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Updated: Jun 23, 2025
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Usability Evaluation of Augmented Reality: A Neuro-Information-Systems Study
Published on: November 30, 2022
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AMFL: Resource-Efficient Adaptive Metaverse-Based Federated Learning for the Human-Centric Augmented Reality
Summary
This study introduces an adaptive algorithm for Metaverse augmented reality (AR) applications using federated learning (FL). The novel approach enhances quality of experience (QoE) and reduces costs, even with challenging Non-IID data.
Area of Science:
- Computer Science
- Artificial Intelligence
- Telecommunications
Background:
- 5G technology enables immersive Metaverse augmented reality (AR) experiences.
- Integrating federated learning (FL) with Metaverse AR (MAR) systems offers edge intelligence services.
- Non-independent and identically distributed (Non-IID) data and limited resources challenge MAR applications.
Purpose of the Study:
- To propose a novel adaptive resource-efficient Metaverse-based FL (AMFL) algorithm for AR applications.
- To mitigate the negative effects of Non-IID data and reduce resource costs.
- To improve the quality of experience (QoE) in MAR systems.
Main Methods:
- Analysis of wireless communication factors (CPU frequency, bandwidth, transmission power) on FL training performance.
- Formulation of a QoE maximization problem considering Non-IID degree, model accuracy, and resource consumption.
- Employment of a deep reinforcement learning (DRL)-based method for adaptive resource allocation.
Main Results:
- The proposed AMFL algorithm significantly improves QoE by up to 30.28%.
- Communication rounds and energy costs are reduced by up to 81.08% and 72.20%, respectively.
- The algorithm performs effectively even under the worst Non-IID data conditions.
Conclusions:
- AMFL effectively addresses Non-IID data challenges in MAR systems.
- The algorithm optimizes resource allocation for enhanced QoE and reduced costs.
- This work advances edge intelligence services for immersive AR applications.
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