【摘要】 联邦学习(Federated Learning)是为了解决机器学习中以隐私保护为前提的数据碎片化和隔离问题。各客户端节点在本地训练数据，将训练的模型参数信息上传到中央服务器，由参数服务器聚合参数信息以达到共同训练的目的。由于现实环境中，各节点数据之间的分布往往不一致，通过分析非独立同分布数据对联邦学习准确率的影响，来证明传统联邦学习方法得到的模型精度较低。因此，采用多样化抽样策略模拟数据倾斜度分布，提出了基于DBSCAN(Density-Based Spatial Clustering of Applications with Noise)聚类的集群联邦学习算法(DBSCAN Based Cluster Federated Learning, DCFL),解决了联邦学习中不同节点的数据非独立同分布降低了学习准确率的问题。在Mnist和Cifar-10标准数据集上进行了实验，相比传统的联邦学习算法，基于DBSCAN聚类的集群联邦学习算法对模型的准确率有较大的提升。更多还原

【Abstract】 Federated learning is to solve the problem of data fragmentation and isolation in machine learning based on privacy protection.Each client node trains the data locally and uploads the model parameter information to the central server, which aggregates the parameter information to achieve the purpose of common training.In the real environment, the distribution of data among nodes is often inconsistent.By analyzing the influence of independent identically distributed data on the accuracy of federated learning, it is proved that the accuracy of the model obtained by the traditional federated learning method is low.Therefore, a diversified sampling strategy is adopted to simulate the data inclination distribution, and a Clustered Federated Learning Methods algorithm based on DBSCAN clustering(DCFL) is proposed, which solves the problem that the learning accuracy is reduced when the data of different nodes are not independently and identically distributed in federated learning.Through the experimental comparison of Mnist and Cifar-10 standard data sets, compared with the traditional federated learning algorithm, DCFL can greatly improve the accuracy of the model.更多还原