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Mar, 2024
HETAL:具有同态加密的高效隐私保护迁移学习
HETAL: Efficient Privacy-preserving Transfer Learning with Homomorphic Encryption
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Seewoo Lee, Garam Lee, Jung Woo Kim, Junbum Shin, Mun-Kyu Lee
TL;DR
HETAL是一种高效的基于同态加密的迁移学习算法,通过使用CKKS同态加密方案对客户数据进行加密,并采用基于验证的早停方法,实现了对客户隐私的保护,并达到了非加密训练的准确性。
Abstract
transfer learning
is a de facto standard method for efficiently training
machine learning
models for data-scarce problems by adding and fine-tuning new classification layers to a model pre-trained on large datase
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