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CG-FKAN: Compressed-Grid Federated Kolmogorov-Arnold Networks for Communication Constrained Environment

arXiv 2511.01433 ↗First posted 3 Nov 2025cs.LGcited by 0

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Abstract (arXiv)

Federated learning (FL), widely used in privacy-critical applications, suffers from limited interpretability, whereas Kolmogorov-Arnold Networks (KAN) address this limitation via learnable spline functions. However, existing FL studies applying KAN overlook the communication overhead introduced by grid extension, which is essential for modeling complex functions. In this letter, we propose CG-FKAN, which compresses extended grids by sparsifying and transmitting only essential coefficients under a communication budget. Experiments show that CG-FKAN achieves up to 13.6% lower RMSE than fixed-grid KAN in communication-constrained settings. In addition, we derive a theoretical upper bound on its approximation error.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 3 Nov 2025.

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References · 12

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2024KAN: Kolmogorov-Arnold Networkscited by 634
  4. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  5. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 247
  6. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  7. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 94
  8. 2018Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communicationcited by 51
  9. 2024F-KANs: Federated Kolmogorov-Arnold Networkscited by 14
  10. 2022Compressed Particle-Based Federated Bayesian Learning and Unlearningcited by 5
  11. 2024Enabling Trustworthy Federated Learning in Industrial IoT: Bridging the Gap Between Interpretability and Robustnesscited by 3
  12. 2025A Unified Benchmark of Federated Learning with Kolmogorov-Arnold Networks for Medical Imagingcited by 3

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