Motivated by the emergence of decentralized machine learning (ML) ecosystems, we study the delegation of data collection. Taking the field of contract theory as our starting point, we design optimal and near-optimal contracts that deal with two fundamental information asymmetries that arise in decentralized ML: uncertainty in the assessment of model quality and uncertainty regarding the optimal performance of any model. We show that a principal can cope with such asymmetry via simple linear contracts that achieve 1-1/e fraction of the optimal utility. To address the lack of a priori knowledge regarding the optimal performance, we give a convex program that can adaptively and efficiently compute the optimal contract. We also study linear contracts and derive the optimal utility in the more complex setting of multiple interactions.

翻译：受去中心化机器学习生态系统兴起的启发，我们研究了数据收集的委托问题。以契约理论为出发点，我们设计了最优和近最优的契约，以应对去中心化机器学习中出现的两类基本信息不对称：模型质量评估的不确定性以及任何模型最优性能的不确定性。我们证明，委托人可以通过简单的线性契约应对此类不对称，并实现最优效用的1-1/e比例。为解决缺乏最优性能先验知识的问题，我们给出一个能够自适应且高效计算最优契约的凸规划。我们还研究了线性契约，并在更复杂的多轮交互场景中推导出了最优效用。