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Analysis Study Research Paper | Information Technology | United States of America | Volume 10 Issue 11, November 2021 | Rating: 5.1 / 10
Scalable Distributed Training Algorithms for Machine Learning Models: A Code - Centric Approach
Abstract: The rapid growth of data and model complexity in machine learning has necessitated the development of scalable distributed training algorithms. Traditional single - machine training approaches have become increasingly inadequate due to the immense computational demands of modern machine learning models and the vast datasets they require. As a result, researchers and practitioners have turned to distributed training techniques that leverage multiple machines or devices to accelerate the training process and manage larger models and datasets more efficiently. This paper provides a comprehensive review of current distributed training techniques, focusing on a code - centric approach to implementation. By examining various algorithms such as synchronous and asynchronous stochastic gradient descent (SGD), model parallelism, and federated learning, we explore how these methods address the challenges posed by large - scale machine learning. Each technique is evaluated based on its scalability, efficiency, and suitability for different types of machine learning tasks. We delve into the specifics of implementing these algorithms, offering practical code examples and case studies. These examples not only illustrate the theoretical concepts but also provide hands - on guidance for developers looking to implement distributed training in their own projects. The paper highlights the strengths and weaknesses of different methodologies, such as the communication overhead and potential for stale gradients in asynchronous methods, or the privacy - preserving benefits and challenges of federated learning.
Keywords: distributed training, machine learning, scalable algorithms, synchronous SGD, federated learning
Edition: Volume 10 Issue 11, November 2021,
Pages: 1546 - 1554