mlsanityData to 14 Oct 2026

FedX: Explanation-Guided Pruning for Communication-Efficient Federated Learning in Remote Sensing

arXiv 2508.06256 ↗First posted 8 Aug 2025cs.CVcited by 1

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Abstract (arXiv)

Federated learning (FL) enables the collaborative training of deep neural networks across decentralized data archives (i.e., clients), where each client stores data locally and only shares model updates with a central server. This makes FL a suitable learning paradigm for remote sensing (RS) image classification tasks, where data centralization may be restricted due to legal and privacy constraints. However, a key challenge in applying FL to RS tasks is the communication overhead caused by the frequent exchange of large model updates between clients and the central server. To address this issue, in this paper we propose a novel strategy (denoted as FedX) that uses explanation-guided pruning to reduce communication overhead by minimizing the size of the transmitted models without compromising performance. FedX leverages backpropagation-based explanation methods to estimate the task-specific importance of model components and prunes the least relevant ones at the central server. The resulting sparse global model is then sent to clients, substantially reducing communication overhead. We evaluate FedX on multi-label scene classification using the BigEarthNet-S2 dataset and single-label scene classification using the EuroSAT dataset. Experimental results show the success of FedX in significantly reducing the number of shared model parameters while enhancing the generalization capability of the global model, compared to both unpruned model and state-of-the-art pruning methods. The code of FedX will be available at https://git.tu-berlin.de/rsim/FedX.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 27 of its 48 references that are in the map. First posted on arXiv 8 Aug 2025.

Builds on

27 of 48 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  4. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,496
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  6. 2023ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoderscited by 712
  7. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 682
  8. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  9. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 222
  10. 2021BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrievalcited by 92
  11. 2019Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruningcited by 89
  12. 2021Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Bettercited by 55
15 more
  1. 2024reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysiscited by 48
  2. 2022Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classificationcited by 25
  3. 2023FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clientscited by 23
  4. 2023FedICT: Federated Multi-task Distillation for Multi-access Edge Computingcited by 17
  5. 2022Distributed Pruning Towards Tiny Neural Networks in Federated Learningcited by 11
  6. 2024Pruning By Explaining Revisited: Optimizing Attribution Methods to Prune CNNs and Transformerscited by 11
  7. 2023Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classificationcited by 10
  8. 2023FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learningcited by 7
  9. 2023AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategycited by 7
  10. 2022Learning crop type mapping from regional label proportions in large-scale SAR and optical imagerycited by 5
  11. 2023FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learningcited by 5
  12. 2024Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side Distillationcited by 4
  13. 2024Leveraging feature communication in federated learning for remote sensing image classificationcited by 4
  14. 2025On the Effectiveness of Methods and Metrics for Explainable AI in Remote Sensing Image Scene Classificationcited by 3
  15. 2023FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularizationcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026FedMAD: Modulation-Aware Directional Aggregation for Federated Learning in Remote Sensing Image Classificationcited by 0