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Towards bandwidth efficient edge–cloud collaborative deep learning with Data Importance driven Compression
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DOI：10.1016/j.neucom.2025.130835.png)
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Abstract
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• Autoencoders that retain classification information can better recover samples for training. • The contribution of data determines the degree of compression. • Edge-cloud collaboration framework balances bandwidth consumption and classification accuracy.
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6.5
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2.5W
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6.5W
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PROCEEDINGS OF THE IEEE
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PROCEEDINGS OF THE IEEEIF25.9
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