The rapid development of artificial intelligence (AI) has been made possible by the swift advancement of technology. Recently, there has been a significant increase in interest in the concept of the Metaverse, which is viewed as the ultimate manifestation of the internet. However, building a vast Metaverse requires a substantial allocation of resources to maintain it. Such circumstances pose several challenges, including the disclosure of sensitive data and the problem of non-independent and homogeneous data distribution. In this study, we will focus on explaining federated learning (FL), a methodology that addresses these issues by training localized models and then consolidating them on a global scale to create the ultimate model without sharing the original data. Additionally, we will also discuss the current challenges and potential developments that should be considered in the upcoming years.