mlsanityData to 14 Oct 2026

Transformer-based Federated Learning for Multi-Label Remote Sensing Image Classification

arXiv 2405.15405 ↗First posted 24 May 2024cs.CVcited by 5

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Abstract (arXiv)

Federated learning (FL) aims to collaboratively learn deep learning model parameters from decentralized data archives (i.e., clients) without accessing training data on clients. However, the training data across clients might be not independent and identically distributed (non-IID), which may result in difficulty in achieving optimal model convergence. In this work, we investigate the capability of state-of-the-art transformer architectures (which are MLP-Mixer, ConvMixer, PoolFormer) to address the challenges related to non-IID training data across various clients in the context of FL for multi-label classification (MLC) problems in remote sensing (RS). The considered transformer architectures are compared among themselves and with the ResNet-50 architecture in terms of their: 1) robustness to training data heterogeneity; 2) local training complexity; and 3) aggregation complexity under different non-IID levels. The experimental results obtained on the BigEarthNet-S2 benchmark archive demonstrate that the considered architectures increase the generalization ability with the cost of higher local training and aggregation complexities. On the basis of our analysis, some guidelines are derived for a proper selection of transformer architecture in the context of FL for RS MLC. The code of this work is publicly available at https://git.tu-berlin.de/rsim/FL-Transformer.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 22 of its 26 references that are in the map. First posted on arXiv 24 May 2024.

Builds on

22 of 26 references.

References · 22

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  4. 2019Advances and Open Problems in Federated Learningcited by 3,046
  5. 2021MLP-Mixer: An all-MLP Architecture for Visioncited by 1,446
  6. 2021Model-Contrastive Federated Learningcited by 766
  7. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  8. 2021MetaFormer Is Actually What You Need for Visioncited by 524
  9. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
  10. 2016Semi-supervised Knowledge Transfer for Deep Learning from Private Training Datacited by 348
  11. 2022Patches Are All You Need?cited by 201
  12. 2022FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correctioncited by 181
10 more
  1. 2021Local Learning Matters: Rethinking Data Heterogeneity in Federated Learningcited by 114
  2. 2021Rethinking Architecture Design for Tackling Data Heterogeneity in Federated Learningcited by 111
  3. 2022Layer-wised Model Aggregation for Personalized Federated Learningcited by 96
  4. 2021BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrievalcited by 92
  5. 2022On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classificationcited by 17
  6. 2023Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classificationcited by 10
  7. 2023Learning Across Decentralized Multi-Modal Remote Sensing Archives with Federated Learningcited by 8
  8. 2022Deep Active Learning for Multi-Label Classification of Remote Sensing Imagescited by 6
  9. 2024Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Frameworkcited by 6
  10. 2022Generative Reasoning Integrated Label Noise Robust Deep Image Representation Learningcited by 5

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Cited by year

Citing papers per year: 2025: 5.

5 papers.

  1. 2025FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Modelscited by 11
  2. 2025FedRS-Bench: Realistic Federated Learning Datasets and Benchmarks in Remote Sensingcited by 3
  3. 2025A Multi-Modal Federated Learning Framework for Remote Sensing Image Classificationcited by 1
  4. 2025Communication-Efficient Federated Learning Based on Explanation-Guided Pruning for Remote Sensing Image Classificationcited by 0
  5. 2025CF-Net: A Cross-Feature Reconstruction Network for High-Accuracy 1-Bit Target Classificationcited by 0