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