arXiv:2002.05377
공식 코드 · Papers with Code
High Performance Logistic Regression for Privacy-Preserving Genome Analysis
원문 초록
In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function. To the best of our knowledge, we present the fastest existing secure Multi-Party Computation implementation for training logistic regression models on high dimensional genome data distributed across a local area network.
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Privacy-Preserving Visual Content Tagging using Graph Transformer Networks
Differentially Private Federated Low Rank Adaptation Beyond Fixed-Matrix
Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptation
Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
S2FGL: Spatial Spectral Federated Graph Learning
Active Learning for Manifold Gaussian Process Regression
토론
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