KAN模型在医疗影像联邦学习中表现优于传统MLP，更省资源。
A Unified Benchmark of Federated Learning with Kolmogorov-Arnold Networks for Medical Imaging
- 用KAN替代传统MLP，在联邦学习中实现更优性能。
- 在非独立同分布数据下，网格大小与网络结构影响模型效果。
- 宽度优化比深度增加更能提升联邦学习中的性能，适合医疗隐私场景。
联邦学习（FL）可在不共享原始数据的前提下跨分布式设备训练模型，从而保护医疗等敏感领域的隐私。本文在血细胞分类数据集上，评估了柯尔莫哥洛夫-阿诺德网络（KAN）与传统多层感知机（MLP）在六种先进联邦学习算法中的表现。实验表明，KAN可有效替代联邦环境中的MLP，以更简单的架构实现更优性能。此外，我们分析了关键超参数——网格大小与网络结构——在不同非独立同分布（Non-IID）数据分布下的影响。消融研究进一步揭示，在联邦设置中保持最小深度、优化宽度能获得最佳表现。这些发现确立了KAN在分布式医疗影像隐私保护应用中的潜力。据我们所知，这是首个针对医疗影像任务的联邦学习中KAN的综合性基准。
原文摘要 · Abstract (English)
Federated Learning (FL) enables model training across decentralized devices without sharing raw data, thereby preserving privacy in sensitive domains like healthcare. In this paper, we evaluate Kolmogorov-Arnold Networks (KAN) architectures against traditional MLP across six state-of-the-art FL algorithms on a blood cell classification dataset. Notably, our experiments demonstrate that KAN can effectively replace MLP in federated environments, achieving superior performance with simpler architectures. Furthermore, we analyze the impact of key hyperparameters-grid size and network architecture-on KAN performance under varying degrees of Non-IID data distribution. In addition, our ablation studies reveal that optimizing KAN width while maintaining minimal depth yields the best performance in federated settings. As a result, these findings establish KAN as a promising alternative for privacy-preserving medical imaging applications in distributed healthcare. To the best of our knowledge, this is the first comprehensive benchmark of KAN in FL settings for medical imaging task.
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