Federated Learning for Distributed IoT Security: A Privacy-Preserving Approach to Intrusion Detection

Billions of resource-constrained IoT devices now stream security-critical yet privacy-sensitive traffic across the Internet. To detect intrusions without exposing raw data, we propose a federated learning (FL) framework that trains collaboratively on the edge while preserving privacy. At its core is...

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Bibliografiske detaljer

Principais autores:	Chandu Gutti, Karthik Thumula, Parag Balbudhe
Format:	Artigo
Sprog:	Inglês
Udgivet:	IEEE 2025-01-01
Serier:	IEEE Access
Fag:	Federated learning IoT security intrusion detection system privacy-preserving distributed learning cybersecurity
Online adgang:	https://ieeexplore.ieee.org/document/11095679/
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