Efficient Privacy Preserving Edge Intelligent Computing Framework for Image Classification in IoT

Abstract

To extract knowledge from the large data collected by edge devices, a traditional cloud-based approach that requires data upload may not be feasible due to communication bandwidth limitations as well as privacy and security concerns of end-users. A novel privacy-preserving edge intelligent computing framework for image classification in IoT is proposed to address these challenges. Specifically, the autoencoder will be trained unsupervised at each edge device individually, and then the obtained latent vectors transmitted to the edge server for the training of a classifier. This framework would reduce the communication overhead and protect end-users’ data. Compared to federated learning, the training of the classifier in the proposed framework is not subject to the constraints of the edge devices, and the autoencoder can be trained independently at each edge device without any server involvement. Compared to collaborative intelligence such as SplitNN, the proposed method does not suffer from high communication cost as noticed in SplitNN. Furthermore, the privacy of the end-users’ data is protected by transmitting latent vectors and without the 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.