The Recognition of Remote Sensing Image based on Federated Generation Adversarial Network

Abstract

Recently, the processing of Synthetic Aperture Radar(SAR) has been gradually transitioning from traditional electromagnetic simulation to intelligent detection and recognition. As an intelligent architecture, Federated Learning(FL) has attracted increasing attention for the processing of SAR images due to its capacity for data protection and cooperative gain. In this paper, a new FL scheme named Fed-CWGP is proposed, where Wasserstein Generation Adversarial Network with gradient penalty(WGAN-gp) is considered to expand samples locally for the issue of few-shot learning. Additionally, the Conditional Generation Adversarial Network(CGAN) is used to control the direction of sample generation, solving the problem of non-independent and non-identical distribution of data distribution between nodes. This proposed scheme allows local classifiers and cloud servers to exchange and collaboratively learn model parameters, which improves the performance of global and node classification models and reduces the transmission delay between models. To enhance the privacy of model uploading, differential privacy is introduced on the gradient of each node model. Simulation results state that the proposed Fed-CWGP outperforms the classical schemes.