标题： 基于自进化高斯聚类的联邦学习 显示英文标题

标题： Federated Learning based on Self-Evolving Gaussian Clustering

摘要： 在本研究中，我们提出了一种在联邦学习背景下的动态模糊系统，该系统能够随着新聚类的添加而动态适应，因此不需要预先选择聚类的数量。 与传统方法不同，联邦学习允许模型在客户端设备上本地训练，仅与中央服务器共享模型参数而非数据。 我们的方法使用PyTorch实现，在聚类和分类任务上进行了测试。 结果表明，我们的方法在几个著名的UCI数据集上优于现有的分类方法。 尽管由于重叠条件计算而计算量较大，但所提出的方法在去中心化数据处理中表现出显著优势。 显示英文摘要

摘要： In this study, we present an Evolving Fuzzy System within the context of Federated Learning, which adapts dynamically with the addition of new clusters and therefore does not require the number of clusters to be selected apriori. Unlike traditional methods, Federated Learning allows models to be trained locally on clients' devices, sharing only the model parameters with a central server instead of the data. Our method, implemented using PyTorch, was tested on clustering and classification tasks. The results show that our approach outperforms established classification methods on several well-known UCI datasets. While computationally intensive due to overlap condition calculations, the proposed method demonstrates significant advantages in decentralized data processing.