7 citations · 25 across the 18 of their papers we have counts for
15 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…
On the Local Cache Update Rules in Streaming Federated Learning
Heqiang Wang, Jieming Bian, Jie Xu
In this study, we address the emerging field of Streaming Federated Learning (SFL) and propose local cache update rules to manage dynamic data distributions and limited cache capac…
Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying
Jieming Bian, Jie Xu
This paper presents a study on asynchronous Federated Learning (FL) in a mobile network setting. The majority of FL algorithms assume that communication between clients and the ser…
FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning
Jieming Bian, Zhu Fu, Jie Xu
Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framework, has received extensive research attention in recent years. The majority of…