Abstract
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.
Abstract (translated)
在应用于远程 sensing图像分类领域的联邦学习（FL）领域，本文介绍并评估了几个创新性的通信策略。我们的探索包括基于特征的通信、伪权重合并和结合使用权重和特征的方法。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、提高隐私和减少网络信息交互。这项研究为FL中基于特征的通信提供了宝贵的洞见，为远程 sensing场景提供了潜在的应用。
URL
https://arxiv.org/abs/2403.13575
