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Federated Binary Gating with Server-Side Vision-Language Inference for Surveillance Anomaly Classification

arXiv · AI, language, vision and robotics · article · Sep 7, 2026 · UTC

Privacy-sensitive surveillance systems could benefit from large vision-language models (VLMs), but such models typically require centralized access to raw video. In federated learning settings, this challenge is amplified by non-independent and identically distributed (non-IID) client data, which can make direct multiclass anomaly classification unstable, especially for rare categories. We propose a hybrid two-stage architecture that combines a federated binary convolutional neural network (CNN) gate with server-side zero-shot VLM inference. The lightweight LiteCNN3D gate performs local anomal

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