Federated Transfer Learning combines two important concepts: Federated Learning and Transfer Learning.
Federated Learning enables the joint training of models on distributed data sources without centralizing the data.
Transfer Learning uses knowledge learned from one domain or task to better solve another, similar task.
In Federated Transfer Learning, models or features from local data sources are transferred and aggregated to improve learning performance, even if the data formats or domains are different.
This method is especially useful when data is heterogeneously distributed and direct data sharing is not possible.
Typical applications can be found in medicine, financial data, or IoT environments.