  This study delves into the classification of interference signals to global
navigation satellite systems (GNSS) stemming from mobile jammers such as
unmanned aerial vehicles (UAVs) across diverse wireless communication zones,
employing federated learning (FL) and transfer learning (TL). Specifically, we
employ a neural network classifier, enhanced with FL to decentralize data
processing and TL to hasten the training process, aiming to improve
interference classification accuracy while preserving data privacy. Our
evaluations span multiple data scenarios, incorporating both independent and
identically distributed (IID) and non-identically distributed (non-IID), to
gauge the performance of our approach under different interference conditions.
Our results indicate an improvement of approximately $8\%$ in classification
accuracy compared to basic convolutional neural network (CNN) model,
accompanied by expedited convergence in networks utilizing pre-trained models.
Additionally, the implementation of FL not only developed privacy but also
matched the robustness of centralized learning methods, particularly under IID
scenarios. Moreover, the federated averaging (FedAvg) algorithm effectively
manages regional interference variability, thereby enhancing the regional
communication performance indicator, $C/N_0$, by roughly $5\text{dB}\cdot
\text{Hz}$ compared to isolated setups.
