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Showing 1 to 2 of 2 for “"Privacy-preserved machine learning"”.
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Personalized and Communication Cost Reduction Models in Federated Learning
… that sometimes raise concern regarding data privacy; especially in medical applications. Federated learning is used to solve this problem of user privacy. Most of the federated models are implemented by connecting billions of edge devices for privacy-preserved, on-device training. However, …
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PFHE: partially homomorphic encryption on CNN inference
… poses significant challenges for practical deep learning inference. In many application scenarios, high-resolution images may only contain a small portion of sensitive information. We noticed that none of the previous works consider this, so in this work, we propose a new framework that …