Global Prototype

Global prototypes represent a powerful approach in machine learning, aiming to create shared, generalized representations of data classes across diverse datasets and learning scenarios. Current research focuses on developing methods to efficiently learn and utilize these prototypes, particularly within federated learning frameworks and for addressing data heterogeneity, often employing contrastive learning and k-means clustering algorithms to generate and refine these representations. This approach improves model performance, particularly in few-shot and zero-shot learning settings, and offers advantages in terms of communication efficiency and privacy preservation in distributed learning environments.