  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 for RS image classification problems.
To this end, we initially provide a systematic review of the FL algorithms
presented in the computer vision and machine learning 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 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. After the
theoretical comparison, experimental analyses are presented to compare them
under different decentralization scenarios. For the experimental analyses, we
focus our attention on multi-label image classification 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.
