This paper provides a comprehensive survey of Federated Transfer Learning (FTL), a novel paradigm that integrates transfer learning (TL) into federated learning (FL) to address the challenges of data heterogeneity, system heterogeneity, incremental data, and labeled data scarcity in FL. The authors categorize FTL into six scenarios: homogeneous FTL, heterogeneous FTL, dynamic heterogeneous FTL, model adaptive FTL, semi-supervised FTL, and unsupervised FTL. They discuss the definitions, challenges, and corresponding solutions for each scenario, including data-based and model-based strategies. The paper also outlines common settings, available datasets, and significant research contributions in FTL, highlighting the importance of privacy-preserving mechanisms and communication architectures. The key contributions of the survey include providing a systematic review of FTL, detailing the challenges and solutions, and summarizing the current research status and future prospects.This paper provides a comprehensive survey of Federated Transfer Learning (FTL), a novel paradigm that integrates transfer learning (TL) into federated learning (FL) to address the challenges of data heterogeneity, system heterogeneity, incremental data, and labeled data scarcity in FL. The authors categorize FTL into six scenarios: homogeneous FTL, heterogeneous FTL, dynamic heterogeneous FTL, model adaptive FTL, semi-supervised FTL, and unsupervised FTL. They discuss the definitions, challenges, and corresponding solutions for each scenario, including data-based and model-based strategies. The paper also outlines common settings, available datasets, and significant research contributions in FTL, highlighting the importance of privacy-preserving mechanisms and communication architectures. The key contributions of the survey include providing a systematic review of FTL, detailing the challenges and solutions, and summarizing the current research status and future prospects.