Social Metaverse: Challenges and Solutions
Yuntao Wang, Zhou Su, and Miao Yan

TL;DR
This paper introduces SocialFL, a social-aware hierarchical federated learning framework for privacy-preserving AI in the social metaverse, addressing challenges like privacy-utility tradeoff, model theft, and reliability.
Contribution
It proposes a novel social-aware hierarchical FL framework, an aggregator-free blockchain-based FL mechanism, and an AI ownership provenance system using smart contracts and watermarks.
Findings
Framework is feasible and effective
Blockchain-based FL enhances robustness
Provenance mechanism prevents AI thefts
Abstract
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…
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