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Hierarchical Federated Learning (HFL) is introduced as a promising techniquethat allows model owners to fully exploit computational resources and bandwidthresources to train the global model. However, due to the high training cost, asingle model owner may not be able to deploy HFL. To address this issue, wedevelop a smart contract based trust crowdfunding mechanism for HFL, whichenables multiple model owners to obtain a crowdfunding model with high socialutility for multiple crowdfunding participants. To ensure the authenticity ofthe crowdfunding mechanism, we implemented the Vickey-Clark-Croves (VCG)mechanism to encourage all crowdfunding participants and clients to providerealistic bids and offers. At the same time, in order to ensure guaranteedtrustworthiness of crowdfunding and automatic distribution of funds, we developand implement a smart contract to record the crowdfunding process and trainingresults in the blockchain. We prove that the proposed scheme satisfies thebudget balance and participant constraint. Finally, we implement a prototype ofthis smart contract on an Ethereoum private chain and evaluate the proposed VCGmechanism. The experimental results demonstrate that the proposed scheme caneffectively improve social utility while ensuring the authenticity andtrustworthiness of the crowdfunding process.

A Smart Contract based Crowdfunding Mechanism for Hierarchical Federated Learning