  In the realm of Federated Learning (FL) applied to remote sensing image
classification, this study introduces and assesses several innovative
communication strategies. Our exploration includes feature-centric
communication, pseudo-weight amalgamation, and a combined method utilizing both
weights and features. Experiments conducted on two public scene classification
datasets unveil the effectiveness of these strategies, showcasing accelerated
convergence, heightened privacy, and reduced network information exchange. This
research provides valuable insights into the implications of feature-centric
communication in FL, offering potential applications tailored for remote
sensing scenarios.
