Federated learning (FL) enables the collaboration of multiple deep learning models to learn from decentralized data archives (i.e., clients) without accessing data on clients. Although FL offers ample opportunities in knowledge discovery from distributed image archives, it is seldom considered in remote sensing (RS). In this paper, as a first time in RS, we present a comparative study of state-of-the-art FL algorithms. To this end, we initially provide a systematic review of the FL algorithms presented in the computer vision community for image classification problems, and select several state-of-the-art FL algorithms based on their effectiveness with respect to training data heterogeneity across clients (known as non-IID data). After presenting an extensive overview of the selected algorithms, a theoretical comparison of the algorithms is conducted based on their: 1) local training complexity; 2) aggregation complexity; 3) learning efficiency; 4) communication cost; and 5) scalability in terms of number of clients. As the classification task, we consider multi-label classification (MLC) problem since RS images typically consist of multiple classes, and thus can simultaneously be associated with multi-labels. After the theoretical comparison, experimental analyses are presented to compare them under different decentralization scenarios in terms of MLC performance. Based on our comprehensive analyses, we finally derive a guideline for selecting suitable FL algorithms in RS. The code of this work will be publicly available at https://git.tu-berlin.de/rsim/FL-RS.

翻译：联邦学习（FL）允许多个深度学习模型协同学习来自去中心化数据档案库（即客户端）的知识，而无需访问客户端数据。尽管FL在从分布式图像档案库进行知识发现方面提供了充足机遇，但该技术在遥感（RS）领域鲜有应用。本文首次在遥感领域对最先进的FL算法进行对比研究。为此，我们首先系统梳理了计算机视觉领域针对图像分类问题提出的FL算法，并基于其应对客户端间训练数据异质性（即非独立同分布数据）的有效性，选取了若干代表性FL算法。在对所选算法进行全面概述后，我们从以下五个维度展开理论对比：1）局部训练复杂度；2）聚合复杂度；3）学习效率；4）通信开销；5）客户端数量可扩展性。由于遥感图像通常包含多个类别并可同时关联多个标签，故将多标签分类（MLC）问题作为基准任务。在理论对比之后，我们通过实验分析比较了不同去中心化场景下各算法的MLC性能。基于综合分析，最终推导出在遥感领域选择合适FL算法的指导原则。本工作代码将公开于https://git.tu-berlin.de/rsim/FL-RS。