论文标题
基于在线拍卖的激励机制设计用于横向联邦学习和预算限制
Online Auction-Based Incentive Mechanism Design for Horizontal Federated Learning with Budget Constraint
论文作者
论文摘要
联合学习使各方都有隔离的各方可以协作有效地训练模型，同时满足隐私保护。为了获得高质量的模型，需要一种激励机制来激励更多具有数据和计算能力的高质量工人。现有的激励机制应用于脱机场景，任务发布者在任务之前收集所有投标并选择工人。但是，实际上，不同的工人在任务之前或期间以不同的订单在线到达。因此，我们提出了一种基于反向拍卖的在线激励机制，用于通过预算限制的水平联合学习。工人在网上到达时提交竞标。预算有限的任务出版商利用了到达工人的信息来决定是否选择新工人。理论分析证明，我们的机制满足了预算可行性，计算效率，个人理性，消费者主权，时间真实性和成本真实性，并具有足够的预算。实验结果表明，我们的在线机制是有效的，可以获得高质量的模型。
Federated learning makes it possible for all parties with data isolation to train the model collaboratively and efficiently while satisfying privacy protection. To obtain a high-quality model, an incentive mechanism is necessary to motivate more high-quality workers with data and computing power. The existing incentive mechanisms are applied in offline scenarios, where the task publisher collects all bids and selects workers before the task. However, it is practical that different workers arrive online in different orders before or during the task. Therefore, we propose a reverse auction-based online incentive mechanism for horizontal federated learning with budget constraint. Workers submit bids when they arrive online. The task publisher with a limited budget leverages the information of the arrived workers to decide on whether to select the new worker. Theoretical analysis proves that our mechanism satisfies budget feasibility, computational efficiency, individual rationality, consumer sovereignty, time truthfulness, and cost truthfulness with a sufficient budget. The experimental results show that our online mechanism is efficient and can obtain high-quality models.
