{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T21:04:04Z","timestamp":1776891844733,"version":"3.51.2"},"reference-count":41,"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\/501100007162","name":"Department of Science and Technology of Guangdong Province","doi-asserted-by":"publisher","award":["2024B0101010003"],"award-info":[{"award-number":["2024B0101010003"]}],"id":[{"id":"10.13039\/501100007162","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.neucom.2026.133454","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T18:12:23Z","timestamp":1774462343000},"page":"133454","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["RBFL: Defending against sophisticated poisoning attacks in resilient blockchain-empowered federated learning"],"prefix":"10.1016","volume":"682","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3205-0087","authenticated-orcid":false,"given":"Cunnian","family":"Gao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8725-860X","authenticated-orcid":false,"given":"Wenfeng","family":"Deng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7316-432X","authenticated-orcid":false,"given":"Nan","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Xiaojun","family":"Liang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1755-7887","authenticated-orcid":false,"given":"Wei","family":"Cui","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.133454_bib0005","series-title":"Artificial Intelligence and Statistics","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"McMahan","year":"2017"},{"issue":"5","key":"10.1016\/j.neucom.2026.133454_bib0010","doi-asserted-by":"crossref","first-page":"3259","DOI":"10.1109\/TITS.2023.3324962","article-title":"Federated Learning in intelligent transportation systems: recent applications and open problems","volume":"25","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"4","key":"10.1016\/j.neucom.2026.133454_bib0015","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3501813","article-title":"Federated learning for Healthcare: systematic review and architecture proposal","volume":"13","author":"Antunes","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol."},{"issue":"1","key":"10.1016\/j.neucom.2026.133454_bib0020","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-020-69250-1","article-title":"Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data","volume":"10","author":"Sheller","year":"2020","journal-title":"Sci. Rep."},{"issue":"4","key":"10.1016\/j.neucom.2026.133454_bib0025","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MIS.2020.2988604","article-title":"Fedhealth: a federated transfer learning framework for wearable healthcare","volume":"35","author":"Chen","year":"2020","journal-title":"IEEE Intell. Syst."},{"key":"10.1016\/j.neucom.2026.133454_bib0030","series-title":"Proceedings of the International Conference on Big Data","first-page":"18","article-title":"Ffd: a federated learning based method for credit card fraud detection","author":"Yang","year":"2019"},{"issue":"5","key":"10.1016\/j.neucom.2026.133454_bib0035","first-page":"2438","article-title":"Deepchain: auditable and privacy-preserving Deep learning with blockchain-based incentive","volume":"18","author":"Weng","year":"2019","journal-title":"IEEE Trans. Dependable Secure Comput."},{"issue":"6","key":"10.1016\/j.neucom.2026.133454_bib0040","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1109\/LCOMM.2019.2921755","article-title":"Blockchained on-device federated learning","volume":"24","author":"Kim","year":"2020","journal-title":"IEEE Commun. Lett."},{"issue":"11","key":"10.1016\/j.neucom.2026.133454_bib0045","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3570953","article-title":"Blockchain-empowered federated learning: challenges, solutions, and future directions","volume":"55","author":"Zhu","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.neucom.2026.133454_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.120377","article-title":"Bpfl: blockchain-based privacy-preserving federated learning against poisoning attack","volume":"665","author":"Ren","year":"2024","journal-title":"Information Sci."},{"key":"10.1016\/j.neucom.2026.133454_bib0055","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"3396","article-title":"Mpaf: model poisoning attacks to federated Learning based on fake clients","author":"Cao","year":"2022"},{"key":"10.1016\/j.neucom.2026.133454_bib0060","series-title":"Proceedings of the 29th USENIX Security Symposium","first-page":"1605","article-title":"Local model poisoning attacks to{Byzantine-Robust} federated learning","author":"Fang","year":"2020"},{"key":"10.1016\/j.neucom.2026.133454_bib0065","article-title":"A little is enough: circumventing defenses for distributed learning","volume":"32","author":"Baruch","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2026.133454_bib0070","article-title":"Machine learning with adversaries: byzantine tolerant gradient descent","volume":"30","author":"Blanchard","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"7","key":"10.1016\/j.neucom.2026.133454_bib0075","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1109\/TPDS.2020.3044223","article-title":"Biscotti: a blockchain system for private and secure federated learning","volume":"32","author":"Shayan","year":"2020","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"10.1016\/j.neucom.2026.133454_bib0080","series-title":"Proceedings of the NDSS","article-title":"Manipulating the byzantine: optimizing model poisoning attacks and defenses for federated learning","author":"Shejwalkar","year":"2021"},{"key":"10.1016\/j.neucom.2026.133454_bib0085","doi-asserted-by":"crossref","first-page":"3877","DOI":"10.1109\/TIFS.2025.3555193","article-title":"Enhanced model poisoning attack and multi-strategy defense in federated learning","volume":"20","author":"Yang","year":"2025","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.neucom.2026.133454_bib0090","series-title":"Proceedings of the European Symposium on Research in Computer Security","first-page":"455","article-title":"Contra: defending against poisoning attacks in federated learning","author":"Awan","year":"2021"},{"key":"10.1016\/j.neucom.2026.133454_bib0095","series-title":"Proceedings of the NDSS","article-title":"Fltrust: byzantine-robust federated learning via trust bootstrapping","author":"Cao","year":"2021"},{"issue":"2","key":"10.1016\/j.neucom.2026.133454_bib0100","first-page":"15","article-title":"A peer-to-peer electronic cash system","volume":"4","author":"Nakamoto","year":"2008","journal-title":"Bitcoin"},{"key":"10.1016\/j.neucom.2026.133454_bib0105","first-page":"1","article-title":"Ethereum: a secure decentralised generalised transaction ledger","volume":"151","author":"Wood","year":"2014","journal-title":"Ethereum Project Yellow Paper"},{"key":"10.1016\/j.neucom.2026.133454_bib0110","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.future.2021.11.028","article-title":"A framework for privacy-preservation of IOT healthcare data using federated learning and blockchain technology","volume":"129","author":"Singh","year":"2022","journal-title":"Futur. Gener. Comput. Syst."},{"issue":"3","key":"10.1016\/j.neucom.2026.133454_bib0115","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1109\/JIOT.2020.3017377","article-title":"Privacy-preserving blockchain-based federated learning for IOT devices","volume":"8","author":"Zhao","year":"2020","journal-title":"IEEE Internet Things J."},{"issue":"7","key":"10.1016\/j.neucom.2026.133454_bib0120","doi-asserted-by":"crossref","first-page":"5926","DOI":"10.1109\/JIOT.2020.3032544","article-title":"Blockchain-based federated learning for device failure detection in industrial IOT","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.neucom.2026.133454_bib0125","series-title":"Proceedings of the European Symposium on Research in Computer Security","first-page":"480","article-title":"Data poisoning attacks against federated learning systems","author":"Tolpegin","year":"2020"},{"key":"10.1016\/j.neucom.2026.133454_bib0130","series-title":"Advances in Neural Information Processing Systems","first-page":"16070","article-title":"Attack of the Tails: yes, you really can backdoor federated learning","volume":"vol. 33","author":"Wang","year":"2020"},{"key":"10.1016\/j.neucom.2026.133454_bib0135","series-title":"Advances in Neural Information Processing Systems","article-title":"Data poisoning attacks on factorization-based collaborative filtering","volume":"vol. 29","author":"Li","year":"2016"},{"key":"10.1016\/j.neucom.2026.133454_bib0140","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.ins.2023.02.025","article-title":"Model poisoning attack in differential privacy-based federated learning","volume":"630","author":"Yang","year":"2023","journal-title":"Inf. Sci."},{"key":"10.1016\/j.neucom.2026.133454_bib0145","series-title":"Proceedings of the 29th USENIX Security Symposium","first-page":"1605","article-title":"Local model poisoning attacks to Byzantine-Robust federated learning","author":"Fang","year":"2020"},{"key":"10.1016\/j.neucom.2026.133454_bib0150","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops","first-page":"3396","article-title":"Mpaf: model poisoning attacks to federated Learning based on fake clients","author":"Cao","year":"2022"},{"key":"10.1016\/j.neucom.2026.133454_bib0155","author":"Fung"},{"key":"10.1016\/j.neucom.2026.133454_bib0160","series-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"2545","article-title":"Fldetector: defending federated learning against model poisoning attacks via detecting malicious clients","author":"Zhang","year":"2022"},{"key":"10.1016\/j.neucom.2026.133454_bib0165","doi-asserted-by":"crossref","first-page":"1639","DOI":"10.1109\/TIFS.2022.3169918","article-title":"Shieldfl: mitigating model poisoning attacks in privacy-preserving federated learning","volume":"17","author":"Ma","year":"2022","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"7","key":"10.1016\/j.neucom.2026.133454_bib0170","doi-asserted-by":"crossref","first-page":"3743","DOI":"10.1109\/TAI.2024.3376651","article-title":"Defending against poisoning attacks in federated Learning with blockchain","volume":"5","author":"Dong","year":"2024","journal-title":"IEEE Trans. Artif. Intell."},{"key":"10.1016\/j.neucom.2026.133454_bib0175","doi-asserted-by":"crossref","first-page":"2632","DOI":"10.1109\/TIFS.2025.3533907","article-title":"Toward efficient and certified recovery from poisoning attacks in federated learning","volume":"20","author":"Jiang","year":"2025","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.neucom.2026.133454_bib0180","series-title":"Proceedings of the IEEE International Conference on Big Data","first-page":"7822","article-title":"Efficient federated unlearning with adaptive differential privacy preservation","author":"Jiang","year":"2024"},{"key":"10.1016\/j.neucom.2026.133454_bib0185","doi-asserted-by":"crossref","first-page":"13143","DOI":"10.1109\/TIFS.2025.3636788","article-title":"Certifying the right to be forgotten: primal\u2013dual optimization for sample and label unlearning in vertical federated learning","volume":"20","author":"Jiang","year":"2025","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.neucom.2026.133454_bib0190","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"4652","article-title":"Multi-metrics adaptively identifies backdoors in federated learning","author":"Huang","year":"2023"},{"key":"10.1016\/j.neucom.2026.133454_bib0195","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"9046","article-title":"Game of gradients: mitigating irrelevant clients in federated learning","volume":"vol. 35","author":"Nagalapatti","year":"2021"},{"issue":"2","key":"10.1016\/j.neucom.2026.133454_bib0200","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1109\/TAI.2024.3355362","article-title":"A credible and fair federated learning framework based on blockchain","volume":"6","author":"Chen","year":"2025","journal-title":"IEEE Trans. Artif. Intell."},{"key":"10.1016\/j.neucom.2026.133454_bib0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2022.102907","article-title":"Privacy, accuracy, and model fairness trade-offs in federated learning","volume":"122","author":"Gu","year":"2022","journal-title":"Comput. Secur."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226008519?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226008519?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:29:57Z","timestamp":1776889797000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226008519"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":41,"alternative-id":["S0925231226008519"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133454","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"RBFL: Defending against sophisticated poisoning attacks in resilient blockchain-empowered federated learning","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133454","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":"133454"}}