# Mobile Augmented Reality with Federated Learning in the Metaverse

arXiv: https://arxiv.org/abs/2212.08324 · HTML: https://mlsanity.com/paper/2212.08324/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 16 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [A Survey on Metaverse: Fundamentals, Security, and Privacy](https://mlsanity.com/paper/2203.02662/) (2022)
- [A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges](https://mlsanity.com/paper/2203.05471/) (2022)
- [YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design](https://mlsanity.com/paper/2009.05697/) (2020)
- [Blockchain-based Federated Learning for Industrial Metaverses: Incentive Scheme with Optimal AoI](https://mlsanity.com/paper/2206.07384/) (2022)
- [Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality](https://mlsanity.com/paper/2211.08705/) (2022)

## Built on by (most cited first, 3 of 3)

- [Federated Learning for Metaverse: A Survey](https://mlsanity.com/paper/2303.17987/) (2023)
- [A Survey on Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2304.00524/) (2023)
- [Metaverse Survey & Tutorial: Exploring Key Requirements, Technologies, Standards, Applications, Challenges, and Perspectives](https://mlsanity.com/paper/2405.04718/) (2024)

## Abstract (arXiv)

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.