FedMarket: A Cryptocurrency Driven Marketplace for Mobile Federated Learning Services

【Author】 Yousafzai, Abdullah; Khan, Latif U.; Majeed, Umer; Hakeem, Owais; Hong, Choong Seon

【Source】IEEE ACCESS

【影响因子】3.476

【Abstract】Federated learning (FL) enables the training of a shared collaborative machine learning model while keeping all the confidential training data on distributed devices. The FL state-of-the-art considers a monopolist FL task publisher. However, we present a FL marketplace where multiple FL task publishers and mobile devices co-exist for a set of diverse and varying learning tasks. Mobile devices participating in the training of FL models provides pay-as-you-go (i.e. using blockchain-based cryptocurrencies) FL training services to the FL task publishers. In the proposed framework, multiple FL task publishers may compete with each other and the participating workers (i.e. mobile devices) can choose one FL task publisher over another for participation in the training of a global model. We utilize code offloading for enabling customized FL pipelines in mobile devices and mitigating the model heterogeneity inherent in varying and changing FL tasks published by the task publishers. Experimental results indicate the efficacy of the proposed framework.

【Keywords】Collaborative work; Mobile handsets; Training data; Servers; Data models; Machine learning; Blockchains; Smart contracts; Computational modeling; Federated learning; blockchain; smart contract; marketplace; computational offloading

【发表时间】2022

【收录时间】2022-09-06

【文献类型】理论模型

【主题类别】

区块链技术-协同技术-联邦学习