Incentive Mechanism Design for Multi-server Federated Learning with Wireless Differential Privacy
Federated learning,as an emerging machine learning model,has received widespread atten-tion due to its excellent privacy protection characteristics.The users participating in federated learning are independent and selfish individuals,so it is particularly important to design an effective incentive mecha-nism to enable users to spontaneously participate in federated learning tasks.This paper proposes an in-centive mechanism for multi-server federated learning with differential privacy to address the lack of analy-sis of privacy protection performance in existing federated learning incentive mechanisms and the lack of incentive mechanisms in multi-server scenarios.First of all,a federated learning model with multiple serv-ers is constructed,and the differential privacy protection is provided for the gradient of local models up-loaded by users with wireless channel noise.The utility of users is quantified by the privacy budget,and the interaction between the server and users is modeled as a Stackelberg game,while the competition among servers is modeled as a non-cooperative game.Subsequently,through theoretical analysis,the opti-mal transmission power of user under different reward rate combinations can be obtained,and a reward rate selection algorithm based on stochastic learning automata(SLA)is proposed to solve the mixed-strat-egy Nash equilibrium of the game between servers.Finally,the social welfare and privacy budget of the system are analyzed through simulation,and the simulation results shows that the proposed incentive mechanism can improve social welfare while providing good privacy protection.
federated learningincentive mechanismdifferential privacygame theorywireless communication