{"slug": "on-device-language-models-for-privacy-preserving-stress-prediction-a-multimodal", "title": "On-Device Language Models for Privacy-Preserving Stress Prediction: A Multimodal Evaluation on Mobile Health", "summary": "A paper submitted to arXiv on 11 August 2026 evaluated on-device language models (ODLMs) for multimodal stress prediction on mobile health data, finding that objective sensor features marginally outperform subjective self-reports on average. The study used zero-shot prompting and measured predictive accuracy alongside latency and throughput, reporting that lightweight sub-2B models achieve low latency with predictable resource usage. The authors conclude that ODLMs show promise for privacy-preserving mobile mental health inference without cloud dependency, but face practical resource constraints.", "body_md": "# Computer Science > Machine Learning\n\n  [Submitted on 11 Aug 2026]\n\n# Title:On-Device Language Models for Privacy-Preserving Stress Prediction: A Multimodal Evaluation on Mobile Health\n\n[View PDF](/pdf/2609.11961)\n\n[HTML (experimental)](https://arxiv.org/html/2609.11961v1)\n\nAbstract:Stress is a pervasive determinant of mental health and a key target for mobile health interventions. On-device language models (ODLMs) offer privacy-preserving inference without cloud dependency, yet their feasibility for health prediction under mobile resource constraints remains underexplored. We evaluate ODLMs for multi-modal stress prediction using zero-shot prompting, measuring predictive accuracy alongside latency and throughput. Our results show that objective sensor features marginally outperform subjective self-reports on average, and that lightweight sub-2B models achieve low latency with predictable resource usage. Our findings highlight both the promise and the practical constraints of ODLMs for mobile mental health.\n    \n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/on-device-language-models-for-privacy-preserving-stress-prediction-a-multimodal", "canonical_source": "https://arxiv.org/abs/2609.11961", "published_at": "2026-09-14 04:00:00+00:00", "updated_at": "2026-09-14 04:26:44.992652+00:00", "lang": "en", "topics": ["large-language-models", "machine-learning", "ai-research", "ai-safety"], "entities": ["arXiv", "on-device language models", "ODLMs"], "alternates": {"html": "https://wpnews.pro/news/on-device-language-models-for-privacy-preserving-stress-prediction-a-multimodal", "markdown": "https://wpnews.pro/news/on-device-language-models-for-privacy-preserving-stress-prediction-a-multimodal.md", "text": "https://wpnews.pro/news/on-device-language-models-for-privacy-preserving-stress-prediction-a-multimodal.txt", "jsonld": "https://wpnews.pro/news/on-device-language-models-for-privacy-preserving-stress-prediction-a-multimodal.jsonld"}}