MetaFed通过联邦学习实现元宇宙的节能、隐私与性能平衡
MetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse Systems
- 用多智能体强化学习动态选参与设备，提升资源调度效率
- 在MNIST和CIFAR-10上碳排放降低25%，精度高且通信开销小
- 适合关注绿色计算与隐私保护的元宇宙系统开发者
沉浸式元宇宙应用的快速发展带来了性能、隐私与环境可持续性之间的复杂挑战。集中式架构难以满足这些需求，常导致能耗高、延迟大和隐私风险。本文提出MetaFed，一种去中心化的联邦学习框架，支持元宇宙环境中的可持续智能资源编排。该框架集成三项核心技术：(i) 基于多智能体强化学习的动态客户端选择；(ii) 基于同态加密的隐私保护联邦学习；(iii) 与可再生能源可用性对齐的碳感知调度。在MNIST和CIFAR-10数据集上使用轻量级ResNet模型的评估表明，相比传统方法，MetaFed可实现最高25%的碳排放减少，同时保持高准确率和极低通信开销。结果表明，MetaFed是构建环境友好且符合隐私合规要求的元宇宙基础设施的可扩展方案。
原文摘要 · Abstract (English)
The rapid expansion of immersive Metaverse applications introduces complex challenges at the intersection of performance, privacy, and environmental sustainability. Centralized architectures fall short in addressing these demands, often resulting in elevated energy consumption, latency, and privacy concerns. This paper proposes MetaFed, a decentralized federated learning (FL) framework that enables sustainable and intelligent resource orchestration for Metaverse environments. MetaFed integrates (i) multi-agent reinforcement learning for dynamic client selection, (ii) privacy-preserving FL using homomorphic encryption, and (iii) carbon-aware scheduling aligned with renewable energy availability. Evaluations on MNIST and CIFAR-10 using lightweight ResNet architectures demonstrate that MetaFed achieves up to 25% reduction in carbon emissions compared to conventional approaches, while maintaining high accuracy and minimal communication overhead. These results highlight MetaFed as a scalable solution for building environmentally responsible and privacy-compliant Metaverse infrastructures.
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