Results 1 to 10 of about 495,712 (260)
A Partition Based Gradient Compression Algorithm for Distributed Training in AIoT [PDF]
Sensors, 2021 Running Deep Neural Networks (DNNs) in distributed Internet of Things (IoT) nodes is a promising scheme to enhance the performance of IoT systems. However, due to the limited computing and communication resources of the IoT nodes, the communication ...
Bingjun Guo, Yazhi Liu, Chunyang Zhang
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Survey of edge–edge collaborative training for edge intelligence
工程科学学报, 2023 With the rapid arrival of the Internet of Everything era, massive data resources are generated on edge sides, causing problems such as large network load, high energy consumption, and privacy security in traditional distributed training based on cloud ...
Rui WANG +7 more
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Increasing Momentum-Like Factors: A Method for Reducing Training Errors on Multiple GPUs
Tsinghua Science and Technology, 2022 In distributed training, increasing batch size can improve parallelism, but it can also bring many difficulties to the training process and cause training errors.
Yu Tang +6 more
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Distributed Training of Graph Convolutional Networks [PDF]
IEEE Transactions on Signal and Information Processing over Networks, 2021 Published on IEEE Transactions on Signal and Information Processing over ...
Scardapane S, Spinelli I, Di Lorenzo P
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FedADT: An Adaptive Method Based on Derivative Term for Federated Learning
Sensors, 2023 Federated learning is served as a novel distributed training framework that enables multiple clients of the internet of things to collaboratively train a global model while the data remains local.
Huimin Gao +4 more
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Secure Distributed Training at Scale
CoRR, 2021 Many areas of deep learning benefit from using increasingly larger neural networks trained on public data, as is the case for pre-trained models for NLP and computer vision. Training such models requires a lot of computational resources (e.g., HPC clusters) that are not available to small research groups and independent researchers.
Eduard Gorbunov +3 more
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Distributed Training and Optimization of Neural Networks [PDF]
, 2022 20 pages, 4 figures, 2 tables, Submitted for review.
Vlimant, Jean-Roch, Yin, Junqi
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Distributed training method for deep neural networks
Dianzi Jishu Yingyong, 2023 ： Deep neural networks have achieved great success in classification and prediction of high-dimensional data. Training deep neural networks is a data-intensive task, which needs to collect large-scale data from multiple data sources.
Yuan Ye, Tian Yuan, Jiang Qibing
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Bridging the Gap Between Memory and Communication Efficiency on Distributed Deep Learning Systems
IEEE Access, 2021 Large-scale distributed deep learning is of great importance in various applications. For data-parallel distributed training systems, limited hardware resources (e.g., GPU memory and interconnection bandwidth) often become a performance bottleneck, and ...
Shaofeng Zhao +3 more
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Efficient Distributed Training Framework for Federated Learning [PDF]
Jisuanji kexue, 2023 Federated learning effectively solves the problem of isolated data island,but there are some challenges.Firstly,the training nodes of federated learning have a large hardware heterogeneity,which has an impact on the training speed and model performance ...
FENG Chen, GU Jingjing
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