Data Sharing

Data sharing in machine learning aims to leverage distributed datasets for improved model training while addressing privacy and security concerns. Current research focuses on developing federated learning frameworks, employing techniques like differential privacy, synthetic data generation, and blockchain technologies to enable collaborative model training without direct data exchange. These methods are crucial for advancing fields like healthcare and manufacturing, where data is often siloed due to privacy regulations or competitive interests, ultimately improving model accuracy and efficiency while protecting sensitive information.