Publications (15)
FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure
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…
Stability of Rarefaction Waves Under Periodic Perturbation for A Rate-Type Viscoelastic System
lin Chang, Duo Liu, Weiqiang Zhang
In this paper, a rarefaction wave under space-periodic perturbation for the 3 times 3 rate-type viscoelastic system is considered. It is shown that if the initial perturbation arou…
: 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…
Generalized Category Discovery via Reciprocal Learning and Class-Wise Distribution Regularization
Duo Liu, Zhiquan Tan, Linglan Zhao +3
Generalized Category Discovery (GCD) aims to identify unlabeled samples by leveraging the base knowledge from labeled ones, where the unlabeled set consists of both base and novel…
Randomized block coordinate descent method for linear ill-posed problems
Qinian Jin, Duo Liu
Consider the linear ill-posed problems of the form , where, for each , is a bounded linear operator between two Hilbert spaces and ${\math…
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…