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

2024-10-25

arXiv.org

AI Summary

To address high latency, user privacy leakage, and service-provider computational overload in vehicular metaverses, this paper proposes the first privacy-preserving, low-latency model trading framework tailored for车载 scenarios. Methodologically, it (1) introduces a multidimensional immersion metric (IoM) to quantify model value; (2) formulates a bilevel incentive mechanism grounded in equilibrium-programming-with-equilibrium-constraints (EPEC); and (3) designs a distributed deep reinforcement learning algorithm for dynamic reward allocation—operating without access to private user data. Evaluated on MNIST and GTSRB, the framework improves IoM by 38.3% and 37.2%, respectively, and reduces training time required to achieve target accuracy by 43.5% and 49.8%. The approach jointly optimizes model quality, privacy preservation, and service real-time performance.

Technology Category

• Machine Learning: Privacy
• Computer Vision: Large Vision Models
• Multiagent Systems: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Security and Privacy: Large-scale security measurements

Abstract

Timely updating of Internet of Things (IoT) data is crucial for immersive 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 continuous collection of high-quality data. To address these issues, we propose an immersion-aware model trading framework that facilitates data provision for services while ensuring privacy through federated learning (FL). Specifically, we first develop a novel multi-dimensional metric, the immersion of model (IoM), which assesses model value comprehensively by considering freshness and accuracy of learning models, as well as the amount and potential value of raw data used for training. Then, we design an incentive mechanism to incentivize metaverse users (MUs) to contribute high-value models 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. Furthermore, considering dynamic network conditions and privacy concerns, we formulate the reward decisions of MSPs as a multi-agent Markov decision process. To solve this, we develop a fully distributed dynamic reward algorithm based on deep reinforcement learning, without accessing any private information about MUs and other MSPs. Experimental results demonstrate 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.

Problem

• Reducing latency and privacy risks in vehicular metaverse data updates
• Balancing model value and resource constraints for metaverse services
• Optimizing reward decisions under dynamic network and privacy conditions

Innovation

• Uses federated learning for privacy-preserving data collection
• Introduces immersion of model (IoM) multi-dimensional metric
• Develops distributed dynamic reward algorithm via deep reinforcement learning