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

2025-06-19

AI Summary

To address the challenge of real-time streaming transmission in social metaverses—where privacy sensitivity and highly dynamic bandwidth coexist—this paper proposes an end-edge collaborative adaptive bitrate (ABR) control framework. Our method innovatively integrates federated learning with multi-agent proximal policy optimization (F-MAPPO), enabling distributed, privacy-preserving joint decision-making while ensuring sensitive user biometric data remains strictly localized and never uploaded to remote servers. The system incorporates edge-aware bitrate adaptation and distributed state modeling to significantly enhance network responsiveness. Experimental results demonstrate that, under volatile network conditions, the framework improves user satisfaction by ≥14%, reduces end-to-end latency by 22%, and simultaneously guarantees privacy security and streaming quality.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Privacy
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Large-scale security measurements
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

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.

Problem

• Ensuring privacy in social metaverse biometric data collection
• Achieving high-quality low-latency metaverse streaming
• Optimizing bandwidth for real-time immersive rendering

Innovation

• Federated Multi-Agent Proximal Policy Optimization
• Dynamic bit rate adjustment for streaming
• Privacy-preserving federated learning integration