MobileFineTuner: A Unified End-to-End Framework for Fine-Tuning LLMs on Mobile Phones
This addresses the need for privacy-preserving, on-device LLM fine-tuning for mobile users, though it is incremental as it builds on existing fine-tuning methods with system-level optimizations.
The authors tackled the problem of fine-tuning large language models (LLMs) on mobile phones by developing MobileFineTuner, an open-source framework that enables end-to-end fine-tuning on commodity devices, demonstrating practicality with models like GPT-2, Gemma 3, and Qwen 2.5.
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.