2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2025
H2Tune: Federated Foundation Model Fine-Tuning with Hybrid Heterogeneity
Wei Guo, Siyuan Lu, Yiqi Tong +5
Different from existing federated fine-tuning (FFT) methods for foundation models, hybrid heterogeneous federated fine-tuning (HHFFT) is an under-explored scenario where clients ex…
cs.LG2025
Proto-EVFL: Enhanced Vertical Federated Learning via Dual Prototype with Extremely Unaligned Data
Wei Guo, Yiyang Duan, Zhaojun Hu +7
In vertical federated learning (VFL), multiple enterprises address aligned sample scarcity by leveraging massive locally unaligned samples to facilitate collaborative learning. How…
cs.LG2024★ 2 cited
A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications
Wei Guo, Fuzhen Zhuang, Xiao Zhang +2
Federated learning (FL) is a novel distributed machine learning paradigm that enables participants to collaboratively train a centralized model with privacy preservation by elimina…