Fed-DTCN: A Federated Disentangled Learning Framework for Unsupervised Zero-Day Anomaly Detection in IoT with...

The proliferation of Internet of Things (IoT) devices continues to expand the network attack surface while introducing stringent privacy requirements that challenge effective intrusion detection. Federated learning enables collaborative model training without centralizing raw network telemetry. However, existing federated intrusion detection approaches often degrade under statistical heterogeneity and remain vulnerable to zero-day attacks when they rely on labeled data or reconstruction-based objectives. This work proposes Fed-DTCN (Federated Dual Temporal Contrastive Network), an unsupervised federated framework for zero-day anomaly detection in IoT environments. Fed-DTCN learns robust representations of benign IoT traffic using contrastive learning with semantic-preserving augmentations. A dual-encoder architecture disentangles globally shared features from client-specific patterns, improving generalization under heterogeneous federated deployments. Personalization and privacy are preserved by selectively aggregating only the shared encoder parameters. The framework employs a compact temporal convolutional backbone together with a soft-weighted contrastive objective to constrain benign representations, thereby enabling reliable detection of out-of-distribution threats. Extensive experiments on the TON_IoT and CSE-CIC-IDS2018 benchmarks show that Fed-DTCN matches or surpasses a state-of-the-art supervised baseline on standard attacks, achieving an F1-score of 99.99% on TON_IoT. In a zero-day evaluation where the Botnet class is withheld during training, Fed-DTCN attains an F1-score of 96%, compared to 0.52% for the supervised baseline. Ablation studies validate the effectiveness of the proposed augmentations, while evaluations under heterogeneous client partitions demonstrate reduced inter-client variance and consistent per-client improvements, indicating suitability for realistic IoT deployments.