关键词: 联邦学习, 客户端选择, 数据异构性, 系统异构性, 服务质量, 互信息。

Abstract: The practical deployment of federated learning is limited by dual client heterogeneity, which involves non-Independent and Identically Distributed data and varied system resources. This phenomenon constrains both training efficiency and global model performance. Client selection strategy is a key method to address these issues. However, existing client selection strategies often focus on a single dimension of heterogeneity and show poor adaptation to dynamic client environments. To address the dynamic changes in client computing resources and network status, as well as the problem of dual heterogeneity, a service-defined federated learning client selection algorithm, FedQos, is proposed. It draws inspiration from the "Everything as a Service" paradigm in service computing. The algorithm first redefines federated learning clients as orchestratable and measurable AI microservice units. It uses a Quality of Service aware scheduling algorithm to dynamically evaluate client response times and prioritizes clients with high system reliability to overcome system heterogeneity. Second, the algorithm quantifies the potential value of client data by calculating the mutual information between the local data of a candidate client and the global model. It selects clients with greater potential value to accelerate convergence. At the same time, the algorithm incorporates an information-theoretic regularization method during local client training. This method addresses data heterogeneity and enhances the generalization performance of the federated learning model. Experimental results on the CIFAR-10 dataset show that the FedQos algorithm improves test accuracy by up to 4.62 percentage points. Regarding training efficiency, the algorithm requires the shortest cumulative time to complete the same number of training rounds across all test scenarios.

Key words: federated learning, client selection, data heterogeneity, system heterogeneity, quality of service, mutual information.