Free lunch for federated remote sensing target fine-grained classification: a parameter-efficient framework

Remote Sensing Target Fine-grained Classification (TFGC) is of great significance in both military and civilian fields. Due to location differences, growth in data size, and centralized server storage constraints, these data are usually stored under different databases across regions/countries. However, privacy laws and national security concerns constrain researchers from accessing these sensitive remote sensing images for further analysis. Moreover, low-resource remote sensing devices face challenges in communication overhead and efficiency when dealing with the ever-increasing data and model scales. To address these challenges, this paper proposes a novel Privacy-Reserving TFGC Framework based on Federated Learning, dubbed PRFL. The proposed framework allows each client to learn global and local knowledge to enhance the local representation of private data in environments with extreme statistical heterogeneity (non. Independent and Identically Distributed, IID). It provides highly customized models to clients with differentiated data distributions. Furthermore, the framework minimizes communication overhead and improves efficiency while ensuring satisfactory performance, enhancing robustness and practical applicability under resource-scarce conditions. We demonstrate the effectiveness of PRFL on the classical TFGC task using four public datasets.