# MetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse Systems

arXiv: https://arxiv.org/abs/2508.17341 · HTML: https://mlsanity.com/paper/2508.17341/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 24 Aug 2025.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda](https://mlsanity.com/paper/2110.05352/) (2021)
- [A Survey on Metaverse: Fundamentals, Security, and Privacy](https://mlsanity.com/paper/2203.02662/) (2022)
- [A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges](https://mlsanity.com/paper/2203.05471/) (2022)
- [Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms](https://mlsanity.com/paper/2002.08196/) (2020)
- [Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing](https://mlsanity.com/paper/1510.00888/) (2015)
- [A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications](https://mlsanity.com/paper/1703.10750/) (2017)
- [Computing Power Network: A Survey](https://mlsanity.com/paper/2210.06080/) (2022)
- [A Survey of Mobile Edge Computing for the Metaverse: Architectures, Applications, and Challenges](https://mlsanity.com/paper/2212.00481/) (2022)
- [MOHAF: A Multi-Objective Hierarchical Auction Framework for Scalable and Fair Resource Allocation in IoT Ecosystems](https://mlsanity.com/paper/2508.14830/) (2025)

## Abstract (arXiv)

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.