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Federated learning (FL) enables the collaboration of multiple deep learningmodels to learn from decentralized data archives (i.e., clients) withoutaccessing data on clients. Although FL offers ample opportunities in knowledgediscovery from distributed image archives, it is seldom considered in remotesensing (RS). In this paper, as a first time in RS, we present a comparativestudy of state-of-the-art FL algorithms. To this end, we initially provide asystematic review of the FL algorithms presented in the computer visioncommunity for image classification problems, and select severalstate-of-the-art FL algorithms based on their effectiveness with respect totraining data heterogeneity across clients (known as non-IID data). Afterpresenting an extensive overview of the selected algorithms, a theoreticalcomparison of the algorithms is conducted based on their: 1) local trainingcomplexity; 2) aggregation complexity; 3) learning efficiency; 4) communicationcost; and 5) scalability in terms of number of clients. As the classificationtask, we consider multi-label classification (MLC) problem since RS imagestypically consist of multiple classes, and thus can simultaneously beassociated with multi-labels. After the theoretical comparison, experimentalanalyses are presented to compare them under different decentralizationscenarios in terms of MLC performance. Based on our comprehensive analyses, wefinally derive a guideline for selecting suitable FL algorithms in RS. The codeof this work will be publicly available at https://git.tu-berlin.de/rsim/FL-RS.

Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification