Incentivizing Federated Learning With Contract Theory Under Strong Information Asymmetry

Incentive mechanism is an effective approach to encourage user participation in the Federated Learning (FL) process and improve training efficiency. However, current research often focuses on scenarios with complete information or weak information asymmetry between the server and users, and few studies consider incentive mechanism design in strong asymmetric information scenarios. Meanwhile, most works assume that users' resource contributions to model performance are independent of each other, which is not consistent with practical situations. To tackle these challenges, we design an incentive contract tailored for scenarios with strong information asymmetry. Our contract leverages the probability distribution of user types to ensure its appropriateness. Furthermore, taking into account the correlation of the users' resource contributions, we propose an iteration algorithm to determine the set of optimal contract items that satisfy the constraints of individual rationality (IR) and incentive compatibility (IC). Our simulation results show that our contract can effectively motivate multiple users to take part in the training process, enabling the server to achieve utility close to those in weak asymmetric information scenarios while maintaining robustness.