Exploring Federated Learning for Data Integration: A Structured Literature Review

Data integration is utilized to integrate heterogeneous data from multiple sources, representing a crucial step to improve information value in data analysis and mining. Incorporating machine and deep learning into data integration has proven beneficial, particularly for messy data. Yet, a significant challenge is the scarcity of training data, impeding the development of robust models. Federated learning has emerged as a promising solution to address the challenge of limited training data in various research domains. Through collaborative model training, robust model training is enabled while upholding data privacy and security. This paper explores the potential of applying federated learning to data integration through a structured literature review, offering insights into the current state-of-the-art and future directions in this interdisciplinary field.