Dual-Detector Reoptimization for Federated Weakly Supervised Video Anomaly Detection via Adaptive Dynamic Recursive Mapping

Federated weakly supervised video anomaly detection represents a significant advancement in privacy-preserving collaborative learning, enabling distributed clients to train anomaly detectors using only video-level annotations. However, the inherent challenges of optimizing noisy representation with coarse-grained labels often result in substantial local model errors, which are exacerbated during federated aggregation, particularly in heterogeneous scenarios. To address these limitations, we propose a novel dual-detector framework incorporating adaptive dynamic recursive mapping, which significantly enhances local model accuracy and robustness against representation noise. Our framework integrates two complementary components: a channel-averaged anomaly detector and a channel-statistical anomaly detector, which interact through cross-detector adaptive decision parameters to enable iterative optimization and stable anomaly scoring across all instances. Furthermore, we introduce the scene-similarity adaptive local aggregation algorithm, which dynamically aggregates and learns private models based on scene similarity, thereby enhancing generalization capabilities across diverse scenarios. Extensive experiments conducted on the NVIDIA Jetson AGX Xavier platform using the ShanghaiTech and UBnormal datasets demonstrate the superior performance of our approach in both centralized and federated settings. Notably, in federated environments, our method achieves remarkable improvements of 6.2% and 12.3% in AUC compared to state-of-the-art methods, underscoring its effectiveness in resource-constrained scenarios and its potential for real-world applications in distributed video surveillance systems.