Data Silo

Data silos, representing isolated datasets held by different organizations, hinder collaborative data analysis and model training due to privacy concerns and data heterogeneity. Current research focuses on overcoming these limitations using federated learning, which allows collaborative model training without direct data sharing, often incorporating techniques like multi-task learning and contrastive learning to improve model accuracy and handle non-identically distributed data. These methods are being applied across diverse domains, including healthcare and multi-agent systems, with the goal of enabling more powerful and efficient machine learning models while preserving data privacy and security.