论文标题
一种基于VCG的公平奖励机制，用于联合学习
A VCG-based Fair Incentive Mechanism for Federated Learning
论文作者
论文摘要
Vickrey-Clarke-Groves（VCG）机制的持久价值由于Facebook广告拍卖的采用而被强调。我们的研究深入研究了其在合作虚拟商品生产（CVGP）游戏中的实用性，该游戏在联邦学习和众包等领域中找到了应用程序，在这种领域中，投标人扮演供应商而不是消费者的角色。我们将采购-VCG（PVCG）共享规则介绍到现有的VCG机制中，以便它们可以处理CVGP游戏中反向拍卖设置的持续策略空间特征。我们的主要理论贡献提供了数学证据，以证明PVCG是CVGP游戏环境中第一个同时实现真实性，帕累托效率，个人合理性和弱预算平衡的方法。这些特性表明在数字计划经济中有可能产生帕累托效率的生产。此外，要计算嘈杂的经济环境中的PVCG付款，我们提出了报告互动最大化方法（RIM）方法。 RIM通过与供应商的迭代互动来促进学习最佳采购水平和PVCG付款。
The enduring value of the Vickrey-Clarke-Groves (VCG) mechanism has been highlighted due to its adoption by Facebook ad auctions. Our research delves into its utility in the collaborative virtual goods production (CVGP) game, which finds application in realms like federated learning and crowdsourcing, in which bidders take on the roles of suppliers rather than consumers. We introduce the Procurement-VCG (PVCG) sharing rule into existing VCG mechanisms such that they can handle capacity limits and the continuous strategy space characteristic of the reverse auction setting in CVGP games. Our main theoretical contribution provides mathematical proofs to show that PVCG is the first in the CVGP game context to simultaneously achieve truthfulness, Pareto efficiency, individual rationality, and weak budget balance. These properties suggest the potential for Pareto-efficient production in the digital planned economy. Moreover, to compute the PVCG payments in a noisy economic environment, we propose the Report-Interpolation-Maximization (RIM) method. RIM facilitates the learning of the optimal procurement level and PVCG payments through iterative interactions with suppliers.
