In this paper, we present an innovative federated learning (FL) approach that utilizes Kolmogorov-Arnold Networks (KANs) for classification tasks. By utilizing the adaptive activation capabilities of KANs in a federated framework, we aim to improve classification capabilities while preserving privacy. The study evaluates the performance of federated KANs (F- KANs) compared to traditional Multi-Layer Perceptrons (MLPs) on classification task. The results show that the F-KANs model significantly outperforms the federated MLP model in terms of accuracy, precision, recall, F1 score and stability, and achieves better performance, paving the way for more efficient and privacy-preserving predictive analytics.

翻译：本文提出了一种创新的联邦学习方法，该方法利用柯尔莫哥洛夫-阿诺德网络（KANs）进行分类任务。通过在联邦框架中利用KANs的自适应激活能力，我们旨在提升分类性能的同时保护数据隐私。本研究评估了联邦KANs（F-KANs）与传统多层感知机（MLPs）在分类任务上的性能对比。结果表明，F-KANs模型在准确率、精确率、召回率、F1分数和稳定性方面均显著优于联邦MLP模型，实现了更优的性能，为更高效且保护隐私的预测分析开辟了新途径。