  In order to extract knowledge from the large data collected by edge devices,
traditional cloud based approach that requires data upload may not be feasible
due to communication bandwidth limitation as well as privacy and security
concerns of end users. To address these challenges, a novel privacy preserving
edge computing framework is proposed in this paper for image classification.
Specifically, autoencoder will be trained unsupervised at each edge device
individually, then the obtained latent vectors will be transmitted to the edge
server for the training of a classifier. This framework would reduce the
communications overhead and protect the data of the end users. Comparing to
federated learning, the training of the classifier in the proposed framework
does not subject to the constraints of the edge devices, and the autoencoder
can be trained independently at each edge device without any server
involvement. Furthermore, the privacy of the end users' data is protected by
transmitting latent vectors without additional cost of encryption. Experimental
results provide insights on the image classification performance vs. various
design parameters such as the data compression ratio of the autoencoder and the
model complexity.
