37 citations · 97 across the 26 of their papers we have counts for
4 papers · 1 filter
Communication-Efficient Personalized Federal Graph Learning via Low-Rank Decomposition
Ruyue Liu, Rong Yin, Xiangzhen Bo +5
Federated graph learning (FGL) has gained significant attention for enabling heterogeneous clients to process their private graph data locally while interacting with a centralized…
Key-Point-Driven Mathematical Reasoning Distillation of Large Language Model
Xunyu Zhu, Jian Li, Can Ma +1
Large Language Models (LLMs) have demonstrated exceptional proficiency in mathematical reasoning tasks due to their extensive parameter counts and training on vast datasets. Despit…
FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning
Jian Li, Yong Liu, Wei Wang +2
Recent Newton-type federated learning algorithms have demonstrated linear convergence with respect to the communication rounds. However, communicating Hessian matrices is often unf…
Distilling Mathematical Reasoning Capabilities into Small Language Models
Xunyu Zhu, Jian Li, Yong Liu +2
This work addresses the challenge of democratizing advanced Large Language Models (LLMs) by compressing their mathematical reasoning capabilities into sub-billion parameter Small L…