A Trust-Aware Federated Learning Framework Based on Zero Trust Architecture for Secure Cloud Intrusion Detection

Abstract

Cloud computing provides scalable infrastructure but introduces critical challenges to data privacy, trust management, and intrusion detection. To address these issues, we present a trust-aware framework that integrates a Federated Learning (FL)-based distributed ensemble with Zero Trust Architecture (ZTA) for secure cloud environments. The framework enables collaborative learning while keeping data local and private. Zero Trust is implemented by periodically verifying identity and evaluating client contributions with a lightweight local trust filter before aggregation. For reliability and agreement with the local model, an ensemble of Random Forest and Decision Tree classifiers is used. Contributions are only aggregated when satisfying the predefined trust and performance thresholds, thereby reducing the influence of potentially unreliable or malicious inputs under the assumed threat model. The proposed framework is evaluated on the UNSW-NB15 dataset under non-IID client data distribution, achieving 96% accuracy, 95.1% precision, 94%recall, 0.96 AUC-ROC, and an average per-round latency of 180 ms. Comparative results demonstrate that the framework provides a better trade-off between detection accuracy, privacy preservation, and trust enforcement than several state-of-the-art baselines, positioning it as a practical solution for cloud intrusion detection.