# Federated Transfer Learning: concept and applications

arXiv: https://arxiv.org/abs/2010.15561 · HTML: https://mlsanity.com/paper/2010.15561/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2020.

## Built on by (most cited first, 30 of 32)

- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [Mitigating Data Heterogeneity in Federated Learning with Data Augmentation](https://mlsanity.com/paper/2206.09979/) (2022)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sources](https://mlsanity.com/paper/2305.06217/) (2023)
- [A Recent Survey of Heterogeneous Transfer Learning](https://mlsanity.com/paper/2310.08459/) (2023)
- [PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection](https://mlsanity.com/paper/2406.02318/) (2024)
- [Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees](https://mlsanity.com/paper/2406.17887/) (2024)
- [FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients](https://mlsanity.com/paper/2502.20639/) (2025)
- [A Survey on Contribution Evaluation in Vertical Federated Learning](https://mlsanity.com/paper/2405.02364/) (2024)
- [Position: AI Scaling: From Up to Down and Out](https://mlsanity.com/paper/2502.01677/) (2025)
- [Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms](https://mlsanity.com/paper/2207.02337/) (2022)
- [Exploring the Practicality of Federated Learning: A Survey Towards the Communication Perspective](https://mlsanity.com/paper/2405.20431/) (2024)
- [DACFL: Dynamic Average Consensus Based Federated Learning in Decentralized Topology](https://mlsanity.com/paper/2111.05505/) (2021)
- [PFSL: Personalized & Fair Split Learning with Data & Label Privacy for thin clients](https://mlsanity.com/paper/2303.10624/) (2023)
- [A Survey on Secure and Private Federated Learning Using Blockchain: Theory and Application in Resource-constrained Computing](https://mlsanity.com/paper/2303.13727/) (2023)
- [Seeing the Forest through the Trees: Data Leakage from Partial Transformer Gradients](https://mlsanity.com/paper/2406.00999/) (2024)
- [Combining Federated Learning and Control: A Survey](https://mlsanity.com/paper/2407.11069/) (2024)
- [The Effects of Data Imbalance Under a Federated Learning Approach for Credit Risk Forecasting](https://mlsanity.com/paper/2401.07234/) (2024)
- [On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions](https://mlsanity.com/paper/2508.13730/) (2025)
- [An Efficient Industrial Federated Learning Framework for AIoT: A Face Recognition Application](https://mlsanity.com/paper/2206.13398/) (2022)
- [Federated Selective Aggregation for Knowledge Amalgamation](https://mlsanity.com/paper/2207.13309/) (2022)
- [Online Transfer Learning for RSV Case Detection](https://mlsanity.com/paper/2402.01987/) (2024)
- [Federated Transfer Component Analysis Towards Effective VNF Profiling](https://mlsanity.com/paper/2404.17553/) (2024)
- [Federated learning in food research](https://mlsanity.com/paper/2406.06202/) (2024)
- [Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysis](https://mlsanity.com/paper/2609.15681/) (2026)
- [TablePuppet: A Generic Framework for Relational Federated Learning](https://mlsanity.com/paper/2403.15839/) (2024)
- [CrowdTransfer: Enabling Crowd Knowledge Transfer in AIoT Community](https://mlsanity.com/paper/2407.06485/) (2024)
- [Privacy Attack in Federated Learning is Not Easy: An Experimental Study](https://mlsanity.com/paper/2409.19301/) (2024)
- [Artificial Intelligence in Pediatric Echocardiography: Exploring Challenges, Opportunities, and Clinical Applications with Explainable AI and Federated Learning](https://mlsanity.com/paper/2411.10255/) (2024)
- [Traversal Learning: A Lossless And Efficient Distributed Learning Framework](https://mlsanity.com/paper/2504.07471/) (2025)

## 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.