159_b14","series-title":"IEEE INFOCOM 2023-IEEE Conference on Computer Communications","first-page":"1","article-title":"Asynchronous federated unlearning","author":"Su","year":"2023"},{"key":"10.1016\/j.csi.2026.104159_b15","series-title":"2023 IEEE International Conference on Big Data","first-page":"5559","article-title":"Fedcio: Efficient exact federated unlearning with clustering, isolation, and one-shot aggregation","author":"Qiu","year":"2023"},{"key":"10.1016\/j.csi.2026.104159_b16","article-title":"Guaranteeing data privacy in federated unlearning with dynamic user participation","author":"Liu","year":"2024","journal-title":"IEEE Trans. Dependable Secur. Comput."},{"issue":"7","key":"10.1016\/j.csi.2026.104159_b17","doi-asserted-by":"crossref","first-page":"8653","DOI":"10.1109\/JIOT.2024.3502666","article-title":"Hier-FUN: Hierarchical federated learning and unlearning in heterogeneous edge computing","volume":"12","author":"Ma","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.csi.2026.104159_b18","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.neucom.2021.07.098","article-title":"Federated learning on non-IID data: A survey","volume":"465","author":"Zhu","year":"2021","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.csi.2026.104159_b19","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/TMC.2021.3075291","article-title":"Adaptive batch size for federated learning in resource-constrained edge computing","volume":"22","author":"Ma","year":"2021","journal-title":"IEEE Trans. Mob. Comput."},{"key":"10.1016\/j.csi.2026.104159_b20","doi-asserted-by":"crossref","unstructured":"X. Sheng, W. Bao, L. Ge, Robust federated unlearning, in: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024, pp. 2034\u20132044.","DOI":"10.1145\/3627673.3679817"},{"issue":"4","key":"10.1016\/j.csi.2026.104159_b21","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.26599\/BDMA.2024.9020001","article-title":"A multi-task based clustering personalized federated learning method","volume":"7","author":"Xiong","year":"2024","journal-title":"Big Data Min. Anal."},{"issue":"4","key":"10.1016\/j.csi.2026.104159_b22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3674973","article-title":"FedGK: Communication-efficient federated learning through group-guided knowledge distillation","volume":"24","author":"Zhang","year":"2024","journal-title":"ACM Trans. Internet Technol."},{"key":"10.1016\/j.csi.2026.104159_b23","series-title":"Robust federated learning in a heterogeneous environment","author":"Ghosh","year":"2019"},{"issue":"3","key":"10.1016\/j.csi.2026.104159_b24","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1109\/TNSE.2022.3163279","article-title":"Improving accuracy and convergence in group-based federated learning on non-IID data","volume":"10","author":"He","year":"2022","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"issue":"18","key":"10.1016\/j.csi.2026.104159_b25","doi-asserted-by":"crossref","first-page":"3682","DOI":"10.3390\/electronics13183682","article-title":"SoFL: Clustered federated learning based on dual clustering for heterogeneous data","volume":"13","author":"Zhang","year":"2024","journal-title":"Electronics"},{"issue":"8","key":"10.1016\/j.csi.2026.104159_b26","doi-asserted-by":"crossref","first-page":"3710","DOI":"10.1109\/TNNLS.2020.3015958","article-title":"Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints","volume":"32","author":"Sattler","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.csi.2026.104159_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2023.109938","article-title":"How to cope with malicious federated learning clients: an unsupervised learning-based approach","volume":"234","author":"Onsu","year":"2023","journal-title":"Comput. Netw."},{"key":"10.1016\/j.csi.2026.104159_b28","doi-asserted-by":"crossref","first-page":"6055","DOI":"10.1109\/TSP.2021.3115952","article-title":"Fedpd: A federated learning framework with adaptivity to non-iid data","volume":"69","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Signal Process."},{"key":"10.1016\/j.csi.2026.104159_b29","series-title":"Federated learning with non-iid data","author":"Zhao","year":"2018"},{"key":"10.1016\/j.csi.2026.104159_b30","series-title":"Flower: A friendly federated learning framework","author":"Beutel","year":"2022"},{"key":"10.1016\/j.csi.2026.104159_b31","series-title":"Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"key":"10.1016\/j.csi.2026.104159_b32","series-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"}],"container-title":["Computer Standards & Interfaces"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0920548926000334?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0920548926000334?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T06:39:17Z","timestamp":1776148757000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0920548926000334"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":32,"alternative-id":["S0920548926000334"],"URL":"https:\/\/doi.org\/10.1016\/j.csi.2026.104159","relation":{},"ISSN":["0920-5489"],"issn-type":[{"value":"0920-5489","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"ASG-FU: An adaptive and secure grouping framework for federated unlearning","name":"articletitle","label":"Article Title"},{"value":"Computer Standards & Interfaces","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.csi.2026.104159","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":"104159"}}