  This paper presents the development and application of Wavelet
Kolmogorov-Arnold Networks (Wav-KAN) in federated learning. We implemented
Wav-KAN \cite{wav-kan} in the clients. Indeed, we have considered both
continuous wavelet transform (CWT) and also discrete wavelet transform (DWT) to
enable multiresolution capabaility which helps in heteregeneous data
distribution across clients. Extensive experiments were conducted on different
datasets, demonstrating Wav-KAN's superior performance in terms of
interpretability, computational speed, training and test accuracy. Our
federated learning algorithm integrates wavelet-based activation functions,
parameterized by weight, scale, and translation, to enhance local and global
model performance. Results show significant improvements in computational
efficiency, robustness, and accuracy, highlighting the effectiveness of wavelet
selection in scalable neural network design.
