Copertina

FedKRSO: Communication and Memory Efficient Federated Fine-Tuning of Large Language Models

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Yang, Guohao, Wu, Tongle, Guo, Yuanxiong, Sun, Ying, Gong, Yanmin
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

FedPT: Federated Proxy-Tuning of Large Language Models on Resource-Constrained Edge Devices
di: Gao, Zhidong, et al.
Pubblicazione: (2024)

One Node Per User: Node-Level Federated Learning for Graph Neural Networks
di: Gao, Zhidong, et al.
Pubblicazione: (2024)

Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning
di: Gao, Zhidong, et al.
Pubblicazione: (2024)

Online Client Scheduling and Resource Allocation for Efficient Federated Edge Learning
di: Gao, Zhidong, et al.
Pubblicazione: (2024)

DP$^2$-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization
di: Zhang, Zhenxiao, et al.
Pubblicazione: (2024)

FedPFT: Federated Proxy Fine-Tuning of Foundation Models
di: Peng, Zhaopeng, et al.
Pubblicazione: (2024)

Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models
di: Fang, Zihan, et al.
Pubblicazione: (2024)

FedMomentum: Preserving LoRA Training Momentum in Federated Fine-Tuning
di: Yan, Peishen, et al.
Pubblicazione: (2026)

Efficient and Scalable Fine-Tune of Language Models for Genome Understanding
di: Zhan, Huixin, et al.
Pubblicazione: (2024)

SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large Pre-Trained Models over Resource-Limited Devices
di: Cao, Linxiao, et al.
Pubblicazione: (2024)

FedNano: Toward Lightweight Federated Tuning for Pretrained Multimodal Large Language Models
di: Zhang, Yao, et al.
Pubblicazione: (2025)

Efficient Federated Fine-Tuning of Large Language Models with Layer Dropout
di: Wang, Shilong, et al.
Pubblicazione: (2025)

Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression
di: Zhang, Zhenxiao, et al.
Pubblicazione: (2024)

FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning
di: Tamirisa, Rishub, et al.
Pubblicazione: (2024)

Federated Data-Efficient Instruction Tuning for Large Language Models
di: Qin, Zhen, et al.
Pubblicazione: (2024)

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion
di: Fan, Tao, et al.
Pubblicazione: (2026)

FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning
di: Liu, Junkang, et al.
Pubblicazione: (2026)

A Bayesian Hybrid Parameter-Efficient Fine-Tuning Method for Large Language Models
di: Chai, Yidong, et al.
Pubblicazione: (2025)

FedAdamW: A Communication-Efficient Optimizer with Convergence and Generalization Guarantees for Federated Large Models
di: Liu, Junkang, et al.
Pubblicazione: (2025)

FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning
di: Tamirisa, Rishub, et al.
Pubblicazione: (2023)

LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning
di: Pan, Rui, et al.
Pubblicazione: (2024)

AdaZeta: Adaptive Zeroth-Order Tensor-Train Adaption for Memory-Efficient Large Language Models Fine-Tuning
di: Yang, Yifan, et al.
Pubblicazione: (2024)

Sparsity-Aware Low-Rank Representation for Efficient Fine-Tuning of Large Language Models
di: Zhang, Longteng, et al.
Pubblicazione: (2026)

On the Entropy Dynamics in Reinforcement Fine-Tuning of Large Language Models
di: Wang, Shumin, et al.
Pubblicazione: (2026)

Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation
di: Yeh, Shih-Ying, et al.
Pubblicazione: (2023)

Preserving Diversity in Supervised Fine-Tuning of Large Language Models
di: Li, Ziniu, et al.
Pubblicazione: (2024)

ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training
di: Wang, Hui-Po, et al.
Pubblicazione: (2021)

Crafting Efficient Fine-Tuning Strategies for Large Language Models
di: Oliver, Michael, et al.
Pubblicazione: (2024)

Task-Centric Personalized Federated Fine-Tuning of Language Models
di: Talasso, Gabriel U., et al.
Pubblicazione: (2026)

RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models
di: Wei, Quan, et al.
Pubblicazione: (2025)

FLAME: Towards Federated Fine-Tuning Large Language Models Through Adaptive SMoE
di: Le, Khiem, et al.
Pubblicazione: (2025)

Hyperbolic Fine-Tuning for Large Language Models
di: Yang, Menglin, et al.
Pubblicazione: (2024)

CEPerFed: Communication-Efficient Personalized Federated Learning for Multi-Pulse MRI Classification
di: Li, Ludi, et al.
Pubblicazione: (2025)

The Effectiveness of Approximate Regularized Replay for Efficient Supervised Fine-Tuning of Large Language Models
di: Riemer, Matthew, et al.
Pubblicazione: (2025)

Understanding and Guiding Layer Placement in Parameter-Efficient Fine-Tuning of Large Language Models
di: Xu, Yichen, et al.
Pubblicazione: (2026)

FedTreeLoRA: Reconciling Statistical and Functional Heterogeneity in Federated LoRA Fine-Tuning
di: Bian, Jieming, et al.
Pubblicazione: (2026)

H2Tune: Federated Foundation Model Fine-Tuning with Hybrid Heterogeneity
di: Guo, Wei, et al.
Pubblicazione: (2025)

MELINOE: Fine-Tuning Enables Memory-Efficient Inference for Mixture-of-Experts Models
di: Raje, Arian, et al.
Pubblicazione: (2026)

Linearization Explains Fine-Tuning in Large Language Models
di: Afzal, Zahra Rahimi, et al.
Pubblicazione: (2026)

A Survey on Parameter-Efficient Fine-Tuning for Foundation Models in Federated Learning
di: Bian, Jieming, et al.
Pubblicazione: (2025)