Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Shilong, Liu, Jianchun, Xu, Hongli, Yan, Jiaming, Gao, Xianjun |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning
by: Huo, Yujia, et al.
Published: (2025)
by: Huo, Yujia, et al.
Published: (2025)
Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices
by: Liu, Jun, et al.
Published: (2024)
by: Liu, Jun, et al.
Published: (2024)
FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models
by: Ramesh, Hariharan, et al.
Published: (2025)
by: Ramesh, Hariharan, et al.
Published: (2025)
Caesar: A Low-deviation Compression Approach for Efficient Federated Learning
by: Yan, Jiaming, et al.
Published: (2024)
by: Yan, Jiaming, et al.
Published: (2024)
Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout
by: Liu, Ji, et al.
Published: (2025)
by: Liu, Ji, et al.
Published: (2025)
HSplitLoRA: A Heterogeneous Split Parameter-Efficient Fine-Tuning Framework for Large Language Models
by: Lin, Zheng, et al.
Published: (2025)
by: Lin, Zheng, et al.
Published: (2025)
FedQuad: Adaptive Layer-wise LoRA Deployment and Activation Quantization for Federated Fine-Tuning
by: Li, Rukuo, et al.
Published: (2025)
by: Li, Rukuo, et al.
Published: (2025)
SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large Pre-Trained Models over Resource-Limited Devices
by: Cao, Linxiao, et al.
Published: (2024)
by: Cao, Linxiao, et al.
Published: (2024)
A Robust Federated Learning Framework for Undependable Devices at Scale
by: Wang, Shilong, et al.
Published: (2024)
by: Wang, Shilong, et al.
Published: (2024)
DLoRA: Distributed Parameter-Efficient Fine-Tuning Solution for Large Language Model
by: Gao, Chao, et al.
Published: (2024)
by: Gao, Chao, et al.
Published: (2024)
Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
by: Zhao, Wanru, et al.
Published: (2024)
by: Zhao, Wanru, et al.
Published: (2024)
Efficient Deployment of Large Language Models on Resource-constrained Devices
by: Yao, Zhiwei, et al.
Published: (2025)
by: Yao, Zhiwei, et al.
Published: (2025)
Learning Like Humans: Resource-Efficient Federated Fine-Tuning through Cognitive Developmental Stages
by: Wu, Yebo, et al.
Published: (2025)
by: Wu, Yebo, et al.
Published: (2025)
The Future of Large Language Model Pre-training is Federated
by: Sani, Lorenzo, et al.
Published: (2024)
by: Sani, Lorenzo, et al.
Published: (2024)
Mitigating Persistent Client Dropout in Asynchronous Decentralized Federated Learning
by: Stępka, Ignacy, et al.
Published: (2025)
by: Stępka, Ignacy, et al.
Published: (2025)
A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning
by: Chen, Minghui, et al.
Published: (2025)
by: Chen, Minghui, et al.
Published: (2025)
Adaptive Rank Allocation for Federated Parameter-Efficient Fine-Tuning of Language Models
by: Wu, Fei, et al.
Published: (2025)
by: Wu, Fei, et al.
Published: (2025)
Collaborative Speculative Inference for Efficient LLM Inference Serving
by: Gao, Luyao, et al.
Published: (2025)
by: Gao, Luyao, et al.
Published: (2025)
LoRAFusion: Efficient LoRA Fine-Tuning for LLMs
by: Zhu, Zhanda, et al.
Published: (2025)
by: Zhu, Zhanda, et al.
Published: (2025)
CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models
by: Wu, Huiwen, et al.
Published: (2024)
by: Wu, Huiwen, et al.
Published: (2024)
FedRef: Bayesian Fine-Tuning using a Reference Model to Mitigate Catastrophic Forgetting for Heterogeneous Federated Learning
by: Yoon, Taehwan, et al.
Published: (2025)
by: Yoon, Taehwan, et al.
Published: (2025)
Resource-Efficient Personal Large Language Models Fine-Tuning with Collaborative Edge Computing
by: Ye, Shengyuan, et al.
Published: (2024)
by: Ye, Shengyuan, et al.
Published: (2024)
SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning
by: Yang, Yuning, et al.
Published: (2024)
by: Yang, Yuning, et al.
Published: (2024)
Towards the Next Frontier of LLMs, Training on Private Data: A Cross-Domain Benchmark for Federated Fine-Tuning
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2026)
by: Jimenez-Gutierrez, Daniel M., et al.
Published: (2026)
Towards Communication-Efficient Decentralized Federated Graph Learning over Non-IID Data
by: Wang, Shilong, et al.
Published: (2025)
by: Wang, Shilong, et al.
Published: (2025)
Federated Fine-Tuning of Sparsely-Activated Large Language Models on Resource-Constrained Devices
by: Chen, Fahao, et al.
Published: (2025)
by: Chen, Fahao, et al.
Published: (2025)
Symbiosis: Multi-Adapter Inference and Fine-Tuning
by: Gupta, Saransh, et al.
Published: (2025)
by: Gupta, Saransh, et al.
Published: (2025)
HAFLQ: Heterogeneous Adaptive Federated LoRA Fine-tuned LLM with Quantization
by: Su, Yang, et al.
Published: (2024)
by: Su, Yang, et al.
Published: (2024)
Exploring Selective Layer Fine-Tuning in Federated Learning
by: Sun, Yuchang, et al.
Published: (2024)
by: Sun, Yuchang, et al.
Published: (2024)
Communication-Efficient Federated Fine-Tuning
by: Theologitis, Michael, et al.
Published: (2025)
by: Theologitis, Michael, et al.
Published: (2025)
FedTrans: Efficient Federated Learning via Multi-Model Transformation
by: Zhu, Yuxuan, et al.
Published: (2024)
by: Zhu, Yuxuan, et al.
Published: (2024)
Fine-Tuning GPT-5 for GPU Kernel Generation
by: Tehrani, Ali, et al.
Published: (2026)
by: Tehrani, Ali, et al.
Published: (2026)
Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models
by: Liu, Ji, et al.
Published: (2024)
by: Liu, Ji, et al.
Published: (2024)
FedPop: Federated Population-based Hyperparameter Tuning
by: Chen, Haokun, et al.
Published: (2023)
by: Chen, Haokun, et al.
Published: (2023)
Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation
by: Zhang, Zikai, et al.
Published: (2025)
by: Zhang, Zikai, et al.
Published: (2025)
SPD: Sync-Point Drop for Efficient Tensor Parallelism of Large Language Models
by: Kim, Han-Byul, et al.
Published: (2025)
by: Kim, Han-Byul, et al.
Published: (2025)
FedTLU: Federated Learning with Targeted Layer Updates
by: Park, Jong-Ik, et al.
Published: (2024)
by: Park, Jong-Ik, et al.
Published: (2024)
Federated Neural Architecture Search with Model-Agnostic Meta Learning
by: Huang, Xinyuan, et al.
Published: (2025)
by: Huang, Xinyuan, et al.
Published: (2025)
Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
by: Gao, Zhidong, et al.
Published: (2024)
by: Gao, Zhidong, et al.
Published: (2024)
Efficient Asynchronous Federated Learning with Sparsification and Quantization
by: Jia, Juncheng, et al.
Published: (2023)
by: Jia, Juncheng, et al.
Published: (2023)
Similar Items
-
Mitigating Catastrophic Forgetting with Adaptive Transformer Block Expansion in Federated Fine-Tuning
by: Huo, Yujia, et al.
Published: (2025) -
Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devices
by: Liu, Jun, et al.
Published: (2024) -
FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models
by: Ramesh, Hariharan, et al.
Published: (2025) -
Caesar: A Low-deviation Compression Approach for Efficient Federated Learning
by: Yan, Jiaming, et al.
Published: (2024) -
Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout
by: Liu, Ji, et al.
Published: (2025)