Cascade Learning

Cascade learning is a machine learning technique that trains a series of models sequentially, where each model's output informs the subsequent model's training. Current research focuses on improving efficiency and robustness in various applications, including federated learning, large language model deployment, and online learning scenarios. This approach addresses challenges like memory limitations in resource-constrained devices and high computational costs of large models by leveraging a hierarchical structure to achieve better accuracy, generalization, and interpretability while reducing overall resource consumption. The resulting improvements in efficiency and performance have significant implications for deploying complex models in real-world applications.