Quality (philosophy)

Incentive

Mechanism (biology)

Quantum mechanics

Common value auction

Philosophy

Reverse auction

Microeconomics

Economics

Power (physics)

Computer science

Business

Physics

Mechanism design

Task (project management)

Incentive compatibility

Management

Reputation

Bidding

Epistemology

Sociology

Budget constraint

Reliability (semiconductor)

Industrial organization

Profit (economics)

Social science

Current research on federated learning mainly focuses on joint optimization, improving efficiency and effectiveness, and protecting privacy. However, there are relatively few studies on incentive mechanisms. Most studies fail to consider the fact that if there is no profit, participants have no incentive to provide data and training models, and task requesters cannot identify and select reliable participants with high-quality data. Therefore, this paper proposes a federated learning incentive mechanism based on reputation and reverse auction theory. Participants bid for tasks, and reputation indirectly reflects their reliability and data quality. In this federated learning program, we select and reward participants by combining the reputation and bids of the participants under a limited budget. Theoretical analysis proves that the mechanism satisfies computational efficiency, individual rationality, budget feasibility, and truthfulness. The simulation results show the effectiveness of the mechanism.

Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction