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A lightweight federated MIL-VLM cascade in which only a compact MIL scorer is trained across clients, while a frozen VLM verifies high-scoring suspect segments post hoc is introduced.
A hybrid two-stage architecture that combines a federated binary convolutional neural network (CNN) gate with server-side zero-shot VLM inference and routing rules can be adjusted to trade server-side VLM usage for higher anomaly sensitivity is proposed.
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