arXiv:2602.13069v2 Announce Type: replace Abstract: On-device fine-tuning enables privacy-preserving personalization of large language models, but mobile devices impose severe memory constraints, typically 6--12GB shared across all workloads. Existing approaches force a trade-off between exact gradients with high memory (MeBP) and low memory with noisy estimates (MeZO). We propose Memory-efficient Structured Backpropagation (MeSP), which bridges this gap by manually deriving backward passes that exploit LoRA's low-rank structure. Our key insight is that the intermediate projection $h = xA$ can
The proliferation of Large Language Models (LLMs) and the demand for personalized, private AI experiences necessitate efficient on-device fine-tuning solutions, given the current memory constraints of mobile hardware.
This development addresses a critical bottleneck for widespread, private LLM adoption, enabling more powerful AI capabilities directly on user devices without reliance on cloud infrastructure for fine-tuning.
The ability to perform memory-efficient, precise fine-tuning on consumer devices shifts the paradigm from cloud-centric to edge-centric personalization for LLMs, enhancing privacy and reducing latency.
- · Mobile device manufacturers
- · On-device AI developers
- · Users prioritizing data privacy
- · Low-power edge AI chip designers
- · Cloud AI service providers specializing in fine-tuning
- · Developers solely reliant on server-side LLM personalization
Increased adoption and utility of AI features on smartphones and other edge devices due to enhanced personalization and privacy.
Reduced data transfer requirements and computational load on central servers, leading to lower operating costs for AI service providers and potentially less data collected.
New application categories emerge that leverage highly personalized, private, and always-on AI assistants directly integrated into daily routines without internet dependency for learning.
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Read at arXiv cs.LG