Many-Task Federated Fine-Tuning via Unified Task Vectors
Fuente:
arXiv
Saved in:
| Main Authors: | Tsouvalas, Vasileios, Ozcelebi, Tanir, Meratnia, Nirvana |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side Distillation
by: Tsouvalas, Vasileios, et al.
Published: (2024)
by: Tsouvalas, Vasileios, et al.
Published: (2024)
EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters
by: Tsouvalas, Vasileios, et al.
Published: (2024)
by: Tsouvalas, Vasileios, et al.
Published: (2024)
EFU: Enforcing Federated Unlearning via Functional Encryption
by: Mohammadi, Samaneh, et al.
Published: (2025)
by: Mohammadi, Samaneh, et al.
Published: (2025)
Fine-tuning Multimodal Transformers on Edge: A Parallel Split Learning Approach
by: Fudala, Timo, et al.
Published: (2025)
by: Fudala, Timo, et al.
Published: (2025)
Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning
by: Huang, Brandon, et al.
Published: (2024)
by: Huang, Brandon, et al.
Published: (2024)
Towards Unified Modeling in Federated Multi-Task Learning via Subspace Decoupling
by: Wei, Yipan, et al.
Published: (2025)
by: Wei, Yipan, et al.
Published: (2025)
Many Perception Tasks are Highly Redundant Functions of their Input Data
by: Ramesh, Rahul, et al.
Published: (2024)
by: Ramesh, Rahul, et al.
Published: (2024)
Task-Specific Directions: Definition, Exploration, and Utilization in Parameter Efficient Fine-Tuning
by: Si, Chongjie, et al.
Published: (2024)
by: Si, Chongjie, et al.
Published: (2024)
Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune
by: Luo, Mengen, et al.
Published: (2024)
by: Luo, Mengen, et al.
Published: (2024)
FedVLMBench: Benchmarking Federated Fine-Tuning of Vision-Language Models
by: Zheng, Weiying, et al.
Published: (2025)
by: Zheng, Weiying, et al.
Published: (2025)
Adaptive Parametric Activation: Unifying and Generalising Activation Functions Across Tasks
by: Alexandridis, Konstantinos Panagiotis, et al.
Published: (2024)
by: Alexandridis, Konstantinos Panagiotis, et al.
Published: (2024)
Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning
by: Sun, Guangyu, et al.
Published: (2022)
by: Sun, Guangyu, et al.
Published: (2022)
TwinTURBO: Semi-Supervised Fine-Tuning of Foundation Models via Mutual Information Decompositions for Downstream Task and Latent Spaces
by: Quétant, Guillaume, et al.
Published: (2025)
by: Quétant, Guillaume, et al.
Published: (2025)
The Unified Balance Theory of Second-Moment Exponential Scaling Optimizers in Visual Tasks
by: Zhang, Gongyue, et al.
Published: (2024)
by: Zhang, Gongyue, et al.
Published: (2024)
Redefining non-IID Data in Federated Learning for Computer Vision Tasks: Migrating from Labels to Embeddings for Task-Specific Data Distributions
by: Borazjani, Kasra, et al.
Published: (2025)
by: Borazjani, Kasra, et al.
Published: (2025)
Taming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning
by: Wang, Yuhua, et al.
Published: (2026)
by: Wang, Yuhua, et al.
Published: (2026)
LoRA-FAIR: Federated LoRA Fine-Tuning with Aggregation and Initialization Refinement
by: Bian, Jieming, et al.
Published: (2024)
by: Bian, Jieming, et al.
Published: (2024)
Quantifying Task Priority for Multi-Task Optimization
by: Jeong, Wooseong, et al.
Published: (2024)
by: Jeong, Wooseong, et al.
Published: (2024)
FedHCA$^2$: Towards Hetero-Client Federated Multi-Task Learning
by: Lu, Yuxiang, et al.
Published: (2023)
by: Lu, Yuxiang, et al.
Published: (2023)
SGW-based Multi-Task Learning in Vision Tasks
by: Zhang, Ruiyuan, et al.
Published: (2024)
by: Zhang, Ruiyuan, et al.
Published: (2024)
Transporting Task Vectors across Different Architectures without Training
by: Rinaldi, Filippo, et al.
Published: (2026)
by: Rinaldi, Filippo, et al.
Published: (2026)
Knowledge Composition using Task Vectors with Learned Anisotropic Scaling
by: Zhang, Frederic Z., et al.
Published: (2024)
by: Zhang, Frederic Z., et al.
Published: (2024)
Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data
by: Ardıç, Emre, et al.
Published: (2026)
by: Ardıç, Emre, et al.
Published: (2026)
GFPL: Generative Federated Prototype Learning for Resource-Constrained and Data-Imbalanced Vision Task
by: Lu, Shiwei, et al.
Published: (2026)
by: Lu, Shiwei, et al.
Published: (2026)
pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models
by: Ghiasvand, Sajjad, et al.
Published: (2025)
by: Ghiasvand, Sajjad, et al.
Published: (2025)
On the Robustness Tradeoff in Fine-Tuning
by: Li, Kunyang, et al.
Published: (2025)
by: Li, Kunyang, et al.
Published: (2025)
Task Switching Without Forgetting via Proximal Decoupling
by: Shamsolmoali, Pourya, et al.
Published: (2026)
by: Shamsolmoali, Pourya, et al.
Published: (2026)
Preserving Domain Generalization in Fine-Tuning via Joint Parameter Selection
by: Pan, Bin, et al.
Published: (2025)
by: Pan, Bin, et al.
Published: (2025)
CGL: Advancing Continual GUI Learning via Reinforcement Fine-Tuning
by: Yao, Zhenquan, et al.
Published: (2026)
by: Yao, Zhenquan, et al.
Published: (2026)
Mitigating Parameter Interference in Model Merging via Sharpness-Aware Fine-Tuning
by: Lee, Yeoreum, et al.
Published: (2025)
by: Lee, Yeoreum, et al.
Published: (2025)
Robust and Generalizable GNN Fine-Tuning via Uncertainty-aware Adapter Learning
by: Jiang, Bo, et al.
Published: (2025)
by: Jiang, Bo, et al.
Published: (2025)
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning
by: Chen, Jun, et al.
Published: (2023)
by: Chen, Jun, et al.
Published: (2023)
Gradient-Sign Masking for Task Vector Transport Across Pre-Trained Models
by: Rinaldi, Filippo, et al.
Published: (2025)
by: Rinaldi, Filippo, et al.
Published: (2025)
Rep-MTL: Unleashing the Power of Representation-level Task Saliency for Multi-Task Learning
by: Wang, Zedong, et al.
Published: (2025)
by: Wang, Zedong, et al.
Published: (2025)
Meta ControlNet: Enhancing Task Adaptation via Meta Learning
by: Yang, Junjie, et al.
Published: (2023)
by: Yang, Junjie, et al.
Published: (2023)
Merging Multi-Task Models via Weight-Ensembling Mixture of Experts
by: Tang, Anke, et al.
Published: (2024)
by: Tang, Anke, et al.
Published: (2024)
GRPO-RM: Fine-Tuning Representation Models via GRPO-Driven Reinforcement Learning
by: Xu, Yanchen, et al.
Published: (2025)
by: Xu, Yanchen, et al.
Published: (2025)
Selecting Fine-Tuning Examples by Quizzing VLMs
by: Ji, Tenghao, et al.
Published: (2025)
by: Ji, Tenghao, et al.
Published: (2025)
Task-customized Masked AutoEncoder via Mixture of Cluster-conditional Experts
by: Liu, Zhili, et al.
Published: (2024)
by: Liu, Zhili, et al.
Published: (2024)
Split-Ensemble: Efficient OOD-aware Ensemble via Task and Model Splitting
by: Chen, Anthony, et al.
Published: (2023)
by: Chen, Anthony, et al.
Published: (2023)
Similar Items
-
Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side Distillation
by: Tsouvalas, Vasileios, et al.
Published: (2024) -
EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters
by: Tsouvalas, Vasileios, et al.
Published: (2024) -
EFU: Enforcing Federated Unlearning via Functional Encryption
by: Mohammadi, Samaneh, et al.
Published: (2025) -
Fine-tuning Multimodal Transformers on Edge: A Parallel Split Learning Approach
by: Fudala, Timo, et al.
Published: (2025) -
Multimodal Task Vectors Enable Many-Shot Multimodal In-Context Learning
by: Huang, Brandon, et al.
Published: (2024)