A dual-threshold, multi-exit local/server co-inference framework with a channel-adaptive offloading policy is proposed and tested on retinal images for rare-event classification.
Bottlenet++: An end-to-end approach for feature compression in device-edge co-inference systems,
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Communication Efficient Cooperative Edge AI via Event-Triggered Computation Offloading
A dual-threshold, multi-exit local/server co-inference framework with a channel-adaptive offloading policy is proposed and tested on retinal images for rare-event classification.