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