mlsanityData to 7 Oct 2026

A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications

arXiv 2403.01387 ↗First posted 3 Mar 2024cs.LG cs.DCcited by 8

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Abstract (arXiv)

Federated learning (FL) is a novel distributed machine learning paradigm that enables participants to collaboratively train a centralized model with privacy preservation by eliminating the requirement of data sharing. In practice, FL often involves multiple participants and requires the third party to aggregate global information to guide the update of the target participant. Therefore, many FL methods do not work well due to the training and test data of each participant may not be sampled from the same feature space and the same underlying distribution. Meanwhile, the differences in their local devices (system heterogeneity), the continuous influx of online data (incremental data), and labeled data scarcity may further influence the performance of these methods. To solve this problem, federated transfer learning (FTL), which integrates transfer learning (TL) into FL, has attracted the attention of numerous researchers. However, since FL enables a continuous share of knowledge among participants with each communication round while not allowing local data to be accessed by other participants, FTL faces many unique challenges that are not present in TL. In this survey, we focus on categorizing and reviewing the current progress on federated transfer learning, and outlining corresponding solutions and applications. Furthermore, the common setting of FTL scenarios, available datasets, and significant related research are summarized in this survey.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 166 of its 293 references that are in the map. First posted on arXiv 3 Mar 2024.

Builds on

166 of 293 references.

References · 166

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 3.

8 papers.

  1. 2024Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraintscited by 4
  2. 2024Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clientscited by 3
  3. 2026Learning Adaptive Distribution Alignment with Neural Characteristic Function for Graph Domain Adaptationcited by 1
  4. 2025An Adaptive ML Framework for Power Converter Monitoring via Federated Transfer Learningcited by 0
  5. 2025FedFusion: Federated Learning with Diversity- and Cluster-Aware Encoders for Robust Adaptation under Label Scarcitycited by 0
  6. 2025FTTE: Enabling Federated and Resource-Constrained Deep Edge Intelligencecited by 0
  7. 2026Federated Learning and Class Imbalancescited by 0
  8. 2026Revisiting Label Inference Attacks in Vertical Federated Learning: Why They Are Vulnerable and How to Defendcited by 0