F-KANs: Federated Kolmogorov-Arnold Networks

• URL: http://arxiv.org/abs/2407.20100v2
• Date: Tue, 30 Jul 2024 11:27:55 GMT
• Title: F-KANs: Federated Kolmogorov-Arnold Networks
• Authors: Engin Zeydan, Cristian J. Vaca-Rubio, Luis Blanco, Roberto Pereira, Marius Caus, Abdullah Aydeger,
• Abstract summary: We present an innovative federated learning (FL) approach that utilizes Kolmogorov-Arnold Networks (KANs) for classification tasks. The study evaluates the performance of federated KANs compared to traditional Multi-Layer Perceptrons (MLPs) classification task.
• Score: 3.8277268808551512
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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