Federated Transfer Learning: A Comprehensive Overview

Federated Transfer Learning (FTL) combines the strengths of Federated Learning (FL) and TransferLearning (TL) to enable collaborative model training without sharing sensitive data. In today’slandscape, where privacy concerns are critical and data is often fragmented or scarce, FTL offers apractical and secure solution. FL facilitates decentralized learning by keeping data local, thusminimizing privacy risks, while TL leverages knowledge from related domains, enhancing modelperformance, particularly when labeled data is limited. This paper explores the foundational conceptsand methodologies of FTL, focusing on its applications in critical fields such as healthcare, wherepatient confidentiality is vital; finance, where protecting sensitive financial information is essential;Internet of Things (IoT), where devices operate under diverse conditions; and natural languageprocessing, which deals with language diversity and cultural nuances. Additionally, we addresschallenges such as managing heterogeneous data, ensuring scalability, and maintaining privacy,proposing future research directions to overcome these obstacles. FTL emerges as a promisingtechnology for privacy-preserving, collaborative machine learning across various industries, offeringpractical and secure solutions in an increasingly data-driven world.

کلیدواژه ها:

Federated Transfer Learning (FTL) ، Federated Learning (FL) ، Transfer Learning (TL) ، Data privacy ، Collaborative model training ، Decentralized learning ، Privacy-preserving technology