Dynamic Fourier Federated Learning for Fully Connected Distributed Network

As the demand for seamless connectivity across distributed networks increases, traditional federated learning (FL) models struggle to maintain accuracy and efficiency in dynamic environment. Conventional approaches, such as Random Fourier Features-based Kernel Least Mean Squares (RFF-KLMS), often fail to adapt to changing data distributions, leading to performance degradation. To address this issue, the Dynamic Fourier Federated Learning (DFFL) algorithm is introduced, incorporating adaptive Fourier features that iteratively evolve to better align with current data patterns. Simulation results demonstrate that the DFFL algorithm significantly enhances the convergence rate of FL models, ensuring reliable and seamless connectivity in real-world applications where data characteristics are continuously changing.