Federated Learning for the Metaverse: A Short Survey

Abstract

The Metaverse, a 3-dimensional virtual realm mirroring real-world objects, promises transformative experiences. Its potential is tempered by data collection, confidentiality, and privacy concerns. In tandem with AI-enabled technologies, edge computing can provide a practical solution to overcoming many of these challenges. This paper argues that combining AI-enabled technologies with edge computing-specifically via federated learning (FL) can address these challenges. FL, as a privacy-centric distributed machine learning (ML) approach, enables knowledge sharing among Metaverse clients without compromising user data. However, while FL posits potential resolutions for the Metaverse, a discernible lacuna remains in the comprehensive study of its overarching consequences. The literature misses an extensive study of adopting these new deployment architectures, giving it significant research impact. This paper bridges this knowledge gap, exploring the symbiosis between the Metaverse and FL. We first introduce the foundational concepts of both domains and detail enabling technologies such as digital twins, the Internet of Things, brain-computer interfaces, blockchain, and extended reality. Next, we delve into practical applications of FL within the metaverse, spanning sectors like healthcare, education, e-commerce, gaming, and the military. Finally, the paper highlights the key challenges and future directions for integrating FL within the metaverse ecosystem.