mlsanityData to 14 Oct 2026

FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensing

arXiv 2505.08325 ↗First posted 13 May 2025cs.LG cs.AIcited by 3

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Abstract (arXiv)

Remote sensing (RS) images are usually produced at an unprecedented scale, yet they are geographically and institutionally distributed, making centralized model training challenging due to data-sharing restrictions and privacy concerns. Federated learning (FL) offers a solution by enabling collaborative model training across decentralized RS data sources without exposing raw data. However, there lacks a realistic federated dataset and benchmark in RS. Prior works typically rely on manually partitioned single dataset, which fail to capture the heterogeneity and scale of real-world RS data, and often use inconsistent experimental setups, hindering fair comparison. To address this gap, we propose a realistic federated RS dataset, termed FedRS. FedRS consists of eight datasets that cover various sensors and resolutions and builds 135 clients, which is representative of realistic operational scenarios. Data for each client come from the same source, exhibiting authentic federated properties such as skewed label distributions, imbalanced client data volumes, and domain heterogeneity across clients. These characteristics reflect practical challenges in federated RS and support evaluation of FL methods at scale. Based on FedRS, we implement 10 baseline FL algorithms and evaluation metrics to construct the comprehensive FedRS-Bench. The experimental results demonstrate that FL can consistently improve model performance over training on isolated data silos, while revealing performance trade-offs of different methods under varying client heterogeneity and availability conditions. We hope FedRS-Bench will accelerate research on large-scale, realistic FL in RS by providing a standardized, rich testbed and facilitating fair comparisons across future works. The source codes and dataset are available at https://fedrs-bench.github.io/.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 32 of its 50 references that are in the map. First posted on arXiv 13 May 2025.

Builds on

32 of 50 references.

References · 32

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,496
  5. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  6. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  7. 2017Remote Sensing Image Scene Classification: Benchmark and State of the Artcited by 797
  8. 2018LEAF: A Benchmark for Federated Settingscited by 792
  9. 2021Model-Contrastive Federated Learningcited by 766
  10. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  11. 2016AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classificationcited by 347
  12. 2022FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correctioncited by 181
20 more
  1. 2022SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understandingcited by 167
  2. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 125
  3. 2024VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understandingcited by 121
  4. 2022FedPrompt: Communication-Efficient and Privacy Preserving Prompt Tuning in Federated Learningcited by 91
  5. 2023FedDisco: Federated Learning with Discrepancy-Aware Collaborationcited by 67
  6. 2024FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Modelscited by 30
  7. 2023FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clientscited by 23
  8. 2022FLAIR: Federated Learning Annotated Image Repositorycited by 22
  9. 2023FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregationcited by 22
  10. 2025FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Modelscited by 11
  11. 2023Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classificationcited by 10
  12. 2023Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learningcited by 8
  13. 2024pfl-research: simulation framework for accelerating research in Private Federated Learningcited by 8
  14. 2024FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injectioncited by 8
  15. 2024Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classificationcited by 5
  16. 2020User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimizationcited by 4
  17. 2024Leveraging feature communication in federated learning for remote sensing image classificationcited by 4
  18. 2025Vertical Federated Learning in Practice: The Good, the Bad, and the Uglycited by 4
  19. 2025A Multi-Modal Federated Learning Framework for Remote Sensing Image Classificationcited by 1
  20. 2025SAFE: Self-Adjustment Federated Learning Framework for Remote Sensing Collaborative Perceptioncited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Revisiting Backdoor Threat in Federated Instruction Tuning from a Signal Aggregation Perspectivecited by 5
  2. 2026ProtegoFed: Backdoor-Free Federated Instruction Tuning with Interspersed Poisoned Datacited by 2
  3. 2026FedRG: Unleashing the Representation Geometry for Federated Learning with Noisy Clientscited by 0