计算机科学
透明度(行为)
联合学习
激励
协议(科学)
质量(理念)
工作(物理)
计算机安全
块链
人工智能
医学
机械工程
认识论
工程类
哲学
病理
经济
微观经济学
替代医学
作者
Shuaicheng Ma,Yang Cao,Li Xiong
标识
DOI:10.1109/icdew53142.2021.00023
摘要
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.
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