  Federated learning utilizes various resources provided by participants to
collaboratively train a global model, which potentially address the data
privacy issue of machine learning. In such promising paradigm, the performance
will be deteriorated without sufficient training data and other resources in
the learning process. Thus, it is quite crucial to inspire more participants to
contribute their valuable resources with some payments for federated learning.
In this paper, we present a comprehensive survey of incentive schemes for
federate learning. Specifically, we identify the incentive problem in federated
learning and then provide a taxonomy for various schemes. Subsequently, we
summarize the existing incentive mechanisms in terms of the main techniques,
such as Stackelberg game, auction, contract theory, Shapley value,
reinforcement learning, blockchain. By reviewing and comparing some impressive
results, we figure out three directions for the future study.
