{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:08:37Z","timestamp":1779149317381,"version":"3.51.4"},"reference-count":28,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":441,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Internet of Things and Cyber-Physical Systems"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1016\/j.iotcps.2026.03.006","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T17:34:28Z","timestamp":1774373668000},"page":"232-240","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Adaptive privacy-preserving federated learning for robust IoT systems: A defense against data poisoning attacks"],"prefix":"10.1016","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6608-5065","authenticated-orcid":false,"given":"Sajjad","family":"Khan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3020-9556","authenticated-orcid":false,"given":"Davor","family":"Svetinovic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.iotcps.2026.03.006_bib1","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.iotcps.2023.04.001","article-title":"Security of federated learning with IoT systems: issues, limitations, challenges, and solutions","volume":"3","author":"Yaacoub","year":"2023","journal-title":"Internet of Things and Cyber-Physical Systems"},{"key":"10.1016\/j.iotcps.2026.03.006_bib2","doi-asserted-by":"crossref","first-page":"4551","DOI":"10.1016\/j.iotcps.2023.01.003","article-title":"A blockchain-based decentralized collaborative learning model for reliable energy digital twins","volume":"3","author":"Qiao","year":"2023","journal-title":"Internet of Things and Cyber-Physical Systems"},{"key":"10.1016\/j.iotcps.2026.03.006_bib3","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2022.109048","article-title":"Fusion of federated learning and industrial internet of things: a survey","volume":"212","author":"Boobalan","year":"2022","journal-title":"Comput. Netw."},{"key":"10.1016\/j.iotcps.2026.03.006_bib4","series-title":"Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security","first-page":"13101321","article-title":"Privacy-preserving deep learning","author":"Shokri","year":"2015"},{"issue":"4","key":"10.1016\/j.iotcps.2026.03.006_bib5","first-page":"1","article-title":"Grnn: generative regression neural Network\u2014A data leakage attack for federated learning","volume":"13","author":"Ren","year":"2022","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.iotcps.2026.03.006_bib6","doi-asserted-by":"crossref","first-page":"167185","DOI":"10.1016\/j.iotcps.2023.12.003","article-title":"Machine learning techniques for IoT security: current research and future vision with generative ai and large language models","volume":"4","author":"Alwahedi","year":"2024","journal-title":"Internet of Things and Cyber-Physical Systems"},{"key":"10.1016\/j.iotcps.2026.03.006_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2024.110383","article-title":"Robust and privacy-preserving federated learning with distributed additive encryption against poisoning attacks","volume":"245","author":"Zhang","year":"2024","journal-title":"Comput. Netw."},{"key":"10.1016\/j.iotcps.2026.03.006_bib8","series-title":"Network and Distributed Systems Security (NDSS) Symposium","article-title":"Local and central differential privacy for robustness and privacy in federated learning","author":"Naseri","year":"2022"},{"key":"10.1016\/j.iotcps.2026.03.006_bib9","doi-asserted-by":"crossref","first-page":"110128","DOI":"10.1016\/j.iotcps.2023.09.003","article-title":"Deep learning for cyber threat detection in iot networks: a review","volume":"4","author":"Aldhaheri","year":"2024","journal-title":"Internet of Things and Cyber-Physical systems"},{"key":"10.1016\/j.iotcps.2026.03.006_bib10","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.comnet.2024.110321","article-title":"Towards robust and privacy-preserving federated learning in edge computing","volume":"243","author":"Zhou","year":"2024","journal-title":"Comput. Netw."},{"issue":"5","key":"10.1016\/j.iotcps.2026.03.006_bib11","doi-asserted-by":"crossref","first-page":"4619","DOI":"10.1109\/TDSC.2024.3354736","article-title":"Efficient and secure federated learning against backdoor attacks","volume":"21","author":"Miao","year":"2024","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"10.1016\/j.iotcps.2026.03.006_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2024.110542","article-title":"Smart contract assisted secure aggregation scheme for model update in federated learning","volume":"250","author":"Wu","year":"2024","journal-title":"Comput. Netw."},{"key":"10.1016\/j.iotcps.2026.03.006_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.comnet.2024.110465","article-title":"Edge server enhanced secure and privacy preserving federated learning","volume":"249","author":"Xu","year":"2024","journal-title":"Comput. Netw."},{"issue":"2","key":"10.1016\/j.iotcps.2026.03.006_bib14","first-page":"19331945","article-title":"Enabling secure cross-modal search over encrypted data via federated learning","volume":"12","author":"Wang","year":"2024","journal-title":"IEEE Internet Things J."},{"issue":"2","key":"10.1016\/j.iotcps.2026.03.006_bib15","first-page":"11651175","article-title":"Byzantine-robust aggregation in federated learning empowered industrial iot","volume":"19","author":"Li","year":"2021","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"10","key":"10.1016\/j.iotcps.2026.03.006_bib16","doi-asserted-by":"crossref","first-page":"6526","DOI":"10.1109\/TII.2022.3156645","article-title":"ADFL: a poisoning attack defense framework for horizontal federated learning","volume":"18","author":"Guo","year":"2022","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"1","key":"10.1016\/j.iotcps.2026.03.006_bib17","first-page":"437450","article-title":"Lomar: a local defense against poisoning attack on federated learning","volume":"20","author":"Li","year":"2021","journal-title":"IEEE Trans. Dependable Secure Comput."},{"issue":"5","key":"10.1016\/j.iotcps.2026.03.006_bib18","doi-asserted-by":"crossref","first-page":"4481","DOI":"10.1109\/TDSC.2024.3353317","article-title":"Sine: similarity is not enough for mitigating local model poisoning attacks in federated learning","volume":"21","author":"Kasyap","year":"2024","journal-title":"IEEE Trans. Dependable Secure Comput."},{"issue":"9","key":"10.1016\/j.iotcps.2026.03.006_bib19","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":"2021","journal-title":"IEEE Trans. Mobile Comput."},{"issue":"2","key":"10.1016\/j.iotcps.2026.03.006_bib20","first-page":"1364","article-title":"Privacy-preserving federated deep learning with irregular users","volume":"19","author":"Xu","year":"2020","journal-title":"IEEE Trans. Dependable Secure Comput."},{"issue":"10","key":"10.1016\/j.iotcps.2026.03.006_bib21","doi-asserted-by":"crossref","first-page":"2291","DOI":"10.1109\/TPDS.2021.3137321","article-title":"Privacy-preserving efficient federated-learning model debugging","volume":"33","author":"Li","year":"2021","journal-title":"IEEE Trans. Parallel Distr. Syst."},{"issue":"6","key":"10.1016\/j.iotcps.2026.03.006_bib22","doi-asserted-by":"crossref","first-page":"5005","DOI":"10.1109\/TDSC.2023.3239007","article-title":"Pile: robust privacy-preserving federated learning via verifiable perturbations","volume":"20","author":"Tang","year":"2023","journal-title":"IEEE Trans. Dependable Secure Comput."},{"key":"10.1016\/j.iotcps.2026.03.006_bib23","doi-asserted-by":"crossref","first-page":"4329","DOI":"10.1109\/TIFS.2023.3295949","article-title":"Privacy-preserving federated learning with malicious clients and honest-but-curious servers","volume":"18","author":"Le","year":"2023","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.iotcps.2026.03.006_bib24","doi-asserted-by":"crossref","first-page":"5749","DOI":"10.1109\/TIFS.2023.3315125","article-title":"APFed: anti-Poisoning attacks in privacy-preserving heterogeneous federated learning","volume":"18","author":"Chen","year":"2023","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"10.1016\/j.iotcps.2026.03.006_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.iot.2023.100956","article-title":"Dynamic behavior assessment protocol for secure decentralized federated learning","volume":"24","author":"Khan","year":"2023","journal-title":"Internet Things"},{"key":"10.1016\/j.iotcps.2026.03.006_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.iot.2024.101174","article-title":"OpenFL: a scalable and secure decentralized federated learning system on the ethereum blockchain","volume":"26","author":"Wahrst\u00e4tter","year":"2024","journal-title":"Internet Things"},{"issue":"24","key":"10.1016\/j.iotcps.2026.03.006_bib27","doi-asserted-by":"crossref","first-page":"21294","DOI":"10.1109\/JIOT.2023.3282732","article-title":"A game theory-based incentive mechanism for collaborative security of federated learning in energy blockchain environment","volume":"10","author":"He","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.iotcps.2026.03.006_bib28","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."}],"container-title":["Internet of Things and Cyber-Physical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2667345226000064?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2667345226000064?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T23:33:37Z","timestamp":1779147217000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2667345226000064"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":28,"alternative-id":["S2667345226000064"],"URL":"https:\/\/doi.org\/10.1016\/j.iotcps.2026.03.006","relation":{},"ISSN":["2667-3452"],"issn-type":[{"value":"2667-3452","type":"print"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Adaptive privacy-preserving federated learning for robust IoT systems: A defense against data poisoning attacks","name":"articletitle","label":"Article Title"},{"value":"Internet of Things and Cyber-Physical Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.iotcps.2026.03.006","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.","name":"copyright","label":"Copyright"}]}}