Unmanned Aerial Vehicles (UAVs), or drones, are gaining popularity across several industries because of their adaptability, efficiency, and capacity to reach difficult-to-access regions. However, certain UAV applications carry critical or hazardous risks due to the environments in which they are used or the nature of the task. The main challenge associated with UAV communication is different types of cyberattacks. Hence, in this paper, a federated learning-based cyber-physical Intrusion Detection System (IDS) is proposed for UAV communication. A lightweight ANN model is embedded with each UAV as well as the central server. During learning, a chunk of data is used to train the local ANN models and, their weights are shared with the global model. The global model performs its weight updation and sends these weights back to each local model. The performance of the global model in terms of accuracy is compared against an isolated ANN model trained over the same data. An appreciable increase in the accuracy of the global model can be observed from this comparison. In addition, the performance of the local model and the global model is determined based on recall, precision, and F1-score.