Blockchain-based Auction Mechanism for Federated Learning in Edge-enabled Metaverse

Abstract

Federated Learning (FL) can accelerate the speed of distributed computing in a meta-universe in a wireless environment. We investigate the integration of machine learning models with irreplaceable tokens (NFT) to enable wireless metaverse users (MUs) to control ownership and participate in the economic value allocation by applying FL (FL-NFT). The MUs are grouped into a decentralized-autonomous organization (DAO) to train the global model. To find a cost-benefit tradeoff, MUs and metaverse service providers (MSPs) need to use Stackelberg games to find better strategies and derive the optimal solution by backward induction. We have designed a novel blockchain-based secure auction mechanism (SAM). Theoretical analysis and simulation results show that SAM can enhance FL-NFT and realize the inherent characteristics of incentive mechanisms.