F-KANs: Federated Kolmogorov-Arnold Networks
This work addresses privacy-preserving predictive analytics for federated learning users, but it appears incremental as it applies an existing method (KANs) to a federated setting.
The paper tackled classification tasks by proposing federated Kolmogorov-Arnold Networks (F-KANs), which significantly outperformed federated MLPs in accuracy, precision, recall, F1 score, and stability.
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.