Federated Learning for Metaverse: A Survey. (arXiv:2303.17987v1 [cs.CR])

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 …

collection data data collection data leakage deployment development devices dilemma distributed edge edge devices features federated learning innovation large locally machine machine learning metaverse own paradigm privacy private private data problem problems process solution stage survey the metaverse tool training