Efficient Privacy Preserving Edge Computing Framework for Image Classification
This addresses privacy and bandwidth issues for edge computing users in image classification, but it is incremental compared to existing methods like federated learning.
The paper tackles the problem of privacy and communication overhead in edge-based image classification by proposing a framework where edge devices train autoencoders locally and transmit latent vectors to a server for classifier training, reducing communication and protecting user data without encryption. Experimental results analyze performance trade-offs with parameters like compression ratio and model complexity.
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.