  We propose a privacy-preserving ensemble infused enhanced Deep Neural Network
(DNN) based learning framework in this paper for Internet-of-Things (IoT),
edge, and cloud convergence in the context of healthcare. In the convergence,
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 dataset 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 edge
server. We conduct several experiments to evaluate the performance of our
proposed framework.
