  In traditional machine learning, the central server first collects the data
owners' private data together and then trains the model. However, people's
concerns about data privacy protection are dramatically increasing. The
emerging paradigm of federated learning efficiently builds machine learning
models while allowing the private data to be kept at local devices. The success
of federated learning requires sufficient data owners to jointly utilize their
data, computing and communication resources for model training. In this paper,
we propose an auction based market model for incentivizing data owners to
participate in federated learning. We design two auction mechanisms for the
federated learning platform to maximize the social welfare of the federated
learning services market. Specifically, we first design an approximate
strategy-proof mechanism which guarantees the truthfulness, individual
rationality, and computational efficiency. To improve the social welfare, we
develop an automated strategy-proof mechanism based on deep reinforcement
learning and graph neural networks. The communication traffic congestion and
the unique characteristics of federated learning are particularly considered in
the proposed model. Extensive experimental results demonstrate that our
proposed auction mechanisms can efficiently maximize the social welfare and
provide effective insights and strategies for the platform to organize the
federated training.
