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From distributed machine to distributed deep learning: a comprehensive survey
Artificial intelligence has made remarkable progress in handling complex tasks, thanks to advances in hardware acceleration and machine learning...
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Distributed Deep Reinforcement Learning: A Survey and a Multi-player Multi-agent Learning Toolbox
With the breakthrough of AlphaGo, deep reinforcement learning has become a recognized technique for solving sequential decision-making problems....
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Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models
This study focused on comparing distributed learning models with centralized and local models, assessing their efficacy in predicting specific...
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Instance segmentation on distributed deep learning big data cluster
Distributed deep learning is a promising approach for training and deploying large and complex deep learning models. This paper presents a...
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Exploring the distributed learning on federated learning and cluster computing via convolutional neural networks
Distributed learning has led to the development of federated learning and cluster computing; however, the two methods are very different. Therefore,...
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Exploring workplace-based learning in distributed healthcare settings: a qualitative study
BackgroundDistributed healthcare settings such as district hospitals, primary care, and public health facilities are becoming the real-life settings...
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Distributed few-shot learning with prototype distribution correction
Few-shot learning aims to learn a classifier that can perform well even if a few labeled samples are used for training. Many methods based on...
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Communication-efficient federated continual learning for distributed learning system with Non-IID data
Due to the privacy preserving capabilities and the low communication costs, federated learning has emerged as an efficient technique for distributed...
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Deep reinforcement learning-based scheduling in distributed systems: a critical review
Many fields of research use parallelized and distributed computing environments, including astronomy, earth science, and bioinformatics. Due to an...
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Cloud data security for distributed embedded systems using machine learning and cryptography
In the growing demand for distributed embedded systems that efficiently execute complex processes and high-end applications, safeguarding sensitive...
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Towards Distributed Graph Representation Learning
Distributed graph representation learning refers to the process of learning graph data representation in a distributed computing environment. In the...
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FedSL: Federated split learning on distributed sequential data in recurrent neural networks
Federated Learning (FL) and Split Learning (SL) are privacy-preserving Machine-Learning (ML) techniques that enable training ML models over data...
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A combined priority scheduling method for distributed machine learning
Algorithms and frameworks for distributed machine learning have been widely used in numerous artificial intelligence engineering applications. A...
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Mining multi-center heterogeneous medical data with distributed synthetic learning
Overcoming barriers on the use of multi-center data for medical analytics is challenging due to privacy protection and data heterogeneity in the...
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Distributed Machine Learning and Computing Theory and Applications
This book focuses on a wide range of distributed machine learning and computing algorithms and their applications in healthcare and engineering...
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DC-SHAP Method for Consistent Explainability in Privacy-Preserving Distributed Machine Learning
Ensuring the transparency of machine learning models is vital for their ethical application in various industries. There has been a concurrent trend...
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Distributed Multi-agent Target Search and Tracking With Gaussian Process and Reinforcement Learning
Deploying multiple robots for target search and tracking has many practical applications, yet the challenge of planning over unknown or partially...
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Distributed Learning Ecosystems Concepts, Resources, and Repositories
This open-access book is based on the observation that learning ecosystems are increasingly established in higher education institutions. However, an...
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Distributed Ensemble Method Using Deep Learning to Detect DDoS Attacks in IoT Networks
The widespread adoption of Internet of Things (IoT) devices has increased exponentially in recent years. Consequently, the security risks and...
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Multi-consensus decentralized primal-dual fixed point algorithm for distributed learning
Decentralized distributed learning has recently attracted significant attention in many applications in machine learning and signal processing. To...
