New paradigm of distributed artificial intelligence for LLM implementation and its key technologies

Abstract

With the Internet’s development and information technology advancement, current network applications and services, such as e-commerce, industrial automation, and vehicular automation, have experienced substantial expansion. Foundation models, represented by large language models (LLMs), have emerged in response to growing demands. Their broad range of applications has brought significant advancements to various industries. While such developments have improved people’s economic lives and social activities, the challenges posed by the rapid growth of data volume and network traffic cannot be overlooked. Intelligent systems aimed at enhancing knowledge computation and learning capabilities are gradually gaining attention. Nevertheless, efficient and flexible intelligent systems are still in their early stages, leaving ample space for further optimization. This study provides an overview of Distributed Artificial Intelligence (DAI) with its related paradigm, briefly introduces the evolution of LLMs, and proposes a novel optimization framework named PCD Tri-Tuning for DAI workflows: leveraging caching-related technologies to enhance perceptual capabilities, adopting load-balancing techniques for computational optimization, and developing reasoning methodologies and cooperation techniques to improve decision-making. Subsequently, the study examines the pivotal role of the proposed optimization framework in practical domains such as e-commerce, smart manufacturing, and vehicular automation while also discussing the challenges and outlining strategies for further development.