Robust Incentive Mechanism of Federated Learning for Data Quality Uncertainty

Abstract

In order to make the incentive mechanism more suitable for the actual training situation and improve the efficiency of the model, the robust incentive mechanism of federated leaning is proposed to deal with uncertainty of the data quality. (1) Firstly, the incentive mechanism of federated learning is constructed by the use of Stackelberg game to optimize the central server and data owner utilities, respectively. (2) Secondly, the uncertainty of data quality of the data owners is present by two robust uncertainty sets, and the corresponding incentive mechanism of the robust Stackelberg game is given. (3) Thirdly, the existence of equilibrium solution of the game is proved and the equilibrium solution of the whole game is derived. (4) Finally, the feasibility and robustness of the model are verified, and in the comparative experiments, the central server can select the optimal combination of perturbation ratio and uncertainty level according to the preference for uncertainty risk to obtain the optimal incentive mechanism. The incentive mechanism designed in this article not only considers the uncertainty in actual training, but also has a good incentive effect on model training under different risk preferences.