Federated learning of large language models with parameter-efficient prompt tuning and adaptive optimization
Federated learning (FL) is a promising paradigm to enable collaborative model training with
decentralized data. However, the training process of Large Language Models (LLMs) …
decentralized data. However, the training process of Large Language Models (LLMs) …
SLoRA: Federated parameter efficient fine-tuning of language models
Transfer learning via fine-tuning pre-trained transformer models has gained significant
success in delivering state-of-the-art results across various NLP tasks. In the absence of …
success in delivering state-of-the-art results across various NLP tasks. In the absence of …
Feddat: An approach for foundation model finetuning in multi-modal heterogeneous federated learning
Recently, foundation models have exhibited remarkable advancements in multi-modal
learning. These models, equipped with millions (or billions) of parameters, typically require a …
learning. These models, equipped with millions (or billions) of parameters, typically require a …
Unlocking the potential of prompt-tuning in bridging generalized and personalized federated learning
Abstract Vision Transformers (ViT) and Visual Prompt Tuning (VPT) achieve state-of-the-art
performance with improved efficiency in various computer vision tasks. This suggests a …
performance with improved efficiency in various computer vision tasks. This suggests a …
Fedbpt: Efficient federated black-box prompt tuning for large language models
Pre-trained language models (PLM) have revolutionized the NLP landscape, achieving
stellar performances across diverse tasks. These models, while benefiting from vast training …
stellar performances across diverse tasks. These models, while benefiting from vast training …
Advances and open challenges in federated learning with foundation models
The integration of Foundation Models (FMs) with Federated Learning (FL) presents a
transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while …
transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while …
Adaptive parameterization of deep learning models for federated learning
MF Elvebakken, A Iosifidis, L Esterle - arXiv preprint arXiv:2302.02949, 2023 - arxiv.org
Federated Learning offers a way to train deep neural networks in a distributed fashion. While
this addresses limitations related to distributed data, it incurs a communication overhead as …
this addresses limitations related to distributed data, it incurs a communication overhead as …
Language-Guided Transformer for Federated Multi-Label Classification
Federated Learning (FL) is an emerging paradigm that enables multiple users to
collaboratively train a robust model in a privacy-preserving manner without sharing their …
collaboratively train a robust model in a privacy-preserving manner without sharing their …
[PDF][PDF] 語言引導之變換器於聯邦式多標籤分類
劉亦傑 - 2024 - tdr.lib.ntu.edu.tw
摘要聯邦學習(FL) 是一種新興的模型學習框架, 該方法使多個用戶能夠在不共享私人數據的情況
下協作訓練一個強大的模型, 以保護隱私. 大多數現有的FL 方法僅考慮傳統的單標籤圖像分類 …
下協作訓練一個強大的模型, 以保護隱私. 大多數現有的FL 方法僅考慮傳統的單標籤圖像分類 …
Federated Scaling of Pre-trained Models for Deep Facial Expression Recognition
PVNP Srihitha, M Verma, MVNK Prasad - International Conference on …, 2023 - Springer
Building an efficient deep learning-based Facial Expression Recognition (FER) system is
challenging due to the requirements of large amounts of personal data and the rise in data …
challenging due to the requirements of large amounts of personal data and the rise in data …