mlsanityData to 7 Oct 2026

Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification

arXiv 2311.06141 ↗First posted 10 Nov 2023cs.CVcited by 10

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) enables the collaboration of multiple deep learning models to learn from decentralized data archives (i.e., clients) without accessing data on clients. Although FL offers ample opportunities in knowledge discovery from distributed image archives, it is seldom considered in remote sensing (RS). In this paper, as a first time in RS, we present a comparative study of state-of-the-art FL algorithms for RS image classification problems. To this end, we initially provide a systematic review of the FL algorithms presented in the computer vision and machine learning communities. Then, we select several state-of-the-art FL algorithms based on their effectiveness with respect to training data heterogeneity across clients (known as non-IID data). After presenting an extensive overview of the selected algorithms, a theoretical comparison of the algorithms is conducted based on their: 1) local training complexity; 2) aggregation complexity; 3) learning efficiency; 4) communication cost; and 5) scalability in terms of number of clients. After the theoretical comparison, experimental analyses are presented to compare them under different decentralization scenarios. For the experimental analyses, we focus our attention on multi-label image classification problems in RS. Based on our comprehensive analyses, we finally derive a guideline for selecting suitable FL algorithms in RS. The code of this work is publicly available at https://git.tu-berlin.de/rsim/FL-RS.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 28 of its 55 references that are in the map. First posted on arXiv 10 Nov 2023.

Builds on

28 of 55 references.

References · 28

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,488
  6. 2016HyperNetworkscited by 1,003
  7. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  8. 2021Model-Contrastive Federated Learningcited by 762
  9. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  10. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  11. 2020Federated Learning with Matched Averagingcited by 564
  12. 2021Federated Learning Based on Dynamic Regularizationcited by 555
16 more
  1. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 380
  2. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  3. 2022FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correctioncited by 181
  4. 2020FedBE: Making Bayesian Model Ensemble Applicable to Federated Learningcited by 176
  5. 2020Privacy and Robustness in Federated Learning: Attacks and Defensescited by 163
  6. 2021Local Learning Matters: Rethinking Data Heterogeneity in Federated Learningcited by 114
  7. 2022Layer-wised Model Aggregation for Personalized Federated Learningcited by 95
  8. 2021BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrievalcited by 91
  9. 2021Papaya: Practical, Private, and Scalable Federated Learningcited by 89
  10. 2021FedAUX: Leveraging Unlabeled Auxiliary Data in Federated Learningcited by 67
  11. 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
  12. 2021LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learningcited by 63
  13. 2020Cost-Effective Federated Learning Designcited by 61
  14. 2021Communication Efficiency in Federated Learning: Achievements and Challengescited by 40
  15. 2022FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning by Penalizing KL Divergencecited by 29
  16. 2023Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learningcited by 8

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 5.

10 papers.

  1. 2024Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classificationcited by 4
  2. 2025A Multi-Modal Federated Learning Framework for Remote Sensing Image Classificationcited by 1
  3. 2025FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensingcited by 1
  4. 2025FedX: Explanation-Guided Pruning for Communication-Efficient Federated Learning in Remote Sensingcited by 1
  5. 2026Towards Realistic Remote Sensing Dataset Distillation with Discriminative Prototype-guided Diffusioncited by 1
  6. 2025Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image Classificationcited by 0
  7. 2026Leveraging Membership Inference Attacks for Privacy Measurement in Federated Learning for Remote Sensing Imagescited by 0
  8. 2026FedEU: Evidential Uncertainty-Driven Federated Fine-Tuning of Vision Foundation Models for Remote Sensing Image Segmentationcited by 0
  9. 2026On the Effectiveness of Adaptation Strategies for VLM-Based Federated Learning in Remote Sensingcited by 0
  10. 2026FedMAD: Modulation-Aware Directional Aggregation for Federated Learning in Remote Sensing Image Classificationcited by 0