Prototype Based Regularization

Prototype-based regularization is a technique enhancing machine learning model performance, particularly in federated learning and few-shot learning scenarios, by leveraging prototype representations of data clusters. Current research focuses on improving prototype generation and utilization, often incorporating variance-aware clustering, sparsity-inducing losses, and multi-level prototype hierarchies within federated and dynamic learning frameworks. This approach addresses challenges like data heterogeneity and concept drift, leading to improved model generalization and convergence speed across diverse datasets and learning paradigms. The resulting advancements have significant implications for privacy-preserving collaborative learning and efficient learning from limited data.