Search Results for author:
Found 4 papers, 0 papers with code
Private Model Compression via Knowledge Distillation
To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA.
Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud
To benefit from the cloud data center without the privacy risk, we design, evaluate, and implement a cloud-based framework ARDEN which partitions the DNN across mobile devices and cloud data centers.
Deep Learning Towards Mobile Applications
In this paper, we provide an overview of the current challenges and representative achievements about pushing deep learning on mobile devices from three aspects: training with mobile data, efficient inference on mobile devices, and applications of mobile deep learning.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
