Privacy-Preserving Ensemble Infused Enhanced Deep Neural Network Framework for Edge Cloud Convergence
Veronika Stephanie, Ibrahim Khalil, Mohammad Saidur Rahman, Mohammed Atiquzzaman
Abstract
We propose a privacy-preserving ensemble infused enhanced deep neural network (DNN)-based learning framework in this article for Internet of Things (IoT), edge, and cloud convergence in the context of healthcare. In the convergence, the edge server is used for both storing IoT produced bioimage and hosting DNN algorithm for local model training. The cloud is used for ensembling local models. The DNN-based training process of a model with a local data set suffers from low accuracy, which can be improved by the aforementioned convergence and ensemble learning. The ensemble learning allows multiple participants to outsource their local model for producing a generalized final model with high accuracy. Nevertheless, ensemble learning elevates the risk of leaking sensitive private data from the final model. The proposed framework presents a differential privacy-based privacy-preserving DNN with transfer learning for a local model generation to ensure minimal loss and higher efficiency at the edge server. We conduct several experiments to evaluate the performance of our proposed framework.