  Hierarchical Federated Learning (HFL) is introduced as a promising technique
that allows model owners to fully exploit computational resources and bandwidth
resources to train the global model. However, due to the high training cost, a
single model owner may not be able to deploy HFL. To address this issue, we
develop a smart contract based trust crowdfunding mechanism for HFL, which
enables multiple model owners to obtain a crowdfunding model with high social
utility for multiple crowdfunding participants. To ensure the authenticity of
the crowdfunding mechanism, we implemented the Vickey-Clark-Croves (VCG)
mechanism to encourage all crowdfunding participants and clients to provide
realistic bids and offers. At the same time, in order to ensure guaranteed
trustworthiness of crowdfunding and automatic distribution of funds, we develop
and implement a smart contract to record the crowdfunding process and training
results in the blockchain. We prove that the proposed scheme satisfies the
budget balance and participant constraint. Finally, we implement a prototype of
this smart contract on an Ethereoum private chain and evaluate the proposed VCG
mechanism. The experimental results demonstrate that the proposed scheme can
effectively improve social utility while ensuring the authenticity and
trustworthiness of the crowdfunding process.
