Federated Transfer Learning (FTL) combines two important concepts in modern machine learning: Federated Learning (FL) and Transfer Learning (TL). Federated learning allows decentralized training of models across multiple devices or institutions without sharing raw data, while transfer learning enables a model to leverage knowledge from one task or domain to improve performance on a different task or domain. FTL is crucial in scenarios where data is distributed, privacy is essential, and models need to be transferred across domains.Federated Transfer Learning (FTL) is a powerful framework that combines the benefits of federated learning and transfer learning, allowing models to learn from decentralized, privacy-preserving data while also leveraging knowledge from different domains.