Cross-Silo Federated Domain Adaptation for Trustworthy and Privacy-Preserving IoT Security in 5G-to-6G Wireless Systems
Year: 2026
Type: conference-paper
Abstract: The convergence of large-scale Internet of Things (IoT) deployments with emerging 5G-to-6G wireless systems introduces unprecedented security challenges due to extreme network heterogeneity, evolving traffic dynamics, and stringent privacy requirements. Deep learning (DL)-based intrusion detection systems (IDS) often suffer from severe performance degradation when deployed across heterogeneous domains, while centralized training is increasingly impractical in 6G multi-stakeholder ecosystems due to data privacy and trust constraints. This paper proposes a unified, privacy-preserving, and trustworthy security framework that integrates cross-silo federated learning (FL), progressive domain adaptation (DA), and knowledge distillation (KD) to enable generalizable intrusion detection for next-generation wireless systems. The framework first performs federated pre-training across distributed IoT silos to construct a global backbone model without sharing raw data, supporting privacy-by-design principles essential for 6G architectures. A progressive two-stage DA strategy based on correlation alignment is then employed to mitigate distribu-tional discrepancies between IoT and 5G-to-6G traffic domains. Finally, KD compresses the adapted teacher model into a lightweight student network suitable for real-time deployment on resource-constrained edge devices. Extensive evaluations on the IDSIoT2024 and 5G-NIDD datasets demonstrate that the proposed approach achieves over 90% accuracy on the source domain while maintaining 78-80% accuracy and F1-scores above 0.82 on an unseen target domain, with a compact model size of 0.19 M B and sub-millisecond inference latency. These results highlight the framework's effectiveness in delivering trustworthy, privacy-aware, and adaptive security intelligence, positioning it as a practical enabler for AI-enabled 6G wireless systems.
Institutions: Nile University, University of the West of England
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7212407151