Resource-Efficient Personal Large Language Models Fine-Tuning With Collaborative Edge Computing

Large language models (LLMs) have unlocked a plethora of powerful applications at the network edge, such as intelligent personal assistants. Data privacy and security concerns have prompted a shift towards edge-based fine-tuning of personal LLMs, away from cloud reliance. However, this raises issues of computational intensity and resource scarcity, hindering training efficiency and feasibility. While current studies investigate parameter-efficient fine-tuning (PEFT) techniques to mitigate resource constraints, our analysis indicates that these techniques are not sufficiently resource-efficient for edge devices. Other studies focus on exploiting the potential of edge devices through resource management optimization, yet are ultimately bottlenecked by the resource wall of individual devices. To tackle these challenges, we proposePAC+, a resource efficient collaborative edge AI framework for in-situ personal LLMs fine-tuning.PAC+breaks the resource wall of personal LLMs fine-tuning with a sophisticated algorithm-system co-design. (1) Algorithmically,PAC+implements a personal LLMs fine-tuning technique that is efficient in terms of parameters, time, and memory. It utilizes Parallel Adapters to circumvent the need for a full backward pass through the LLM backbone. Additionally, an activation cache mechanism further streamlining the process by negating the necessity for repeated forward passes across multiple epochs. (2) Systematically,PAC+leverages edge devices in close proximity, pooling them as a collective resource for in-situ personal LLMs fine-tuning, utilizing a hybrid data and pipeline parallelism to orchestrate distributed training. The use of the activation cache eliminates the need for forward pass through the LLM backbone, enabling exclusive fine-tuning of the Parallel Adapters using data parallelism. Extensive evaluation of the prototype implementation demonstrates thatPAC+significantly outperforms existing collaborative edge training systems, achieving up to a$9.7\times$end-to-end speedup. Furthermore, compared to mainstream LLM fine-tuning algorithms,PAC+reduces memory footprint by up to$88.16\%$.