1 citations · 1 across the 3 of their papers we have counts for
4 papers
Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging
Kuangpu Guo, Aijing Yu, Jian Liang +4
Model merging has emerged as a promising paradigm for enabling multi-task capabilities without additional training. However, traditional basic merging methods often experience perf…
Cooperative Pseudo Labeling for Unsupervised Federated Classification
Kuangpu Guo, Lijun Sheng, Yongcan Yu +3
Unsupervised Federated Learning (UFL) aims to collaboratively train a global model across distributed clients without sharing data or accessing label information. Previous UFL work…
Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion
Yuguang Zhang, Kuangpu Guo, Zhihe Lu +2
Federated learning (FL) enables collaborative model training across decentralized clients without sharing local data, but is challenged by heterogeneity in data, computation, and c…
An Enhanced Federated Prototype Learning Method under Domain Shift
Liang Kuang, Kuangpu Guo, Jian Liang +1
Federated Learning (FL) allows collaborative machine learning training without sharing private data. Numerous studies have shown that one significant factor affecting the performan…