Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality

• URL: http://arxiv.org/abs/2211.08705v3
• Date: Thu, 7 Dec 2023 09:22:03 GMT
• Title: Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality
• Authors: Xinyu Zhou, Chang Liu, Jun Zhao
• Abstract summary: Metaverse applications via mobile augmented reality (MAR) require rapid and accurate object detection to mix digital data with the real world. Federated learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics. We formulate an optimization problem to minimize a weighted combination of total energy consumption, completion time and model accuracy.
• Score: 13.954907748381743
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Metaverse 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. Federated learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics. Due to privacy concerns and the limited computation resources on mobile devices, we incorporate FL into MAR systems of the Metaverse to train a model cooperatively. Besides, to balance the trade-off between energy, execution latency and model accuracy, thereby accommodating different demands and application scenarios, we formulate an optimization problem to minimize a weighted combination of total energy consumption, completion time and model accuracy. Through decomposing the non-convex optimization problem into two subproblems, we devise a resource allocation algorithm to determine the bandwidth allocation, transmission power, CPU frequency and video frame resolution for each participating device. We further present the convergence analysis and computational complexity of the proposed algorithm. Numerical results show that our proposed algorithm has better performance (in terms of energy consumption, completion time and model accuracy) under different weight parameters compared to existing benchmarks.

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