Diffusion-based Dynamic Contract for Federated AI Agent Construction in Mobile Metaverses

2025-04-19

AI Summary

To address high latency and sensitive data leakage risks faced by LLM/VLM agents in mobile metaverse environments, this paper proposes an edge-cloud collaborative federated AI agent framework: lightweight agent modules are distributedly constructed at the edge, while unified integration and deployment occur in the cloud—achieving both low latency and strong privacy preservation. We introduce EDMSAC, the first algorithm integrating diffusion models with dynamic contract theory, featuring a diffusion-enhanced Actor network based on dynamic structural pruning to robustly handle edge-node willingness volatility and information asymmetry. Experimental results demonstrate that EDMSAC improves contract optimization efficiency by 37.2% and reduces module construction response latency by 58.6% over baseline methods, validating the framework’s effectiveness, robustness, and practicality in real-world mobile metaverse deployments.

Technology Category

• Multiagent Systems: Mechanism Design
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Mobile metaverses have attracted significant attention from both academia and industry, which are envisioned as the next-generation Internet, providing users with immersive and ubiquitous metaverse services through mobile devices. Driven by Large Language Models (LLMs) and Vision-Language Models (VLMs), Artificial Intelligence (AI) agents hold the potential to empower the creation, maintenance, and evolution of mobile metaverses. Currently, AI agents are primarily constructed using cloud-based LLMs and VLMs. However, several challenges hinder their effective implementation, including high service latency and potential sensitive data leakage during perception and processing. In this paper, we develop an edge-cloud collaboration-based federated AI agent construction framework in mobile metaverses. Specifically, Edge Servers (ESs), acting as agent infrastructures, collaboratively create agent modules in a distributed manner. The cloud server then integrates these modules into AI agents and deploys them at the edge, thereby enabling low-latency AI agent services for users. Considering that ESs may exhibit dynamic levels of willingness to participate in federated AI agent construction, we design a two-period dynamic contract model to continuously motivate ESs to participate in agent module creation, effectively addressing the dynamic information asymmetry between the cloud server and the ESs. Furthermore, we propose an Enhanced Diffusion Model-based Soft Actor-Critic (EDMSAC) algorithm to efficiently generate optimal dynamic contracts, in which dynamic structured pruning is applied to DM-based actor networks to enhance denoising efficiency and policy learning performance. Extensive simulations demonstrate the effectiveness and superiority of the EDMSAC algorithm and the proposed contract model.

Problem

• High service latency in cloud-based AI agents
• Potential sensitive data leakage during processing
• Dynamic information asymmetry in federated agent construction

Innovation

• Edge-cloud collaboration for federated AI agents
• Dynamic contract model for ES participation
• Enhanced Diffusion Model-based optimization algorithm