Federated Document Visual Question Answering: A Pilot Study

Khanh Nguyen, Dimosthenis Karatzas·May 10, 2024

Summary

The research explores the application of federated learning (FL) in document visual question answering (DocVQA), addressing privacy concerns by allowing local data processing without sharing. Studies employ FL for DocVQA, using techniques like self-pretraining, adaptive optimization, and diverse datasets to enhance model performance. Federated benchmarks are created by combining datasets, and research highlights the benefits of FL in scaling models, adapting to non-IID data, and preserving privacy. Key findings include improved performance over baselines, the importance of hyperparameter tuning, and the potential for FL to advance document understanding tasks while respecting data restrictions. The work also investigates the impact of client participation, pretraining strategies, and optimization methods on FL's effectiveness in DocVQA, demonstrating comparable results to centralized models in some cases. Overall, the research contributes to the understanding of FL's role in multimodal AI while maintaining privacy and data security.

Key findings

6

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of training document analysis models due to the scattered nature of documents in private data silos, hindering large-scale training over heterogeneous data . This problem is not new, as existing documents are often copyrighted or contain private information, making it difficult to create centralized, large-scale document datasets . The use of federated learning (FL) is proposed as a solution to train a shared model on decentralized private document data, specifically focusing on Document Visual Question Answering (DocVQA) tasks .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the application of federated learning (FL) in training a shared model on decentralized private document data for Document Visual Question Answering (DocVQA) tasks . The study explores the effectiveness of FL in training DocVQA models using data scattered across private data silos, enabling collaboration among multiple clients without data exchange . The hypothesis focuses on demonstrating the viability of FL for training large-scale multimodal Language- and Vision-based models like the ones used for DocVQA, aiming to achieve results comparable to centralized models while preserving privacy and enhancing generalization by training over heterogeneous data .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Federated Document Visual Question Answering: A Pilot Study" introduces several innovative ideas, methods, and models in the field of Federated Learning for Document Visual Question Answering (DocVQA) . One key contribution is the utilization of Federated Learning techniques to address the challenge of limited availability of large-scale generic datasets due to sensitive content and copyright issues in documents . By leveraging privacy-preserving methods like Federated Learning, the paper enables the use of distributed and private datasets among different entities, facilitating collaborative training without data exchange .

The proposed model architecture is based on a text-only pre-trained language model (PLM) T5 as the backbone, enhanced with visual features extracted from documents to accommodate multimodal input for DocVQA tasks . This approach allows for ease of fine-tuning and robust performance on DocVQA tasks, focusing on privacy-preserving techniques like Federated Learning on multimodal data .

Furthermore, the paper explores the concept of self-pretraining in a multimodal setting like DocVQA, demonstrating that continuing pretraining PLMs on unlabeled documents in downstream DocVQA datasets can improve the DocVQA performance of finetuning in the same tasks . This highlights the importance of leveraging self-pretraining with limited-scale downstream data and high-level reasoning pretraining objectives to enhance performance in complex domains like DocVQA .

Additionally, the study delves into the application of Federated Learning for training DocVQA models on decentralized document data from different heterogeneous sources, showcasing the viability of Federated Learning for both pre-training and fine-tuning large-scale multimodal LLM models used in DocVQA tasks . The results indicate that Federated Learning can achieve comparable results to centralized models, potentially enabling researchers to leverage document collections scattered across private data silos for better generalization . The paper "Federated Document Visual Question Answering: A Pilot Study" introduces novel characteristics and advantages compared to previous methods in the field of Federated Learning for Document Visual Question Answering (DocVQA) .

  1. Self-Pretraining and Pretraining Objectives: The study explores the concept of self-pretraining in a multimodal setting like DocVQA, demonstrating that continuing pretraining Pre-trained Language Models (PLMs) on unlabeled documents in downstream DocVQA datasets can enhance DocVQA performance during fine-tuning . The proposed self-supervised tasks in Federated Self-Pretraining (FSP) aim to learn the alignment between semantic and layout information from documents without QA annotations, utilizing denoising objectives inspired by T5 .

  2. Federated Learning Strategies: The paper introduces the Federated Self-Pretraining (FSP) strategy, which provides a warm start to PLMs, allowing better adaptation to document data and achieving significant performance improvements in FeDocVQA tasks . The FSP strategy is shown to be beneficial for DocVQA under Federated Learning (FL), consistently improving performance across different configurations, with FedAdam being considered a better design choice in heterogeneous systems .

  3. Collaborative Training and Model Performance: The study focuses on collaboratively training a DocVQA model among clients without data exchange, utilizing Federated Learning to minimize the objective function and achieve effective training across multiple clients with varying dataset sizes and computational capabilities . The results demonstrate that FL is a viable approach for both pre-training and fine-tuning large-scale multimodal LLM models used in DocVQA, achieving results comparable to centralized models and enabling researchers to leverage decentralized document data for better generalization .

  4. Hyperparameter Tuning and Performance Optimization: The paper emphasizes the importance of hyperparameter tuning for complex settings like DocVQA with Federated Learning, highlighting the need for extensive tuning to improve the performance of FL strategies like FedAvg and FedAdam across different configurations . The study also delves into the impact of the number of communication rounds on FSP and DocVQA federated training, showcasing improvements in performance with increased communication rounds .

Overall, the paper's innovative approaches, such as self-pretraining, Federated Self-Pretraining (FSP), and collaborative training using Federated Learning, offer significant advancements in the field of Document Visual Question Answering, providing insights into effective training strategies and performance optimization in decentralized document data settings .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of Federated Document Visual Question Answering. Noteworthy researchers in this field include Tito, R., Karatzas, D., Valveny, E., Nguyen, K., Tobaben, M., Kerkouche, R., Souibgui, M.A., Jung, K., Kang, L., Honkela, A., Fritz, M., Borchmann, L., Pietruszka, M., Joziak, P., Powalski, R., Jurkiewicz, D., Coustaty, M., Anckaert, B., and Van Landeghem, J. . The key to the solution mentioned in the paper involves utilizing federated learning techniques to train a shared model on decentralized private document data, focusing on Document Visual Question Answering (DocVQA) tasks. This approach enables training over heterogeneous document datasets, enriching DocVQA models by combining self-pretraining with a Federated DocVQA training method using centralized adaptive optimization, which outperforms the FedAvg baseline .


How were the experiments in the paper designed?

The experiments in the paper were designed to explore the application of federated learning (FL) in training a Document Visual Question Answering (DocVQA) model on decentralized document data from different sources . The study focused on the problem of DocVQA, which requires reasoning capabilities across diverse domains, making it suitable for FL approaches . The experiments aimed to train a shared DocVQA model collaboratively among clients without exchanging data, minimizing the objective function through a central server . The experiments involved comparing different strategies such as FedAvg and FedAdam, assessing the impact of hyperparameters like the number of clients (K) and client fraction (C), and studying the effects of pretraining objectives and communication rounds on performance . The study demonstrated that FL is a viable approach for training large-scale multimodal models like DocVQA, achieving results comparable to centralized models and enabling training over heterogeneous document datasets .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the WikiTableQuestions (WTQ) dataset, which comprises logical questions over HTML tables from Wikipedia . The code for the study is available as open source at the following link: https://github.com/khanhnguyen21006/fldocvqa .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study conducted a comprehensive analysis of Federated Document Visual Question Answering (FeDocVQA) using a federated learning approach . The experiments involved exploring the impact of various factors such as the number of clients, sampling probability, pretraining objectives, and communication rounds on the performance of the model .

The results demonstrated the effectiveness of the Federated Self-Pretraining (FSP) strategy in improving FeDocVQA performance, showcasing consistent improvements across different configurations . Additionally, the study compared different strategies like FedAvg and FedAdam, highlighting the benefits of FedAdam in dealing with a high level of heterogeneity among clients .

Moreover, the experiments delved into the effects of the client fraction during pretraining and finetuning, revealing that the choice of client fraction had a notable impact on the performance of the model . The study's detailed analysis of these factors and their impact on FeDocVQA performance provides strong empirical evidence supporting the scientific hypotheses under investigation.


What are the contributions of this paper?

The paper "Federated Document Visual Question Answering: A Pilot Study" makes several contributions:

  • It explores the use of federated learning (FL) to train a shared model on decentralized private document data, addressing the challenge of training over scattered private data silos .
  • The focus is on Document Visual Question Answering (DocVQA), a task suitable for FL due to the diverse reasoning capabilities required across different domains, enriching DocVQA models by training over heterogeneous document datasets .
  • The paper proposes a combination of self-pretraining and Federated DocVQA training method using centralized adaptive optimization, outperforming the FedAvg baseline .
  • Extensive experiments and analysis are presented on training DocVQA models with FL, providing insights for future research in this area, demonstrating the effectiveness of pretraining strategies and the importance of tuning hyperparameters for practical document tasks under federation .
  • The study showcases that pretraining strategies effectively learn and scale up under federated training with diverse DocVQA datasets, highlighting the significance of hyperparameter tuning for complex settings like DocVQA with FL .

What work can be continued in depth?

To delve deeper into the research, further exploration can be conducted on the effectiveness of Federated Self-Pretraining (FSP) in multimodal settings like DocVQA. This involves continuing the investigation on how pretraining a model on unlabeled training data for a specific task before finetuning impacts performance, especially in complex multimodal problems such as DocVQA . Additionally, the study can focus on the applicability and benefits of self-pretraining in a federated manner, where each client performs self-supervised training on its private documents, aiming to obtain a domain-adapted initialization for subsequent training .

Tables

3

Introduction
Background
Overview of DocVQA and its challenges
Importance of privacy in multimodal AI
Objective
To investigate FL application in DocVQA
Address privacy concerns through local data processing
Enhance model performance with FL techniques
Methodology
Data Collection
Federated dataset aggregation
Inclusion of diverse datasets
Data Preprocessing
Adaptation to non-IID data
Privacy-preserving data preprocessing techniques
Federated Learning Techniques
Self-pretraining
Pretraining models on local data
Adaptive Optimization
Customized optimization algorithms for DocVQA
Federated Benchmarks
Creation of standardized evaluation platforms
Experiments and Evaluation
Performance comparison with baselines
Hyperparameter tuning impact
Client participation analysis
Pretraining strategies for FL in DocVQA
Optimization methods' effect on model effectiveness
Privacy and Security
Preserving data privacy during model training
Assessing privacy leakage risks
Results and Findings
Improved performance in DocVQA tasks
Comparable results to centralized models
Importance of FL for document understanding under data restrictions
Discussion
Advantages of FL in scaling and adapting to DocVQA
Limitations and future research directions
Conclusion
Summary of key contributions
The role of FL in multimodal AI with privacy preservation
Implications for real-world applications and industry adoption
Basic info
papers
computer vision and pattern recognition
computation and language
machine learning
artificial intelligence
Advanced features
Insights
How does federated learning address privacy concerns in the field of DocVQA?
What does the research focus on in the context of document visual question answering?
What techniques are employed in the studies to enhance model performance using FL?
What are the key findings of the research regarding the benefits and effectiveness of FL in DocVQA?

Federated Document Visual Question Answering: A Pilot Study

Khanh Nguyen, Dimosthenis Karatzas·May 10, 2024

Summary

The research explores the application of federated learning (FL) in document visual question answering (DocVQA), addressing privacy concerns by allowing local data processing without sharing. Studies employ FL for DocVQA, using techniques like self-pretraining, adaptive optimization, and diverse datasets to enhance model performance. Federated benchmarks are created by combining datasets, and research highlights the benefits of FL in scaling models, adapting to non-IID data, and preserving privacy. Key findings include improved performance over baselines, the importance of hyperparameter tuning, and the potential for FL to advance document understanding tasks while respecting data restrictions. The work also investigates the impact of client participation, pretraining strategies, and optimization methods on FL's effectiveness in DocVQA, demonstrating comparable results to centralized models in some cases. Overall, the research contributes to the understanding of FL's role in multimodal AI while maintaining privacy and data security.
Mind map
Assessing privacy leakage risks
Preserving data privacy during model training
Creation of standardized evaluation platforms
Federated Benchmarks
Customized optimization algorithms for DocVQA
Adaptive Optimization
Pretraining models on local data
Self-pretraining
Privacy and Security
Federated Learning Techniques
Inclusion of diverse datasets
Federated dataset aggregation
Enhance model performance with FL techniques
Address privacy concerns through local data processing
To investigate FL application in DocVQA
Importance of privacy in multimodal AI
Overview of DocVQA and its challenges
Implications for real-world applications and industry adoption
The role of FL in multimodal AI with privacy preservation
Summary of key contributions
Limitations and future research directions
Advantages of FL in scaling and adapting to DocVQA
Importance of FL for document understanding under data restrictions
Comparable results to centralized models
Improved performance in DocVQA tasks
Experiments and Evaluation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Discussion
Results and Findings
Methodology
Introduction
Outline
Introduction
Background
Overview of DocVQA and its challenges
Importance of privacy in multimodal AI
Objective
To investigate FL application in DocVQA
Address privacy concerns through local data processing
Enhance model performance with FL techniques
Methodology
Data Collection
Federated dataset aggregation
Inclusion of diverse datasets
Data Preprocessing
Adaptation to non-IID data
Privacy-preserving data preprocessing techniques
Federated Learning Techniques
Self-pretraining
Pretraining models on local data
Adaptive Optimization
Customized optimization algorithms for DocVQA
Federated Benchmarks
Creation of standardized evaluation platforms
Experiments and Evaluation
Performance comparison with baselines
Hyperparameter tuning impact
Client participation analysis
Pretraining strategies for FL in DocVQA
Optimization methods' effect on model effectiveness
Privacy and Security
Preserving data privacy during model training
Assessing privacy leakage risks
Results and Findings
Improved performance in DocVQA tasks
Comparable results to centralized models
Importance of FL for document understanding under data restrictions
Discussion
Advantages of FL in scaling and adapting to DocVQA
Limitations and future research directions
Conclusion
Summary of key contributions
The role of FL in multimodal AI with privacy preservation
Implications for real-world applications and industry adoption
Key findings
6

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of training document analysis models due to the scattered nature of documents in private data silos, hindering large-scale training over heterogeneous data . This problem is not new, as existing documents are often copyrighted or contain private information, making it difficult to create centralized, large-scale document datasets . The use of federated learning (FL) is proposed as a solution to train a shared model on decentralized private document data, specifically focusing on Document Visual Question Answering (DocVQA) tasks .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the application of federated learning (FL) in training a shared model on decentralized private document data for Document Visual Question Answering (DocVQA) tasks . The study explores the effectiveness of FL in training DocVQA models using data scattered across private data silos, enabling collaboration among multiple clients without data exchange . The hypothesis focuses on demonstrating the viability of FL for training large-scale multimodal Language- and Vision-based models like the ones used for DocVQA, aiming to achieve results comparable to centralized models while preserving privacy and enhancing generalization by training over heterogeneous data .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Federated Document Visual Question Answering: A Pilot Study" introduces several innovative ideas, methods, and models in the field of Federated Learning for Document Visual Question Answering (DocVQA) . One key contribution is the utilization of Federated Learning techniques to address the challenge of limited availability of large-scale generic datasets due to sensitive content and copyright issues in documents . By leveraging privacy-preserving methods like Federated Learning, the paper enables the use of distributed and private datasets among different entities, facilitating collaborative training without data exchange .

The proposed model architecture is based on a text-only pre-trained language model (PLM) T5 as the backbone, enhanced with visual features extracted from documents to accommodate multimodal input for DocVQA tasks . This approach allows for ease of fine-tuning and robust performance on DocVQA tasks, focusing on privacy-preserving techniques like Federated Learning on multimodal data .

Furthermore, the paper explores the concept of self-pretraining in a multimodal setting like DocVQA, demonstrating that continuing pretraining PLMs on unlabeled documents in downstream DocVQA datasets can improve the DocVQA performance of finetuning in the same tasks . This highlights the importance of leveraging self-pretraining with limited-scale downstream data and high-level reasoning pretraining objectives to enhance performance in complex domains like DocVQA .

Additionally, the study delves into the application of Federated Learning for training DocVQA models on decentralized document data from different heterogeneous sources, showcasing the viability of Federated Learning for both pre-training and fine-tuning large-scale multimodal LLM models used in DocVQA tasks . The results indicate that Federated Learning can achieve comparable results to centralized models, potentially enabling researchers to leverage document collections scattered across private data silos for better generalization . The paper "Federated Document Visual Question Answering: A Pilot Study" introduces novel characteristics and advantages compared to previous methods in the field of Federated Learning for Document Visual Question Answering (DocVQA) .

  1. Self-Pretraining and Pretraining Objectives: The study explores the concept of self-pretraining in a multimodal setting like DocVQA, demonstrating that continuing pretraining Pre-trained Language Models (PLMs) on unlabeled documents in downstream DocVQA datasets can enhance DocVQA performance during fine-tuning . The proposed self-supervised tasks in Federated Self-Pretraining (FSP) aim to learn the alignment between semantic and layout information from documents without QA annotations, utilizing denoising objectives inspired by T5 .

  2. Federated Learning Strategies: The paper introduces the Federated Self-Pretraining (FSP) strategy, which provides a warm start to PLMs, allowing better adaptation to document data and achieving significant performance improvements in FeDocVQA tasks . The FSP strategy is shown to be beneficial for DocVQA under Federated Learning (FL), consistently improving performance across different configurations, with FedAdam being considered a better design choice in heterogeneous systems .

  3. Collaborative Training and Model Performance: The study focuses on collaboratively training a DocVQA model among clients without data exchange, utilizing Federated Learning to minimize the objective function and achieve effective training across multiple clients with varying dataset sizes and computational capabilities . The results demonstrate that FL is a viable approach for both pre-training and fine-tuning large-scale multimodal LLM models used in DocVQA, achieving results comparable to centralized models and enabling researchers to leverage decentralized document data for better generalization .

  4. Hyperparameter Tuning and Performance Optimization: The paper emphasizes the importance of hyperparameter tuning for complex settings like DocVQA with Federated Learning, highlighting the need for extensive tuning to improve the performance of FL strategies like FedAvg and FedAdam across different configurations . The study also delves into the impact of the number of communication rounds on FSP and DocVQA federated training, showcasing improvements in performance with increased communication rounds .

Overall, the paper's innovative approaches, such as self-pretraining, Federated Self-Pretraining (FSP), and collaborative training using Federated Learning, offer significant advancements in the field of Document Visual Question Answering, providing insights into effective training strategies and performance optimization in decentralized document data settings .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of Federated Document Visual Question Answering. Noteworthy researchers in this field include Tito, R., Karatzas, D., Valveny, E., Nguyen, K., Tobaben, M., Kerkouche, R., Souibgui, M.A., Jung, K., Kang, L., Honkela, A., Fritz, M., Borchmann, L., Pietruszka, M., Joziak, P., Powalski, R., Jurkiewicz, D., Coustaty, M., Anckaert, B., and Van Landeghem, J. . The key to the solution mentioned in the paper involves utilizing federated learning techniques to train a shared model on decentralized private document data, focusing on Document Visual Question Answering (DocVQA) tasks. This approach enables training over heterogeneous document datasets, enriching DocVQA models by combining self-pretraining with a Federated DocVQA training method using centralized adaptive optimization, which outperforms the FedAvg baseline .


How were the experiments in the paper designed?

The experiments in the paper were designed to explore the application of federated learning (FL) in training a Document Visual Question Answering (DocVQA) model on decentralized document data from different sources . The study focused on the problem of DocVQA, which requires reasoning capabilities across diverse domains, making it suitable for FL approaches . The experiments aimed to train a shared DocVQA model collaboratively among clients without exchanging data, minimizing the objective function through a central server . The experiments involved comparing different strategies such as FedAvg and FedAdam, assessing the impact of hyperparameters like the number of clients (K) and client fraction (C), and studying the effects of pretraining objectives and communication rounds on performance . The study demonstrated that FL is a viable approach for training large-scale multimodal models like DocVQA, achieving results comparable to centralized models and enabling training over heterogeneous document datasets .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the WikiTableQuestions (WTQ) dataset, which comprises logical questions over HTML tables from Wikipedia . The code for the study is available as open source at the following link: https://github.com/khanhnguyen21006/fldocvqa .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that needed verification. The study conducted a comprehensive analysis of Federated Document Visual Question Answering (FeDocVQA) using a federated learning approach . The experiments involved exploring the impact of various factors such as the number of clients, sampling probability, pretraining objectives, and communication rounds on the performance of the model .

The results demonstrated the effectiveness of the Federated Self-Pretraining (FSP) strategy in improving FeDocVQA performance, showcasing consistent improvements across different configurations . Additionally, the study compared different strategies like FedAvg and FedAdam, highlighting the benefits of FedAdam in dealing with a high level of heterogeneity among clients .

Moreover, the experiments delved into the effects of the client fraction during pretraining and finetuning, revealing that the choice of client fraction had a notable impact on the performance of the model . The study's detailed analysis of these factors and their impact on FeDocVQA performance provides strong empirical evidence supporting the scientific hypotheses under investigation.


What are the contributions of this paper?

The paper "Federated Document Visual Question Answering: A Pilot Study" makes several contributions:

  • It explores the use of federated learning (FL) to train a shared model on decentralized private document data, addressing the challenge of training over scattered private data silos .
  • The focus is on Document Visual Question Answering (DocVQA), a task suitable for FL due to the diverse reasoning capabilities required across different domains, enriching DocVQA models by training over heterogeneous document datasets .
  • The paper proposes a combination of self-pretraining and Federated DocVQA training method using centralized adaptive optimization, outperforming the FedAvg baseline .
  • Extensive experiments and analysis are presented on training DocVQA models with FL, providing insights for future research in this area, demonstrating the effectiveness of pretraining strategies and the importance of tuning hyperparameters for practical document tasks under federation .
  • The study showcases that pretraining strategies effectively learn and scale up under federated training with diverse DocVQA datasets, highlighting the significance of hyperparameter tuning for complex settings like DocVQA with FL .

What work can be continued in depth?

To delve deeper into the research, further exploration can be conducted on the effectiveness of Federated Self-Pretraining (FSP) in multimodal settings like DocVQA. This involves continuing the investigation on how pretraining a model on unlabeled training data for a specific task before finetuning impacts performance, especially in complex multimodal problems such as DocVQA . Additionally, the study can focus on the applicability and benefits of self-pretraining in a federated manner, where each client performs self-supervised training on its private documents, aiming to obtain a domain-adapted initialization for subsequent training .

Tables
3
Scan the QR code to ask more questions about the paper
© 2025 Powerdrill. All rights reserved.