"Privacy-preserving Offloading in Edge Intelligence Systems with Induct" by Jude Tchaye-Kondi, Yanlong Zhai et al.
We address privacy and latency issues in edge-cloud computing environments where the neural network training is centralized. This paper considers the scenario where the edge devices are the only data sources for the deep learning model to be trained on the central server. Improper access to the massive amounts of data generated by edge devices could lead to privacy concerns. As a result, existing solutions for preserving privacy and reducing network latency in the edge environment rely on auxiliary datasets with no privacy risks or pre-trained models to build the client side feature extractor....
Source: uow.edu.au
