Han discusses the challenges of distributed training, particularly the communication bottlenecks that hinder scalability. He introduces deep gradient compression as a solution aimed at making efficient training accessible even on low-cost networking infrastructures. This approach seeks to empower a broader range of machine learning practitioners who may not have access to advanced resources.
