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

arXiv: https://arxiv.org/abs/2403.13575 · HTML: https://mlsanity.com/paper/2403.13575/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification](https://mlsanity.com/paper/1608.05167/) (2016)
- [NormFace: L2 Hypersphere Embedding for Face Verification](https://mlsanity.com/paper/1704.06369/) (2017)
- [Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning](https://mlsanity.com/paper/2306.00792/) (2023)

## Built on by (most cited first, 4 of 4)

- [FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models](https://mlsanity.com/paper/2501.02461/) (2025)
- [FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing](https://mlsanity.com/paper/2505.08325/) (2025)
- [FedX: Explanation-Guided Pruning for Communication-Efficient Federated Learning in Remote Sensing](https://mlsanity.com/paper/2508.06256/) (2025)
- [Leveraging Membership Inference Attacks for Privacy Measurement in Federated Learning for Remote Sensing Images](https://mlsanity.com/paper/2601.06200/) (2026)

## Abstract (arXiv)

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.