Decentralized Knowledge Distillation

Decentralized knowledge distillation focuses on training accurate machine learning models from data distributed across multiple agents without directly sharing sensitive information. Current research emphasizes efficient communication strategies, often involving one-shot communication or distillation of datasets rather than model parameters, and explores the impact of data heterogeneity on model performance. This approach addresses the limitations of centralized learning in privacy-sensitive applications and resource-constrained environments, offering significant potential for improving the scalability and robustness of machine learning systems. The development of novel algorithms, such as those incorporating multiple auxiliary heads or decentralized composite knowledge distillation, aims to enhance both accuracy and efficiency in these decentralized settings.