Federated Augmentation-Enabled Low-Altitude Economy Networks: Challenges, Methodologies, and Applications

Low-Altitude Economy (LAE) networks, driven by UAVs, smart drones, and aerial-ground collaborative platforms, transform applications like surveillance, inspection, and precision agriculture. In these systems, data are inherently distributed across numerous edge devices, making centralized data aggregation impractical. Federated Learning (FL) delivers a distributed machine learning paradigm, enabling on-device model updates without centralized data collection and powering critical functions like aerial image classification and anomaly detection. However, FL still faces severe challenges in LAE environments, including data imbalance, extreme feature diversity, and weak generalization capabilities across highly heterogeneous edge nodes. The objective of this work is to address these challenges by introducing Federated Augmentation (FA) as a novel framework specifically adapted to LAE networks. Unlike conventional FL methods, our approach enriches local training with synthetic feature generation while preserving privacy and communication efficiency, thereby tackling both data scarcity and non-IID feature skew. We present an image classification case study demonstrating how SDP integration into the FA pipeline enhances privacy protection without compromising model utility. Finally, we outline key research directions to advance efficient, adaptive, and privacy-aware learning in future low-altitude systems.