Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Framework

• URL: http://arxiv.org/abs/2401.01493v1
• Date: Wed, 3 Jan 2024 01:45:00 GMT
• Title: Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Framework
• Authors: Shengchao Chen, Ting Shu, Huan Zhao, Jiahao Wang, Sufen Ren, Lina Yang
• Abstract summary: This paper proposes a novel Privacy-Reserving TFGC Framework based on Federated Learning, dubbed PRFL. We demonstrate the effectiveness of the proposed PRFL on the classical TFGC task by leveraging four public datasets.
• Score: 23.933367972846312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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