Optimized Click Prediction on Mobile Devices via Device-Cloud Synergy

The rapid growth of deep learning-based services and applications underscores the need for efficient neural network model deployment. Traditional cloud-centric solutions, despite their computational power, face significant challenges such as high energy consumption, network transmission delays, and user privacy concerns. Conversely, performing high-performance inference on resource-constrained mobile devices, especially for tasks like advertising click prediction, presents its own set of difficulties. To address these challenges, we propose a device-cloud collaboration system utilizing a difficult-case discriminator. This system classifies input samples based on semantic information into difficult and simple cases. Difficult cases are processed in the cloud using a large model, while simple cases are handled on the device by a smaller model. This approach maximizes system resources and protects user privacy. Evaluations on public datasets show that our system significantly outperforms other advertisement methods in click prediction accuracy and uploading efficiency. Compared to the device-only approach, our system improves the area under the curve (AUC) by 8.9%, and compared to the cloud-centric approach, it reduces the upload ratio by 34%. Moreover, deploying our system on a specific smartphone demonstrates substantial improvements in private real datasets.