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MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services

Hongjia Wu, Hui Zeng, Zehui Xiong, Jiawen Kang, Zhiping Cai, Tse-Tin Chan, Dusit Niyato, Zhu Han

TL;DR

This work tackles data freshness, privacy, and resource-burden challenges in vehicular metaverse AR by proposing an immersion-aware model trading framework that leverages federated learning. It introduces the IoM metric to jointly quantify model freshness, accuracy, data quantity, and data potential, and models MSP–MU interactions as a two-level EPEC, with a distributed MDDR DRL mechanism to adapt rewards under dynamic conditions. Theoretical results prove existence and uniqueness of equilibria, and simulations on MNIST and GTSRB show IoM improvements of up to 38.3% and 37.2% and substantial reductions in training time, demonstrating effective incentivization of MUs and adaptive, privacy-preserving data provisioning in multi-MSP/multi-MU vehicular metaverse services.

Abstract

Timely updating of Internet of Things data is crucial for achieving immersion in vehicular metaverse services. However, challenges such as latency caused by massive data transmissions, privacy risks associated with user data, and computational burdens on metaverse service providers (MSPs) hinder the continuous collection of high-quality data. To address these challenges, we propose an immersion-aware model trading framework that enables efficient and privacy-preserving data provisioning through federated learning (FL). Specifically, we first develop a novel multi-dimensional evaluation metric for the immersion of models (IoM). The metric considers the freshness and accuracy of the local model, and the amount and potential value of raw training data. Building on the IoM, we design an incentive mechanism to encourage metaverse users (MUs) to participate in FL by providing local updates to MSPs under resource constraints. The trading interactions between MSPs and MUs are modeled as an equilibrium problem with equilibrium constraints (EPEC) to analyze and balance their costs and gains, where MSPs as leaders determine rewards, while MUs as followers optimize resource allocation. To ensure privacy and adapt to dynamic network conditions, we develop a distributed dynamic reward algorithm based on deep reinforcement learning, without acquiring any private information from MUs and other MSPs. Experimental results show that the proposed framework outperforms state-of-the-art benchmarks, achieving improvements in IoM of 38.3% and 37.2%, and reductions in training time to reach the target accuracy of 43.5% and 49.8%, on average, for the MNIST and GTSRB datasets, respectively. These findings validate the effectiveness of our approach in incentivizing MUs to contribute high-value local models to MSPs, providing a flexible and adaptive scheme for data provisioning in vehicular metaverse services.

MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services

TL;DR

This work tackles data freshness, privacy, and resource-burden challenges in vehicular metaverse AR by proposing an immersion-aware model trading framework that leverages federated learning. It introduces the IoM metric to jointly quantify model freshness, accuracy, data quantity, and data potential, and models MSP–MU interactions as a two-level EPEC, with a distributed MDDR DRL mechanism to adapt rewards under dynamic conditions. Theoretical results prove existence and uniqueness of equilibria, and simulations on MNIST and GTSRB show IoM improvements of up to 38.3% and 37.2% and substantial reductions in training time, demonstrating effective incentivization of MUs and adaptive, privacy-preserving data provisioning in multi-MSP/multi-MU vehicular metaverse services.

Abstract

Timely updating of Internet of Things data is crucial for achieving immersion in vehicular metaverse services. However, challenges such as latency caused by massive data transmissions, privacy risks associated with user data, and computational burdens on metaverse service providers (MSPs) hinder the continuous collection of high-quality data. To address these challenges, we propose an immersion-aware model trading framework that enables efficient and privacy-preserving data provisioning through federated learning (FL). Specifically, we first develop a novel multi-dimensional evaluation metric for the immersion of models (IoM). The metric considers the freshness and accuracy of the local model, and the amount and potential value of raw training data. Building on the IoM, we design an incentive mechanism to encourage metaverse users (MUs) to participate in FL by providing local updates to MSPs under resource constraints. The trading interactions between MSPs and MUs are modeled as an equilibrium problem with equilibrium constraints (EPEC) to analyze and balance their costs and gains, where MSPs as leaders determine rewards, while MUs as followers optimize resource allocation. To ensure privacy and adapt to dynamic network conditions, we develop a distributed dynamic reward algorithm based on deep reinforcement learning, without acquiring any private information from MUs and other MSPs. Experimental results show that the proposed framework outperforms state-of-the-art benchmarks, achieving improvements in IoM of 38.3% and 37.2%, and reductions in training time to reach the target accuracy of 43.5% and 49.8%, on average, for the MNIST and GTSRB datasets, respectively. These findings validate the effectiveness of our approach in incentivizing MUs to contribute high-value local models to MSPs, providing a flexible and adaptive scheme for data provisioning in vehicular metaverse services.

Paper Structure

This paper contains 29 sections, 2 theorems, 43 equations, 10 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Edge-Enabled Vehicular Metaverse Services
  4. Federated Learning for AR in the Metaverse
  5. Incentive Mechanism for Data Synchronization in the Metaverse.
  6. System Model
  7. System Overview
  8. FL Framework Mechanism Adopted
  9. Immersion Metric of Local Model
  10. Contribution Prediction $I$
  11. Age of Information $\Delta$
  12. Game Formulation
  13. Utility of MU
  14. Utility of MSP
  15. Multi-MSP Multi-MU Game as EPEC
  16. ...and 14 more sections

Key Result

Theorem 1

Problem 1 is strictly concave and has a unique globally optimal solution in the lower level. That is, for each MU $m$, there exists a unique resource allocation tuple $(\bm{f}_{m}^{*}, \bm{B}_{m}^{*})$ that maximizes its utility.

Figures (10)

  • Figure 1: An example of AR services in the vehicular metaverse.
  • Figure 2: The outline of an immersion-aware framework for FL-assisted vehicular metaverse.
  • Figure 3: Workflow of the immersion-aware model trading framework.
  • Figure 4: Illustration of the FL mechanism with $n$ tasks for MU $m$.
  • Figure 5: The AoI evolution of the local updates sent from MU $m$ to MSP $n$.
  • ...and 5 more figures

Theorems & Definitions (8)

  • Definition 1
  • Definition 2
  • Theorem 1
  • proof
  • Theorem 2
  • proof
  • proof
  • proof