[2301.12085] Resource Allocation of Federated Learning Assisted Mobile Augmented Reality System in the Metaverse

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.