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Hierarchical Training of Deep Neural Networks Using Early Exiting
In this study, a novel hierarchical training method for deep neural networks is proposed that uses early exits in a divided architecture between edge and cloud workers to reduce the communication cost, training runtime and privacy concerns.
Privacy-Preserving Image Acquisition Using Trainable Optical Kernel
Also, in contrast with the previous optical privacy-preserving methods that cannot be trained, our method is data-driven and optimized for the specific application at hand.
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