【Author】
Ma, Shuaicheng; Cao, Yang; Xiong, Li
【Source】2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING WORKSHOPS (ICDEW 2021)
【Abstract】Federated Learning is a promising machine learning paradigm when multiple parties collaborate to build a high-quality machine learning model. Nonetheless, these parties are only willing to participate when given enough incentives, such as a fair reward based on their contributions. Many studies explored Shapley value based methods to evaluate each party's contribution to the learned model. However, they commonly assume a semi-trusted server to train the model and evaluate the data owners' model contributions, which lacks transparency and may hinder the success of federated learning in practice. In this work, we propose a blockchain-based federated learning framework and a protocol to transparently evaluate each participant's contribution. Our framework protects all parties' privacy in the model building phase and transparently evaluates contributions based on the model updates. The experiment with the handwritten digits dataset demonstrates that the proposed method can effectively evaluate the contributions.
【Keywords】Blockchain; Federated Learning; Contribution Evaluation; Transparency; Privacy
【摘要】当多方合作构建高质量的机器学习模型时，联邦学习是一种很有前途的机器学习范式。尽管如此，这些各方只有在获得足够的激励时才愿意参与，例如基于他们的贡献的公平奖励。许多研究探索了基于 Shapley 价值的方法来评估各方对学习模型的贡献。然而，他们通常假设一个半可信的服务器来训练模型并评估数据所有者的模型贡献，这缺乏透明度，可能会阻碍联邦学习在实践中的成功。在这项工作中，我们提出了一个基于区块链的联邦学习框架和一个协议，以透明地评估每个参与者的贡献。我们的框架在模型构建阶段保护各方隐私，并根据模型更新透明地评估贡献。手写数字数据集的实验表明，所提出的方法可以有效地评估贡献。
【关键词】区块链；联邦学习；贡献评估；透明度;隐私
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