Federated Learning Architecture for Cross-Domain Threat Detection in Distributed IoT Networks with Dynamic Adversarial Adaptation

The fast growth of the Internet of Things (IoT) infrastructure has provided tremendous amounts of networks of interconnected gadgets, each with a potential point of entry for advanced cyber threats. Traditional centralized security models are not able to handle the scale, diversity, and dynamics of the existing IoT settings, resulting in performance bottlenecks and privacy violations. As a way of overcoming these limitations, this paper introduces a Federated Learning (FL)-based Intrusion Detection Systems (IDS) to identify threats within distributed IoT networks in a secure and privacy-friendly way. The proposed system integrates cross-domain learning and adaptive adversarial systems, where IoT devices can cooperate and define new threats without sharing sensitive raw data. Experiments were conducted on real-world IoT sensor data, such as temperature and humidity, motion, gas, acceleration, light intensity, and GPS to test the applicability of IoT sensors to smart homes, industrial automation, and environmental monitoring. The system offered substantial enhancements in flexibility and communication effectiveness compared to centralized models by simulating attacks, including false data injection, signal interference, and digital tampering. The devices learn and exchange encrypted model updates independently, while keeping bandwidth to a minimum without affecting detection accuracy. The results show that the FL-based IDS can enhance the cybersecurity of large-scale IoT systems while ensuring data privacy and scalability across heterogeneous domains, making it a promising approach for implementing next-generation distributed cybersecurity systems.