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Federated Domain Generalization: A Survey

arXiv 2306.01334 ↗First posted 2 Jun 2023cs.LG cs.AIcited by 25

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Abstract (arXiv)

Machine learning typically relies on the assumption that training and testing distributions are identical and that data is centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly and data is often distributed across different devices, organizations, or edge nodes. Consequently, it is imperative to develop models that can effectively generalize to unseen distributions where data is distributed across different domains. In response to this challenge, there has been a surge of interest in federated domain generalization (FDG) in recent years. FDG combines the strengths of federated learning (FL) and domain generalization (DG) techniques to enable multiple source domains to collaboratively learn a model capable of directly generalizing to unseen domains while preserving data privacy. However, generalizing the federated model under domain shifts is a technically challenging problem that has received scant attention in the research area so far. This paper presents the first survey of recent advances in this area. Initially, we discuss the development process from traditional machine learning to domain adaptation and domain generalization, leading to FDG as well as provide the corresponding formal definition. Then, we categorize recent methodologies into four classes: federated domain alignment, data manipulation, learning strategies, and aggregation optimization, and present suitable algorithms in detail for each category. Next, we introduce commonly used datasets, applications, evaluations, and benchmarks. Finally, we conclude this survey by providing some potential research topics for the future.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 142 of its 335 references that are in the map. First posted on arXiv 2 Jun 2023.

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142 of 335 references.

References · 142

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38 more
  1. 2015Learning Transferable Features with Deep Adaptation Networkscited by 1,323
  2. 2018Federated Learning with Non-IID Datacited by 1,099
  3. 2020In Search of Lost Domain Generalizationcited by 1,042
  4. 2019Towards Federated Learning at Scale: System Designcited by 826
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  13. 2021Domain Generalization with MixStylecited by 563
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  15. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
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  21. 2020Self-Challenging Improves Cross-Domain Generalizationcited by 426
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Citing papers per year: 2023: 4; 2024: 8; 2025: 9; 2026: 4.

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  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  2. 2023Domain Generalization for Medical Image Analysis: A Reviewcited by 47
  3. 2024Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boostingcited by 27
  4. 2024A Systematic Review of Generalization Research in Medical Image Classificationcited by 8
  5. 2024Federated Unsupervised Domain Generalization using Global and Local Alignment of Gradientscited by 7
  6. 2024The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regretcited by 6
  7. 2025When Shift Happens - Confounding Is to Blamecited by 5
  8. 2024RaffeSDG: Random Frequency Filtering enabled Single-source Domain Generalization for Medical Image Segmentationcited by 3
  9. 2025pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Modelscited by 3
  10. 2023CommunityAI: Towards Community-based Federated Learningcited by 2
  11. 2024Intelligent Cross-Organizational Process Mining: A Survey and New Perspectivescited by 2
  12. 2024FedCCRL: Federated Domain Generalization with Cross-Client Representation Learningcited by 2
13 more
  1. 2025Federated Adapter on Foundation Models: An Out-Of-Distribution Approachcited by 2
  2. 2024Federated Domain Generalization via Prompt Learning and Aggregationcited by 1
  3. 2023Federated Active Learning for Target Domain Generalisationcited by 0
  4. 2025Decentralized Domain Generalization with Style Sharing: Formal Model and Convergence Analysiscited by 0
  5. 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoningcited by 0
  6. 2025Improving Learning of New Diseases through Knowledge-Enhanced Initialization for Federated Adapter Tuningcited by 0
  7. 2025FedDAPL: Toward Client-Private Generalization in Federated Learningcited by 0
  8. 2025Gains: Fine-grained Federated Domain Adaptation in Open Setcited by 0
  9. 2025Federated Domain Generalization with Latent Space Inversioncited by 0
  10. 2026Federated Learning for Cross-Modality Medical Image Segmentation via Augmentation-Driven Generalizationcited by 0
  11. 2026Joint Routing and Model Pruning for Decentralized Federated Learning in Bandwidth-Constrained Multi-Hop Wireless Networkscited by 0
  12. 2026Clustered Edge Intelligence: Beyond Just Convergence of Edge Computing and AIcited by 0
  13. 2026FedCKA: Representation-Guided Layer Personalization for Federated 3D Perception Across Driving Domainscited by 0