Adaptive Social Metaverse Streaming based on Federated Multi-Agent Deep Reinforcement Learning

--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, immer-sive rendering, and bandwidth optimization. T o 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. Index T erms --Social metaverse, adaptive bit rate streaming, Multi-agent reinforcement learning, federated learning, extended reality. The metaverse is seen as the next evolution of the Internet, offering a seamless digital space where users can meet, socialize, play games, and collaborate in immersive 3D environments [1]. As adoption grows, it has gained significant global attention. Gartner predicts that by 2026, 25% of people will spend at least an hour per day in metaverse environments [2].