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.
翻译:联邦学习组织是一个很有希望的机器学习模式,当多方合作建立一个高质量的机器学习模式时,这种模式是一个很有希望的机器学习模式;然而,这些缔约方只有在给予足够的奖励时,才愿意参与,例如根据他们的贡献给予公平奖励;许多研究探讨了以Shapley价值为基础的方法来评价每一方对学习模式的贡献;然而,它们通常假定一个半受托服务器来培训模型和评价数据所有者的模式贡献,这种模型贡献缺乏透明度,并可能妨碍联合会在实践中的成功学习;在这项工作中,我们提议了一个基于链式的联邦学习框架和一个透明评估每个参与者贡献的议定书。我们的框架保护所有当事方在模型建设阶段的隐私,并根据模型更新情况对贡献进行透明评价。对手写数字数据集的实验表明,拟议的方法能够有效地评价贡献。