F-KANs: Federated Kolmogorov-Arnold Networks
<|reference_start|>F-KANs: Federated Kolmogorov-Arnold Networks: 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.<|reference_end|>
@article{zeydan2024f-kans:,
    title={F-KANs: Federated Kolmogorov-Arnold Networks},
    author={Engin Zeydan, Cristian J. Vaca-Rubio, Luis Blanco, Roberto Pereira,
  Marius Caus, Abdullah Aydeger},
    journal={arXiv preprint arXiv:2407.20100},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20100},
    primaryClass={cs.LG cs.AI cs.CR cs.NI}
}