000170129 001__ 170129
000170129 005__ 20260326144912.0
000170129 0247_ $$2doi$$a10.1145/3767740
000170129 0248_ $$2sideral$$a148679
000170129 037__ $$aART-2026-148679
000170129 041__ $$aeng
000170129 100__ $$aGarcía-Sáez, Luis Miguel
000170129 245__ $$aPoisoning-Resilient Federated Learning for MEC-IoT Environments Using Blockchain
000170129 260__ $$c2026
000170129 5203_ $$aThe rise of distributed architectures in Internet of Things (IoT) environments has significantly advanced both data processing and artificial intelligence. Notably, Multi-access Edge Computing (MEC) represents a distributed form of the Edge Computing paradigm, focussing on heterogeneous protocol management. In contrast, Federated Learning (FL) is an application-level framework designed to enable decentralised Machine Learning (ML) across devices without centralising data. Nevertheless, the combination of both technologies enables the creation of more efficient, scalable, and responsive systems. However, their integration into IoT brings substantial security challenges, including data poisoning, model manipulation, and the insertion of false nodes, all of which threaten the reliability of FL systems. Blockchain technology emerges as a promising solution to these challenges. It offers a decentralised, transparent, and immutable framework that ensures the authenticity and verification of data across the network. Through blockchain, node interactions are automated and secured, enhancing the integrity and trust in the learning process. This article proposes a blockchain-based architecture for FL within MEC-IoT systems, designed to mitigate security threats. The architecture emphasises data integrity, secure node interactions, and transparent audit trails while maintaining optimal model performance and accuracy, even under attack. It highlights the low resource consumption and minimal time overhead of blockchain integration, ensuring efficiency is not compromised. This integrated approach improves data security, supports secure collaborative learning, and fosters a more resilient and trustworthy IoT ecosystem.
000170129 536__ $$9info:eu-repo/grantAgreement/ES/AEI/PID2024-158682OB-C32$$9info:eu-repo/grantAgreement/ES/DGA/T21-23R$$9info:eu-repo/grantAgreement/ES/MCIU/PID2023-151467OA-I00$$9info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131115A-I00$$9info:eu-repo/grantAgreement/ES/MICINN/PID2022-142332OA-I00
000170129 540__ $$9info:eu-repo/semantics/closedAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000170129 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000170129 700__ $$aRuiz-Villafranca, Sergio
000170129 700__ $$aRoldán-Gómez, José$$uUniversidad de Zaragoza
000170129 700__ $$aCarrillo-Mondéjar, Javier$$uUniversidad de Zaragoza
000170129 700__ $$aMartínez, José Luis
000170129 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000170129 773__ $$g26, 1 (2026), 9 [30 pp.]$$pACM Transactions on Internet Technology$$tACM Transactions on Internet Technology$$x1533-5399
000170129 8564_ $$s1115666$$uhttps://zaguan.unizar.es/record/170129/files/texto_completo.pdf$$yVersión publicada
000170129 8564_ $$s2035387$$uhttps://zaguan.unizar.es/record/170129/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000170129 909CO $$ooai:zaguan.unizar.es:170129$$particulos$$pdriver
000170129 951__ $$a2026-03-26-14:48:27
000170129 980__ $$aARTICLE