MobileFineTuner: A Unified End-to-End Framework for Fine-Tuning LLMs on Mobile Phones

• URL: http://arxiv.org/abs/2512.08211v1
• Date: Tue, 09 Dec 2025 03:41:01 GMT
• Title: MobileFineTuner: A Unified End-to-End Framework for Fine-Tuning LLMs on Mobile Phones
• Authors: Jiaxiang Geng, Lunyu Zhao, Yiyi Lu, Bing Luo,
• Abstract summary: MobileFineTuner is a unified open-source framework that enables end-to-end LLM fine-tuning on commodity mobile phones.<n>To address the memory and energy limitations inherent to mobile phones, we introduce system-level optimizations.<n>We demonstrate the practicality of MobileFineTuner by fine-tuning GPT-2, Gemma 3, and Qwen 2.5 on real mobile phones.
• Score: 4.325104899424185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mobile phones are the most ubiquitous end devices, generating vast amounts of human-authored data and serving as the primary platform for end-side applications. As high-quality public data for large language models (LLMs) approaches exhaustion, on-device fine-tuning provides an opportunity to leverage private user data while preserving privacy. However, existing approaches are predominantly simulation-based or rely on IoT devices and PCs, leaving commodity mobile phones largely unexplored. A key gap is the absence of an open-source framework that enables practical LLM fine-tuning on mobile phones. We present MobileFineTuner, a unified open-source framework that enables end-to-end LLM fine-tuning directly on commodity mobile phones. MobileFineTuner is designed for efficiency, scalability, and usability, supporting full-parameters fine-tuning (Full-FT) and parameter-efficient fine-tuning (PEFT). To address the memory and energy limitations inherent to mobile phones, we introduce system-level optimizations including parameter sharding, gradient accumulation, and energy-aware computation scheduling. We demonstrate the practicality of MobileFineTuner by fine-tuning GPT-2, Gemma 3, and Qwen 2.5 on real mobile phones. Extensive experiments and ablation studies validate the effectiveness of the proposed optimizations and establish MobileFineTuner as a viable foundation for future research on on-device LLM training.

Related papers

• Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation [56.36237936346563]
  Foundation models (FMs) exhibit remarkable generalization but require adaptation to downstream tasks.<n>Due to data privacy regulations, cloud-based FMs cannot directly access private edge data.<n>We introduce Practical Semi-Supervised Federated Learning (PSSFL), where edge devices hold only unlabeled, low-resolution data.<n>Our work paves the way for scalable and privacy-preserving FM adaptation in federated scenarios.
  arXiv  Detail & Related papers  (2025-08-22T17:47:02Z)
• Fed MobiLLM: Efficient Federated LLM Fine-Tuning over Heterogeneous Mobile Devices via Server Assisted Side-Tuning [16.47223778897796]
  Large language models (LLMs) over heterogeneous mobile devices foster immense potential applications of personalized intelligence.<n> Conventional federated LLM FT approaches place prohibitive computational and memory burdens on mobile hardware.<n>We propose Fed MobiLLM, a novel design to facilitate efficient federated LLM FT across mobile devices with diverse computing/communication speeds and local model architectures.
  arXiv  Detail & Related papers  (2025-08-09T00:41:48Z)
• Dissecting the Impact of Mobile DVFS Governors on LLM Inference Performance and Energy Efficiency [20.904706759529237]
  Large Language Models (LLMs) are increasingly being integrated into various applications and services running on billions of mobile devices.<n>Currently, deploying LLMs on resource-limited mobile devices faces a significant challenge due to their high demand for computation, memory, and ultimately energy.
  arXiv  Detail & Related papers  (2025-07-02T20:47:40Z)
• Mobile-MMLU: A Mobile Intelligence Language Understanding Benchmark [45.28023118459497]
  We introduce Mobile-MMLU, a large-scale benchmark dataset tailored for mobile intelligence.<n>It consists of 16,186 questions across 80 mobile-related fields, designed to evaluate LLM performance in realistic mobile scenarios.<n>A challenging subset, Mobile-MMLU-Pro, provides advanced evaluation similar in size to MMLU-Pro but significantly more difficult than our standard full set.
  arXiv  Detail & Related papers  (2025-03-26T17:59:56Z)
• Are We There Yet? A Measurement Study of Efficiency for LLM Applications on Mobile Devices [5.926813659185372]
  Small-size large language models (LLMs) can run successfully on powerful mobile devices, though they exhibit quality limitations compared to larger models.<n>Only small-size LLMs can run successfully on powerful mobile devices, though they exhibit quality limitations compared to larger models.
  arXiv  Detail & Related papers  (2025-03-10T16:27:17Z)
• Smooth Handovers via Smoothed Online Learning [48.953313950521746]
  We first analyze an extensive dataset from a commercial mobile network operator (MNO) in Europe with more than 40M users, to understand and reveal important features and performance impacts on HOs.<n>Our findings highlight a correlation between HO failures/delays, and the characteristics of radio cells and end-user devices.<n>We propose a realistic system model for smooth and accurate HOs that extends existing approaches by incorporating device and cell features on HO optimization.
  arXiv  Detail & Related papers  (2025-01-14T13:16:33Z)
• SlimLM: An Efficient Small Language Model for On-Device Document Assistance [60.971107009492606]
  We present SlimLM, a series of SLMs optimized for document assistance tasks on mobile devices.<n>SlimLM is pre-trained on SlimPajama-627B and fine-tuned on DocAssist.<n>We evaluate SlimLM against existing SLMs, showing comparable or superior performance.
  arXiv  Detail & Related papers  (2024-11-15T04:44:34Z)
• Understanding Large Language Models in Your Pockets: Performance Study on COTS Mobile Devices [13.721403954332267]
  Large language models (LLMs) increasingly integrate into every aspect of our work and daily lives.<n>There are growing concerns about user privacy, which push the trend toward local deployment of these models.<n>As a rapidly emerging application, we are concerned about their performance on commercial-off-the-shelf mobile devices.
  arXiv  Detail & Related papers  (2024-10-04T17:14:59Z)
• PocketLLM: Enabling On-Device Fine-Tuning for Personalized LLMs [5.063806958859058]
  On mobile devices, the wealth of valuable, non-public data generated daily holds great promise for locally fine-tuning personalized LLMs. We propose employing derivative-free optimization techniques to enable on-device fine-tuning of LLM, even on memory-limited mobile devices. Empirical results demonstrate that the RoBERTa-large model and OPT-1.3B can be fine-tuned locally on the OPPO Reno 6 smartphone using around 4GB and 6.5GB of memory respectively.
  arXiv  Detail & Related papers  (2024-07-01T07:26:56Z)
• MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases [81.70591346986582]
  We introduce MobileAIBench, a benchmarking framework for evaluating Large Language Models (LLMs) and Large Multimodal Models (LMMs) on mobile devices. MobileAIBench assesses models across different sizes, quantization levels, and tasks, measuring latency and resource consumption on real devices.
  arXiv  Detail & Related papers  (2024-06-12T22:58:12Z)
• To Talk or to Work: Flexible Communication Compression for Energy Efficient Federated Learning over Heterogeneous Mobile Edge Devices [78.38046945665538]
  federated learning (FL) over massive mobile edge devices opens new horizons for numerous intelligent mobile applications. FL imposes huge communication and computation burdens on participating devices due to periodical global synchronization and continuous local training. We develop a convergence-guaranteed FL algorithm enabling flexible communication compression.
  arXiv  Detail & Related papers  (2020-12-22T02:54:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.