FEDERATED LEARNING-DRIVEN THREAT DETECTION: A PRIVACY-PRESERVING FRAMEWORK FOR ZERO-DAY ANOMALY DETECTION IN EDGE-COMPUTING NETWORKS
Indonesia Indonesia Indonesia 
Abstract
Edge-computing networks increasingly support critical digital services, yet their distributed architecture exposes heterogeneous devices to evolving cyber threats, including zero-day attacks that evade signature-based detection. This study aimed to develop and evaluate a privacy-preserving federated learning framework for detecting previously unseen anomalies without centralizing sensitive network data. A quantitative experimental design was employed using benchmark cybersecurity datasets and a simulated edge environment comprising twenty clients. The proposed framework integrated supervised classification, autoencoder-based anomaly detection, differential privacy, secure aggregation, communication compression, and malicious-update validation. Its performance was compared with centralized learning and conventional federated averaging across heterogeneous, communication-constrained, and adversarial scenarios. Results showed that the framework achieved 96.30% accuracy, a 96.00% F1-score, and a 91.70% zero-day detection rate, outperforming both comparison models. Membership-inference success decreased to 51.60%, transmitted data volume fell to 1.62 GB, and model performance declined by only 2.90 percentage points when 20% of clients conducted poisoning attacks. Inferential analysis confirmed significant differences with large effect sizes. These outcomes demonstrate practical utility under dynamic traffic conditions, limited bandwidth, and compromised participation. The study concludes that combining privacy protection, anomaly-based learning, and robust aggregation provides an effective, scalable, and resilient approach for securing edge-computing networks against unknown and adversarial threats.
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