  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.
