论文标题
多方机器学习机理设计
Mechanism Design for Multi-Party Machine Learning
论文作者
论文摘要
在多方机器学习系统中，不同政党通过以隐私的方式共享数据来合作优化更好的模型。学习的主要挑战是激励问题。例如，如果各方之间存在竞争，则可以从战略上隐藏他的数据，以防止其他方获得更好的模型。 在本文中，我们通过机制设计的镜头研究问题，并在我们的环境中纳入了多方学习的特征。首先，每个代理的估值都具有取决于他人的类型和行动的外部性。其次，每个代理只能歪曲比他的真实类型低的类型，但相反的类型也不会。我们称此设置与类型依赖性动作空间相互依存的值。我们在准单酮实用程序设置中提供了最佳的真实机制。在最普遍的情况下，我们还为真实的机制提供了必要的条件。最后，我们表明，这种机制的存在受到市场增长率的高度影响，并提供了经验分析。
In a multi-party machine learning system, different parties cooperate on optimizing towards better models by sharing data in a privacy-preserving way. A major challenge in learning is the incentive issue. For example, if there is competition among the parties, one may strategically hide his data to prevent other parties from getting better models. In this paper, we study the problem through the lens of mechanism design and incorporate the features of multi-party learning in our setting. First, each agent's valuation has externalities that depend on others' types and actions. Second, each agent can only misreport a type lower than his true type, but not the other way round. We call this setting interdependent value with type-dependent action spaces. We provide the optimal truthful mechanism in the quasi-monotone utility setting. We also provide necessary and sufficient conditions for truthful mechanisms in the most general case. Finally, we show the existence of such mechanisms is highly affected by the market growth rate and provide empirical analysis.
