49 citations · 56 across the 13 of their papers we have counts for
12 papers · 1 filter
Breaking the Structural Identity: Personalized Federated LoRA Fine-tuning under Rank Heterogeneity
Lei Wang, Jieming Bian, Letian Zhang +1
Large Language Models (LLMs) have achieved remarkable success across diverse domains, but their adaptation to privacy-sensitive, distributed datasets remains a challenge. While Fed…
FedTreeLoRA: Reconciling Statistical and Functional Heterogeneity in Federated LoRA Fine-Tuning
Jieming Bian, Lei Wang, Letian Zhang +1
Federated Learning (FL) with Low-Rank Adaptation (LoRA) has become a standard for privacy-preserving LLM fine-tuning. However, existing personalized methods predominantly operated…
Adaptive LoRA Experts Allocation and Selection for Federated Fine-Tuning
Lei Wang, Jieming Bian, Letian Zhang +1
Large Language Models (LLMs) have demonstrated impressive capabilities across various tasks, but fine-tuning them for domain-specific applications often requires substantial domain…
FedEL: Federated Elastic Learning for Heterogeneous Devices
Letian Zhang, Bo Chen, Jieming Bian +2
Federated learning (FL) enables distributed devices to collaboratively train machine learning models while maintaining data privacy. However, the heterogeneous hardware capabilitie…
Multimodal Federated Learning: A Survey through the Lens of Different FL Paradigms
Yuanzhe Peng, Jieming Bian, Lei Wang +2
Multimodal Federated Learning (MFL) lies at the intersection of two pivotal research areas: leveraging complementary information from multiple modalities to improve downstream infe…
A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning
Jieming Bian, Yuanzhe Peng, Lei Wang +2
Foundation models have revolutionized artificial intelligence by providing robust, versatile architectures pre-trained on large-scale datasets. However, adapting these massive mode…
FedALT: Federated Fine-Tuning through Adaptive Local Training with Rest-of-World LoRA
Jieming Bian, Lei Wang, Letian Zhang +1
Fine-tuning large language models (LLMs) in federated settings enables privacy-preserving adaptation but suffers from cross-client interference due to model aggregation. Existing f…
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…
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers
Jieming Bian, Lei Wang, Shaolei Ren +1
Training large-scale artificial intelligence (AI) models demands significant computational power and energy, leading to increased carbon footprint with potential environmental repe…