Free Knowledge Transfer

Free knowledge transfer in machine learning focuses on leveraging the knowledge embedded within pre-trained models without accessing their original training data, addressing data privacy and resource constraints. Current research emphasizes techniques like knowledge distillation, often employing transformers and graph neural networks, to efficiently transfer knowledge between models, sometimes using prompts or other data-free methods to guide the process. This field is significant because it enables the deployment of powerful models on resource-limited devices and facilitates knowledge sharing across diverse domains while respecting data ownership and privacy.