In order to ensure that the training users can continuously participate in the whole federa-ted learning and training process under the limited base station budget,a federated learning incentive mechanism based on multi-dimensional auction and Stackelberg game model is proposed.Combined with the user's own data quality,quantity and historical reputation,the mechanism uses the multi-dimensional auction method to select the top K users from all the users who intend to participate in the auction,to participate in the federated learning training process.The Stackelberg game method is used to obtain the optimal reward of the base station and the optimal training cost of the training us-er,and the Nash equilibrium solution of the game between the two sides is determined.Simulation results show that compared with the fixed incentive mechanism and the unincentive mechanism,the accuracy and global training loss of the proposed mechanism are better than those of the comparison mechanisms,which can guarantee that the training users will continuously participate in the whole federated learning and training process with data of independent identically distribution(IID)and non-independent identically distribution(Non-IID)data,which ensures that the training user can continuously participate in the whole federated learning and training process.
mobile edge computingfederated learningmulti-dimensional auctionsStackelberg gameNash equilibrium
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