Journal Article10.48550/arXiv.2306.11297
Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation
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TL;DR: In this article , a decentralized and trustworthy quantum federated learning (QFL) framework is proposed to ensure that the underlying systems are transparent, secure, and trustworthy in the Metaverse.
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Abstract: With the emerging developments of the Metaverse, a virtual world where people can interact, socialize, play, and conduct their business, it has become critical to ensure that the underlying systems are transparent, secure, and trustworthy. To this end, we develop a decentralized and trustworthy quantum federated learning (QFL) framework. The proposed QFL leverages the power of blockchain to create a secure and transparent system that is robust against cyberattacks and fraud. In addition, the decentralized QFL system addresses the risks associated with a centralized server-based approach. With extensive experiments and analysis, we evaluate classical federated learning (CFL) and QFL in a distributed setting and demonstrate the practicality and benefits of the proposed design. Our theoretical analysis and discussions develop a genuinely decentralized financial system essential for the Metaverse. Furthermore, we present the application of blockchain-based QFL in a hybrid metaverse powered by a metaverse observer and world model. Our implementation details and code are publicly available 1.
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Citations
Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges
Enrique Tom'as Mart'inez Beltr'an,Mario Quiles P'erez,Pedro Miguel S'anchez S'anchez,Sergio L'opez Bernal,Gérôme Bovet,Manuel Gil P'erez,Gregorio Mart'inez P'erez,Alberto Huertas Celdr'an +7 more
TL;DR: In this paper , the main fundamentals of federated learning in terms of federation architectures, topologies, communication mechanisms, security approaches, and key performance indicators are identified and analyzed, and the most relevant features of the current DFL frameworks are reviewed and compared.
Adaptive Sampling and Transmission for Minimizing Age of Information in Metaverse
Yuquan Xiao,Qinghe Du,Wenchi Cheng,Wei Zhang +3 more
TL;DR: This paper proposes adaptive sampling and transmission schemes to minimize age of information in the metaverse, using fractional programming and Dinkelbach's transform, and demonstrates lower statistical AoI than baseline schemes for metaverse users with varying channel conditions and attention.
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Quantum-Driven Context-Aware Federated Learning in Heterogeneous Vehicular Metaverse Ecosystem
Bishmita Hazarika,Keshav Singh,T. Duong,Octavia A. Dobre +3 more
- 09 Jun 2024
TL;DR: This study introduces QV-MetaFL, a quantum-based federated learning framework for vehicular metaverse, addressing heterogeneity and communication costs with Q-STP and Q-VCG, and a composite loss function, demonstrating transformative capabilities through comprehensive simulations.
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TL;DR: Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future as mentioned in this paper, which will be useful tools for exploring many-body quantum physics, and may have other useful applications.
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Variational Quantum Algorithms
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TL;DR: An overview of the field of Variational Quantum Algorithms is presented and strategies to overcome their challenges as well as the exciting prospects for using them as a means to obtain quantum advantage are discussed.
Metaverse for Social Good: A University Campus Prototype
Haihan Duan,Jiaye Li,Sizheng Fan,Zhonghao Lin,Xiao Wu,Wei Cai +5 more
- 17 Oct 2021
TL;DR: Wang et al. as discussed by the authors proposed a three-layer metaverse architecture from a macro perspective, containing infrastructure, interaction, and ecosystem, which journey toward both a historical and novel metaverse with a detailed timeline and table of specific attributes.
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Proceedings Article
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- 07 Feb 2022
TL;DR: Data2vec is a framework that uses the same learning method for either speech, NLP or computer vision to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture.