Model Sharing

Model sharing in machine learning explores methods for collaboratively training and utilizing models while addressing concerns about data privacy, resource constraints, and intellectual property. Current research focuses on federated learning techniques, including the development of novel architectures like Dual-LiGO for efficient model growth without direct model exchange, and the use of proxy models to facilitate collaboration while enhancing privacy. This field is crucial for enabling large-scale collaborative AI development, particularly in sensitive domains like healthcare and finance, and for making advanced models accessible to resource-constrained researchers and institutions.