发布日期: 2025-09-20

更新日期: 2025-09-22

文章字数: 1.3k

阅读时长: 5 分

阅读次数:

⚠️ 以下所有内容总结都来自于大语言模型的能力，如有错误，仅供参考，谨慎使用
🔴 请注意：千万不要用于严肃的学术场景，只能用于论文阅读前的初筛！
💗 如果您觉得我们的项目对您有帮助 ChatPaperFree ，还请您给我们一些鼓励！⭐️ HuggingFace免费体验

2025-09-20 更新

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

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

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.

物联网数据的及时更新对于实现车载元宇宙服务沉浸至关重要。然而，由于大规模数据传输带来的延迟、与用户数据相关的隐私风险和元宇宙服务提供商（MSP）的计算负担等挑战，持续收集高质量数据受到阻碍。为了应对这些挑战，我们提出了一种沉浸感知模型交易框架，通过联邦学习（FL）实现高效且保护隐私的数据供应。具体来说，我们首先为模型沉浸（IoM）开发了一种新型的多维度评估指标。该指标考虑了本地模型的新鲜度和准确性，以及原始训练数据的大小和潜在价值。基于IoM，我们设计了一种激励机制，通过向MSP提供本地更新，鼓励元宇宙用户（MU）参与联邦学习。MSP和MU之间的交易互动被建模为带有均衡约束的均衡问题（EPEC），以分析和平衡他们的成本和收益，其中MSP作为领导者确定奖励，而MU作为追随者优化资源配置。为了确保隐私并适应动态网络条件，我们开发了一种基于深度强化学习的分布式动态奖励算法，该算法无需从MU和其他MSP获取任何私人信息。实验结果表明，所提出的框架优于最新基准测试，在MNIST和GTSRB数据集上，IoM提高了38.3%和37.2%，达到目标准确度的训练时间平均减少了43.5%和49.8%。这些发现验证了我们方法的有效性，能够激励MU向MSP提供高价值的本地模型，为车载元宇宙服务的数据供应提供了一种灵活且适应性强的方案。

论文及项目相关链接

PDF

Summary
物联网数据及时更新对于实现车载元宇宙服务至关重要。面临的挑战包括大规模数据传输导致的延迟、用户数据隐私风险以及元宇宙服务提供商的计算负担。为此，我们提出了一个沉浸感知模型交易框架，通过联邦学习实现高效且隐私保护的数据供应。该框架设计了一种新型多维模型沉浸度评价指标，激励用户在资源受限情况下参与联邦学习并向服务提供商提供本地更新。服务提供商和用户之间的交易互动被建模为均衡问题进行分析和平衡。为保障隐私并适应动态网络条件，我们开发了一种基于深度强化学习的分布式动态奖励算法。实验结果显示，该框架优于现有基准测试，在MNIST和GTSRB数据集上，模型沉浸度提高38.3%和37.2%，达到目标准确度的训练时间平均减少43.5%和49.8%。

Key Takeaways