Swing Distillation

Swing distillation is a knowledge distillation technique focused on improving the efficiency and privacy of model training. Current research emphasizes developing methods to transfer knowledge from large, complex "teacher" models to smaller, faster "student" models while mitigating the risk of inadvertently leaking sensitive data from the teacher's training set. This involves exploring novel distillation algorithms, such as those employing adaptive temperature scaling or noise injection, and applying these techniques to diverse model architectures, including diffusion models and multi-agent systems. The resulting smaller, more efficient models have significant implications for resource-constrained applications and enhance privacy in machine learning.