7 citations · 25 across the 18 of their papers we have counts for
4 papers · 2 filters
LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement
Jieming Bian, Lei Wang, Letian Zhang +1
Foundation models (FMs) achieve strong performance across diverse tasks with task-specific fine-tuning, yet full parameter fine-tuning is often computationally prohibitive for larg…
Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning
Jieming Bian, Lei Wang, Jie Xu
Federated Learning (FL) is a distributed machine learning approach that enables devices to collaboratively train models without sharing their local data, ensuring user privacy and…
Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains
Lei Wang, Jieming Bian, Letian Zhang +2
Federated learning (FL) allows collaborative machine learning training without sharing private data. While most FL methods assume identical data domains across clients, real-world…
Federated Learning with Instance-Dependent Noisy Label
Lei Wang, Jieming Bian, Jie Xu
Federated learning (FL) with noisy labels poses a significant challenge. Existing methods designed for handling noisy labels in centralized learning tend to lose their effectivenes…