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F-KANs: Federated Kolmogorov-Arnold Networks
- Publication Year :
- 2024
-
Abstract
- 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.<br />Comment: This work has been submitted to IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Related Code: https://github.com/ezeydan/F-KANs.git
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2407.20100
- Document Type :
- Working Paper