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

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.