Compromising in cyber security, especially regarding data leaks and single points of failure, pose a problem to the rapid growth of the Internet of Things (IoT). A solution for these issues is Blockchain and Federated Learning (FL) integration, which offer a more private and decentralized method for ensuring the security of IoT devices. This integration improves the integrity of data autonomously, while authentication and secure model aggregation block any attempts to tamper. Moreover, FL allows Artificial Intelligence (AI) to be trained on devices, making the exposure of data less likely. In this paper, we analyse the attack surface and emerging vulnerabilities of Blockchain-FL based IoT systems while constructing a taxonomy of the threats posed. They focus on novel security frameworks aimed at constructing next generation IoT system infrastructure, which is scalable, robust, intelligent and above all, secure.

Decentralized Edge Intelligence: A Big Picture on Integrating Blockchain and Federated Learning for IoT Security

Antonella Longo
Methodology
;
2025-01-01

Abstract

Compromising in cyber security, especially regarding data leaks and single points of failure, pose a problem to the rapid growth of the Internet of Things (IoT). A solution for these issues is Blockchain and Federated Learning (FL) integration, which offer a more private and decentralized method for ensuring the security of IoT devices. This integration improves the integrity of data autonomously, while authentication and secure model aggregation block any attempts to tamper. Moreover, FL allows Artificial Intelligence (AI) to be trained on devices, making the exposure of data less likely. In this paper, we analyse the attack surface and emerging vulnerabilities of Blockchain-FL based IoT systems while constructing a taxonomy of the threats posed. They focus on novel security frameworks aimed at constructing next generation IoT system infrastructure, which is scalable, robust, intelligent and above all, secure.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11587/559486
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