用联邦强化学习动态调节元宇宙流媒体质量，兼顾隐私与体验。
Adaptive Social Metaverse Streaming based on Federated Multi-Agent Deep Reinforcement Learning
- 通过联邦多智能体强化学习动态调整码率，保护用户隐私。
- 在多种网络条件下提升用户体验至少14%。
- 适合对隐私和实时性要求高的元宇宙应用开发者。
社交元宇宙是融合虚拟与现实的数字生态系统，支持社交、工作、购物和娱乐互动。然而，沉浸式交互需持续采集生物特征与行为数据，带来隐私风险；同时，实时互动、沉浸渲染和带宽优化使高质量低延迟流媒体难以实现。为此，本文提出基于联邦多智能体近端策略优化（F-MAPPO）的自适应社交元宇宙流媒体系统ASMS。该系统结合联邦学习（FL）与深度强化学习（DRL），在不集中收集数据的前提下动态调节视频码率，保障用户隐私。实验表明，在不同网络条件下，ASMS相比现有方法至少提升用户体验14%。该方案在资源受限且动态变化的网络中仍能提供流畅沉浸的流媒体体验。
原文摘要 · Abstract (English)
The social metaverse is a growing digital ecosystem that blends virtual and physical worlds. It allows users to interact socially, work, shop, and enjoy entertainment. However, privacy remains a major challenge, as immersive interactions require continuous collection of biometric and behavioral data. At the same time, ensuring high-quality, low-latency streaming is difficult due to the demands of real-time interaction, immersive rendering, and bandwidth optimization. To address these issues, we propose ASMS (Adaptive Social Metaverse Streaming), a novel streaming system based on Federated Multi-Agent Proximal Policy Optimization (F-MAPPO). ASMS leverages F-MAPPO, which integrates federated learning (FL) and deep reinforcement learning (DRL) to dynamically adjust streaming bit rates while preserving user privacy. Experimental results show that ASMS improves user experience by at least 14% compared to existing streaming methods across various network conditions. Therefore, ASMS enhances the social metaverse experience by providing seamless and immersive streaming, even in dynamic and resource-constrained networks, while ensuring that sensitive user data remains on local devices.
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