mlsanityData to 7 Oct 2026

F-KANs: Federated Kolmogorov-Arnold Networks

arXiv 2407.20100 ↗First posted 29 Jul 2024cs.LG cs.AI cs.CRcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 7 of its 13 references that are in the map. First posted on arXiv 29 Jul 2024.

Builds on

7 of 13 references.

References · 7

  1. 2024KAN: Kolmogorov-Arnold Networkscited by 634
  2. 2024Kolmogorov-Arnold Networks (KANs) for Time Series Analysiscited by 97
  3. 2024Convolutional Kolmogorov-Arnold Networkscited by 76
  4. 2024Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretabilitycited by 69
  5. 2024GKAN: Graph Kolmogorov-Arnold Networkscited by 52
  6. 2024Demonstrating the Efficacy of Kolmogorov-Arnold Networks in Vision Taskscited by 39
  7. 2024Kolmogorov-Arnold Graph Neural Networkscited by 31

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 10; 2026: 1.

14 papers.

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