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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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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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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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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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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...
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OSGAN: One-shot distributed learning using generative adversarial networks
With the advancements in mobile technology, a large amount of data is generated by end devices, which has created a renewed interest in develo**...
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Distributed sparse learning for stochastic configuration networks via alternating direction method of multipliers
As a class of randomized learning algorithms, stochastic configuration networks (SCNs) have demonstrated excellent capabilities in various...
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Large scale performance analysis of distributed deep learning frameworks for convolutional neural networks
Continuously increasing data volumes from multiple sources, such as simulation and experimental measurements, demand efficient algorithms for an...
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Application of deep reinforcement learning to intelligent distributed humidity control system
The indoor environment of buildings is complex and changeable, and it is difficult to ensure that the indoor humidity is uniform and stable while...
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Enhancing trust and privacy in distributed networks: a comprehensive survey on blockchain-based federated learning
While centralized servers pose a risk of being a single point of failure, decentralized approaches like blockchain offer a compelling solution by...
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From distributed machine learning to federated learning: a survey
In recent years, data and computing resources are typically distributed in the devices of end users, various regions or organizations. Because of...
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Distributed Reinforcement Learning
This chapter explores the use of distributed reinforcement learning, which involves multiple agents running in parallel to interact with the...
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LBB: load-balanced batching for efficient distributed learning on heterogeneous GPU cluster
As the cost of deep learning training increases, using heterogeneous GPU clusters is a reasonable way to scale cluster resources to support...
