Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality
This work addresses resource efficiency and privacy for Metaverse applications via mobile augmented reality, but it is incremental as it applies existing federated learning and optimization techniques to a specific domain.
The authors tackled the problem of training object detection models for mobile augmented reality in the Metaverse using federated learning, by formulating an optimization problem to balance energy, latency, and accuracy, and devised a resource allocation algorithm that outperformed benchmarks in these metrics under various weight parameters.
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.