Title: HyperKAN: A Plug-and-Play Tool for Personalized Weights Generation
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Personalized weight generation, Kolmogorov- Arnold Networks, Personalized Federated Learning

Abstract: Personalized weight generation is vital for adapting models to tailored patterns in real-world applications. However, existing methods struggle to capture nuanced feature variations across heterogeneous data distributions, resulting in suboptimal personalization performance. In this paper, we introduce HyperKAN, a plug-and-play personalized weights generator that integrates Kolmogorov-
Arnold Networks (KANs) for capturing feature variations among clients and enhancing personalization capacity. We design a novel
Personalized Federated Learning (pFL) framework that embed HyperKAN, enabling tailored model aggregation for each client with faster convergence. Our evaluations on four datasets present HyperKAN’s versatility and effectiveness, achieving up to 48% higher ac-
curacy than state-of-the-art methods. In a nutshell, HyperKAN offers a highly adaptable solution for enhancing model personalization, par-
ticularly in non-IID scenarios that challenge traditional weight generation approaches.