PFAE: Personalized Federated Learning for Anomaly Detection Over Heterogeneous IoT Domains

Existing federated learning (FL)-based anomaly detection (AD) methods suffer from performance degradation due to domain heterogeneity, e.g., differences in features, data distributions, and data types across clients from different domains. This challenge is particularly pronounced in IoT systems and is more challenging than the well-known problem of non-i.i.d. data issues. To address this problem, we propose Personalized Federated Heterogeneous Autoencoder (PFAE), an AD framework that introduces a private head for processing domain-specific inputs and a public backbone for shared representation learning. PFAE design enables each client to effectively detect anomalies within its own data domain while leveraging global knowledge through model aggregation. We theoretically prove that the difference in the expected anomaly score of PFAE calculated using the public backbone for benign samples from any pair of clients is bounded. This implies that the distribution of benign samples is shared across domains, so benign samples from a client may not be misidentified as anomalies by other clients during inference. PFAE has lower training complexity than existing autoencoder-based methods. Experiments on eight non-i.i.d. datasets from different domains, where each client is trained on a single dataset, show that PFAE enhances the generalization of anomaly scores for benign samples across domains, especially under high anomaly ratios.