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F-KANs: Federated Kolmogorov-Arnold Networks
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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.
Forward citations
Cited by 2 Pith papers
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Concurrent training methods for Kolmogorov-Arnold networks: Disjoint datasets and FPGA implementation
Three concurrency tricks — group-wise pre-training, disjoint-batch training with parameter averaging, and fixed-point FPGA execution — speed up Newton-Kaczmarz KAN training, with measured CPU gains of ~7-9x over the a...
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Enhancing Federated Learning with Kolmogorov-Arnold Networks: A Comparative Study Across Diverse Aggregation Strategies
KANs achieve higher or comparable accuracy to MLPs across four tabular datasets in simulated federated learning, using fewer communication rounds.
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