mlsanityData to 7 Oct 2026

Federated Multi-Target Domain Adaptation

arXiv 2108.07792 ↗First posted 17 Aug 2021cs.CVcited by 44

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Abstract (arXiv)

Federated learning methods enable us to train machine learning models on distributed user data while preserving its privacy. However, it is not always feasible to obtain high-quality supervisory signals from users, especially for vision tasks. Unlike typical federated settings with labeled client data, we consider a more practical scenario where the distributed client data is unlabeled, and a centralized labeled dataset is available on the server. We further take the server-client and inter-client domain shifts into account and pose a domain adaptation problem with one source (centralized server data) and multiple targets (distributed client data). Within this new Federated Multi-Target Domain Adaptation (FMTDA) task, we analyze the model performance of exiting domain adaptation methods and propose an effective DualAdapt method to address the new challenges. Extensive experimental results on image classification and semantic segmentation tasks demonstrate that our method achieves high accuracy, incurs minimal communication cost, and requires low computational resources on client devices.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). Builds on 35 of its 45 references that are in the map. First posted on arXiv 17 Aug 2021.

Builds on

35 of 45 references.

References · 35

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,155
  4. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,773
  5. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  6. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
  7. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,327
  8. 2017Adversarial Discriminative Domain Adaptationcited by 1,309
  9. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,282
  10. 2016Deep CORAL: Correlation Alignment for Deep Domain Adaptationcited by 1,194
  11. 2017CyCADA: Cycle-Consistent Adversarial Domain Adaptationcited by 1,079
  12. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
23 more
  1. 2016Playing for Data: Ground Truth from Computer Gamescited by 816
  2. 2017Maximum Classifier Discrepancy for Unsupervised Domain Adaptationcited by 682
  3. 2017Federated Multi-Task Learningcited by 669
  4. 2016Deep Transfer Learning with Joint Adaptation Networkscited by 609
  5. 2014Deep Domain Confusion: Maximizing for Domain Invariancecited by 597
  6. 2019Confidence Regularized Self-Trainingcited by 418
  7. 2019Contrastive Adaptation Network for Unsupervised Domain Adaptationcited by 407
  8. 2017DualGAN: Unsupervised Dual Learning for Image-to-Image Translationcited by 310
  9. 2017Learning to Discover Cross-Domain Relations with Generative Adversarial Networkscited by 278
  10. 2019Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptationcited by 235
  11. 2016Coupled Generative Adversarial Networkscited by 216
  12. 2018DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptationcited by 212
  13. 2020Salvaging Federated Learning by Local Adaptationcited by 142
  14. 2019Federated Adversarial Domain Adaptationcited by 140
  15. 2020Federated Visual Classification with Real-World Data Distributioncited by 126
  16. 2020Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learningcited by 112
  17. 2017No More Discrimination: Cross City Adaptation of Road Scene Segmenterscited by 110
  18. 2016Beyond Sharing Weights for Deep Domain Adaptationcited by 80
  19. 2018Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approachcited by 63
  20. 2018A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulationcited by 60
  21. 2014Domain Adaptive Neural Networks for Object Recognitioncited by 51
  22. 2018Multi-target Unsupervised Domain Adaptation without Exactly Shared Categoriescited by 15
  23. 2018Secure Federated Transfer Learningcited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 10; 2023: 14; 2024: 8; 2025: 9; 2026: 1.

44 papers.

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  1. 2022Improving Generalization in Federated Learning by Seeking Flat Minimacited by 98
  2. 2022Source-Free Unsupervised Domain Adaptation: A Surveycited by 79
  3. 2022Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learningcited by 35
  4. 2023Asynchronous Federated Continual Learningcited by 29
  5. 2023MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labelingcited by 25
  6. 2023Federated Domain Generalization: A Surveycited by 25
  7. 2023Federated Generative Learning with Foundation Modelscited by 23
  8. 2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
  9. 2022Mitigating Data Heterogeneity in Federated Learning with Data Augmentationcited by 21
  10. 2021Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Settingcited by 19
  11. 2022Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phasescited by 18
  12. 2022Feature Distribution Matching for Federated Domain Generalizationcited by 18
32 more
  1. 2022Knowledge-Aware Federated Active Learning with Non-IID Datacited by 15
  2. 2024Training-Free Model Merging for Multi-target Domain Adaptationcited by 15
  3. 2024Synergizing Foundation Models and Federated Learning: A Surveycited by 14
  4. 2022FedForgery: Generalized Face Forgery Detection with Residual Federated Learningcited by 12
  5. 2023Federated Learning without Full Labels: A Surveycited by 12
  6. 2023FedWon: Triumphing Multi-domain Federated Learning Without Normalizationcited by 11
  7. 2022FedHarmony: Unlearning Scanner Bias with Distributed Datacited by 10
  8. 2024Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalizationcited by 10
  9. 2023Multi-Source to Multi-Target Decentralized Federated Domain Adaptationcited by 6
  10. 2025FedAlign: Federated Domain Generalization with Cross-Client Feature Alignmentcited by 5
  11. 2022Federated and Generalized Person Re-identification through Domain and Feature Hallucinatingcited by 4
  12. 2024Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Datacited by 4
  13. 2023FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous Drivingcited by 3
  14. 2025FedSC: Federated Learning with Semantic-Aware Collaborationcited by 3
  15. 2024FedCCRL: Federated Domain Generalization with Cross-Client Representation Learningcited by 2
  16. 2023FACT: Federated Adversarial Cross Trainingcited by 1
  17. 2023Population Expansion for Training Language Models with Private Federated Learningcited by 1
  18. 2023Trustworthy Representation Learning Across Domainscited by 1
  19. 2023A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasetscited by 1
  20. 2025FedS2R: One-Shot Federated Domain Generalization for Synthetic-to-Real Semantic Segmentation in Autonomous Drivingcited by 1
  21. 2025Cyst-X: A Multi-Center MRI Benchmark and Federated Learning Framework for Malignancy-Risk Stratification of Pancreatic Cystic Neoplasmcited by 1
  22. 2026FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devicescited by 1
  23. 2023SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysiscited by 0
  24. 2023Model-Contrastive Federated Domain Adaptationcited by 0
  25. 2024Federated Learning with New Knowledge: Fundamentals, Advances, and Futurescited by 0
  26. 2024Federated Black-Box Adaptation for Semantic Segmentationcited by 0
  27. 2024Hybrid-Regularized Magnitude Pruning for Robust Federated Learning under Covariate Shiftcited by 0
  28. 2025Federated Unsupervised Semantic Segmentationcited by 0
  29. 2025Generalizable Federated Learning using Client Adaptive Focal Modulationcited by 0
  30. 2025Rethinking the Backbone in Class Imbalanced Federated Source Free Domain Adaptation: The Utility of Vision Foundation Modelscited by 0
  31. 2025FedDAF: Federated Domain Adaptation Using Model Functional Distancecited by 0
  32. 2025FedSCAl: Leveraging Server and Client Alignment for Unsupervised Federated Source-Free Domain Adaptationcited by 0