5 citations · 5 across the 4 of their papers we have counts for
4 papers
Rethinking LoRA for Data Heterogeneous Federated Learning: Subspace and State Alignment
Hongyi Peng, Han Yu, Xiaoxiao Li +1
Low-Rank Adaptation (LoRA) is widely used for federated fine-tuning. Yet under non-IID settings, it can substantially underperform full-parameter fine-tuning. Through with-high-pro…
Toward Federated Large Language Models in Medicine: A Parameter-Efficient Framework for Privacy-Preserving, Multi-Institutional Adaptation
Anran Li, Yuanyuan Chen, Wenjun Long +16
Large language models (LLMs) are increasingly adapted for medical applications, but most are trained using data from a single institution because privacy and governance constraints…
FedCal: Achieving Local and Global Calibration in Federated Learning via Aggregated Parameterized Scaler
Hongyi Peng, Han Yu, Xiaoli Tang +1
Federated learning (FL) enables collaborative machine learning across distributed data owners, but data heterogeneity poses a challenge for model calibration. While prior work focu…
Advances and Open Challenges in Federated Foundation Models
Chao Ren, Han Yu, Hongyi Peng +9
The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI). This integration offers enhanced capabil…