论文标题
通过联合学习在元元中进行移动增强现实
Mobile Augmented Reality with Federated Learning in the Metaverse
论文作者
论文摘要
Metavers被认为是互联网的下一个演变，最近受到了很多关注。通过移动增强现实（MAR）进行的元应用应用需要快速准确的对象检测，以将数字数据与现实世界混合。随着移动设备的发展，它们的计算功能正在增加，因此可以利用其计算资源来训练机器学习模型。鉴于对用户隐私和数据安全的越来越关注，联邦学习（FL）已成为具有隐私性分析的有希望的分布式学习框架。在本文中，FL和MAR被汇总在一起。我们讨论了FL和MAR组合的必要性和合理性。还讨论了支持Metavers中FL和MAR的前瞻性技术。此外，还提出了防止在元评估中实现FL和MAR的现有挑战，并提出了几种应用程序方案。最后，证明了三个元摩尔摩尔系统的三个案例研究。
The Metaverse is deemed the next evolution of the Internet and has received much attention recently. Metaverse applications via mobile augmented reality (MAR) require rapid and accurate object detection to mix digital data with the real world. As mobile devices evolve, their computational capabilities are increasing, and thus their computational resources can be leveraged to train machine learning models. In light of the increasing concerns of user privacy and data security, federated learning (FL) has become a promising distributed learning framework for privacy-preserving analytics. In this article, FL and MAR are brought together in the Metaverse. We discuss the necessity and rationality of the combination of FL and MAR. The prospective technologies that support FL and MAR in the Metaverse are also discussed. In addition, existing challenges that prevent the fulfillment of FL and MAR in the Metaverse and several application scenarios are presented. Finally, three case studies of Metaverse FL-MAR systems are demonstrated.
