Federated learning (FL) enables the collaboration of multiple deep learning (DL) models to learn from decentralized data archives (i.e., clients) without accessing data on the clients. Although FL offers ample opportunities in knowledge discovery from distributed image archives, it is seldom considered in remote sensing (RS). In this article, for the first time in RS, we present a comparative study of state-of-the-art FL algorithms for RS image classification problems. To this end, we initially provide a systematic review of the FL algorithms presented in the computer vision (CV) and machine learning (ML) communities. Then, we select several state-of-the-art FL algorithms based on their effectiveness with respect to training data heterogeneity across clients [known as not independent and identically distributed (non-IID) data]. After presenting an extensive overview of the selected algorithms, we conduct a theoretical comparison of them 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. After the theoretical comparison, experimental analyses are presented to compare the algorithms under different decentralization scenarios. For the experimental analyses, we focus our attention on multilabel image classification (MLC) problems in RS. Based on our comprehensive analyses, we finally derive a guideline for selecting suitable FL algorithms in RS. The code of this work is publicly available at https://git.tu-berlin.de/rsim/FL-RS .

中文翻译：

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跨分散和非共享档案的遥感图像分类联合学习：综述


联邦学习 (FL) 允许多个深度学习 (DL) 模型协作，从分散的数据档案（即客户端）中学习，而无需访问客户端上的数据。尽管 FL 为从分布式图像档案中发现知识提供了充足的机会，但在遥感 (RS) 中很少考虑它。在本文中，我们首次在 RS 领域针对 RS 图像分类问题对最先进的 FL 算法进行了比较研究。为此，我们首先对计算机视觉 (CV) 和机器学习 (ML) 社区中提出的 FL 算法进行系统回顾。然后，我们根据客户端训练数据异质性（称为非独立同分布（非 IID）数据）的有效性，选择几种最先进的 FL 算法。在对所选算法进行广泛概述之后，我们根据它们的 1）本地训练复杂性，2）聚合复杂性，3）学习效率，4）通信成本和 5）在数量方面的可扩展性对它们进行了理论比较。客户。经过理论比较后，进行了实验分析，以比较不同去中心化场景下的算法。对于实验分析，我们将注意力集中在 RS 中的多标签图像分类（MLC）问题。基于我们的综合分析，我们最终得出了在 RS 中选择合适的 FL 算法的指南。这项工作的代码可在 https://git.tu-berlin.de/rsim/FL-RS 上公开获取。