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The Metaverse has received much attention recently. Metaverse applications via mobile augmented reality (MAR) require rapid and accurate object detection to mix digital data with the real world. Federated learning (FL) is an intriguing…

机器学习 · 计算机科学 2023-12-08 Xinyu Zhou , Chang Liu , Jun Zhao

Metaverse has become a buzzword recently. Mobile augmented reality (MAR) is a promising approach to providing users with an immersive experience in the Metaverse. However, due to limitations of bandwidth, latency and computational…

社会与信息网络 · 计算机科学 2023-03-13 Xinyu Zhou , Yang Li , Jun Zhao

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…

密码学与安全 · 计算机科学 2023-04-03 Yao Chen , Shan Huang , Wensheng Gan , Gengsen Huang , Yongdong Wu

Recent technological advancements have considerately improved healthcare systems to provide various intelligent healthcare services and improve the quality of life. Federated learning (FL), a new branch of artificial intelligence (AI),…

Social metaverse is a shared digital space combining a series of interconnected virtual worlds for users to play, shop, work, and socialize. In parallel with the advances of artificial intelligence (AI) and growing awareness of data privacy…

计算机与社会 · 计算机科学 2023-08-24 Yuntao Wang , Zhou Su , Miao Yan

Federated Learning (FL) is a machine learning technique that enables multiple entities to collaboratively learn a shared model without exchanging their local data. Over the past decade, FL systems have achieved substantial progress, scaling…

机器学习 · 计算机科学 2025-03-04 Katharine Daly , Hubert Eichner , Peter Kairouz , H. Brendan McMahan , Daniel Ramage , Zheng Xu

In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. Traditional…

网络与互联网体系结构 · 计算机科学 2020-03-02 Wei Yang Bryan Lim , Nguyen Cong Luong , Dinh Thai Hoang , Yutao Jiao , Ying-Chang Liang , Qiang Yang , Dusit Niyato , Chunyan Miao

The metaverse, emerging as a revolutionary platform for social and economic activities, provides various virtual services while posing security and privacy challenges. Wearable devices serve as bridges between the real world and the…

密码学与安全 · 计算机科学 2024-10-30 Wenbo Liu , Handi Chen , Edith C. H. Ngai

Over the past few years, significant advancements have been made in the field of machine learning (ML) to address resource management, interference management, autonomy, and decision-making in wireless networks. Traditional ML approaches…

机器学习 · 计算机科学 2023-11-07 Xiaonan Liu , Yansha Deng , Arumugam Nallanathan , Mehdi Bennis

Metaverse applications that incorporate Mobile Augmented Reality (MAR) provide mixed and immersive experiences by amalgamating the virtual with the physical world. Notably, due to their multi-modality such applications are demanding in…

网络与互联网体系结构 · 计算机科学 2022-09-20 Zhaohui Huang , Vasilis Friderikos

Federated Learning (FL) enables distributed machine learning training while preserving privacy, representing a paradigm shift for data-sensitive and decentralized environments. Despite its rapid advancements, FL remains a complex and…

机器学习 · 计算机科学 2025-05-14 Frederico Vicente , Cláudia Soares , Dušan Jakovetić

Federated learning (FL) was proposed to facilitate the training of models in a distributed environment. It supports the protection of (local) data privacy and uses local resources for model training. Until now, the majority of research has…

Federated learning (FL) is a system in which a central aggregator coordinates the efforts of multiple clients to solve machine learning problems. This setting allows training data to be dispersed in order to protect privacy. The purpose of…

机器学习 · 计算机科学 2022-06-27 Subrato Bharati , M. Rubaiyat Hossain Mondal , Prajoy Podder , V. B. Surya Prasath

With the development of blockchain and communication techniques, the Metaverse is considered as a promising next-generation Internet paradigm, which enables the connection between reality and the virtual world. The key to rendering a…

信号处理 · 电气工程与系统科学 2022-09-29 Peiyuan Si , Jun Zhao , Huimei Han , Kwok-Yan Lam , Yang Liu

Federated Learning (FL) is emerging as a promising technology to build machine learning models in a decentralized, privacy-preserving fashion. Indeed, FL enables local training on user devices, avoiding user data to be transferred to…

机器学习 · 计算机科学 2020-11-19 Nicolas Kourtellis , Kleomenis Katevas , Diego Perino

Robust machine learning (ML) models can be developed by leveraging large volumes of data and distributing the computational tasks across numerous devices or servers. Federated learning (FL) is a technique in the realm of ML that facilitates…

Federated Learning (FL) enables collaborative model training across diverse entities while safeguarding data privacy. However, FL faces challenges such as data heterogeneity and model diversity. The Meta-Federated Learning (Meta-FL)…

机器学习 · 计算机科学 2024-06-25 Zahir Alsulaimawi

As an evolving successor to the mobile Internet, the Metaverse creates the impression of an immersive environment, integrating the virtual as well as the real world. In contrast to the traditional mobile Internet based on servers, the…

密码学与安全 · 计算机科学 2023-05-17 Jianrui Chen , Jingjing Wang , Chunxiao Jiang , Yong Ren , Lajos Hanzo

Mobile Augmented Reality (MAR) integrates computer-generated virtual objects with physical environments for mobile devices. MAR systems enable users to interact with MAR devices, such as smartphones and head-worn wearables, and performs…

人机交互 · 计算机科学 2022-08-29 Jacky Cao , Kit-Yung Lam , Lik-Hang Lee , Xiaoli Liu , Pan Hui , Xiang Su

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…

机器学习 · 计算机科学 2025-11-06 Muhammet Anil Yagiz , Zeynep Sude Cengiz , Polat Goktas
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