Strategic Analysis of the Parameter Servers and Participants in Federated Learning: An Evolutionary Game Perspective

Abstract

Federated learning (FL) is a new decentralized deep learning paradigm developed for collaborative model training and solving the problem of data privacy and has received extensive attention from both the academic and business worlds. However, FL still faces challenges in encouraging participants to contribute private data and computational resources. Although many studies have applied game theory models to improve the incentive mechanism design of FL, they assume that the players are absolutely rational and that the game models are static. In this study, a mathematical model based on evolutionary game theory (EGT) is established to analyze the interaction between parameter servers and participants, considering that the participants are not completely rational in the long-term dynamic decision-making process. The evolutionarily stable status of the FL system and the strategies of the parameter servers and participants were analyzed under eight different scenarios. Based on the model analysis and results of the numerical experiments, managerial insights for maintaining a sustainable FL system are summarized.