CF-HFC:Calibrated Federated based Hardware-aware Fuzzy Clustering for Intrusion Detection in Heterogeneous IoTs
Saadat Izadi, Mahmood Ahmadi

TL;DR
This paper introduces CF-HFC, a federated learning framework with hardware-aware clustering and adaptive calibration, significantly improving intrusion detection accuracy and efficiency in heterogeneous IoT environments.
Contribution
It proposes a novel three-tier architecture combining hardware-aware fuzzy clustering, stabilized aggregation, and dynamic calibration to address heterogeneity challenges in federated IoT intrusion detection.
Findings
Achieves over 99% detection accuracy on multiple datasets
Faster convergence and lower communication latency
Effectively mitigates device and data heterogeneity
Abstract
The rapid expansion of heterogeneous Internet of Things (IoT) environments has heightened security risks, as resource-constrained devices remain vulnerable to diverse cyberattacks. Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative intrusion detection; however, device and data heterogeneity introduce major challenges, including straggler delays, unstable convergence, and unbalanced error rates. This paper presents a Calibrated Federated Learning method with Hardware-aware Fuzzy Clustering (CF-HFC) to enhance intrusion detection performance in heterogeneous IoT networks. The proposed three-tier Edge-Fog-Cloud architecture integrates three complementary components: (1) hardware-aware fuzzy clustering, which organizes clients by computational capacity to mitigate straggler effects; (2) Fuzzy-FedProx aggregation, which stabilizes optimization under…
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Taxonomy
TopicsNetwork Security and Intrusion Detection · IoT and Edge/Fog Computing · Anomaly Detection Techniques and Applications
