# Leveraging feature communication in federated learning for remote sensing image classification

Source: [Leveraging feature communication in federated learning for remote sensing image classification](<https://www.sota2.com/research/paper/68fa73411e209131cea5b51a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:45:17.730Z

Authors: Anh-Kiet Duong, Ho\\\`ang-\\^An L\\^e, Minh-Tan Pham

Published: 2024-03-20T13:20:05.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.13575>)

[PDF](<https://arxiv.org/pdf/2403.13575.pdf>)