Federated Learning for Metaverse: A Survey

Abstract

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.

A Survey on Federated Learning for Reconfigurable Intelligent Metasurfaces-Aided Wireless Networks

“…Metaverse: The metaverse, an amalgamation of AR, VR, XR, and immersive digital environments, acts as a virtual shared space where users engage in various activities such as interaction, socialization, and work [47]. The integration of FL within the metaverse framework is not only pivotal for ensuring the privacy of user data but also plays a crucial role in reducing the need for extensive computing resources [48]. This makes FL an efficient and privacy-preserving solution for enabling the metaverse.…”