Mobile Augmented Reality with Federated Learning in the Metaverse
This work addresses privacy and efficiency issues for Metaverse applications using mobile devices, but it is incremental as it combines existing FL and MAR concepts without new empirical results.
The paper explores integrating federated learning (FL) with mobile augmented reality (MAR) in the Metaverse to address privacy concerns while leveraging mobile device computational resources, presenting case studies and discussing challenges and technologies.
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.