Mingzhe Han

FedHUR is a federated recommendation framework for learning hierarchical utility-guided client relations that consistently outperforms existing federated recommendation baselines, demonstrating the effectiveness of hierarchical utility-guided client relation learning.

Open-source QUEST agents, trained solely on 8K synthetic tasks, rival or surpass proprietary research agents, proving that scaling data synthesis can unlock frontier performance.

LLMs are poised to flip the script on personalization, giving users unprecedented control over their data and how it's used across platforms.