Federated Learning Across Decentralized and Unshared Archives for Remote
  Sensing Image Classification
Barış Büyüktaş Gencer Sumbul Begüm Demir
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.