Adaptive Privacy-Preserving AI Framework For Predictive Mobile Application Performance And User Experience Optimization

Abstract

Mobile applications are increasingly reliant on Artificial Intelligence (AI). Predictive AI enables personalized predictions, anticipates resource usage, and prevents performance degradation, but the data used to train these systems may be sensitive, device-specific, and subject to increasing regulatory scrutiny. This study presents the Adaptive Privacy-Preserving AI (APP-AI) framework, which integrates on-device AI inference, federated model training, local differential privacy (LDP), and a user experience (UX) controller into a single package to optimize predictive performance, resource utilization, and privacy guarantees on Android devices. The framework was deployed using TensorFlow Lite for on-device inference and tested on a heterogeneous testbed of low-, mid-, and high-tier Android devices, where telemetry collected battery and memory usage, inference latency, crash occurrences, and user interactions. Compared with two baselines: the cloud-only baseline and the classic on-device machine learning pipeline, the proposed framework achieved a 65 percent reduction in battery drain, a 78 percent decrease in crash rate, a 91.5 percent validation accuracy at a moderate privacy budget (ε = 8), and an 81 percent reduction in median inference latency. A systematic analysis of privacy-utility was used to quantify the competing impacts of a differentially private budget on model accuracy and the estimated risk of re-identification. A user experience study using six composite metrics showed significant improvements in perceived responsiveness, system usability, and privacy trust compared with a traditional cloud-based application. The results indicate that on-device intelligence, both adaptive and privacy-conscious, could improve technical performance and user-perceived quality of service, and provide a template for mobile software engineering in a privacy-driven context.