Free Lunch for Federated Remote Sensing Target Fine-Grained
  Classification: A Parameter-Efficient Framework
Shengchao Chen Ting Shu Huan Zhao Jiahao Wang Sufen Ren Lina Yang
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.
Additionally, low-resource remote sensing devices encounter challenges in terms
of communication overhead and efficiency when dealing with the ever-increasing
data and model scales. To solve the above 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). Thus, it provides highly customized models to clients with
differentiated data distributions. Moreover, the framework minimizes
communication overhead and improves efficiency while ensuring satisfactory
performance, thereby enhancing robustness and practical applicability under
resource-scarce conditions. We demonstrate the effectiveness of the proposed
PRFL on the classical TFGC task by leveraging four public datasets.