Cover Image

Task-Centric Personalized Federated Fine-Tuning of Language Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Talasso, Gabriel U., Kurmanji, Meghdad, de Souza, Allan M., Lane, Nicholas D., Villas, Leandro A.
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Position: Bridge the Gaps between Machine Unlearning and AI Regulation
by: Marino, Bill, et al.
Published: (2025)

Agentic Federated Learning: The Future of Distributed Training Orchestration
by: Jarczewski, Rafael O., et al.
Published: (2026)

LLM Unlearning via Neural Activation Redirection
by: Shen, William F., et al.
Published: (2025)

How Data Inter-connectivity Shapes LLMs Unlearning: A Structural Unlearning Perspective
by: Qiu, Xinchi, et al.
Published: (2024)

MT-DAO: Multi-Timescale Distributed Adaptive Optimizers with Local Updates
by: Iacob, Alex, et al.
Published: (2025)

$f$-FUM: Federated Unlearning via min--max and $f$-divergence
by: Karimian, Radmehr, et al.
Published: (2026)

Beyond Shortest Path: Agentic Vehicular Routing with Semantic Context
by: Braun, Carnot, et al.
Published: (2025)

Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
by: Zhao, Wanru, et al.
Published: (2024)

Implicit Federated In-context Learning For Task-Specific LLM Fine-Tuning
by: Li, Dongcheng, et al.
Published: (2025)

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

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

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

FedKRSO: Communication and Memory Efficient Federated Fine-Tuning of Large Language Models
by: Yang, Guohao, et al.
Published: (2026)

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

From Language to Action in Arabic: Reliable Structured Tool Calling via Data-Centric Fine-Tuning
by: Nacar, Omer, et al.
Published: (2026)

Wireless Federated Multi-Task LLM Fine-Tuning via Sparse-and-Orthogonal LoRA
by: Yang, Nuocheng, et al.
Published: (2026)

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

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

MaZO: Masked Zeroth-Order Optimization for Multi-Task Fine-Tuning of Large Language Models
by: Zhang, Zhen, et al.
Published: (2025)

FL-TAC: Enhanced Fine-Tuning in Federated Learning via Low-Rank, Task-Specific Adapter Clustering
by: Ping, Siqi, et al.
Published: (2024)

Exploring Gradient Subspaces: Addressing and Overcoming LoRA's Limitations in Federated Fine-Tuning of Large Language Models
by: Mahla, Navyansh, et al.
Published: (2024)

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

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

A Closer Look at Personalized Fine-Tuning in Heterogeneous Federated Learning
by: Chen, Minghui, et al.
Published: (2025)

Task-Aware Parameter-Efficient Fine-Tuning of Large Pre-Trained Models at the Edge
by: Hu, Senkang, et al.
Published: (2025)

Federated Fine-Tuning of LLMs: Framework Comparison and Research Directions
by: Yan, Na, et al.
Published: (2025)

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

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

Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning
by: Zhang, Jingyuan, et al.
Published: (2024)

Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning?
by: Li, Zexi, et al.
Published: (2025)

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

Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning
by: Raje, Arian, et al.
Published: (2025)

RPO: Fine-Tuning Visual Generative Models via Rich Vision-Language Preferences
by: Zhao, Hanyang, et al.
Published: (2025)

DEPT: Decoupled Embeddings for Pre-training Language Models
by: Iacob, Alex, et al.
Published: (2024)

Distilling Linearized Behavior into Non-Linear Fine-Tuning for Effective Task Arithmetic
by: Sommariva, Thomas, et al.
Published: (2026)

Rethinking Fine-Tuning when Scaling Test-Time Compute: Limiting Confidence Improves Mathematical Reasoning
by: Chen, Feng, et al.
Published: (2025)

Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models
by: Shu, Yao, et al.
Published: (2024)

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

SFT-GO: Supervised Fine-Tuning with Group Optimization for Large Language Models
by: Kim, Gyuhak, et al.
Published: (2025)

QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models
by: Zhou, Jiajun, et al.
Published: (2025)