Social Metaverse: Challenges and Solutions

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 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.