Privacy-Preserving Real-Time Vietnamese-English Translation on iOS using Edge AI

TL;DR

This paper presents a fully offline, privacy-preserving Vietnamese-English translation system on iOS devices, utilizing optimized lightweight neural models to enable real-time translation without network dependency.

Contribution

It introduces a novel on-device NMT system for Vietnamese-English translation using a quantized Transformer model optimized for resource-limited mobile devices.

Findings

Achieved real-time translation performance on iOS devices.

Demonstrated effective privacy preservation by avoiding cloud-based processing.

Successfully integrated modern NLP models into a seamless mobile application.

Abstract

This research addresses the growing need for privacy-preserving and accessible language translation by developing a fully offline Neural Machine Translation (NMT) system for Vietnamese-English translation on iOS devices. Given increasing concerns about data privacy and unreliable network connectivity, on-device translation offers critical advantages. This project confronts challenges in deploying complex NMT models on resource-limited mobile devices, prioritizing efficiency, accuracy, and a seamless user experience. Leveraging advances such as MobileBERT and, specifically, the lightweight \textbf{TinyLlama 1.1B Chat v1.0} in GGUF format, \textbf{a} quantized Transformer-based model is implemented and optimized. The application is realized as a real-time iOS prototype, tightly integrating modern iOS frameworks and privacy-by-design principles. Comprehensive documentation covers model selection, technical architecture, challenges, and final implementation, including functional Swift code for deployment.