Resource Allocation of Federated Learning-Assisted Mobile Augmented Reality Systems in the Metaverse via Uplink and Downlink NOMA

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 yet be applied on a large scale in the Metaverse. Moreover, federated learning, with its privacy-preserving characteristics, has emerged as a prospective distributed learning framework in the future Metaverse world. This paper proposes a federated learning-assisted MAR system via non-orthogonal multiple access (NOMA) for the Metaverse. Additionally, to optimize a weighted sum of energy and model accuracy, a resource allocation algorithm is devised by setting appropriate transmission power, CPU frequency, and video frame resolution in the uplink and downlink NOMA for each user. Besides, time complexity and convergence analysis of the resource allocation algorithm are provided. Experimental results demonstrate that our proposed algorithm achieves an overall good performance from the following three aspects: 1) under different system parameters, 2) comparisons with baseline algorithms, and 3) adjustment of the weight parameter to balance accuracy and energy consumption.