Dynamic Games in Federated Learning Training Service Market
Yuze Zou, Shaohan Feng, Jing Xu, Shimin Gong, Dusit Tao Niyato, Wenqing Cheng · 2019
With the great success of deep learning and increasingly powerful mobile devices, federated learning gains growing attentions from both academia and industry. It provides high-quality on-device model training services while preserves data privacy. In this paper, we consider a federated learning training service market which consists of model owners as consumers and mobile device groups as providers. A two-layer dynamic game is formulated to analyze the dynamics of this market. In particular, the service selection processes of model owners are modeled as a lower-level evolutionary game while the pricing strategies of mobile device groups are modeled as a higher-level differential game. The solutions of the dynamic games, i.e., dynamic equilibrium are analyzed theoretically and verified via extensive numerical evaluations.