mlsanityData to 14 Oct 2026

Federated Adversarial Domain Adaptation

arXiv 1911.02054 ↗First posted 5 Nov 2019cs.CV cs.LGcited by 140

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Abstract (arXiv)

Federated learning improves data privacy and efficiency in machine learning performed over networks of distributed devices, such as mobile phones, IoT and wearable devices, etc. Yet models trained with federated learning can still fail to generalize to new devices due to the problem of domain shift. Domain shift occurs when the labeled data collected by source nodes statistically differs from the target node's unlabeled data. In this work, we present a principled approach to the problem of federated domain adaptation, which aims to align the representations learned among the different nodes with the data distribution of the target node. Our approach extends adversarial adaptation techniques to the constraints of the federated setting. In addition, we devise a dynamic attention mechanism and leverage feature disentanglement to enhance knowledge transfer. Empirically, we perform extensive experiments on several image and text classification tasks and show promising results under unsupervised federated domain adaptation setting.

Key facts. Cited by 140 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Nov 2019.

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Citing papers per year: 2021: 36; 2022: 29; 2023: 34; 2024: 19; 2025: 19; 2026: 3.

140 papers.

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  1. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  2. 2021Data-Free Knowledge Distillation for Heterogeneous Federated Learningcited by 383
  3. 2023A Comprehensive Survey on Test-Time Adaptation under Distribution Shiftscited by 359
  4. 2021Federated Learning on Non-IID Data: A Surveycited by 346
  5. 2021FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Spacecited by 287
  6. 2022Federated Class-Incremental Learningcited by 127
  7. 2023A Comprehensive Survey on Source-free Domain Adaptationcited by 104
  8. 2021Domain Impression: A Source Data Free Domain Adaptation Methodcited by 91
  9. 2022Source-Free Unsupervised Domain Adaptation: A Surveycited by 80
  10. 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
  11. 2022Divergence-aware Federated Self-Supervised Learningcited by 73
  12. 2021Provably Secure Federated Learning against Malicious Clientscited by 63
38 more
  1. 2023No One Left Behind: Real-World Federated Class-Incremental Learningcited by 53
  2. 2021Style Normalization and Restitution for Domain Generalization and Adaptationcited by 46
  3. 2021Federated Multi-Target Domain Adaptationcited by 44
  4. 2022A Survey on Heterogeneous Federated Learningcited by 42
  5. 2021Federated Learning with Domain Generalizationcited by 41
  6. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 41
  7. 2023Generalizable Heterogeneous Federated Cross-Correlation and Instance Similarity Learningcited by 40
  8. 2021Non-IID data and Continual Learning processes in Federated Learning: A long road aheadcited by 36
  9. 2023Federated Incremental Semantic Segmentationcited by 36
  10. 2021Emerging Trends in Federated Learning: From Model Fusion to Federated X Learningcited by 35
  11. 2022Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customizationcited by 35
  12. 2022FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reductioncited by 35
  13. 2022Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learningcited by 35
  14. 2021Unsupervised Domain Adaptation of Black-Box Source Modelscited by 32
  15. 2021Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimizationcited by 32
  16. 2022Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learningcited by 29
  17. 2024A Survey on Efficient Federated Learning Methods for Foundation Model Trainingcited by 29
  18. 2021Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretabilitycited by 28
  19. 2023Federated Domain Generalization: A Surveycited by 25
  20. 2022Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matchingcited by 24
  21. 2021UMAD: Universal Model Adaptation under Domain and Category Shiftcited by 23
  22. 2021Prototype Guided Federated Learning of Visual Feature Representationscited by 22
  23. 2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
  24. 2022Mitigating Data Heterogeneity in Federated Learning with Data Augmentationcited by 21
  25. 2021Memory-aware curriculum federated learning for breast cancer classificationcited by 20
  26. 2021Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Settingcited by 19
  27. 2022Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phasescited by 18
  28. 2022Feature Distribution Matching for Federated Domain Generalizationcited by 18
  29. 2021FedH2L: Federated Learning with Model and Statistical Heterogeneitycited by 17
  30. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  31. 2023A Simple Data Augmentation for Feature Distribution Skewed Federated Learningcited by 17
  32. 2021Collaborative Semantic Aggregation and Calibration for Federated Domain Generalizationcited by 16
  33. 2022Knowledge-Aware Federated Active Learning with Non-IID Datacited by 16
  34. 2023FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clusteringcited by 15
  35. 2023Federated Learning without Full Labels: A Surveycited by 13
  36. 2025Federated Continual Learning: Concepts, Challenges, and Solutionscited by 13
  37. 2021Auto-weighted Robust Federated Learning with Corrupted Data Sourcescited by 12
  38. 2021Secure Domain Adaptation with Multiple Sourcescited by 12

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