Module Wise

Module-wise approaches are transforming the training and deployment of large neural networks by focusing on the individual components (modules) rather than the entire model. Current research emphasizes knowledge distillation techniques, where smaller "student" modules learn from larger "teacher" modules, improving efficiency and enabling adaptation to diverse tasks and data modalities. This modularity is particularly valuable for resource-constrained environments and privacy-sensitive applications like federated learning in healthcare, leading to more efficient and adaptable AI systems. The resulting smaller, faster models maintain performance comparable to their larger counterparts, offering significant advantages in deployment and scalability.