Abstract
As smart home environments grow increasingly populated with heterogeneous and resource-constrained IoT devices, ensuring secure and adaptive network protection has become a critical challenge. Traditional intrusion detection solutions, i.e., centralized, rule-based, or cloud-dependent, struggle to scale or provide user-friendly insights for non-technical residents. To address these limitations, we propose HiFINS, a Hierarchical Federated Learning-based Interactive Network Security system that integrates hierarchical federated learning (HiF) for real-time, privacy-aware threat detection and a human-centered interface (INS) for intuitive security management. HiFINS distributes model training across home routers, local servers, and edge/cloud infrastructure, enhancing detection performance while minimizing data exposure. Simultaneously, the interactive application enables users to configure routers, view alerts, and respond to threats without needing technical expertise. The system is implemented and validated within a reproducible, container-based test environment that emulates realistic smart home conditions and facilitates transparent benchmarking. Experimental results show that HiFINS outperforms both centralized and existing hierarchical federated approaches in terms of detection accuracy, inference throughput, and precision, while maintaining competitive training efficiency. These gains demonstrate HiFINS's practical viability for securing home networks in real time. Although the hierarchical design introduces coordination overhead, the tradeoff enables more resilient and scalable deployment across diverse environments. The system's modular and reproducible structure supports future adaptation and integration into broader smart home security efforts.