mlsanityData to 7 Oct 2026

Federated Transfer Learning: concept and applications

arXiv 2010.15561 ↗First posted 26 Sep 2020cs.LG cs.CRcited by 32

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Abstract (arXiv)

Development of Artificial Intelligence (AI) is inherently tied to the development of data. However, in most industries data exists in form of isolated islands, with limited scope of sharing between different organizations. This is an hindrance to the further development of AI. Federated learning has emerged as a possible solution to this problem in the last few years without compromising user privacy. Among different variants of the federated learning, noteworthy is federated transfer learning (FTL) that allows knowledge to be transferred across domains that do not have many overlapping features and users. In this work we provide a comprehensive survey of the existing works on this topic. In more details, we study the background of FTL and its different existing applications. We further analyze FTL from privacy and machine learning perspective.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Sep 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 5; 2024: 15; 2025: 5; 2026: 2.

32 papers.

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  1. 2023Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmarkcited by 97
  2. 2022Mitigating Data Heterogeneity in Federated Learning with Data Augmentationcited by 21
  3. 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
  4. 2023Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sourcescited by 10
  5. 2023A Recent Survey of Heterogeneous Transfer Learningcited by 7
  6. 2024PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detectioncited by 7
  7. 2024Federated Dynamical Low-Rank Training with Global Loss Convergence Guaranteescited by 7
  8. 2025FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clientscited by 7
  9. 2024A Survey on Contribution Evaluation in Vertical Federated Learningcited by 5
  10. 2025Position: AI Scaling: From Up to Down and Outcited by 5
  11. 2022Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanismscited by 4
  12. 2024Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspectivecited by 4
20 more
  1. 2021DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topologycited by 3
  2. 2023PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clientscited by 3
  3. 2023A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computingcited by 3
  4. 2024Seeing the Forest through the Trees: Data Leakage from Partial Transformer Gradientscited by 3
  5. 2024The Effects of Data Imbalance Under a Federated Learning Approach for Credit Risk Forecastingcited by 2
  6. 2024Combining Federated Learning and Control: A Surveycited by 2
  7. 2025On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directionscited by 2
  8. 2022An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Applicationcited by 1
  9. 2022Federated Selective Aggregation for Knowledge Amalgamationcited by 1
  10. 2024Online Transfer Learning for RSV Case Detectioncited by 1
  11. 2024Federated Transfer Component Analysis Towards Effective VNF Profilingcited by 1
  12. 2024Federated learning in food researchcited by 1
  13. 2026Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysiscited by 1
  14. 2024TablePuppet: A Generic Framework for Relational Federated Learningcited by 0
  15. 2024CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Communitycited by 0
  16. 2024Privacy Attack in Federated Learning is Not Easy: An Experimental Studycited by 0
  17. 2024Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learningcited by 0
  18. 2025Traversal Learning: A Lossless And Efficient Distributed Learning Frameworkcited by 0
  19. 2025ATR-Bench: A Federated Learning Benchmark for Adaptation, Trust, and Reasoningcited by 0
  20. 2026FedKDX: Federated Learning with Negative Knowledge Distillation for Enhanced Healthcare AI Systemscited by 0