Hierarchical Training

Hierarchical training is a deep learning technique that structures the training process into multiple levels or stages, often mirroring hierarchical data structures or task complexities. Current research focuses on applying this approach to diverse problems, including improving privacy in distributed training, enhancing the accuracy of indoor localization and antibody design, and optimizing resource utilization in edge-cloud computing systems. This methodology offers significant advantages by improving efficiency, scalability, and accuracy across various applications, leading to advancements in areas such as computer vision, natural language processing, and drug discovery.