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：在联邦学习应用于遥感图像分类的领域中，本研究引入并评估了几种创新的通信策略。我们的探索包括以特征为中心的通信、伪权重融合以及同时利用权重和特征的组合方法。在两个公开场景分类数据集上进行的实验揭示了这些策略的有效性，展示了加速收敛、增强隐私保护和减少网络信息交换的效果。本研究为联邦学习中以特征为中心的通信提供了有价值的见解，并为遥感场景量身定制了潜在的应用方向。