  Social metaverse is a shared digital space combining a series of
interconnected virtual worlds for users to play, shop, work, and socialize. In
parallel with the advances of artificial intelligence (AI) and growing
awareness of data privacy concerns, federated learning (FL) is promoted as a
paradigm shift towards privacy-preserving AI-empowered social metaverse.
However, challenges including privacy-utility tradeoff, learning reliability,
and AI model thefts hinder the deployment of FL in real metaverse applications.
In this paper, we exploit the pervasive social ties among users/avatars to
advance a social-aware hierarchical FL framework, i.e., SocialFL for a better
privacy-utility tradeoff in the social metaverse. Then, an aggregator-free
robust FL mechanism based on blockchain is devised with a new block structure
and an improved consensus protocol featured with on/off-chain collaboration.
Furthermore, based on smart contracts and digital watermarks, an automatic
federated AI (FedAI) model ownership provenance mechanism is designed to
prevent AI model thefts and collusive avatars in social metaverse. Experimental
findings validate the feasibility and effectiveness of proposed framework.
Finally, we envision promising future research directions in this emerging
area.
