Incentive Design and Differential Privacy Based Federated Learning: A Mechanism Design Perspective

Due to stricter data management regulations and large size of the training data, distributed learning paradigm such as federated learning (FL) has gained attention recently. FL is capable of significantly preserving end-users' private data from being exposed to external adversaries. However, private information can still be divulged by uploading parameters from users. Therefore, a key challenge in the FL platform is how users participate to build a high-quality learning model with effectively preventing information leakage. To address the above challenge, we design a novel incentive mechanism to attract more data owners to join in the FL process with the consideration of privacy preservation. To implement our proposed scheme, we adopt the concepts of mechanism design (MD) and differential privacy (DP); MD takes an objectives-first approach to designing incentives toward desired objectives, and the DP can provide a theoretical guarantee for users' privacy in FL participations. Based on the DP based incentive mechanism, our joint approach can leverage the full synergy that gives mutual advantages for users and learning operators. Therefore, we can take various benefits in a rational way under the dynamic changing FL environments. Through simulation analysis, the numerical results validate the performance efficiency of our proposed scheme.