Towards Efficient Fine-Tuning of LLMs in Edge-to-Cloud Environments

The rapid evolution of Large Language Models (LLMs) has revolutionized Artificial Intelligence (AI) applications across various domains. In particular, fine-tuning LLMs—essential for adapting models to domain-specific tasks—requires specialized strategies for distributed environments. In this paper, we present a novel framework for LLM fine-tuning in an edge-to-cloud continuum. By leveraging real-time network metrics and edge compute resources, an AI orchestrator dynamically manages workload distribution by selectively eliminating underperforming nodes to optimize fine-tuning efficiency. Experimental results demonstrate that our approach significantly reduces completion time while preserving model convergence, offering an energy-efficient solution for edge-enabled LLM fine-tuning.