Federated Learning(FL)is a distributed machine-learning paradigm that achieves data-privacy protection,and its performance depends on the quality and scale of the data source.The client is a rational individual,and the client s participation in FL incurs costs related to computation,communication,and privacy.Thus,the client must be encouraged to participate through incentives.One of the key factors affecting the successful application of FL is the participation of clients with high-quality data in training.In a multi-task FL environment,clients possess data that are specific to different tasks of varying quality,and their execution capabilities are limited.To improve the overall performance of multiple learning tasks,a task-oriented customer selection and a reward mechanism are designed under budget constraints in this study.By analyzing the important factors that affect the accuracy of the proposed model,a quality-evaluation standard based on the distribution characteristics of client data samples is proposed.Combining this with the client's cost information,an incentive mechanism for reverse auction(EMD-MQMFL)is designed to achieve task assignment and payment strategies for the client.This mechanism has been theoretically analyzed and proven to exhibit honesty,personal rationality,and budget feasibility.Furthermore,its effectiveness in FL performance has been verified via numerous experiments.Experimental results on the MNIST,Fashion-MNIST,and Cifar-10 datasets show that EMD-MQMFL improves the average model accuracy by at least 5.6 percentage points compared with existing mechanisms for cases involving imbalanced data.
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