DDS: An Auction Based on a Variant of Data Shapley for Federated Learning.

Federated learning (FL) has received great attention in recent years due to its good performance and privacy security. However, there are still some problems in FL, such as resource allocation, client selection and incentive mechanism. Auctions, as an incentive mechanism with many advantages, solve these problems well. In this paper, we propose DDS, a lightweight and effective method to evaluate the data value for the scenario of federated learning. Based on DDS, we can pick up clients with high training value with low calculation cost. Furthermore, our method is independent of data aggregation, which makes it possible to implement in federated learning. We also conduct the simulation experiments to demonstrate the effectiveness and robustness of our method.