Federated Transfer Learning (FTL) is an emerging research area that combines federated learning and transfer learning to enable collaborative model training across distributed clients while leveraging knowledge from related tasks or domains. This paradigm addresses scenarios where data is non-i.i.d., privacy-sensitive, or scarce in certain clients, allowing knowledge learned from source clients to be transferred to improve performance on target clients without sharing raw data. Research in FTL explores approaches such as model parameter aggregation with domain adaptation, meta-learning for personalized federated models, privacy-preserving mechanisms using differential privacy or secure multiparty computation, and multi-task learning frameworks for heterogeneous clients. Applications span healthcare, finance, Internet of Things (IoT), smart cities, and edge computing, where cross-domain collaboration enhances predictive accuracy and generalization. Recent studies also investigate communication-efficient algorithms, robustness to client drift, and hybrid architectures combining deep neural networks with federated optimization strategies, establishing FTL as a promising solution for privacy-aware, distributed, and adaptable AI systems.