parameter-efficient fine-tuning

Parameter-Efficient Fine-Tuning (PEFT) is a technique used to adapt pre-trained models to new tasks with minimal changes to the model's parameters. This approach is particularly useful in scenarios where computational resources are limited or when it is desirable to maintain the original model's performance on the initial task.

Papers

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Showing 461–470 of 935 papers

Title	Date	Tasks	Status
Federated Adapter on Foundation Models: An Out-Of-Distribution Approach	May 2, 2025	Federated Learningparameter-efficient fine-tuning	—Unverified
Federated Adversarial Learning for Robust Autonomous Landing Runway Detection	Jun 22, 2024	Federated LearningLane Detection	—Unverified
Federated Fine-tuning of Large Language Models under Heterogeneous Tasks and Client Resources	Feb 18, 2024	Federated Learningparameter-efficient fine-tuning	—Unverified
Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks	Nov 12, 2024	Federated Learningparameter-efficient fine-tuning	—Unverified
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning	Sep 1, 2023	BenchmarkingFederated Learning	—Unverified
FedMCP: Parameter-Efficient Federated Learning with Model-Contrastive Personalization	Aug 28, 2024	Federated Learningparameter-efficient fine-tuning	—Unverified
FedP^2EFT: Federated Learning to Personalize Parameter Efficient Fine-Tuning for Multilingual LLMs	Feb 5, 2025	Federated Learningparameter-efficient fine-tuning	—Unverified
FedPEAT: Convergence of Federated Learning, Parameter-Efficient Fine Tuning, and Emulator Assisted Tuning for Artificial Intelligence Foundation Models with Mobile Edge Computing	Oct 26, 2023	Deep Reinforcement LearningEdge-computing	—Unverified
FedPIA -- Permuting and Integrating Adapters leveraging Wasserstein Barycenters for Finetuning Foundation Models in Multi-Modal Federated Learning	Dec 19, 2024	Federated Learningparameter-efficient fine-tuning	—Unverified
FedSCA: Federated Tuning with Similarity-guided Collaborative Aggregation for Heterogeneous Medical Image Segmentation	Mar 19, 2025	Computational EfficiencyFederated Learning	—Unverified

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All datasets BoolQ HellaSwag WinoGrande

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	LLaMA2-7b	Accuracy (% )	82.63	—	Unverified
2	LLaMA2-7b	Accuracy (% )	82.63	—	Unverified
3	LLaMA2-7b	Accuracy (% )	81.93	—	Unverified
4	LLaMA2-7b	Accuracy (% )	80.28	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LLaMA2-7b	Accuracy (% )	76.68	—	Unverified
2	LLaMA2-7b	Accuracy (% )	76.67	—	Unverified
3	LLaMA2-7b	Accuracy (% )	76.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LLaMA2-7b	Accuracy (% )	70.8	—	Unverified
2	LLaMA2-7b	Accuracy (% )	70.09	—	Unverified
3	LLaMA2-7b	Accuracy (% )	69.85	—	Unverified