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.
View on arXiv@article{wu2025_2410.19665,
title={ MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services },
author={ Hongjia Wu and Hui Zeng and Zehui Xiong and Jiawen Kang and Zhiping Cai and Tse-Tin Chan and Dusit Niyato and Zhu Han },
journal={arXiv preprint arXiv:2410.19665},
year={ 2025 }
}