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Federated learning makes it possible for all parties with data isolation totrain the model collaboratively and efficiently while satisfying privacyprotection. To obtain a high-quality model, an incentive mechanism is necessaryto motivate more high-quality workers with data and computing power. Theexisting incentive mechanisms are applied in offline scenarios, where the taskpublisher collects all bids and selects workers before the task. However, it ispractical that different workers arrive online in different orders before orduring the task. Therefore, we propose a reverse auction-based online incentivemechanism for horizontal federated learning with budget constraint. Workerssubmit bids when they arrive online. The task publisher with a limited budgetleverages the information of the arrived workers to decide on whether to selectthe new worker. Theoretical analysis proves that our mechanism satisfies budgetfeasibility, computational efficiency, individual rationality, consumersovereignty, time truthfulness, and cost truthfulness with a sufficient budget.The experimental results show that our online mechanism is efficient and canobtain high-quality models.

Online Auction-Based Incentive Mechanism Design for Horizontal Federated Learning with Budget Constraint