Resource Allocation of Federated Learning Assisted Mobile Augmented Reality System in the Metaverse

TL;DR

This paper proposes a federated learning assisted mobile augmented reality system for the Metaverse, optimizing resource allocation to balance energy, latency, and accuracy amidst bandwidth and computational constraints.

Contribution

It introduces a novel resource allocation algorithm for federated learning in MAR systems using non-orthogonal multiple access in the Metaverse environment.

Findings

The proposed algorithm outperforms random and greedy algorithms in experiments.

Optimized transmission power, CPU frequency, and video resolution improve system performance.

The system enhances immersive experiences while managing resource limitations.

Abstract

Metaverse has become a buzzword recently. Mobile augmented reality (MAR) is a promising approach to providing users with an immersive experience in the Metaverse. However, due to limitations of bandwidth, latency and computational resources, MAR cannot be applied on a large scale in the Metaverse yet. Moreover, federated learning, with its privacy-preserving characteristics, has emerged as a prospective distributed learning framework in the future Metaverse world. In this paper, we propose a federated learning assisted MAR system via non-orthogonal multiple access for the Metaverse. Additionally, to optimize a weighted sum of energy, latency and model accuracy, a resource allocation algorithm is devised by setting appropriate transmission power, CPU frequency and video frame resolution for each user. Experimental results demonstrate that our proposed algorithm achieves an overall good performance compared to a random algorithm and greedy algorithm.