Federated Contrastive Representation Learning for IoT Anomaly Detection Under Heterogeneous Data

This study proposes a federated contrastive learning based distributed anomaly detection framework to address privacy protection requirements in IoT environments. The framework builds local encoders on each node to embed high-dimensional time series and network behavior features, and uses representation alignment to reduce distribution differences across devices. Based on this, a contrastive learning objective is introduced to strengthen the compactness of normal patterns in the latent space and to enlarge the boundary between normal and abnormal features, which enhances discriminative ability under unsupervised conditions. To avoid sharing raw data, the framework adopts a federated learning strategy that…