基于DBSCAN聚类的联邦学习算法优化

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引文格式：，.基于DBSCAN聚类的联邦学习算法优化[J].理工大学学报（自然科学版），2025，45（2）：92-99.DOI：10. 3969/j. issn.1672-1098. 2025.02.012

中图分类号：TP391 文献标志码：A 文章编号：1672-1098（2025）02-0092-08

Federated Learning Algorithm Optimization Based on DBSCAN Clustering WANG Guoming，LI Miaomiao

（School ofComputerScienceandEngineering，Anhui UniversityofScienceand Technology，HuainanAnhui 232Ool，China） Abstract：Objective In federated learning，each node generates its own local data independently，resulting in heterogeneity between the data.During the training，heterogeneity can cause a gradient drift in the local model generated by federated learning，whichdecreases the accuracy and convergence speed of the federated learning model. Methods To address this isse，a federated learning optimization algorithm were introduced based on DBSCAN clustering，denoted as FLDC，which utilized the DBSCAN algorithm for clustering and grouping local clients.The client data distribution within the layer was similar.Additionally，it utilized a hierarchical sampling method to select clients from each layer and created a subset of clients，improving the diversity of training dataand ensuring thatthe clientdata samples in the subset reflected the global data distribution characteristics.Results Experiments on MNIST and CIFAR-1O showed that FLDC achieved 0.29% to 8.38% higheraccuracyand faster convergencecompared to the benchmark algorithm.Conclusion FLDC efectively reduces the impact of heterogeneous data on model performance in heterogeneous scenarios.

Key Words ： machine learning; federated learning; data heterogeneity； clustering

为了应对集中式机器学习中必须要求用户数据共享的挑战，Google提出了联邦学习（FederatedLearning，FL）[1]。（剩余10472字）