Contract Theory Based Incentive Mechanism for Clustered Federated Learning

Clustered Federated Learning (CFL) can be applied to Internet of Things (IoT) scenarios such as intelligent transportation and healthcare, which can effectively solve the problem of heterogeneous data distribution. However, users may be reluctant to contribute their computation and communication resources to perform learning tasks if the CFL server does not give them proper incentives. In this paper, we would like to address the above issue. Specifically, we design a set of optimal contracts for clusters which satisfy the constraints of individual rationality and incentive compatibility. The proposed contract theory based incentive mechanism not only effectively motivates every cluster, but also overcome the information asymmetry problem to maximize the utility of the CFL server. Finally, simulation results validate the effectiveness of the designed contracts.