Project Background:
Distributed Active Learning is associated with data annotation and model training in scenarios where labeled data is scarce or expensive. Traditional machine learning models often rely on large, labeled datasets for training, and acquiring such datasets can be resource-intensive and time-consuming. Distributed active Learning aims to alleviate this challenge by combining the principles of active Learning with a distributed computing framework. Active Learning involves the selective annotation of the most informative data points, optimizing the learning process with minimal labeled samples. This approach is extended across multiple nodes or devices in a distributed setting, allowing for collaborative and decentralized data annotation. This project seeks to exploit the advantages of both active Learning and distributed computing to create a scalable and efficient framework. This framework optimizes the utilization of labeled data. It accommodates diverse sources and data types distributed across a network, making it particularly relevant in scenarios where centralized annotation or model training is impractical.
Problem Statement
- The problem statement revolves around the challenges of training machine learning models in decentralized environments where labeled data is scarce or difficult to obtain.
- Traditional approaches often require large labeled datasets for effective model training, and in scenarios where such data is distributed across multiple nodes or devices, the conventional centralized annotation becomes impractical.
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The distributed nature of data introduces challenges related to data heterogeneity, privacy concerns, and communication overhead.
- Additionally, selecting the most informative data points for annotation, a crucial aspect of active Learning must be optimized collaboratively and decentralized.
- Ensuring the convergence of models trained across distributed nodes and managing the annotation process in a coordinated yet privacy-preserving manner are key challenges.
Aim and Objectives
- The project aims to enhance the efficiency and effectiveness of machine learning models in decentralized environments with limited labeled data by integrating distributed Active Learning.
- Develop a collaborative and decentralized framework for data annotation and model training.
- Optimize the selection of informative data points using active learning principles in a distributed setting.
- Address challenges related to data heterogeneity, privacy preservation, and communication overhead.
- Ensure convergence and consistency of models trained across distributed nodes.
- Enhance scalability by accommodating diverse sources and data types distributed across a network.
- Evaluate the performance of the distributed active learning framework compared to traditional centralized approaches.
- Provide a solution adaptable to various domains and applications with limited labeled data in decentralized environments.
Contributions to Distributed Active Learning
- The project contributes to the development of methods for decentralized data annotation, enabling collaborative labeling across multiple nodes or devices.
- Innovative strategies are developed and optimized for distributed environments, ensuring the selection of the most informative data points for annotation.
- An implementation of privacy-preserving techniques to safeguard sensitive information during the collaborative annotation process addresses privacy concerns in decentralized learning scenarios.
- It ensures effective convergence of machine learning models trained across distributed nodes, overcoming model consistency and synchronization challenges.
- It involves reducing communication overhead in distributed systems enhancing the efficiency of data exchange and collaboration among nodes.
- The developed framework accommodates diverse sources and types of data distributed across a network, contributing to the scalability and adaptability of the system to various domains and applications.
- Open-source implementations contribute to the research community, fostering collaboration and enabling further advancements in decentralized machine learning.
- It improves resource utilization by distributing the annotation and training processes efficiently using computational resources across the decentralized network.
Deep Learning Algorithms for Distributed Active Learning
- Federated Learning Algorithms
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Distributed Neural Network Training
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Decentralized Gradient Descent
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Ensemble Learning in a Distributed Setting
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Distributed Reinforcement Learning Algorithms
Datasets for Distributed Active Learning
- MNIST (Distributed Version)
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CIFAR-10 (Distributed Version)
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Federated EMNIST (Extended MNIST)
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Federated Shakespeare Dataset
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Federated Learning of Clinical Models (Tumor Prediction, Diabetes Prediction)
Performance Metrics for Distributed Active Learning
- Accuracy
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Federated Model Convergence Rate
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Communication Overhead
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Privacy Preservation Metrics (Differential Privacy Measures)
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Resource Utilization Efficiency
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Scalability Metrics (Performance with Increasing Number of Nodes)
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Federated Averaging Overhead
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Model Consistency Metrics
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Training Time
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Loss Function Values Across Nodes
