Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration
Journal:
arXiv
Published Date:
Jul 1, 2025
Abstract
Edge computing enables real-time data processing closer to its source, thus
improving the latency and performance of edge-enabled AI applications. However,
traditional AI models often fall short when dealing with complex, dynamic tasks
that require advanced reasoning and multimodal data processing. This survey
explores the integration of multi-LLMs (Large Language Models) to address this
in edge computing, where multiple specialized LLMs collaborate to enhance task
performance and adaptability in resource-constrained environments. We review
the transition from conventional edge AI models to single LLM deployment and,
ultimately, to multi-LLM systems. The survey discusses enabling technologies
such as dynamic orchestration, resource scheduling, and cross-domain knowledge
transfer that are key for multi-LLM implementation. A central focus is on
trusted multi-LLM systems, ensuring robust decision-making in environments
where reliability and privacy are crucial. We also present multimodal multi-LLM
architectures, where multiple LLMs specialize in handling different data
modalities, such as text, images, and audio, by integrating their outputs for
comprehensive analysis. Finally, we highlight future directions, including
improving resource efficiency, trustworthy governance multi-LLM systems, while
addressing privacy, trust, and robustness concerns. This survey provides a
valuable reference for researchers and practitioners aiming to leverage
multi-LLM systems in edge computing applications.
