mlsanityData to 14 Oct 2026

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

arXiv 2506.17342 ↗First posted 19 Jun 2025cs.LG cs.AI cs.MMcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 40 references that are in the map. First posted on arXiv 19 Jun 2025.

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11 of 40 references.

References · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  4. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  5. 2018Blockchained On-Device Federated Learningcited by 106
  6. 2022A Survey on Metaverse: Fundamentals, Security, and Privacycited by 51
  7. 2016Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPUcited by 48
  8. 2018Tiyuntsong: A Self-Play Reinforcement Learning Approach for ABR Video Streamingcited by 3
  9. 2022Interacting with New York City Data by HoloLens through Remote Renderingcited by 3
  10. 2023Federated Learning for Metaverse: A Surveycited by 3
  11. 2023Human-Centric Resource Allocation for the Metaverse With Multiaccess Edge Computingcited by 3

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  2. 2023Social Metaverse: Challenges and Solutions ↗
  3. 2024FedRLHF: A Convergence-Guaranteed Federated Framework for Privacy-Preserving and Personalized RLHF
  4. 2025Fed-SE: Federated Self-Evolution for Privacy-Constrained Multi-Environment LLM Agents
  5. 2025Federated Reinforcement Learning for Runtime Optimization of AI Applications in Smart Eyewears
  6. 2026Cooperative Multi-Agent Reinforcement Learning for Adaptive Aggregation in Semi-Supervised Federated Learning with non-IID Data
  7. 2024FairStream: Fair Multimedia Streaming Benchmark for Reinforcement Learning Agents
  8. 2026Edge Learning via Federated Split Decision Transformers for Metaverse Resource Allocation