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.

翻译：元宇宙被认为是互联网的下一轮演进，近期受到广泛关注。通过移动增强现实（MAR）实现的元宇宙应用需要快速且精准的目标检测能力，以便将数字数据与现实世界融合。随着移动设备的发展，其计算能力不断提升，因此可以利用其计算资源来训练机器学习模型。考虑到用户隐私和数据安全日益受到关注，联邦学习（FL）已成为一种具有前景的分布式学习框架，用于隐私保护分析。本文在元宇宙中将FL与MAR结合，探讨了二者融合的必要性与合理性，并讨论了支持元宇宙中FL与MAR的前瞻性技术。此外，还阐述了阻碍FL与MAR在元宇宙中实现的实际挑战及若干应用场景。最后，展示了三种元宇宙FL-MAR系统的案例研究。