170129 20260326144912.0 doi 10.1145/3767740 sideral 148679 ART-2026-148679 eng García-Sáez, Luis Miguel Poisoning-Resilient Federated Learning for MEC-IoT Environments Using Blockchain 2026 The 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. info:eu-repo/grantAgreement/ES/AEI/PID2024-158682OB-C32 info:eu-repo/grantAgreement/ES/DGA/T21-23R info:eu-repo/grantAgreement/ES/MCIU/PID2023-151467OA-I00 info:eu-repo/grantAgreement/EUR/MICINN/TED2021-131115A-I00 info:eu-repo/grantAgreement/ES/MICINN/PID2022-142332OA-I00 info:eu-repo/semantics/closedAccess All rights reserved http://www.europeana.eu/rights/rr-f/ info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Ruiz-Villafranca, Sergio Roldán-Gómez, José Universidad de Zaragoza Carrillo-Mondéjar, Javier Universidad de Zaragoza Martínez, José Luis 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 26, 1 (2026), 9 [30 pp.] ACM Transactions on Internet Technology ACM Transactions on Internet Technology 1533-5399 1115666 http://zaguan.unizar.es/record/170129/files/texto_completo.pdf Versión publicada 2035387 http://zaguan.unizar.es/record/170129/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:170129 articulos driver 2026-03-26-14:48:27 ARTICLE