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。以契约理论领域为出发点，我们设计了处理去中心化机器学习中两个基本信息不对称性的最优及近似最优契约：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，委托方可以通过简单的线性契约来应对这种不对称性，该契约能达到最优效用的 1-1/e 比例。针对最优性能先验知识缺乏的问题，我们提出了一种凸优化程序，能够自适应且高效地计算最优契约。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了最优效用。