PMC

Abstract

Deep learning models have been deployed to a wide range of edge devices. Since the data distribution on edge devices may differ from the cloud where the model was trained, it is typically desirable to customize the model for each edge device to improve accuracy. However, such customization is hard because collecting data from edge devices is usually prohibited due to privacy concerns. In this paper, we propose PMC, a privacy-preserving model customization framework to effectively customize a CNN model from the cloud to edge devices without collecting raw data. Instead, we introduce a method to extract statistical information from the edge, which contains adequate domain-related knowledge for model customization. PMC uses Gaussian distribution parameters to describe the edge data distribution, reweights the cloud data based on the parameters, and uses the reweighted data to train a specialized model for the edge device. During this process, differential privacy can be enforced by adding computed noises to the Gaussian parameters. Experiments on public datasets show that PMC can improve model accuracy by a large margin through customization. Finally, a study on user-generated data demonstrates the effectiveness of PMC in real-world settings.