In the realm of Federated Learning (FL) applied to remote sensing image classification, this study introduces and assesses several innovative communication strategies. Our exploration includes feature-centric communication, pseudo-weight amalgamation, and a combined method utilizing both weights and features. Experiments conducted on two public scene classification datasets unveil the effectiveness of these strategies, showcasing accelerated convergence, heightened privacy, and reduced network information exchange. This research provides valuable insights into the implications of feature-centric communication in FL, offering potential applications tailored for remote sensing scenarios.

翻译：在应用于遥感图像分类的联邦学习（FL）领域，本研究引入并评估了若干创新的通信策略。我们的探索包括以特征为中心的通信、伪权重融合以及一种结合权重与特征的混合方法。在两个公共场景分类数据集上进行的实验揭示了这些策略的有效性，展示了更快的收敛速度、增强的隐私保护以及减少的网络信息交换。本研究为以特征为中心的通信在联邦学习中的影响提供了有价值的见解，并提出了针对遥感场景的潜在应用。