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Nov, 2020
隐私保护协作机器学习的可扩展方法
A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
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Jinhyun So, Basak Guler, A. Salman Avestimehr
TL;DR
该研究提出了COPML算法,这是一个完全去中心化的训练框架,可以保护数据隐私并实现可伸缩性,使用对个体数据进行安全编码的方式在多方之间分发计算负载,并以分布式方式执行训练计算和模型更新。实验演示了COPML相较于基准协议在训练速度上可实现高达16倍的加速。
Abstract
We consider a
collaborative learning
scenario in which multiple data-owners wish to jointly train a
logistic regression
model, while keeping their individual datasets private from the other parties. We propose CO
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