Stones from Other Hills: Intrusion Detection in Statistical Heterogeneous IoT by Self-labeled Personalized Federated Learning

With the fast development of the Internet of Things (IoT), the growing amounts of data transmitted through edge devices tempt hackers to attack vulnerabilities. Because of data fragmentation and heterogeneous data distribution of IoT, attack detection models on edge devices are proficient at detecti...

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Veröffentlicht in:	IEEE internet of things journal 2025-01, p.1-1
Hauptverfasser:	Lu, Wenting, Ye, Ayong, Xiao, Peixin, Liu, Yuanhuang, Yang, Longjing, Zhu, Donglin, Liu, Zhiquan
Format:	Artikel
Sprache:	eng
Schlagworte:	Adaptation models Data models Decoding Feature extraction Federated learning Heterogenous Internet of Things Intrusion Detection Labeling Logic gates Metalearning Performance evaluation Personalized Federated Learning
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