49 citations · 56 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
Adaptive User-Centric Entanglement Routing in Quantum Data Networks
Lei Wang, Jieming Bian, Jie Xu
Distributed quantum computing (DQC) holds immense promise in harnessing the potential of quantum computing by interconnecting multiple small quantum computers (QCs) through a quant…
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…
FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology
Yuanzhe Peng, Jieming Bian, Jie Xu
The fusion of complementary multimodal information is crucial in computational pathology for accurate diagnostics. However, existing multimodal learning approaches necessitate acce…
C2Ideas: Supporting Creative Interior Color Design Ideation with Large Language Model
Yihan Hou, Manling Yang, Hao Cui +3
Interior color design is a creative process that endeavors to allocate colors to furniture and other elements within an interior space. While much research focuses on generating re…
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…