MobileFineTuner: A Mobile-Native Framework for On-Device LLM Fine-Tuning in Real-World Embedded AI Applications
arXiv:2512.08211v2 Announce Type: replace Abstract: Large language models (LLMs) are moving from cloud-centric services toward on-device embedded AI, where models interact with private, longitudinal signals sensed from users and their physical environments. Mobile phones are a natural platform for such applications because they are continuously carried by users, connected to wearable sensors, and deeply integrated with daily mobile applications. However, practical LLM fine-tuning on commodity phones remains difficult. Existing fine-tuning frameworks are largely Python-based and server-oriented
The proliferation of LLMs and the increasing demand for data privacy and real-time interaction are driving innovation towards on-device AI solutions.
This development enables LLMs to process sensitive user data locally, enhancing privacy and reducing reliance on cloud infrastructure, which is crucial for new application development.
The ability to fine-tune LLMs directly on mobile devices shifts power and capability from centralized cloud services to distributed edge computing, impacting application architecture and business models.
- · Mobile device manufacturers
- · On-device AI application developers
- · Users concerned with data privacy
- · Edge computing infrastructure providers
- · Cloud-centric LLM service providers
- · Traditional data centers
- · Developers reliant solely on cloud APIs
Increased real-time, personalized AI experiences due to local data processing.
New privacy-centric business models emerge for AI applications that handle sensitive user data.
Reduced network bandwidth requirements and energy consumption for AI inference, potentially impacting global data traffic and energy grids.
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Read at arXiv cs.LG