CF-HFC:Calibrated Federated based Hardware-aware Fuzzy Clustering for Intrusion Detection in Heterogeneous IoTs

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 non-IID data distributions; and (3) Adaptive Conformal Calibration (ACC), which dynamically adjusts decision thresholds to balance false negative and false positive rates. Extensive experiments on ToN-IoT, BoT-IoT, Edge-IIoTset, and CICDDoS2019 datasets demonstrate that CF-HFC outperforms baseline methods such as FedAvg and FedProx, achieving over 99% detection accuracy, faster convergence, and lower communication latency. Overall, the results verify that CF-HFC effectively mitigates both device- and data-level heterogeneity, compared to existing federated learning approaches, providing accurate and efficient intrusion detection across Heterogeneous IoTs environment.