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.

翻译：社交元宇宙是一个共享的数字空间，它融合了一系列相互连接的虚拟世界，供用户进行娱乐、购物、工作和社交。在人工智能（AI）进步和数据隐私意识日益增强的背景下，联邦学习（FL）被视为向隐私保护型AI赋能社交元宇宙转变的一种范式。然而，隐私-效用权衡、学习可靠性以及AI模型盗窃等挑战阻碍了联邦学习在现实元宇宙应用中的部署。在本文中，我们利用用户/虚拟人之间普遍存在的社交关系，提出了一种社交感知的分层联邦学习框架，即SocialFL，以在社交元宇宙中实现更好的隐私-效用权衡。然后，基于区块链设计了一种无聚合器的鲁棒联邦学习机制，该机制采用新的区块结构和改进的共识协议，并具备链上/链下协作特性。此外，基于智能合约和数字水印，设计了一种自动化的联邦AI（FedAI）模型所有权溯源机制，以防止社交元宇宙中的AI模型盗窃与合谋虚拟人行为。实验验证了所提出框架的可行性和有效性。最后，我们展望了这一新兴领域中有前景的未来研究方向。