8 citations · 18 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Subspace based Federated Unlearning
Guanghao Li, Li Shen, Yan Sun +3
Federated learning (FL) enables multiple clients to train a machine learning model collaboratively without exchanging their local data. Federated unlearning is an inverse FL proces…
FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for Resource and Data Heterogeneity
Guanghao Li, Yue Hu, Miao Zhang +4
Federated Learning (FL) enables training a global model without sharing the decentralized raw data stored on multiple devices to protect data privacy. Due to the diverse capacity o…