Verifying the Quality of Outsourced Training on Clouds

Deep learning training is often outsourced to clouds due to its high computation overhead. However, clouds may not perform model training correctly due to the potential violations on Service Level Agreement (SLA) and attacks, incurring low quality of outsourced training. It is challenging for customers to understand the quality of outsourced training on clouds. They cannot measure the quality by simply testing the trained models because the testing performance is impacted by various factors, e.g., the quality of training and testing data. In order to address these issues, in this paper, we propose a novel framework that allows customers to verify the quality of outsourced training without modifying the processes of model training. Particularly, our framework achieves black-box verification by utilizing an extra training task that can be learned by the model only after the model converges on the original training task. We construct well-designed extra training tasks according to the original tasks, and develop a training quality verification method to measure the model performance on the extra task with a hypothesis testing-based threshold. The experiment results show that the models passing the quality verification achieve at least 96% of their best performance with negligible accuracy loss, i.e., less than 0.25%.