Ensuring reliable participation in UAV-enabled federated edge learning on non-IID (non-Independent and Identically Distributed) data is a complex challenge due to the variability in data distribution and the mobility of UAVs. This research focuses on developing strategies to enhance the reliability and efficiency of federated learning in such settings. By addressing issues such as data heterogeneity, communication overhead, and resource constraints, the study proposes novel mechanisms to optimize model aggregation and training processes. These advancements aim to enable robust and scalable federated learning frameworks, facilitating real-world applications in distributed, dynamic, and resource-constrained environments.
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