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2211.08025
Cited By

FedTune: A Deep Dive into Efficient Federated Fine-Tuning with
Pre-trained Transformers

FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers

15 November 2022

ArXiv (abs)PDF HTML Github

Papers citing "FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers"

26 / 26 papers shown

FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation

FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation

Md Anwar Hossen

357

1

0

24 Dec 2025

Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights

Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical InsightsPeerJ Computer Science (PeerJ CS), 2025

Cosmin Hatfaludi

253

3

0

05 Sep 2025

Communication-Efficient Federated Learning with Adaptive Number of Participants

Communication-Efficient Federated Learning with Adaptive Number of Participants

Vladislav Dorofeev

Dmitry Bylinkin

Daniil Medyakov

Aleksandr Beznosikov

177

0

0

19 Aug 2025

FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA

FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA

Divyansh Jhunjhunwala

Madan Ravi Ganesh

Chaithanya Kumar Mummadi

363

1

0

01 Jun 2025

Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection

Assortment of Attention Heads: Accelerating Federated PEFT with Head Pruning and Strategic Client Selection

Yeshwanth Venkatesha

Priyadarshini Panda

219

1

0

31 May 2025

FedSDAF: Leveraging Source Domain Awareness for Enhanced Federated Domain Generalization

FedSDAF: Leveraging Source Domain Awareness for Enhanced Federated Domain Generalization

564

0

0

05 May 2025

Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI Segmentation

Rate-My-LoRA: Efficient and Adaptive Federated Model Tuning for Cardiac MRI SegmentationIEEE International Symposium on Biomedical Imaging (ISBI), 2025

Dimitris N. Metaxas

249

4

0

06 Jan 2025

Federated Large Language Models: Current Progress and Future Directions

Federated Large Language Models: Current Progress and Future Directions

...

Carlee Joe-Wong

591

27

0

24 Sep 2024

SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large
Pre-Trained Models over Resource-Limited Devices

SFPrompt: Communication-Efficient Split Federated Fine-Tuning for Large Pre-Trained Models over Resource-Limited Devices

251

8

0

24 Jul 2024

Leveraging Foundation Models for Multi-modal Federated Learning with
Incomplete Modality

Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality

Fenglong Ma

271

10

0

16 Jun 2024

PrE-Text: Training Language Models on Private Federated Data in the Age
of LLMs

PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs

Akshat Shrivastava

350

29

0

05 Jun 2024

Accelerating Heterogeneous Federated Learning with Closed-form
Classifiers

Accelerating Heterogeneous Federated Learning with Closed-form Classifiers

Raffaello Camoriano

319

10

0

03 Jun 2024

DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation

DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation

Karthikeyan P. Saravanan

Rogier van Dalen

847

11

0

10 May 2024

FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning
Leveraging Weight Decomposition

FeDeRA:Efficient Fine-tuning of Language Models in Federated Learning Leveraging Weight Decomposition

652

36

0

29 Apr 2024

Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation
Models

Heterogeneous LoRA for Federated Fine-tuning of On-Device Foundation ModelsConference on Empirical Methods in Natural Language Processing (EMNLP), 2024

392

119

0

12 Jan 2024

Towards Integrated Fine-tuning and Inference when Generative AI meets
Edge Intelligence

Towards Integrated Fine-tuning and Inference when Generative AI meets Edge Intelligence

370

4

0

05 Jan 2024

Language-Guided Transformer for Federated Multi-Label Classification

Language-Guided Transformer for Federated Multi-Label ClassificationAAAI Conference on Artificial Intelligence (AAAI), 2023

Yu-Chiang Frank Wang

370

14

0

12 Dec 2023

Leveraging Foundation Models to Improve Lightweight Clients in Federated
Learning

Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning

Filipe Condessa

Madan Ravi Ganesh

307

5

0

14 Nov 2023

Profit: Benchmarking Personalization and Robustness Trade-off in
Federated Prompt Tuning

Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning

315

11

0

06 Oct 2023

Efficient Federated Prompt Tuning for Black-box Large Pre-trained Models

Efficient Federated Prompt Tuning for Black-box Large Pre-trained Models

Li Shen

319

16

0

04 Oct 2023

FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language
Models

FedBPT: Efficient Federated Black-box Prompt Tuning for Large Language ModelsInternational Conference on Machine Learning (ICML), 2023

Ziyue Xu

Yiran Chen

294

38

0

02 Oct 2023

FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal
Heterogeneous Federated Learning

FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated LearningAAAI Conference on Artificial Intelligence (AAAI), 2023

Yao Zhang

Jindong Gu

244

99

0

21 Aug 2023

On the Efficacy of Differentially Private Few-shot Image Classification

On the Efficacy of Differentially Private Few-shot Image Classification

Aliaksandra Shysheya

Santiago Zanella Béguelin

555

17

0

02 Feb 2023

CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated
Learning

CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning

Yingbo Zhou

Ming Hu

160

2

0

28 Jan 2023

When Federated Learning Meets Pre-trained Language Models'
Parameter-Efficient Tuning Methods

When Federated Learning Meets Pre-trained Language Models' Parameter-Efficient Tuning MethodsAnnual Meeting of the Association for Computational Linguistics (ACL), 2022

510

123

0

20 Dec 2022

CLIP-Adapter: Better Vision-Language Models with Feature Adapters

CLIP-Adapter: Better Vision-Language Models with Feature AdaptersInternational Journal of Computer Vision (IJCV), 2021

Yu Qiao

1.4K

1,599

0

09 Oct 2021

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