论文标题
基于拍卖的前付款激励机制设计，用于水平联合学习，并具有声誉和贡献测量
Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement
论文作者
论文摘要
联合学习训练具有分布式数据的设备跨设备模型，同时保护隐私并获得类似于集中式ML的模型。具有数据和计算能力的大量工人是联邦学习的基础。但是，不可避免的成本阻止了自私的工人免费服务。此外，由于数据隔离，任务发布者缺乏有效的方法来选择，评估和支付具有高质量数据的可靠工人。因此，我们设计了一种基于拍卖的激励机制，用于水平联合学习，并具有声誉和贡献测量。通过设计一种衡量贡献的合理方法，我们建立了工人的声誉，这很容易拒绝且难以改善。通过反向拍卖，工人竞标任务，任务出版商选择了将声誉和出价价格结合起来的工人。借助预算限制，获胜的工人是根据绩效支付的。我们证明了我们的机制满足了诚实工人，预算可行性，真实性和计算效率的个人合理性。
Federated learning trains models across devices with distributed data, while protecting the privacy and obtaining a model similar to that of centralized ML. A large number of workers with data and computing power are the foundation of federal learning. However, the inevitable costs prevent self-interested workers from serving for free. Moreover, due to data isolation, task publishers lack effective methods to select, evaluate and pay reliable workers with high-quality data. Therefore, we design an auction-based incentive mechanism for horizontal federated learning with reputation and contribution measurement. By designing a reasonable method of measuring contribution, we establish the reputation of workers, which is easy to decline and difficult to improve. Through reverse auctions, workers bid for tasks, and the task publisher selects workers combining reputation and bid price. With the budget constraint, winning workers are paid based on performance. We proved that our mechanism satisfies the individual rationality of the honest worker, budget feasibility, truthfulness, and computational efficiency.
