Asynchronous and Synchronous Federated Learning-based UAVs

Unmanned aerial vehicles (UAVs) can be used to support data gathering, training of models, and wireless communication by acting as flying Base Stations (BSs). The usage of UAVs for wireless networks is quickly expanding in areas such as tracking as well as surveillance, defense, leading healthcare deliveries, telecommunications, and so on. As Deep Learning (DL)-assisted approaches require sending raw data from model training devices to UAV servers which is problematic because of device privacy concerns and UAVs' limited processing or communication resources. Federated Learning (FL), which is also called distributed deep learning, is presented as a solution, with the core notion of keeping original or raw data where it is created while transferring the only user's localized trained DL models to a centralized organization for aggregation. We will build a Synchronous Federated Learning (SFL) structure for multi-UAVs and also the comparative analysis of Asynchronous Federated Learning (AFL) and SFL.The SFL methodology will take some time to execute, but there will be no data loss or packet loss. AFL, on the other hand, takes less time but results in packet data loss. Simulation findings suggest that our proposed framework and technique improve global rounds and learning accuracy as compared to AFL.