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For distributed machine learning with sensitive data, we demonstrate how minimizing distance correlation between raw data and intermediary representations reduces leakage of sensitive raw data patterns across client communications while maintaining model accuracy.
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2016
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2016
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Y. Koda, J. Park, M. Bennis, T. Nishio, K. Yamamoto, M. Morikura, and K. Nakashima, “Communication-efficient multimodal split learning for mmwave received power prediction,” IEEE Communications Letters , 2020
2020
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2020
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R. Yonetani, V. Naresh Boddeti, K. M. Kitani, and Y. Sato, “Privacy-preserving visual learning using doubly permuted homomorphic encryption,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 2040–2050
2050
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