Enhancing Privacy-Preserving Intrusion Detection Through Federated Learning in Mmwave Technology Using Decentralized Anomaly Detection Algorithms

Abstract

This paper presents a practical intrusion detection framework to enhance privacy preservation in mmWave networks. We leverage Enhancing Privacy-Preserving Intrusion Detection through Federated Learning in mmWave technology using Decentralized Anomaly Detection Algorithms; federated learning and differential privacy techniques tailored to mmWave characteristics through empirical system design, simulations, and comparative benchmarking. The federated learning architecture is optimized via clustering, asynchronous training, and dynamic optimizations for mmWave networks. Rigorous differential privacy is integrated through calibrated Laplace noise injection. Using recent intrusion detection datasets, we validate the framework via experimental convergence analysis, detection accuracy evaluation, and privacy quantifications under different threat models. Results demonstrate significant gains in privacy protections with minimal accuracy loss compared to centralized learning baselines. Detailed algorithm pseudo-codes, mathematical formulations, and performance plots provide valuable practical insights into developing real-world privacy-aware intrusion detection for emerging mmWave systems.