Federated-Cloud Based Deep Neural Networks with Privacy Preserving Image Filtering Techniques

Isabelle Choi, Qiyang Song, Kun Sun

Abstract: Training Deep Neural Network (DNN) models often require significant computational resources due to the large dataset sizes and a huge number of parameters to be optimized. A cloud-based approach may be utilized to accommodate such resource needs with flexibility and efficiency. But, protecting data privacy is a challenge in such approaches. Most of the encryption-based approaches for providing privacy incurs substantial overheads. However, in many instances, only part of data information needs to be protected, and the level of privacy is often dependent on the application requirements. Various types of image filtering techniques are utilized to generate distorted DNN datasets with application-specific privacy requirements satisfied. In general, high distortion level provides strong protection on data privacy but degrades the DNN accuracy. To find the appropriate type and level of image filtering prior to the training process, we identify an image similarity metric that can be used as a DNN accuracy predictor as well as the distortion level indicator. Furthermore, to improve the DNN accuracy of highly distorted datasets, we propose a privacy-preserving federated-cloud DNN training/classification on multiple distorted datasets. Each cloud trains an independent DNN model with a different image filtering algorithm, and then the client combines and utilizes the multiple models to obtain a well-performing model. Experiments were conducted to validate the effectiveness of the proposed schemes.