Privacy-Enhanced Federated Learning Model for Secure Internet of Things Environments

The rapid growth of Internet of Things (IoT) ecosystems has significantly increased cybersecurity threats due to device heterogeneity, resource limitations, and exposure to distributed attacks. Although Federated Learning (FL) has emerged as a promising privacy-preserving machine learning paradigm for decentralized intrusion detection, existing FL approaches often suffer from non-independent and identically distributed (non-IID) data, communication inefficiency, adversarial attacks, and unstable convergence in heterogeneous IoT environments. This study proposes a Privacy-Enhanced Federated Learning (PEFL) framework for adaptive and secure intrusion detection in large-scale IoT networks. The framework integrated differential privacy, secure aggregation, adaptive client selection, trust-aware federated optimization, and edge-assisted hierarchical coordination to improve robustness, scalability, and communication efficiency. The framework was evaluated using benchmark cybersecurity datasets, including CICIDS2017, UNSW-NB15, TON_IoT, and Bot-IoT under heterogeneous and adversarial conditions. Experimental results established that the proposed PEFL framework achieved improved intrusion detection accuracy, faster convergence stability, enhanced resilience against poisoning attacks, and reduced communication overhead compared with conventional FL approaches such as FedAvg and FedProx. The findings further indicated that adaptive client selection and trust-aware aggregation significantly improve model reliability and robustness in resource-constrained IoT environments. This framework will contribute toward the development of scalable, privacy-preserving, and deployable federated intrusion detection systems for next-generation intelligent IoT infrastructures.