FedGPA: Federated Learning with Global-Personalized Collaboration for Edge Anomaly Detection

Edge anomaly detection guarantees the security of Internet-of-Things (IoT). The emerging Federated Learning (FL) can ameliorate the privacy-leakage and data-island issues in edge anomaly detection. However, existing FL-based solutions still reveal limitations in handling statistical and system heterogeneity, thus they cannot adapt to anomaly detection in complex edge environments. To address these problems, we propose FedGPA, a novel Federated learning with Global-Personalized collaboration for edge Anomaly detection. First, we design a conditional calculation component to transform traffic features into global and personalized feature vectors. Next, we introduce contrast and magnitude losses in the global-class embedding module and guide the learning of global feature vectors with the embedding of sample classes. Then, we adopt cross-entropy loss to guide the learning of personalized feature vectors. Finally, the cosine similarity between the updated gradients of cross-entropy and overall losses is used to determine the loss replacement, thereby accelerating the model training. Notably, we prove the FedGPA can converge stably during the aggregation process. Using real-world testbed and traffic datasets, extensive experiments verify the effectiveness of the FedGPA, which efficiently solves statistical and system heterogeneity. Compared to state-of-the-art methods, the FedGPA achieves higher detection accuracy and shorter training time, exhibiting better scalability and convergence.