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Federated learning trains models across devices with distributed data, whileprotecting 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 offederal learning. However, the inevitable costs prevent self-interested workersfrom serving for free. Moreover, due to data isolation, task publishers lackeffective methods to select, evaluate and pay reliable workers withhigh-quality data. Therefore, we design an auction-based incentive mechanismfor horizontal federated learning with reputation and contribution measurement.By designing a reasonable method of measuring contribution, we establish thereputation of workers, which is easy to decline and difficult to improve.Through reverse auctions, workers bid for tasks, and the task publisher selectsworkers combining reputation and bid price. With the budget constraint, winningworkers are paid based on performance. We proved that our mechanism satisfiesthe individual rationality of the honest worker, budget feasibility,truthfulness, and computational efficiency.

Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement