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This paper presents a federated learning approach for indoor fire detection that addresses bandwidth constraints, Byzantine attacks, and fixed-server issues through compressed updates and a rotating coordinator.
This paper proposes a knowledge distillation framework to compress vision-language models for on-device fire detection, showing that compact models can retain most of their teacher's capability while being deployable on embedded hardware.