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

Large language models (LLMs) have enabled transformative applications at the network edge, such as intelligent personal assistants. However, data privacy and security concerns necessitate a shift from cloud-centric paradigms to edge-based fine-tuning for personal LLMs. This transition is significantly hindered by intensive computational requirements and inherent resource scarcity, creating a “resource wall” that compromises training efficiency and feasibility. While current parameter-efficient fine-tuning (PEFT) and resource management strategies attempt to mitigate these constraints, they remain insufficient for the limited capacities of individual edge devices. To address these challenges, we propose PAC+, a resourceefficient collaborative edge AI framework for in-situ personal LLM fine-tuning. PAC+ overcomes the resource bottlenecks through a sophisticated algorithm-system codesign: (1) Algorithmically, PAC+ introduces a fine-tuning technique optimized for parameters, time, and memory. It utilizes Parallel Adapters to circumvent the need for a full backward pass through the LLM backbone. Furthermore, an activation cache mechanism streamlines the process by negating redundant forward passes across multiple epochs. (2) Systematically, PAC+ aggregates proximate edge devices into a collective resource pool, employing hybrid data and pipeline parallelism to orchestrate distributed training. By leveraging the activation cache, PAC+ enables the exclusive fine-tuning of Parallel Adapters via data parallelism, effectively bypassing the backbone's constraints. Extensive evaluation of the prototype implementation demonstrates that PAC+ significantly outperforms existing collaborative edge training systems, achieving up to a 9.7× end-to-end speedup. Furthermore, compared to mainstream LLM fine-tuning algorithms, PAC+ reduces memory footprint by up to 88.16%.