mlsanityData to 14 Oct 2026

Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learning

arXiv 2306.00792 ↗First posted 1 Jun 2023cs.CVcited by 8

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Abstract (arXiv)

The development of federated learning (FL) methods, which aim to learn from distributed databases (i.e., clients) without accessing data on clients, has recently attracted great attention. Most of these methods assume that the clients are associated with the same data modality. However, remote sensing (RS) images in different clients can be associated with different data modalities that can improve the classification performance when jointly used. To address this problem, in this paper we introduce a novel multi-modal FL framework that aims to learn from decentralized multi-modal RS image archives for RS image classification problems. The proposed framework is made up of three modules: 1) multi-modal fusion (MF); 2) feature whitening (FW); and 3) mutual information maximization (MIM). The MF module performs iterative model averaging to learn without accessing data on clients in the case that clients are associated with different data modalities. The FW module aligns the representations learned among the different clients. The MIM module maximizes the similarity of images from different modalities. Experimental results show the effectiveness of the proposed framework compared to iterative model averaging, which is a widely used algorithm in FL. The code of the proposed framework is publicly available at https://git.tu-berlin.de/rsim/MM-FL.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 10 of its 13 references that are in the map. First posted on arXiv 1 Jun 2023.

Builds on

10 of 13 references.

References · 10

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2021Model-Contrastive Federated Learningcited by 766
  5. 2022FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correctioncited by 181
  6. 2020More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classificationcited by 126
  7. 2022Layer-wised Model Aggregation for Personalized Federated Learningcited by 96
  8. 2021BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrievalcited by 92
  9. 2019Unsupervised Domain Adaptation using Feature-Whitening and Consensus Losscited by 67
  10. 2022A Novel Self-Supervised Cross-Modal Image Retrieval Method In Remote Sensingcited by 10

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 4.

8 papers.

  1. 2025FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Modelscited by 11
  2. 2023Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classificationcited by 10
  3. 2024Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Frameworkcited by 6
  4. 2024Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classificationcited by 5
  5. 2024Leveraging feature communication in federated learning for remote sensing image classificationcited by 4
  6. 2025FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensingcited by 3
  7. 2025A Multi-Modal Federated Learning Framework for Remote Sensing Image Classificationcited by 1
  8. 2025Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image Classificationcited by 0