Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality

Zhou, Xinyu; Liu, Chang; Zhao, Jun

doi:10.48550/arxiv.2211.08705

Cited by 2 publications

(3 citation statements)

References 32 publications

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“…Another framework, Metaslicing, groups applications with common functions into MetaInstances, addressing high resource requirements [92]. Zhou et al introduce a federated learning-based resource allocation strategy [93], which optimises the weighted combination of energy consumption, model accuracy, and execution latency, outperforming incumbent benchmarks. Chua et al [94] As highlighted, resource dimensioning in the traditional metaverse optimises computational resources for rendering tasks like 3D graphics, physics simulations, and real-time interactions, ensuring peak performance, scalability, security, cost-efficiency, and future growth.…”

Section: Emotion-aware Resource Dimensioningmentioning

confidence: 99%

Affective Computing and the Road to an Emotionally Intelligent Metaverse

Pervez,

Shoukat,

Usama

et al. 2024

IEEE Open J. Comput. Soc.

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The metaverse is currently undergoing a profound transformation, fundamentally reshaping our perception of reality. It has transcended its origins to become an expansion of human consciousness, seamlessly blending the physical and virtual worlds. Amidst this transformative evolution, numerous applications are striving to mould the metaverse into a digital counterpart capable of delivering immersive human-like experiences. These applications envisage a future where users effortlessly traverse between physical and digital dimensions. Taking a step forward, affective computing technologies can be utilised to identify users' emotional cues and convey authentic emotions, enhancing genuine, meaningful, and contextaware interactions in the digital world. In this paper, we explore how integrating emotional intelligence can enhance the traditional metaverse, birthing an emotionally intelligent metaverse (EIM). Our work illuminates the multifaceted potential of EIM across diverse sectors, including healthcare, education, gaming, automotive, customer service, human resources, marketing, and urban metaverse cyberspace. Through our examination of these sectors, we uncover how infusing emotional intelligence enriches user interactions and experiences within the metaverse. Nonetheless, this transformative journey is riddled with challenges, and we address the obstacles hindering the realisation of EIM's full potential. By doing so, we lay the groundwork for future research endeavours aimed at further enhancing and refining the captivating journey into the world of EIM.

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Section: Emotion-aware Resource Dimensioningmentioning

confidence: 99%

Affective Computing and the Road to an Emotionally Intelligent Metaverse

Pervez,

Shoukat,

Usama

et al. 2024

IEEE Open J. Comput. Soc.

View full text Add to dashboard Cite

show abstract

“…• Applications. Recent studies [58,59] have shown that FL can be broadly applicable in the IoT industry, such as in smart healthcare and smart cities, etc. In addition, FL also helps detect malicious attacks in federated IoT systems.…”

Section: Iot Of Fl4mmentioning

confidence: 99%

“…Some applications seek greater system security, while others seek better model accuracy. Although there are some algorithms and schemes [6,59] proposed to solve the load and resource allocation problems of the metaverse systems, they do not take into account the dynamic cases. The number of system nodes is usually not determined at the time of application deployment, and the allocation of resources should change dynamically as nodes are added or removed.…”

Section: Challengementioning

confidence: 99%

Federated Learning for Metaverse: A Survey

Chen

Huang

Gan

et al. 2023

Companion Proceedings of the ACM Web Conference 2023

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The metaverse, which is at the stage of innovation and exploration, faces the dilemma of data collection and the problem of private data leakage in the process of development. This can seriously hinder the widespread deployment of the metaverse. Fortunately, federated learning (FL) is a solution to the above problems. FL is a distributed machine learning paradigm with privacy-preserving features designed for a large number of edge devices. Federated learning for metaverse (FL4M) will be a powerful tool. Because FL allows edge devices to participate in training tasks locally using their own data, computational power, and model-building capabilities. Applying FL to the metaverse not only protects the data privacy of participants but also reduces the need for high computing power and high memory on servers. Until now, there have been many studies about FL and the metaverse, respectively. In this paper, we review some of the early advances of FL4M, which will be a research direction with unlimited development potential. We first introduce the concepts of metaverse and FL, respectively. Besides, we discuss the convergence of key metaverse technologies and FL in detail, such as big data, communication technology, the Internet of Things, edge computing, blockchain, and extended reality. Finally, we discuss some key challenges and promising directions of FL4M in detail. In summary, we hope that our up-to-date brief survey can help people better understand FL4M and build a fair, open, and secure metaverse. CCS CONCEPTS• Computing methodologies → Federated learning.

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Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality

Cited by 2 publications

References 32 publications

Affective Computing and the Road to an Emotionally Intelligent Metaverse

Affective Computing and the Road to an Emotionally Intelligent Metaverse

Federated Learning for Metaverse: A Survey

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