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cs.LG2025
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…
cs.LG2025
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…
cs.LG2024★ 1 cited
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…