We study the problem of collaborative machine learning markets where multiple parties can achieve improved performance on their machine learning tasks by combining their training data. We discuss desired properties for these machine learning markets in terms of fair revenue distribution and potential threats, including data replication. We then instantiate a collaborative market for cases where parties share a common machine learning task and where parties' tasks are different. Our marketplace incentivizes parties to submit high quality training and true validation data. To this end, we introduce a novel payment division function that is robust-to-replication and customized output models that perform well only on requested machine learning tasks. In experiments, we validate the assumptions underlying our theoretical analysis and show that these are approximately satisfied for commonly used machine learning models.

本研究探讨合作机器学习市场的问题，讨论机器学习市场的公平收入分配和潜在威胁，包括数据复制。我们引入了一个新的付款分割功能和定制输出模型，以在具有不同学习任务的方的情况下刺激方提交高质量的训练和验证数据，并在实验中验证了我们理论分析的假设和这些假设的适用性。

数据复制鲁棒支付的协作机器学习市场