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

arXiv: https://arxiv.org/abs/2410.19665 · HTML: https://mlsanity.com/paper/2410.19665/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 83 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 17 of 17)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction](https://mlsanity.com/paper/2203.11751/) (2022)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [A Survey on Metaverse: Fundamentals, Security, and Privacy](https://mlsanity.com/paper/2203.02662/) (2022)
- [Realizing the Metaverse with Edge Intelligence: A Match Made in Heaven](https://mlsanity.com/paper/2201.01634/) (2022)
- [Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness](https://mlsanity.com/paper/2307.15975/) (2023)
- [Wireless Edge-Empowered Metaverse: A Learning-Based Incentive Mechanism for Virtual Reality](https://mlsanity.com/paper/2111.03776/) (2021)
- [Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach](https://mlsanity.com/paper/2111.10548/) (2021)
- [Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction](https://mlsanity.com/paper/2306.14683/) (2023)
- [Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality](https://mlsanity.com/paper/2211.08705/) (2022)
- [MetaChain: A Novel Blockchain-based Framework for Metaverse Applications](https://mlsanity.com/paper/2201.00759/) (2021)
- [Resource Allocation for Augmented Reality Empowered Vehicular Edge Metaverse](https://mlsanity.com/paper/2212.01325/) (2022)
- [Multi-attribute Auction-based Resource Allocation for Twins Migration in Vehicular Metaverses: A GPT-based DRL Approach](https://mlsanity.com/paper/2406.05418/) (2024)
- [Blockchain-based Pseudonym Management for Vehicle Twin Migrations in Vehicular Edge Metaverse](https://mlsanity.com/paper/2403.15285/) (2024)
- [A Blockchain-based Reliable Federated Meta-learning for Metaverse: A Dual Game Framework](https://mlsanity.com/paper/2408.03694/) (2024)
- [Optimized Detection and Classification on GTRSB: Advancing Traffic Sign Recognition with Convolutional Neural Networks](https://mlsanity.com/paper/2403.08283/) (2024)

## Abstract (arXiv)

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.