PPAI: Enabling Personalized LLM Agent Interoperability for Collaborative Edge Intelligence
This work addresses the challenge of enabling effective collaboration among diverse personalized LLM agents on edge devices, which is important for scalable edge intelligence.
PPAI enables personalized LLM agents on edge devices to collaborate via P2P networks, using a prototype-based query-agent scoring and a Bayesian game for load balancing. It achieves up to 7.96% accuracy improvement and 16.34% latency reduction over baselines.
Deploying large language model (LLM) on edge device enables personalized LLM agents for various users. The growing availability of diverse personalized agents presents a unique opportunity for peer-to-peer (P2P) collaboration, wherein each user can delegate tasks beyond the local agent's expertise to remote agents more suited for the specific query. This paper introduces PPAI, the first personalized LLM agent interoperability system, which enables users to collaborate with each other based on agent specialization. However, the ever-changing pool of agents and their interchangeable capacity introduce new challenges when it comes to matching queries to agents and balancing loads, compared with existing P2P systems. Therefore, we propose a scalable query-agent pair scoring mechanism based on prototypes to identify suitable agents within a P2P network with churn. Moreover, we propose a multi-agent interoperability Bayesian game to balance local demand and global efficiency, when changes in remote agent load occur too quickly to be observed. Finally, we implement a prototype of PPAI and demonstrate that it substantially broadens the range of tasks that could be carried out while maintaining load balance. On average, it achieves an accuracy improvement of up to 7.96% across multiple tasks, while reducing latency by 16.34% compared to the baseline.