mlsanityData to 14 Oct 2026

Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Framework

arXiv 2401.01493 ↗First posted 3 Jan 2024cs.LG cs.AI cs.CRcited by 6

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Abstract (arXiv)

Remote Sensing Target Fine-grained Classification (TFGC) is of great significance in both military and civilian fields. Due to location differences, growth in data size, and centralized server storage constraints, these data are usually stored under different databases across regions/countries. However, privacy laws and national security concerns constrain researchers from accessing these sensitive remote sensing images for further analysis. Additionally, low-resource remote sensing devices encounter challenges in terms of communication overhead and efficiency when dealing with the ever-increasing data and model scales. To solve the above challenges, this paper proposes a novel Privacy-Reserving TFGC Framework based on Federated Learning, dubbed PRFL. The proposed framework allows each client to learn global and local knowledge to enhance the local representation of private data in environments with extreme statistical heterogeneity (non. Independent and Identically Distributed, IID). Thus, it provides highly customized models to clients with differentiated data distributions. Moreover, the framework minimizes communication overhead and improves efficiency while ensuring satisfactory performance, thereby enhancing robustness and practical applicability under resource-scarce conditions. We demonstrate the effectiveness of the proposed PRFL on the classical TFGC task by leveraging four public datasets.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 29 of its 63 references that are in the map. First posted on arXiv 3 Jan 2024.

Builds on

29 of 63 references.

References · 29

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17 more
  1. 2021Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Bettercited by 55
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 1.

6 papers.

  1. 2025FeDaL: Federated Dataset Learning for General Time Series Foundation Modelscited by 10
  2. 2024Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classificationcited by 5
  3. 2026Bi-level Heterogeneous Learning for Time Series Foundation Models: A Federated Learning Approachcited by 2
  4. 2025A Multi-Modal Federated Learning Framework for Remote Sensing Image Classificationcited by 1
  5. 2025Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threatscited by 1
  6. 2024Less is more: Ensemble Learning for Retinal Disease Recognition Under Limited Resourcescited by 0