116 citations · 232 across the 6 of their papers we have counts for
4 papers · 1 filter
: Sparsifying Large Language Models via Dual Taylor Expansion and Attention Distribution Awareness
Lang Xiong, Ning Liu, Ao Ren +6
Large language models (LLMs) face significant deployment challenges due to their massive computational demands. % While pruning offers a promising compression solution, existing me…
Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
Moming Duan, Duo Liu, Xinyuan Ji +4
Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally. Unlike th…
CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework
Yu Zhang, Moming Duan, Duo Liu +5
Federated learning (FL) is an emerging distributed machine learning paradigm that protects privacy and tackles the problem of isolated data islands. At present, there are two main…
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems
Li Li, Moming Duan, Duo Liu +5
Federated Learning (FL) is a novel distributed machine learning which allows thousands of edge devices to train model locally without uploading data concentrically to the server. B…