The rapid adoption of Internet of Things (IoT) technologies in smart cities, healthcare, and industrial automation has increased the vulnerability of networked systems to cyber threats. Due to distributed deployment and limited computational resources, IoT environments are susceptible to attacks such as distributed denial-of-service, botnet propagation, unauthorized access, and previously unseen anomalies. Centralized intrusion detection systems are often inadequate because of high latency, scalability challenges, and privacy risks associated with transmitting sensitive traffic data to cloud servers. This paper proposes a federated edge- intelligence framework for real-time cyber threat detection using explainable deep learning. Deep learning–based anomaly detection models are deployed at edge nodes to enable low-latency detection, while federated learning supports collaborative model training without sharing raw data. Explainable artificial intelligence techniques enhance transparency by identifying influential traffic features. Experimental evaluation demonstrates improved detection accuracy, reduced response time, and enhanced interpretability, making the framework suitable for securing next- generation IoT environments.