基于数据分布的聚类联邦学习

联邦学习(federated learning)可以解决分布式机器学习中基于隐私保护的数据碎片化和数据隔离问题。在联邦学习系统中，各参与者节点合作训练模型，利用本地数据训练局部模型，并将训练好的局部模型上传到服务器节点进行聚合。在真实的应用环境中，各节点之间的数据分布往往具有很大差异，导致联邦学习模型精确度较低。为了解决非独立同分布数据对模型精确度的影响，利用不同节点之间数据分布的相似性，提出了一个聚类联邦学习框架。在Synthetic、CIFAR-10和FEMNIST标准数据集上进行了广泛实验。与其他联邦学习方法相比，基于数据分布的聚类联邦学习对模型的准确率有较大提升，且所需的计算量也更少。

Clustering federated learning based on data distribution

Federated learning is designed to solve the problem of data fragmentation and data isolation based on privacy protection in distributed machine learning. In the federated learning system, participants collaboratively train a model. Each participant uses local data to train the local model, and uploals the trained local model to the server for aggregation. In the real application environment, the data distribution between nodes is often very different, resulting in the accuracy of federated learning model is low. In order to solve the influence of non-independent identically distributed data on the accuracy of the model, this paper proposed a clustering federated learning framework by using the similarity of data distribution between different nodes. Extensive experiments were conducted on Synthetic, CIFAR-10 and FEMNIST standard datasets. Compared with other federated learning methods, clustering federated learning based on data distribution greatly improves the accuracy of the model and requires less computation.