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- W4289761710 abstract "We propose an efficient protocol for decentralized training of deep neural networks from distributed data sources. The proposed protocol allows to handle different phases of model training equally well and to quickly adapt to concept drifts. This leads to a reduction of communication by an order of magnitude compared to periodically communicating state-of-the-art approaches. Moreover, we derive a communication bound that scales well with the hardness of the serialized learning problem. The reduction in communication comes at almost no cost, as the predictive performance remains virtually unchanged. Indeed, the proposed protocol retains loss bounds of periodically averaging schemes. An extensive empirical evaluation validates major improvement of the trade-off between model performance and communication which could be beneficial for numerous decentralized learning applications, such as autonomous driving, or voice recognition and image classification on mobile phones." @default.
- W4289761710 created "2022-08-04" @default.
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- W4289761710 date "2018-07-09" @default.
- W4289761710 modified "2023-10-16" @default.
- W4289761710 title "Efficient Decentralized Deep Learning by Dynamic Model Averaging" @default.
- W4289761710 doi "https://doi.org/10.48550/arxiv.1807.03210" @default.
- W4289761710 hasPublicationYear "2018" @default.
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