提出自演化高斯聚类联邦学习，无需预先设定聚类数。
Federated Learning based on Self-Evolving Gaussian Clustering
- 基于自演化模糊系统动态增删聚类，无需预设聚类数量。
- 在多个UCI数据集上优于传统分类方法，提升模型性能。
- 适合分布式数据场景，尤其适用于隐私敏感的医疗、金融领域。
本研究提出一种基于联邦学习的自演化模糊系统，能够动态适应新聚类的加入，无需预先设定聚类数量。与传统方法不同，联邦学习允许在客户端设备上本地训练模型，仅向中心服务器共享模型参数而非原始数据。该方法使用PyTorch实现，在聚类与分类任务中进行了测试。结果表明，该方法在多个知名UCI数据集上优于现有分类方法。尽管因重叠条件计算导致计算开销较大，但其在去中心化数据处理中展现出显著优势。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。