Centralized Data

Centralized data management in machine learning faces challenges related to privacy, scalability, and data heterogeneity across distributed sources. Current research focuses on developing methods to leverage centralized data while mitigating these issues, including techniques like federated learning that allow collaborative model training without direct data sharing, and algorithms that simulate federated learning environments using centralized datasets to optimize model performance. This work is significant because it enables efficient model training and improved performance while addressing crucial privacy concerns and the inherent complexities of working with diverse datasets.