аналитика - Machine Learning - # Federated Learning with Parameter-Efficient Fine-Tuning

Efficient Fine-tuning of Language Models in Federated Learning Using Weight Decomposition

Q: How can the FeDeRA method be extended to other types of pre-trained models beyond language models

The FeDeRA method can be extended to other types of pre-trained models beyond language models by adapting the SVD-based initialization approach to suit the specific architecture and characteristics of the new models. Since the core idea behind FeDeRA is to leverage the singular value decomposition of the pre-trained weight matrices to initialize adapter modules, this approach can be applied to various types of models that involve weight matrices. For instance, in computer vision tasks, convolutional neural networks (CNNs) have weight matrices that can undergo a similar decomposition process to extract principal components for initialization. By customizing the initialization process based on the structure and requirements of different models, FeDeRA can be effectively extended to a wide range of pre-trained models in various domains.

Q: What are the potential limitations or drawbacks of the SVD-based initialization approach used in FeDeRA

While the SVD-based initialization approach used in FeDeRA offers several advantages, such as improved convergence and performance in federated learning settings with non-IID data, there are potential limitations and drawbacks to consider: Computational Overhead: Performing SVD on large pre-trained weight matrices can be computationally expensive, especially for models with a high number of parameters. This additional computational burden may impact the overall efficiency of the method. Loss of Information: The process of extracting principal components through SVD may lead to some loss of information from the original weight matrices. This loss could potentially affect the model's ability to capture complex patterns and nuances in the data. Sensitivity to Initialization: The effectiveness of the SVD-based initialization approach in FeDeRA may be sensitive to the choice of hyperparameters and the specific characteristics of the pre-trained models. Suboptimal initialization parameters could result in subpar performance.

Q: Could the FeDeRA method be combined with other techniques, such as meta-learning or knowledge distillation, to further improve its performance in federated learning settings with non-IID data

Yes, the FeDeRA method can be combined with other techniques, such as meta-learning or knowledge distillation, to further enhance its performance in federated learning settings with non-IID data: Meta-Learning: By incorporating meta-learning techniques, FeDeRA can adapt its initialization strategy dynamically based on the unique characteristics of the non-IID data distribution on different clients. Meta-learning can help FeDeRA learn to initialize the adapter modules more effectively, leading to improved convergence and performance. Knowledge Distillation: Integrating knowledge distillation methods into FeDeRA can enable the model to transfer knowledge from a teacher model to the adapter modules, enhancing the learning process in federated settings. Knowledge distillation can help FeDeRA leverage the expertise of a centralized model to improve performance on diverse and non-IID data distributions. Ensemble Methods: Combining FeDeRA with ensemble learning techniques can further boost its robustness and generalization capabilities. By aggregating predictions from multiple instances of FeDeRA with different initializations, hyperparameters, or data subsets, the overall performance can be enhanced, especially in scenarios with highly non-IID data.

Основные понятия

The authors propose FeDeRA, a method that leverages Singular Value Decomposition to initialize the adapter modules in the LoRA technique, in order to improve the performance of parameter-efficient fine-tuning in federated learning settings with highly non-IID data.

Аннотация

The paper introduces FeDeRA, a method for efficient fine-tuning of language models in federated learning. The key insights are:

Federated learning with non-IID data leads to a performance gap between parameter-efficient fine-tuning (PEFT) methods like LoRA and full parameter fine-tuning (FT).
FeDeRA initializes the adapter modules in LoRA using Singular Value Decomposition (SVD) of the pre-trained weight matrix. This helps retain more locally learned knowledge during aggregation and provides a better initial direction for fine-tuning.
Extensive experiments on text classification, named entity recognition, and question answering tasks show that FeDeRA outperforms other PEFT methods and is comparable or even better than FT, while reducing training time by over 95% compared to FT.
The authors analyze the magnitude and direction variations of the weight updates in FeDeRA vs. LoRA, demonstrating the improved stability of FeDeRA under non-IID data.
Real-world federated learning experiments on Jetson AGX Orin devices show that FeDeRA achieves the shortest training time to reach target accuracy compared to other methods.

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Статистика

The training time required for FeDeRA to reach 99% of the target accuracy is 95.9%, 97.9%, and 96.9% less than FT on the three tasks using RoBERTa, and 97.3%, 96.5%, and 96.5% less using DeBERTaV3.

Цитаты

"FeDeRA exhibits superior stability in both magnitude variation and direction changes. As a result, it facilitates an enhanced convergence and effectively attenuates the federated learning once exerted by data heterogeneity."
"Compared to FedFT, FeDeRA reduces the training time by 95.9%, 97.9%, and 96.9% respectively on three tasks using RoBERTa and 97.3%, 96.5% and 96.5% using DeBERTaV3."

Ключевые выводы из

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

by Yuxuan Yan,S... в arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18848.pdf

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

Дополнительные вопросы

How can the FeDeRA method be extended to other types of pre-trained models beyond language models

The FeDeRA method can be extended to other types of pre-trained models beyond language models by adapting the SVD-based initialization approach to suit the specific architecture and characteristics of the new models. Since the core idea behind FeDeRA is to leverage the singular value decomposition of the pre-trained weight matrices to initialize adapter modules, this approach can be applied to various types of models that involve weight matrices. For instance, in computer vision tasks, convolutional neural networks (CNNs) have weight matrices that can undergo a similar decomposition process to extract principal components for initialization. By customizing the initialization process based on the structure and requirements of different models, FeDeRA can be effectively extended to a wide range of pre-trained models in various domains.

What are the potential limitations or drawbacks of the SVD-based initialization approach used in FeDeRA

While the SVD-based initialization approach used in FeDeRA offers several advantages, such as improved convergence and performance in federated learning settings with non-IID data, there are potential limitations and drawbacks to consider:

Computational Overhead: Performing SVD on large pre-trained weight matrices can be computationally expensive, especially for models with a high number of parameters. This additional computational burden may impact the overall efficiency of the method.
Loss of Information: The process of extracting principal components through SVD may lead to some loss of information from the original weight matrices. This loss could potentially affect the model's ability to capture complex patterns and nuances in the data.
Sensitivity to Initialization: The effectiveness of the SVD-based initialization approach in FeDeRA may be sensitive to the choice of hyperparameters and the specific characteristics of the pre-trained models. Suboptimal initialization parameters could result in subpar performance.

Could the FeDeRA method be combined with other techniques, such as meta-learning or knowledge distillation, to further improve its performance in federated learning settings with non-IID data

Yes, the FeDeRA method can be combined with other techniques, such as meta-learning or knowledge distillation, to further enhance its performance in federated learning settings with non-IID data:

Meta-Learning: By incorporating meta-learning techniques, FeDeRA can adapt its initialization strategy dynamically based on the unique characteristics of the non-IID data distribution on different clients. Meta-learning can help FeDeRA learn to initialize the adapter modules more effectively, leading to improved convergence and performance.
Knowledge Distillation: Integrating knowledge distillation methods into FeDeRA can enable the model to transfer knowledge from a teacher model to the adapter modules, enhancing the learning process in federated settings. Knowledge distillation can help FeDeRA leverage the expertise of a centralized model to improve performance on diverse and non-IID data distributions.
Ensemble Methods: Combining FeDeRA with ensemble learning techniques can further boost its robustness and generalization capabilities. By aggregating predictions from multiple instances of FeDeRA with different initializations, hyperparameters, or data subsets, the overall performance can be enhanced, especially in scenarios with highly non-IID data.