Model Versioning

Model versioning addresses the challenges of managing the evolution and deployment of machine learning models, particularly in complex scenarios like federated learning and the increasing need for model security. Current research focuses on efficient storage and retrieval of model lineages, robust methods for creating multiple model versions with varying security properties (e.g., resistance to adversarial attacks), and integrating version control into the model development lifecycle to improve collaboration and reproducibility. These advancements are crucial for enhancing the reliability, security, and scalability of machine learning systems across various applications, from computer vision to AIoT.