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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.07583 |
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| _version_ | 1866915458970550272 |
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| author | Le, Cong |
| author_facet | Le, Cong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07583 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Privacy-Preserving Real-Time Vietnamese-English Translation on iOS using Edge AI Le, Cong Software Engineering 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. |
| title | Privacy-Preserving Real-Time Vietnamese-English Translation on iOS using Edge AI |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2505.07583 |