The Non-IID Data Quagmire of Decentralized Machine Learning . The Non-IID Data Quagmire of Decentralized Machine Learning. Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip B. Gibbons. Many large-scale machine learning (ML).
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The Non-IID Data Quagmire of Decentralized Machine Learning. Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip B. Gibbons. Many large-scale machine learning (ML).
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The Non-IID Data Quagmire of Decentralized Machine Learning. This repo is the source code for our paper: The Non-IID Data Quagmire of Decentralized Machine Learning (ICML'20).This repo.
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The Non-IID Data Quagmire of Decentralized Machine Learning. Many large-scale machine learning (ML) applications need to perform decentralized learning over datasets generated at.
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PDF Many large-scale machine learning (ML) applications need to perform decentralized learning over datasets generated at different devices and locations. Such datasets pose a.
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Many large-scale machine learning (ML) applications need to perform decentralized learning over datasets generated at different devices and locations. Such datasets pose a significant.
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Many large-scale machine learning (ML) applications need to perform decentralized learning over datasets generated at different devices and locations. Such datasets pose a.
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SkewScout is presented, a system-level approach that adapts the communication frequency of decentralized learning algorithms to the (skew-induced) accuracy loss between.
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Our study shows that: (i) the problem of non-IID data partitions is fundamental and pervasive, as it exists in all ML applications, DNN models, training datasets, and decentralized.
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Many large-scale machine learning (ML) applications need to train ML models over decentralized datasets that are generated at different devices and locations. These decentralized datasets.
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%0 Conference Paper %T The Non-IID Data Quagmire of Decentralized Machine Learning %A Kevin Hsieh %A Amar Phanishayee %A Onur Mutlu %A Phillip Gibbons %B Proceedings of the.
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Read our recent paper on “The Non-IID Data Quagmire of Decentralized Machine Learning”. Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip B. Gibbons. In: Proceedings.
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The Non-IID Data Quagmire of Decentralized Machine Learning Hsieh et al., 2019 Communication-efficient on-device machine learning: Federated distillation and augmentation.
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The Non-IID Data Quagmire of Decentralized Machine Learning DeepGradientCompression (Lin et al., 2018), a popular algorithm that communicates only a pre-specified amount of gradients.
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The Non-IID Data Quagmire of Decentralized Machine Learning Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip Gibbons ICML 2020. ML Training with Decentralized Data. Challenge 2:.
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The Non-IID Data Quagmire of Decentralized Machine Learning ICML 2020 Kevin Hsieh, Amar Phanishayee,
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1) Partial non-IID data is also problematic. We see that for all three decentralized learning algorithms, partial non-IID data still cause major accuracy loss. Even with a small.
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The Non-IID Data Quagmire of Decentralized Machine Learning Training over skewed label partitions is a fundamental and pervasive problem for decentralized learning. Three.
