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.

翻译：沉浸式元界应用的快速扩张带来了性能、隐私与环境可持续性交叉领域的复杂挑战。集中式架构难以满足这些需求，通常导致能耗增加、延迟加剧及隐私风险。本文提出MetaFed，一种去中心化的联邦学习框架，旨在为元界环境实现可持续的智能资源编排。MetaFed整合了（i）基于多智能体强化学习的动态客户端选择，（ii）采用同态加密的隐私保护联邦学习，以及（iii）与可再生能源供应同步的碳感知调度机制。通过在MNIST和CIFAR-10数据集上使用轻量级ResNet架构进行评估，结果表明MetaFed相较于传统方法可降低高达25%的碳排放，同时保持高精度与低通信开销。这些成果凸显了MetaFed作为构建环境友好且符合隐私规范的元界基础设施的可扩展解决方案。