  Federated learning (FL) becomes popular and has shown great potentials in
training large-scale machine learning (ML) models without exposing the owners'
raw data. In FL, the data owners can train ML models based on their local data
and only send the model updates rather than raw data to the model owner for
aggregation. To improve learning performance in terms of model accuracy and
training completion time, it is essential to recruit sufficient participants.
Meanwhile, the data owners are rational and may be unwilling to participate in
the collaborative learning process due to the resource consumption. To address
the issues, there have been various works recently proposed to motivate the
data owners to contribute their resources. In this paper, we provide a
comprehensive review for the economic and game theoretic approaches proposed in
the literature to design various schemes for stimulating data owners to
participate in FL training process. In particular, we first present the
fundamentals and background of FL, economic theories commonly used in incentive
mechanism design. Then, we review applications of game theory and economic
approaches applied for incentive mechanisms design of FL. Finally, we highlight
some open issues and future research directions concerning incentive mechanism
design of FL.
