49 citations · 52 across the 11 of their papers we have counts for
13 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…
Null-Space Filtering for Data-Free Continual Model Merging: Preserving Stability, Promoting Plasticity
Zihuan Qiu, Lei Wang, Yang Cao +7
Data-free continual model merging (DFCMM) aims to fuse independently fine-tuned models into a single backbone that evolves with incoming tasks without accessing task data. This pap…
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…