8 citations · 9 across the 3 of their papers we have counts for
3 papers
cs.CL2025
Zero Token-Driven Deep Thinking in LLMs: Unlocking the Full Potential of Existing Parameters via Cyclic Refinement
Guanghao Li, Wenhao Jiang, Li Shen +2
Resource limitations often constrain the parameter counts of Large Language Models (LLMs), hindering their performance. While existing methods employ parameter sharing to reuse the…
cs.LG2023★ 1 cited
DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning
Qinglun Li, Li Shen, Guanghao Li +2
To address the communication burden issues associated with federated learning (FL), decentralized federated learning (DFL) discards the central server and establishes a decentraliz…
cs.LG2023★ 8 cited
Visual Prompt Based Personalized Federated Learning
Guanghao Li, Wansen Wu, Yan Sun +3
As a popular paradigm of distributed learning, personalized federated learning (PFL) allows personalized models to improve generalization ability and robustness by utilizing knowle…