  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.
