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.

翻译：异构物联网（IoT）环境的快速扩张加剧了安全风险，资源受限设备仍易受各类网络攻击。联邦学习（FL）作为一种隐私保护的协作入侵检测范式应运而生；然而，设备与数据异构性带来了主要挑战，包括滞后节点延迟、收敛不稳定以及错误率不均衡。本文提出一种结合硬件感知模糊聚类的校准联邦学习方法（CF-HFC），以提升异构物联网网络中的入侵检测性能。所提出的三层边缘-雾-云架构整合了三个互补组件：（1）硬件感知模糊聚类，按计算能力组织客户端以缓解滞后效应；（2）模糊FedProx聚合，在非独立同分布数据条件下稳定优化过程；（3）自适应共形校准（ACC），动态调整决策阈值以平衡漏报率与误报率。在ToN-IoT、BoT-IoT、Edge-IIoTset和CICDDoS2019数据集上的大量实验表明，CF-HFC在检测准确率（超过99%）、收敛速度和通信延迟方面均优于FedAvg、FedProx等基线方法。总体而言，相较于现有联邦学习方法，CF-HFC能有效缓解设备与数据层面的异构性问题，为异构物联网环境提供准确高效的入侵检测。