mlsanityData to 14 Oct 2026

Private Federated Learning with Domain Adaptation

arXiv 1912.06733 ↗First posted 13 Dec 2019cs.LG cs.CR stat.MLcited by 36

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Abstract (arXiv)

Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables multiple parties to jointly re-train a shared model without sharing their data with any other parties, offering advantages in both scale and privacy. We propose a framework to augment this collaborative model-building with per-user domain adaptation. We show that this technique improves model accuracy for all users, using both real and synthetic data, and that this improvement is much more pronounced when differential privacy bounds are imposed on the FL model.

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Dec 2019.

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Citing papers per year: 2020: 1; 2021: 12; 2022: 6; 2023: 8; 2024: 5; 2025: 3; 2026: 1.

36 papers.

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  1. 2022Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learningcited by 172
  2. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 99
  3. 2025A Comprehensive Survey of Mixture-of-Experts: Algorithms, Theory, and Applicationscited by 91
  4. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 41
  5. 2021FedJAX: Federated learning simulation with JAXcited by 32
  6. 2021Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretabilitycited by 28
  7. 2020PFL-MoE: Personalized Federated Learning Based on Mixture of Expertscited by 21
  8. 2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
  9. 2021Memory-aware curriculum federated learning for breast cancer classificationcited by 20
  10. 2021On Bridging Generic and Personalized Federated Learning for Image Classificationcited by 15
  11. 2024Personalized Federated Fine-Tuning for LLMs via Data-Driven Heterogeneous Model Architecturescited by 12
  12. 2022FedHarmony: Unlearning Scanner Bias with Distributed Datacited by 10
24 more
  1. 2023FedHB: Hierarchical Bayesian Federated Learningcited by 8
  2. 2021Practical and Secure Federated Recommendation with Personalized Maskscited by 7
  3. 2024MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classescited by 7
  4. 2023Federated Learning of Shareable Bases for Personalization-Friendly Image Classificationcited by 6
  5. 2021On the Convergence of Differentially Private Federated Learning on Non-Lipschitz Objectives, and with Normalized Client Updatescited by 5
  6. 2022VFed-SSD: Towards Practical Vertical Federated Advertisingcited by 5
  7. 2023FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximationscited by 5
  8. 2021Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenescited by 4
  9. 2022Adaptive Expert Models for Personalization in Federated Learningcited by 4
  10. 2021Personalised Federated Learning: A Combinational Approachcited by 3
  11. 2022MLink: Linking Black-Box Models from Multiple Domains for Collaborative Inferencecited by 2
  12. 2021Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentionscited by 1
  13. 2021Preliminary Steps Towards Federated Sentiment Classificationcited by 1
  14. 2021Modular Gaussian Processes for Transfer Learningcited by 1
  15. 2022Federated Semi-Supervised Domain Adaptation via Knowledge Transfercited by 1
  16. 2023Population Expansion for Training Language Models with Private Federated Learningcited by 1
  17. 2024A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspectivecited by 1
  18. 2024Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clusteringcited by 1
  19. 2023SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysiscited by 0
  20. 2023Global Layers: Non-IID Tabular Federated Learningcited by 0
  21. 2024Personalized Federated Learning for Statistical Heterogeneitycited by 0
  22. 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoningcited by 0
  23. 2025Local Performance vs. Out-of-Distribution Generalization: An Empirical Analysis of Personalized Federated Learning in Heterogeneous Data Environmentscited by 0
  24. 2026Similarity-Aware Personalized Federated Learning in Heterogeneous Environmentscited by 0