Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification: A review
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 git.tu-berlin.de/rsim/FL-RS .
