Project Background:
Using federated learning, natural language processing (NLP) addresses the challenges of training robust and privacy-preserving NLP models in decentralized environments. Traditional NLP approaches often require aggregating large datasets from various sources to train models, raising concerns about data privacy and security. Federated learning offers a solution by enabling model training directly on decentralized devices. This approach allows individual users to retain control over their data while contributing to model improvement. In NLP, federated learning allows for collaborative model training across diverse datasets, capturing the nuances and linguistic variations of different sources.
Additionally, federated learning techniques like model aggregation and differential privacy ensure that sensitive information remains protected during training. The project aims to leverage federated learning to develop more robust and privacy-preserving NLP models for various applications, including language translation, sentiment analysis, and text generation. By harnessing the power of federated learning, NLP systems can benefit from improved model performance, enhanced privacy, and greater scalability, paving the way for more secure and efficient language processing in decentralized environments.
Problem Statement
- Centralized model training in natural language processing (NLP) requires aggregating large datasets from various sources, posing privacy concerns.
- Sharing raw text data for model training raises risks of data leakage and unauthorized access to sensitive information.
- Balancing model performance and privacy preservation in federated learning for NLP applications remains challenging.
- Ensuring interoperability and scalability of federated learning algorithms across diverse NLP tasks and datasets is crucial.
- Developing efficient communication protocols for exchanging model updates while minimizing communication overhead is necessary.
- Evaluating the effectiveness and efficiency of federated learning-based NLP models in real-world scenarios is essential for practical deployment.
Aim and Objectives
- Develop privacy-preserving and robust natural language processing (NLP) models using federated learning.
- Enable model training directly on decentralized devices to preserve data privacy.
- Improve model accuracy and performance across diverse linguistic variations and domains.
- Develop efficient federated learning algorithms for NLP tasks such as language translation and sentiment analysis.
- Ensure interoperability and scalability of federated learning techniques for NLP applications.
- Facilitate model convergence and communication efficiency in federated learning-based NLP systems.
Contributions to Natural Language Processing using Federated Learning
- Enhancing privacy and security by enabling decentralized model training without sharing raw text data.
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Improving model robustness and generalization by leveraging diverse and distributed datasets across decentralized devices.
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Advancing federated learning techniques to address linguistic variations and domain-specific challenges in NLP tasks.
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Facilitating collaboration and knowledge sharing among decentralized devices while preserving data privacy.
- Driving innovation in NLP applications such as language translation, sentiment analysis, and text generation through federated learning.
- Contributing to developing privacy-preserving and scalable NLP solutions for real-world deployment in decentralized environments.
Deep Learning Algorithms for Natural Language Processing using Federated Learning
- Federated Long Short-Term Memory (LSTM)
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Federated Transformer-based Models
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Federated Bidirectional Encoder Representations from Transformers (BERT)
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Federated Convolutional Neural Networks (CNNs)
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Federated Recurrent Neural Networks (RNNs)
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Federated Attention Mechanisms
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Federated Variational Autoencoders (VAEs)
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Federated Generative Adversarial Networks (GANs)
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Federated Capsule Networks (CapsNets)
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Federated Graph Neural Networks (GNNs)
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1.Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2.Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
