Federated Learning (FL) is an emerging machine learning paradigm aimed at collaboratively training AI models with distributed multiple clients in a privacy-preserving manner. However, the performance of FL can be poor, especially in cross-silos FL, without sufficient participants who train models with their data. Therefore, finding relevant datasets and engaging their owners in FL can be one of the key important factors in reality. Motivated by this, in this paper, we introduce a system equipped with efficient ways to search for data owners who possess suitable datasets and to inspire them for cooperation. In our approach, all participants who perform the learning process in FL can receive transparent and automated incentives from every gain of the final output, which can work as a motivation for participation in federated learning.